From f47bdf870d6e63e4f3d80658cc74941ae28ada9e Mon Sep 17 00:00:00 2001 From: sakshimuttha578 Date: Wed, 23 Jul 2025 22:32:40 +0530 Subject: [PATCH 1/5] Added a num guess name - MystiGuess --- programs/mysti_guess.py | 51 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) create mode 100644 programs/mysti_guess.py diff --git a/programs/mysti_guess.py b/programs/mysti_guess.py new file mode 100644 index 0000000..7ecdbbc --- /dev/null +++ b/programs/mysti_guess.py @@ -0,0 +1,51 @@ +# Generate a random num +import random + +# Specifying the range bt taking input +print('Please enter the range of numbers -') +min = int(input('Minimum : ')) +max = int(input('Maximum : ')) +num = random.randint(min,max) + +# attempts +attempts = 5 +guessed = 0 + +# loop +while True : +# take input guess + try: + print(f'You have only {attempts} attempts!') + guess = int(input(f'Guess the number between {min} and {max} : ')) + + # if num < guess : too low + if guess < num : + print('Too low!') + guessed += 1 + attempts -= 1 + if attempts == 0: + print('Attempts Over!') + print('Sorry! Try Next Time!') + print(f'The number was {num}') + break + + # if num > guess : too high + elif guess > num : + print('Too high!') + guessed += 1 + attempts -= 1 + if attempts == 0: + print('Attempts Over!') + print('Sorry! Try Next Time!') + print(f'The number was {num}.') + break + + # else num == guess : well done + else : + guessed += 1 + print(f'Congratulations! You guessed the number in {guessed} attempts!') + break + + # if invalid num : error + except ValueError: + print('Please enter a valid number') From 59484347a90922933111f91fbcc131a0c7033a3e Mon Sep 17 00:00:00 2001 From: Sambarlasagna Date: Sat, 14 Feb 2026 14:22:58 +0530 Subject: [PATCH 2/5] Added Student Registration Form with Enhanced Formatting --- student_form.py | 46 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 student_form.py diff --git a/student_form.py b/student_form.py new file mode 100644 index 0000000..04cfb0d --- /dev/null +++ b/student_form.py @@ -0,0 +1,46 @@ +""" +Student Registration Form +This program collects student details and displays them in a formatted manner. +""" + +def main(): + """Main function to collect and display student information.""" + + print("=" * 60) + print("STUDENT REGISTRATION FORM".center(60)) + print("=" * 60) + print() + + # Collecting user input + name = input("What is your name? ") + reg_number = input("What is your registration number? ") + department = input("What is your department? ") + faculty = input("What is your faculty? ") + course = input("What is your course of study? ") + level = input("What is your current level of study? ") + programme = input("What is your programme of study? ") + state = input("What is your state of residence? ") + nationality = input("What is your nationality? ") + + # Displaying the collected information in a formatted form + print("\n" + "=" * 60) + print("STUDENT DETAILS".center(60)) + print("=" * 60) + print(f""" +Name: {name} +Registration Number: {reg_number} +Department: {department} +Faculty: {faculty} +Course of Study: {course} +Level: {level} +Programme of Study: {programme} +State of Residence: {state} +Nationality: {nationality} +""") + print("=" * 60) + print("Thank you for registering!".center(60)) + print("=" * 60) + + +if __name__ == "__main__": + main() From 714b62a28654751f7f238e27d4cbb83f44481a53 Mon Sep 17 00:00:00 2001 From: Addy06 Date: Sat, 14 Feb 2026 23:39:58 +0530 Subject: [PATCH 3/5] gui calc --- guicalc.py | 90 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 guicalc.py diff --git a/guicalc.py b/guicalc.py new file mode 100644 index 0000000..ad0b019 --- /dev/null +++ b/guicalc.py @@ -0,0 +1,90 @@ +# Import the tkinter library for creating GUI applications +import tkinter as tk + +# Create the main application window +root = tk.Tk() +root.title("Calculator") # Set the window title +root.geometry("300x400") # Set window size (width x height) + +# Create an Entry widget to display numbers and results +entry = tk.Entry( + root, + font=("Arial", 20), # Font style and size + borderwidth=5, # Thickness of border + relief="ridge", # Border style + justify="right" # Align text to the right +) +entry.pack(fill="both", padx=10, pady=10) + +# Function to insert numbers/operators when a button is clicked +def click(value): + entry.insert(tk.END, value) # Insert value at the end of the entry box + +# Function to clear the display +def clear(): + entry.delete(0, tk.END) # Delete everything from entry box + +# Function to evaluate the expression +def calculate(): + try: + # Evaluate the mathematical expression entered by the user + result = eval(entry.get()) + entry.delete(0, tk.END) # Clear entry box + entry.insert(tk.END, result) # Display result + except: + # If any error occurs (invalid expression) + entry.delete(0, tk.END) + entry.insert(tk.END, "Error") + +# List of calculator buttons in order +buttons = [ + '7', '8', '9', '/', + '4', '5', '6', '*', + '1', '2', '3', '-', + '0', '.', '=', '+' +] + +# Create a frame to hold all the buttons +frame = tk.Frame(root) +frame.pack() + +row = 0 +col = 0 + +# Create buttons dynamically using a loop +for btn in buttons: + if btn == "=": + # '=' button performs calculation + tk.Button( + frame, + text=btn, + width=5, + height=2, + command=calculate + ).grid(row=row, column=col) + else: + # Other buttons insert numbers/operators + tk.Button( + frame, + text=btn, + width=5, + height=2, + command=lambda b=btn: click(b) + ).grid(row=row, column=col) + + col += 1 + if col > 3: # Move to next row after 4 buttons + col = 0 + row += 1 + +# Clear button placed below the calculator buttons +tk.Button( + root, + text="Clear", + width=20, + height=2, + command=clear +).pack(pady=10) + +# Start the GUI event loop +root.mainloop() From 77717033ca9cbba0e84a04968935ab38f3b3aa2e Mon Sep 17 00:00:00 2001 From: Pradeep Kumar Date: Sun, 17 May 2026 20:43:57 +0530 Subject: [PATCH 4/5] Update README.md --- README.md | 11874 ++++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 9119 insertions(+), 2755 deletions(-) diff --git a/README.md b/README.md index f1f7400..1ba887f 100644 --- a/README.md +++ b/README.md @@ -2,9 +2,50 @@ > *Click ★ if you like the project. Your contributions are heartily ♡ welcome.* -
+
+ +## Table of Contents + +* [Introduction](#-1-introduction): (Python compilation, bytecode, and VMs) +* [Data Types](#-2-data-types): Data Types & Built-in Scalar Types +* [Dictionary](#-3-dictionary): Hash maps, hash collisions, and Python 3.6+ ordering +* [Operators](#-4-operators): Operators & Bitwise Operations +* [Control Flow Statements](#-5-control-flow-statements): Control Flow Statements & Optimization +* [Core Data Structures](#-6-core-data-structures): Lists, Tuples, Sets, Deques +* [Functions](#-7-functions): Scope, LEGB rule, args/kwargs, closures +* [Lambda Functions](#-8-lambda-functions): Lambda Functions & Functional Programming Patterns +* [Modules and Packages](#-9-modules-and-packages): Import system, sys.modules, namespace packages +* [Object-Oriented Programming](#-10-object-oriented-programming): MRO, C3 Linearization, Descriptors, Metaclasses +* [Exception Handling](#-11-exception-handling): EAFP vs LBYL, custom exceptions, traceback overhead +* [File Handling](#-12-file-handling): Streams, text vs binary +* [Memory Management](#-13-memory-management): Object allocation, PyMalloc, small object caching +* [Garbage Collector](#-14-garbage-collector): Reference counting, generational collection, cyclic references +* [Mutable vs Immutable](#-15-mutable-vs-immutable): Hashability, identity vs equality, side-effects +* [Iterators and Generators](#-16-iterators-and-generators): Iteration protocol, lazy evaluation, memory efficiency +* [Decorators](#-17-decorators): Function decorators, class decorators, functools.wraps, stateful decorators +* [Context Managers](#-18-context-managers): Context management protocol, contextlib, resource management +* [Concurrency and Parallelism](#-19-concurrency-and-parallelism): Threading, Multiprocessing, Asyncio, Event loop, GIL +* [Testing and Debugging](#-20-testing-and-debugging): Unittest, Pytest fixtures, Mocking, profiling with cProfile +* [Miscellaneous](#-21-miscellaneous): Dunder methods, Typing/Type hinting, slots optimization + +**Python for AI & Data Science** + +* [Numerical Computing](#-22-numerical-computing): NumPy arrays, vectorisation +* [Data Manipulation](#-23-data-manipulation): Pandas DataFrames, Series +* [Data Visualisation](#-24-data-visualisation): Matplotlib, Seaborn +* [Machine Learning Libraries](#-25-machine-learning-libraries): Scikit-Learn +* [Deep Learning Frameworks](#-26-deep-learning-frameworks): PyTorch, TensorFlow +* [Model Evaluation Metrics](#-27-model-evaluation-metrics): Precision, Recall, F1 + +
+ + +## # 1. Introduction + +
+ +## Q. What is Python? -## Q.What is Python? Python is a popular programming language. It was created by Guido van Rossum, and released in 1991. It is used for: @@ -17,30 +58,14 @@ mathematics, system scripting. -## Q.What can Python do? +## Q. What can Python do? + Python can be used on a server to create web applications. Python can be used alongside software to create workflows. Python can connect to database systems. It can also read and modify files. Python can be used to handle big data and perform complex mathematics. Python can be used for rapid prototyping, or for production-ready software development. -## Q. How can you improve the following code? - -```py -import string - -i = 0 -for letter in string.letters: - print("The letter at index %i is %s" % (i, letter)) - i = i + 1 -``` - -Bonus points for mentioning `enumerate` and use of `str.format`. - -
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- ## Q. When is using Python the "right choice" for a project? Python is a high-level, interpreted, interactive and object-oriented scripting language. Python is designed to be highly readable. It uses English keywords frequently where as other languages use punctuation, and it has fewer syntactical constructions than other languages. @@ -57,7 +82,7 @@ Although likes and dislikes are highly personal, a developer who is "worth his o - It\'s free and open source! Need we say more?
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## Q. What are some drawbacks of the Python language? @@ -68,7 +93,7 @@ The two most common valid answers to this question are: - Execution speed. Python can be slower than compiled languages since it is interpreted. (Well, sort of. See our earlier discussion on this topic.)
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## Q. We know Python is all the rage these days. But to be truly accepting of a great technology, you must know its pitfalls as well? @@ -86,7 +111,7 @@ And even after these pitfalls, of course. Being easy makes it addictive. Once a So while it has problems, it is also a wonderful tool for a lot of things.
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## Q. What are the key differences between Python 2 and 3? @@ -111,7 +136,7 @@ API changes: - All the elements must be comparable to each other
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## Q. What are some key differences to bear in mind when coding in Python vs. Java? @@ -148,2484 +173,8141 @@ Disclaimer #2. The intent here is not to launch into a religious battle over the - An expert knowledge of Python extends well beyond the technical minutia of the language. A Python expert will have an in-depth understanding and appreciation of Python\'s benefits as well as its limitations. Accordingly, here are some sample questions that can help assess this dimension of a candidate\'s expertise:
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-## Q. What will be the output of the code below in Python 2? - -```py -def div1(x,y): - print "%s/%s = %s" % (x, y, x/y) -def div2(x,y): - print "%s//%s = %s" % (x, y, x//y) +## Q. What are the key features of Python? -div1(5,2) -div1(5.,2) -div2(5,2) -div2(5.,2.) -``` +If it makes for an introductory language to programming, Python must mean something. These are its qualities: +> +- Interpreted. +- Dynamically-typed. +- Object-oriented +- Concise and simple +- Free +- Has a large community -Also, how would the answer differ in Python 3 (assuming, of course, that the above [print] statements were converted to Python 3 syntax)? +## Q. How is a .pyc file different from a .py file? -
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+While both files hold bytecode, .pyc is the compiled version of a Python file. It has platform-independent bytecode. Hence, we can execute it on any platform that supports the .pyc format. Python automatically generates it to improve performance(in terms of load time, not speed). -## Q. What is the difference between range and xrange? +## Q. How does the Python version numbering scheme work? -- As follows: - - `xrange` returns the xrange object while range returns the list, and uses the same memory and no matter what the range size is. - - For the most part, xrange and range are the exact same in terms of functionality. They both provide a way to generate a list of integers for you to use, however you please. - - The only difference is that range returns a Python list object and x range returns an xrange object. This means that xrange doesn\'t actually generate a static list at run-time like range does. It creates the values as you need them with a special technique called yielding. This technique is used with a type of object known as generators. That means that if you have a really gigantic range you'd like to generate a list for, say one billion, xrange is the function to use. - - This is especially true if you have a really memory sensitive system such as a cell phone that you are working with, as range will use as much memory as it can to create your array of integers, which can result in a Memory Error and crash your program. It\'s a memory hungry beast. +Python versions are numbered A.B.C or A.B. -
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+A is the major version number. It is only incremented for major changes in the language. -## Q. What will be the output of the code below? +B is the minor version number, incremented for less earth-shattering changes. -```py -List = ['a', 'b', 'c', 'd', 'e'] -print(list[10:]) -``` +C is the micro-level. It is incremented for each bug fix release. +Not all releases are bug fix releases. -- TypeError: 'type' object is not subscriptable -if proper name given,it will print []. +In the run-up to a new major release, 'A' series of development releases are made denoted as alpha, beta, or release candidate. -
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+Alphas are early releases in which interfaces aren\'t finalized yet; it\'s not unexpected to see an interface change between two alpha releases. -## Q. What is a method? +Betas are more stable, preserving existing interfaces but possibly adding new modules, and release candidates are frozen, making no changes except as needed to fix critical bugs. -A method is a function on some object x that you normally call as x.name(arguments...). Methods are defined as functions inside the class definition: +Alpha, beta and release candidate versions have an additional suffix. -```py -class C: - def meth (self, arg): - return arg*2 + self.attribute -``` +The suffix for an alpha version is "aN" for some small number N, -## Q. How do I call a method defined in a base class from a derived class that overrides it? +The suffix for a beta version is "bN" for some small number N, -If you\'re using new-style classes, use the built-in `super()` function: +And the suffix for a release candidate version is "cN" for some small number N. -```py -class Derived(Base): - def meth (self): - super(Derived, self).meth() -``` +In other words, all versions labeled 2.0aN precede the versions labeled 2.0bN, which precede versions labeled 2.0cN, and those precede 2.0. -If you\'re using classic classes: For a class definition such as -`class Derived(Base):` ... you can call method meth() defined in Base (or one of Base\'s base classes) as Base.meth(self,arguments). Here, Base.meth is an unbound method, so you need to provide the self argument. +You may also find version numbers with a "+" suffix, e.g. "2.2+". These are unreleased versions, built directly from the subversion trunk. In practice, after a final minor release is made, the subversion trunk is incremented to the next minor version, which becomes the "a0" version, e.g. "2.4a0".
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-## Q. How can I organize my code to make it easier to change the base class? - -You could define an alias for the base class, assign the real base class to it before your class definition, and use the alias throughout your class. Then all you have to change is the value assigned to the alias. Incidentally, this trick is also handy if you want to decide dynamically (e.g. depending on availability of resources) which base class to use. +## Q. What Are The Implementation In Python Program? -Example: BaseAlias = class Derived(BaseAlias): def meth(self): BaseAlias.meth(self). +Python program can be implemented by two ways +> + 1. Interactive Mode (Submit statement by statement explicitly) + 2. Batch Mode (Writing all statements and submit all statements) -
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+In Interactive mode python command shell is required. It is available in installation of python cell. -## Q. How do I find the current module name? +In Interactive mode is not suitable for developing the projects & Applications -A module can find out its own module name by looking at the predefined global variable `__name__`. If this has the value `'__main__'`, the program is running as a script. Many modules that are usually used by importing them also provide a command-line interface or a self-test, and only execute this code after checking `__name__`: +Interactive mode is used for predefined function and programs. +Example: ```py -def main(): - print('Running test...') -if __name__ == '__main__': - main() +X=1000 +Y=2000 +X+Y +3000 +Quit(X+Y) ``` +X, Y is not find. +Interactive mode is unfit for looping purpose. -`__import__('x.y.z')` -returns Try: `__import__('x.y.z').y.z` +Interactive Mode: +The concept of submitting one by one python statements explicitly in the python interpreter is known as "Interactive Mode" +In Order to submit the one by one python statements explicitly to the python interpreter we use python command line shell. +Python command line shell is present in python software +We can open the python command line shell by executing python command on command prompt or terminal +Example: +c:/users/mindmajix>python +`3+4` +7 + +`'mindmajix'*3` +'mindmajix mindmajix mindmajix' + +`x=1000` +`y=2000` +`x+y` +3000 +Quit +x+y +c:/users/sailu>python +Error: name 'X' not defined + +Batch Mode: +In the concept of writing the group of python statements in a file, save the file with extension .py and submit that entire file to the python interpreter is known as Batch Mode. + +In Order to develop the python files we use editors or IDE\'s +Different editors are notepad, notepad++, edit+,nano, VI, gedil and so on. +Open the notepad and write the following code: + +Example: ```py -# For more realistic situations, you may have to do something like: -m = __import__(s) - for i in s.split(".")[1:]: m = getattr(m, i) +X=1000 +Y=2000 +print(x+y, x-y, x*y) ``` +Save the file in D drive mindmajix python folder with the demo.py +Open command prompt and execute following commands: -
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+Python D:/mindmajix python/Demo.py -## Q. How do I access a module written in Python from C? +3000 -You can get a pointer to the module object as follows: +-1000 -`module = PyImport_ImportModule("");` +2000.000 -If the module hasn\'t been imported yet (i.e. it is not yet present in sys.modules), this initializes the module; otherwise it simply returns the value of ``sys.modules[""]``. Note that it doesn\'t enter the module into any namespace -- it only ensures it has been initialized and is stored in sys.modules. You can then access the module\'s attributes (i.e. any name defined in the module) as follows: attr = PyObject_GetAttrString(module, ""); Calling PyObject_SetAttrString() to assign to variables in the module also works. +Save another method if we correctly set the path + +D: + +D:/>cd "mindmajix python" + +D:/mindmajix Python>python Demo.py +`3000` +`-1000` +`2000.000`
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-## Q. How do I convert a number to a string? +## Q. What Are Applications of Python? -To convert, e.g., the number 144 to the string '144', use the built-in function str(). If you want a hexadecimal or octal representation, use the built-in functions hex() or oct(). For fancy formatting, use the % operator on strings, e.g. "%04d" % 144 yields '0144' and "%.3f" % (1/3.0) yields '0.333'. See the library reference manual for details. +Applications of Python + +- Automation App +- Data Analytics +- Scientific App +- Web App +- Web Scrapping +- Test Cases +- Network with IOT +- Admin Script +- GUI +- Gaming +- Animation
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-## Q. How is the Implementation of Python\'s dictionaries done? +## Q. Mention five benefits of using Python? -Python dictionary needs to be declared first: -`dict = {}` +- Python comprises of a huge standard library for most Internet platforms like Email, HTML, etc. +- Python does not require explicit memory management as the interpreter itself allocates the memory to newvariables and free them automatically +- Provide easy readability due to use of square brackets +- Easy-to-learn for beginners +- Having the built-in data types saves programming time and effort from declaring variables -Key value pair can be added as: -`dict[key] = value` -or -`objDict.update({key:value})` +
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-Remove element by: -`dict.pop(key)` +## Q. Is Python interpreted or compiled? -Remove all: -`objDict.clear()` +As noted in Why Are There So Many Pythons?, this is, frankly, a bit of a trick question in that it is malformed. Python itself is nothing more than an interface definition (as is true with any language specification) of which there are multiple implementations. Accordingly, the question of whether "Python" is interpreted or compiled does not apply to the Python language itself; rather, it applies to each specific implementation of the Python specification. -A hash value of the key is computed using a hash function, The hash value addresses a location in an array of "buckets" or "collision lists" which contains the (key , value) pair. +Further complicating the answer to this question is the fact that, in the case of CPython (the most common Python implementation), the answer really is "sort of both". Specifically, with CPython, code is first compiled and then interpreted. More precisely, it is not precompiled to native machine code, but rather to bytecode. While machine code is certainly faster, bytecode is more portable and secure. The bytecode is then interpreted in the case of CPython (or both interpreted and compiled to optimized machine code at runtime in the case of PyPy).
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-## Q. What is used to create Unicode string in Python? +## Q. What are some alternative implementations to CPython? When and why might you use them? + +One of the more prominent alternative implementations is Jython, a Python implementation written in Java that utilizes the Java Virtual Machine (JVM). While CPython produces bytecode to run on the CPython VM, Jython produces Java bytecode to run on the JVM. + +Another is IronPython, written in C# and targeting the .NET stack. IronPython runs on Microsoft\'s Common Language Runtime (CLR). -"u" should be added before the string +As also pointed out in Why Are There So Many Pythons?, it is entirely possible to survive without ever touching a non-CPython implementation of Python, but there are advantages to be had from switching, most of which are dependent on your technology stack. -`a = (u'Python')` -`type(a) #will give you unicode` +Another noteworthy alternative implementation is PyPy whose key features include: -Add unicode before the string. Ex: unicode(text) resulting in text. +- Speed. Thanks to its Just-in-Time (JIT) compiler, Python programs often run faster on PyPy. +- Memory usage. Large, memory-hungry Python programs might end up taking less space with PyPy than they do in CPython. +- Compatibility. PyPy is highly compatible with existing python code. It supports cffi and can run popular Python libraries like Twisted and Django. +- Sandboxing. PyPy provides the ability to run untrusted code in a fully secure way. +- Stackless mode. PyPy comes by default with support for stackless mode, providing micro-threads for massive concurrency.
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-## Q. What is the built-in function used in Python to iterate over a sequence of numbers? - -Syntax: `range(start,end,step count)` +## Q. Is Python a compiled language? -Ex: +Python is **both compiled and interpreted**. CPython (the reference implementation) compiles source code to platform-independent **bytecode** (`.pyc` files stored in `__pycache__`), which is then executed by the Python Virtual Machine (PVM). It is not compiled to native machine code (unlike C/C++), which is why we call it "interpreted" in common usage. ```py -a = range(1,10,2) -print (a) -``` -Output: -`[1, 3, 5, 7, 9]` +import dis +import py_compile -If using to iterate +# Compile a source file to bytecode manually +py_compile.compile("hello.py") # produces __pycache__/hello.cpython-3x.pyc -```py -for i in range(1,10): - print (i) +# Inspect bytecode of a function +def add(a: int, b: int) -> int: + return a + b + +dis.dis(add) +# Output (example): +# 2 0 RESUME 0 +# 3 2 LOAD_FAST 0 (a) +# 4 LOAD_FAST 1 (b) +# 6 BINARY_OP 0 (+) +# 10 RETURN_VALUE ``` -Output: +**Alternatives:** +- **PyPy**: JIT-compiles bytecode to native machine code at runtime — typically 3–5× faster than CPython for CPU-bound workloads. +- **Cython**: Transpiles Python-like code to C extensions for near-native performance. -> - 1 - 2 - 3 - 4 - 5 - 6 - 7 - 8 - 9 +**Use Case:** A fintech platform migrated a CPU-bound risk calculation engine from CPython to PyPy, reducing processing time from 45s to 9s with zero code changes, leveraging PyPy\'s JIT compilation.
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-## Q. Does Python have a switch-case statement? +## Q. Why Python instead of Scala on Spark when Scala has better performance? -Ans. In languages like C++, we have something like this: +| Factor | Python (PySpark) | Scala (native Spark) | +|--------|-----------------|---------------------| +| Raw CPU performance | Slower (GIL, serialization overhead) | Faster (JVM, no serialization) | +| Data science ecosystem | Excellent (pandas, NumPy, scikit-learn, PyTorch) | Limited | +| Developer productivity | High | Medium | +| DataFrame/SQL API | Near-identical via PySpark | Native | +| ML integrations | MLflow, Hugging Face, sklearn | MLlib | +| Hiring pool | Much larger | Smaller | - switch(name) - { - case 'Ram': - cout<<"Monday"; - break; - case 'Shiv': - cout<<"Tuesday"; - break; - default: - cout<<"Hi, user"; - } +The performance gap has significantly narrowed because: +1. **PySpark DataFrames/SQL** execute entirely on the JVM — Python is only used for the driver, not the executors. +2. **Apache Arrow** (PySpark 3.0+) enables zero-copy columnar data transfer between JVM and Python, eliminating most serialization overhead. +3. **Pandas UDFs / Vectorized UDFs** use Arrow to process batches in pandas, achieving near-JVM performance for custom transformations. -But in Python, we do not have a switch-case statement. Here, you may write a switch function to use. Else, you may use a set of if-elif-else statements. To implement a function for this, we may use a dictionary. - -```py -def switch(choice): - switcher={ - 'Ram':'Monday', - 'Shiv':'Tuesday', - print(switcher.get(choice,'Hi, user')) - return - switch('Shiv') - Tuesday - switch('Ram') - Monday - switch('Raghav') - Hi, user - } +```py +from pyspark.sql import SparkSession +from pyspark.sql.functions import pandas_udf, col +from pyspark.sql.types import DoubleType +import pandas as pd + +spark = SparkSession.builder.appName("example").getOrCreate() + +df = spark.range(1_000_000).toDF("value") + +# Vectorized UDF — Arrow-backed, runs at near-native speed +@pandas_udf(DoubleType()) +def square_udf(series: pd.Series) -> pd.Series: + return series ** 2 + +result = df.withColumn("squared", square_udf(col("value"))) +result.show(5) +# No Python serialization per-row — entire Arrow batch processed at once ``` -Here, the get() method returns the value of the key. When no key matches, the default value (the second argument) is returned. +**Use Case:** A data science team at an e-commerce company chose PySpark over Scala Spark to reuse their existing `scikit-learn` feature engineering pipelines and `pandas` data validation logic, cutting migration effort by ~70% while keeping 95% of Scala\'s throughput for DataFrame operations.
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-## Q. Does python support switch or case statement in Python? If not what is the reason for the same? +## Q. What is Python? -Dictionary can be used as case/switch. Actually there is no switch statement in the Python programming language but the is a similar construct that can do justice to switch that is the exception handling using try and except1,except2,except3.... and so on. +Python is a **high-level, interpreted, dynamically-typed, multi-paradigm, general-purpose programming language** created by Guido van Rossum and first released in 1991. It emphasizes code readability and developer productivity through its clean syntax and extensive standard library. -## Q. What is the statement that can be used in Python if a statement is required syntactically but the program requires no action? +**Key characteristics:** -`pass` keyword is used to do nothing but it fulfill the syntactical requirements. +| Property | Detail | +|----------|--------| +| Paradigm | Object-oriented, functional, procedural, imperative | +| Typing | Dynamic (types checked at runtime) + optional static typing via `mypy` | +| Memory | Automatic (reference counting + cyclic GC) | +| Execution | CPython: source → bytecode → PVM interpretation | +| License | PSF License (permissive open source) | +| Current version | CPython 3.13 (as of 2025) | ```py -try x[10]: - print(x) -except: - pass -``` +# Python's philosophy is captured in the "Zen of Python" +import this -Use `pass` keyword over there like: +# Demonstrates multi-paradigm nature: -```py -if a > 0: - print("Hello") -else: - pass -``` +# 1. Procedural +def celsius_to_fahrenheit(celsius: float) -> float: + return celsius * 9 / 5 + 32 -
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+# 2. Object-oriented +class Temperature: + def __init__(self, celsius: float) -> None: + self._celsius = celsius -## Q. Does Python support strongly for regular expressions? + @property + def fahrenheit(self) -> float: + return self._celsius * 9 / 5 + 32 -Yes, Python Supports Regular Expressions Well. `re` is an in-buit library for the same. There is a lot of other languages that have good support to RegEx- Perl, Awk, Sed, Java etc. + def __repr__(self) -> str: + return f"Temperature({self._celsius}°C / {self.fahrenheit}°F)" -Regular expressions (called REs, or regexes, or regex patterns) are essentially a tiny, highly specialized programming language embedded inside Python and made available through the re module. Using this little language, you specify the rules for the set of possible strings that you want to match; this set might contain English sentences, or e-mail addresses, or TeX commands, or anything you like. You can then ask questions such as "Does this string match the pattern?", or "Is there a match for the pattern anywhere in this string?". You can also use REs to modify a string or to split it apart in various ways. +# 3. Functional +from functools import reduce +temps_c = [0, 20, 37, 100] +temps_f = list(map(celsius_to_fahrenheit, temps_c)) +above_boiling = list(filter(lambda t: t > 212, temps_f)) +print(above_boiling) # [212.0... only 100°C qualifies at 212°F] -Regular expression patterns are compiled into a series of bytecodes which are then executed by a matching engine written in C. For advanced use, it may be necessary to pay careful attention to how the engine will execute a given RE, and write the RE in a certain way in order to produce bytecode that runs faster. Optimization isn\'t covered in this document, because it requires that you have a good understanding of the matching engine\'s internals. +t = Temperature(37) +print(t) # Temperature(37°C / 98.6°F) +``` + +**Use Case:** Python serves as the lingua franca of data science and ML engineering — TensorFlow, PyTorch, scikit-learn, pandas, and Apache Spark all offer Python-first APIs, making Python the single language teams use across data exploration, model training, API serving, and infrastructure automation.
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-## Q. How do you perform pattern matching in Python? Explain. +## Q. Describe some features of Python. -Regular Expressions/REs/ regexes enable us to specify expressions that can match specific "parts" of a given string. For instance, we can define a regular expression to match a single character or a digit, a telephone number, or an email address, etc. The Python\'s "re" module provides regular expression patterns and was introduce from later versions of Python 2.5. "re" module is providing methods for search text strings, or replacing text strings along with methods for splitting text strings based on the pattern defined. +| Feature | Description | +|---------|-------------| +| **Interpreted** | Executed via CPython bytecode VM — no manual compile step | +| **Dynamically typed** | Variable types resolved at runtime; `mypy` adds optional static typing | +| **Garbage collected** | Reference counting + cyclic GC; no manual `malloc`/`free` | +| **Multi-paradigm** | OOP, functional, procedural, and imperative styles all supported | +| **Extensive stdlib** | 200+ modules covering I/O, networking, math, concurrency, testing | +| **Duck typing** | Interface defined by available methods, not class hierarchy | +| **First-class functions** | Functions are objects; can be passed, stored, returned | +| **Interactive REPL** | Rapid prototyping via `python` shell or Jupyter notebooks | +| **Cross-platform** | Runs on Linux, macOS, Windows, and embedded systems | +| **Readable syntax** | Enforced indentation, minimal boilerplate, PEP 8 style guide | + +```py +# First-class functions + duck typing example +from typing import Callable + +def apply_twice(func: Callable[[int], int], value: int) -> int: + return func(func(value)) + +double = lambda x: x * 2 +print(apply_twice(double, 3)) # 12 + +# Dynamic typing +x = 42 +x = "now I'm a string" # valid — name x rebound to a new object +x = [1, 2, 3] # valid again +``` + +**Use Case:** Python\'s multi-paradigm nature lets a data engineering team write functional pipeline transformations (`map`/`filter`/`reduce`), OOP domain models, and procedural scripts all within the same codebase without language switching.
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-## Q. Write a regular expression that will accept an email id. Use the `re` module. +## Q. How does Python execute code? -Ans. +Python\'s execution pipeline has six stages: -```py -import re -e = re.search(r'[0-9a-zA-Z.]+@[a-zA-Z]+\.(com|co\.in)$' 'JaiRameshwar@gmail.com') -e.group() +``` +Source (.py) + ↓ tokenizer (tokenize.py) +Tokens + ↓ parser (PEG parser, Python 3.9+) +Abstract Syntax Tree (AST) + ↓ compiler (compile.c) +Bytecode (.pyc cached in __pycache__) + ↓ CPython Virtual Machine (ceval.c — eval loop) +Result ``` -'Ramayanwashere@gmail.com' +```py +import ast +import dis +import py_compile -To brush up on regular expressions, check Regular Expressions in Python. +source_code = """ +def factorial(n: int) -> int: + return 1 if n <= 1 else n * factorial(n - 1) +""" -
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+# Stage 1: Parse to AST +tree = ast.parse(source_code) +print(ast.dump(tree, indent=2)) -### _Garbage Collector & Memory Manager_ +# Stage 2: Compile AST to code object +code_obj = compile(source_code, "", "exec") -## Q. What is Garbage Collection? +# Stage 3: Inspect bytecode +exec(code_obj) +import sys +func = sys._getframe().f_locals.get("factorial") or eval("factorial") -The concept of removing unused or unreferenced objects from the memory location is known as a Garbage Collection. While executing the program, if garbage collection takes place then more memory space is available for the program and rest of the program execution becomes faster. +# Disassemble bytecode of the compiled function +def factorial(n: int) -> int: + return 1 if n <= 1 else n * factorial(n - 1) -Garbage collector is a predefined program, which removes the unused or unreferenced objects from the memory location. +dis.dis(factorial) +# Shows RESUME, LOAD_FAST, COMPARE_OP, RETURN_VALUE, BINARY_OP, CALL, etc. -Any object reference count becomes zero then we call that object as a unused or unreferenced object Then no.of reference variables which are pointing the object is known as a reference count of the object. +# Stage 4: .pyc caching +py_compile.compile("myscript.py") # writes __pycache__/myscript.cpython-3x.pyc +``` -While executing the python program if any object reference count becomes zero, then internally python interpreter calls the garbage collector and garbage collector will remove that object from memory location. +**Key points:** +- `.pyc` bytecode is cached to skip re-parsing on subsequent runs (speed up import time, not execution speed) +- The eval loop in `ceval.c` dispatches on opcode using a `switch` statement (CPython 3.11 uses specializing adaptive interpreter) +- PyPy replaces the eval loop with a tracing JIT compiler + +**Use Case:** A Django application with hundreds of modules benefits from `.pyc` caching — on cold start, only changed modules are re-compiled, reducing startup time from ~2s to ~0.3s in production containers. -## Q. How is memory managed in Python? +## Q. What is the difference between Python 2.7+ and Python 3? -Python memory is managed by Python private heap space. All Python objects and data structures are located in a private heap. The programmer does not have an access to this private heap and interpreter. Like other programming language python also has garbage collector which will take care of memory management in python.Python also have an inbuilt garbage collector, which recycle all the unused memory and frees the memory and makes it available to the heap space. The allocation of Python heap space for Python objects is done by Python memory manager. The core API gives access to some tools for the programmer to code. +Python 3 (released 2008, Python 2 EOL January 2020) introduced breaking changes to fix long-standing design flaws. Python 2 is **no longer supported**. -Python has a private heap space to hold all objects and data structures. Being programmers, we cannot access it; it is the interpreter that manages it. But with the core API, we can access some tools. The Python memory manager controls the allocation. +| Feature | Python 2.7 | Python 3.x | +|---------|-----------|-----------| +| `print` | statement: `print "x"` | function: `print("x")` | +| Integer division | `5/2 == 2` (truncates) | `5/2 == 2.5`; use `5//2` for floor | +| `str` default | bytes (ASCII) | Unicode (UTF-8) | +| `unicode` type | separate `u"..."` | all strings are unicode | +| `range()` | returns a list | returns a lazy range object | +| `dict.keys()` | returns list | returns a view | +| `input()` | evaluates input (= py3 `eval(input())`) | returns string safely | +| Exception syntax | `except E, e:` | `except E as e:` | +| `super()` | `super(ClassName, self)` | `super()` (no args) | +| Type hints | limited (PEP 484, backported) | full native support | +| `async`/`await` | not available | native (3.5+) | +| f-strings | not available | native (3.6+) | +| `__future__` | needed for many py3 features | built-in | - +```py +# Division behaviour difference +# Python 2: 5 / 2 == 2 +# Python 3: 5 / 2 == 2.5 +print(5 / 2) # 2.5 (Python 3) +print(5 // 2) # 2 (floor division in both) -## Q. Why isn\'t all memory freed when Python exits? +# String handling +s = "café" +print(type(s)) # — always unicode in Python 3 +print(len(s)) # 4 (characters, not bytes) +print(len(s.encode())) # 5 (UTF-8 bytes — 'é' = 2 bytes) -Objects referenced from the global namespaces of Python modules are not always deallocated when Python exits. This may happen if there are circular references. There are also certain bits of memory ... +# range is lazy in Python 3 +r = range(1_000_000) +print(type(r)) # — O(1) memory -## Q. Whenever you exit Python, is all memory de-allocated? +# super() simplified +class Base: + def hello(self) -> str: return "base" -The answer here is no. The modules with circular references to other objects, or to objects referenced from global namespaces, aren\'t always freed on exiting Python. -Plus, it is impossible to de-allocate portions of memory reserved by the C library. +class Child(Base): + def hello(self) -> str: + return super().hello() + " child" # no args needed in Python 3 +``` -Whenever Python exits, especially those Python modules which are having circular references to other objects or the objects that are referenced from the global namespaces are not always de-allocated or freed.It is impossible to de-allocate those portions of memory that are reserved by the C library.On exit, because of having its own efficient clean up mechanism, Python would try to de-allocate/destroy every other object. +**Use Case:** Migrating a legacy Python 2 data pipeline to Python 3 fixes silent data corruption where `b"bytes" / str` string mixing caused undetected encoding bugs — Python 3\'s strict bytes/str separation forces explicit encoding at system boundaries. -## Q. Is it possible to assign multiple var to values in list? +## Q. Is Python compiled? If yes, how? If not, how? -The multiple assignment trick is a shortcut that lets you assign multiple variables with the values in a list in one line of code. So instead of doing this: +**Both** — Python is compiled to bytecode, then interpreted. It is **not** compiled to native machine code (unless using PyPy\'s JIT or tools like Cython/Nuitka). -```py -cat = ['fat', 'orange', 'loud'] -size = cat[0] -color = cat[1] -disposition = cat[2] +**Execution pipeline:** ``` - -Do this: - -```js -cat = ['fat', 'orange', 'loud'] -size, color, disposition = cat +.py source → (tokenize → parse → AST → compile) → .pyc bytecode → CPython VM ``` - - -## Q. What is `__slots__` and when is it useful? +```py +import dis +import py_compile +import pathlib -In Python, every class can have instance attributes. By default Python uses a `dict` to store an object\'s instance attributes. This is really helpful as it allows setting arbitrary new attributes at runtime. +# Python compiles source to .pyc automatically on import +# Inspect the bytecode of a function +def add(a: int, b: int) -> int: + return a + b -However, for small classes with known attributes it might be a bottleneck. The `dict` wastes a lot of RAM. Python can\'t just allocate a static amount of memory at object creation to store all the attributes. Therefore it sucks a lot of RAM if you create a lot of objects. The usage of `__slots__` to tell Python not to use a `dict`, and only allocate space for a fixed set of attributes. +dis.dis(add) +# RESUME 0 +# LOAD_FAST 0 (a) +# LOAD_FAST 1 (b) +# BINARY_OP 0 (+) +# RETURN_VALUE -**Example:** +# Compile a file manually → writes __pycache__/example.cpython-3x.pyc +py_compile.compile("example.py") -**1. Object without slots:** +# Inspect the .pyc header +pyc = next(pathlib.Path("__pycache__").glob("*.pyc"), None) +if pyc: + data = pyc.read_bytes() + import struct + magic = data[:4].hex() + mtime = struct.unpack(" - ↥ back to top + ↥ back to top -## Q. What Are The Types of Objects Support in Python Language? - -Python supports are two types are of objects. They are: +## Q. What does dynamically / duck typed mean? -Immutable built-in types: +**Dynamic typing:** Variable types are checked at **runtime**, not compile time. A name can be rebound to any type at any time — the name carries no type information itself; the *object* has a type. - Strings - Tuples - Numbers +**Duck typing:** "If it walks like a duck and quacks like a duck, it\'s a duck." Python does not check *what class* an object is — it only checks whether the object has the required method or attribute. Formal class inheritance is not required. -Mutable built-in types: +```py +# Dynamic typing — same name, different types at runtime +x = 42 +print(type(x)) # +x = "hello" +print(type(x)) # +x = [1, 2, 3] +print(type(x)) # - List - Sets - Dictionaries +# Duck typing — anything with .read() works +import io -- Immutable Objects +def count_lines(file_like) -> int: + """Works with any object that has a .read() method.""" + return file_like.read().count("\n") - The objects which doesn\'t allow to modify the contents of those objects are known as 'Immutable Objects' - - Before creating immutable objects with some content python interpreter verifies is already any object is available. In memory location with same content or not. - - If already object is not available then python interpreter creates new objects with that content and store that object address two reference variable. - - If already object is present in memory location with the same content creating new objects already existing object address will be given to the reference variable. +# Works with a real file +# with open("data.txt") as f: +# print(count_lines(f)) - _Program:_ +# Also works with StringIO — no shared base class required +fake_file = io.StringIO("line1\nline2\nline3\n") +print(count_lines(fake_file)) # 3 -```py -i=1000 -print(i) -print(type(i)) -print(id(i)) -j=2000 -print(j) -print(type(j)) -print(id(j)) -x=3000 -print(x) -print(type(x)) -print(id(x)) -y=3000 -print(y) -print(type(y)) -print(id(y)) -``` +# Protocol / structural typing (PEP 544) — formalize duck typing +from typing import Protocol - `int, float, complex, bool, str, tuple` are immutable objects +class Drawable(Protocol): + def draw(self) -> None: ... - Immutable objects performance is high. +class Circle: + def draw(self) -> None: print("drawing circle") - Applying iterations on Immutable objects takes less time. - - All fundamentals types represented classes objects and tuple class objects are immutable objects. +class Square: + def draw(self) -> None: print("drawing square") -- Mutable Objects: - 1. The Objects which allows to modify the contents of those objects are known as 'Mutable Objects' - 2. We can create two different mutable objects with same content +def render(shape: Drawable) -> None: + shape.draw() # duck typed — no inheritance from Drawable needed - Program: +render(Circle()) # drawing circle +render(Square()) # drawing square +``` - ```py - x=[10,20,30] - print(x) - print(type(x)) - print(id(x)) - y=[10,20,30] - print(y) - print(type(y)) - print(id(y)) - ``` +**Dynamic vs Static typing:** - Output: +| | Dynamic (Python) | Static (Java, TypeScript) | +|--|------------------|--------------------------| +| Type check | Runtime | Compile time | +| Flexibility | High | Lower | +| Early error detection | No (use mypy) | Yes | +| Refactoring safety | Lower | Higher | - `List, set, dict` classes objects are mutable objects - - Mutable objects performance is low when compared to immutable objects - Applying Iterations mutable objects takes huge time +**Use Case:** A data pipeline\'s `transform(obj)` function works on DataFrame rows, Pydantic models, and plain dicts equally — as long as each has the expected field access pattern. This eliminates wrapper adapters and lets the pipeline consume multiple data sources without modification. -## Q. Python is Call by Value or Call by Reference? How are arguments passed by value or by reference? - -Everything in Python is an object and all variables hold references to the objects. The references values are according to the functions; as a result you cannot change the value of the references. However, you can change the objects if it is mutable. - -## Q. Explain parameter-passing mechanism in python? - -- To pass its parameters to a function, Python uses pass-by-reference. If you change a parameter within a function, the change reflects in the calling function. This is its default behavior. - -- However, when we pass literal arguments like strings, numbers, or tuples, they pass by value. This is because they are immutable. +## Q. When would I not use Python? -## Q. What are `*args`, `**kwargs` ? +Python is an excellent general-purpose language but has known limitations where other tools are more appropriate: -In cases when we don\'t know how many arguments will be passed to a function, like when we want to pass a list or a tuple of values, we use `*args`. +| Scenario | Reason | Better Alternative | +|----------|--------|--------------------| +| **CPU-bound computation** | GIL limits true thread parallelism; interpreted bytecode is slow | C, C++, Rust, Julia | +| **Mobile apps** | No first-class Android/iOS runtime | Kotlin, Swift, Flutter (Dart) | +| **Real-time / embedded systems** | GC pauses, no deterministic latency | C, C++, Rust, MicroPython | +| **Browser front-end** | Not a native browser language | JavaScript, TypeScript, WASM | +| **High-frequency trading** | Microsecond latency requirements, GC pauses | C++, Java, FPGA | +| **Memory-constrained IoT** | Runtime overhead too large | C, Rust, MicroPython | +| **Type-safe large teams** | Dynamic typing makes large-scale refactoring risky | TypeScript, Go, Kotlin | ```py -def func(*args): - for i in args: - print(i) +# Demonstrating the GIL limitation — threads don't speed up CPU work +import threading +import time -func(3,2,1,4,7) -``` -3 -2 -1 -4 -7 +def cpu_task(n: int) -> int: + return sum(i * i for i in range(n)) -`**kwargs` takes keyword arguments when we don\'t know how many there will be: +# Multi-threaded — NOT faster for CPU work due to GIL +start = time.perf_counter() +threads = [threading.Thread(target=cpu_task, args=(5_000_000,)) for _ in range(4)] +[t.start() for t in threads] +[t.join() for t in threads] +threaded_time = time.perf_counter() - start -```py -def func(**kwargs): - for i in kwargs: - print(i,kwargs[i]) +# Use multiprocessing to bypass GIL +from multiprocessing import Pool +start = time.perf_counter() +with Pool(4) as pool: + pool.map(cpu_task, [5_000_000] * 4) +mp_time = time.perf_counter() - start -func(a=1,b=2,c=7) +print(f"Threads: {threaded_time:.2f}s Multiprocessing: {mp_time:.2f}s") +# Multiprocessing is ~4x faster (true parallelism) ``` -a.1 -b.2 -c.7 -The words `args` and `kwargs` are a convention, and we can use anything in their place. +**When Python is still used despite these limitations:** +- Numpy/Pandas offload heavy computation to C extensions, bypassing the GIL +- `asyncio` handles thousands of I/O-bound concurrent connections efficiently +- Cython / PyPy / numba can make hot paths as fast as C + +**Use Case:** A fintech company uses Python for data analytics and ML model training but writes the order matching engine in C++ with microsecond latency, exposing Python bindings via `ctypes` for integration and testing. -## Q. How can I pass optional or keyword parameters from one function to another? +## Q. What is DRY, and how can I apply it through OOP or FP? -Collect the arguments using the * and ** specifier in the function\'s parameter list; this gives you the positional arguments as a tuple and the keyword arguments as a dictionary. You can then pass these arguments when calling another function by using `*` and `**` : +**DRY (Don\'t Repeat Yourself):** Every piece of knowledge or logic should have a **single authoritative representation** in the codebase. Duplication means two places to update and two places to introduce bugs. ```py -def f(x, *tup, **kwargs): - kwargs['width']='14.3c' - g(x, *tup, **kwargs) -``` +# BAD — validation logic repeated in 3 places +def create_user(name: str, email: str) -> dict: + if not name or len(name) < 2: + raise ValueError("Name too short") + if "@" not in email: + raise ValueError("Invalid email") + return {"name": name, "email": email} + +def update_user(user_id: int, name: str, email: str) -> dict: + if not name or len(name) < 2: # duplicated! + raise ValueError("Name too short") + if "@" not in email: # duplicated! + raise ValueError("Invalid email") + return {"id": user_id, "name": name, "email": email} + +# GOOD — DRY via OOP (extract to a class) +class UserValidator: + @staticmethod + def validate_name(name: str) -> str: + if not name or len(name) < 2: + raise ValueError(f"Name too short: {name!r}") + return name.strip() + + @staticmethod + def validate_email(email: str) -> str: + if "@" not in email or "." not in email.split("@")[-1]: + raise ValueError(f"Invalid email: {email!r}") + return email.lower() + +def create_user_dry(name: str, email: str) -> dict: + return { + "name": UserValidator.validate_name(name), + "email": UserValidator.validate_email(email), + } -In the unlikely case that you care about Python versions older than 2.0, use 'apply': +def update_user_dry(user_id: int, name: str, email: str) -> dict: + return { + "id": user_id, + "name": UserValidator.validate_name(name), + "email": UserValidator.validate_email(email), + } -```python -def f(x, *tup, **kwargs): - kwargs['width']='14.3c' - apply(g, (x,)+tup, kwargs) +# GOOD — DRY via FP (higher-order function / decorator) +from functools import wraps +from typing import Callable + +def validate_positive(func: Callable) -> Callable: + """Decorator: ensures all numeric arguments are positive.""" + @wraps(func) + def wrapper(*args, **kwargs): + for arg in args: + if isinstance(arg, (int, float)) and arg <= 0: + raise ValueError(f"Expected positive number, got {arg}") + return func(*args, **kwargs) + return wrapper + +@validate_positive +def calculate_tax(income: float, rate: float) -> float: + return income * rate + +@validate_positive +def calculate_discount(price: float, pct: float) -> float: + return price * (1 - pct / 100) ``` +**DRY strategies:** +- **Functions** — extract repeated logic into a single callable +- **Classes / inheritance** — share behaviour through base classes +- **Mixins** — reusable capabilities without deep hierarchy +- **Decorators** — apply cross-cutting concerns (logging, validation, retries) once +- **Templates / generics** — parameterize type differences + +**Use Case:** A microservices platform DRYs up HTTP retry logic with a single `@retry(max_attempts=3, backoff=2.0)` decorator applied to all outbound API calls — changing the retry strategy requires editing one decorator, not every service client. + -## Q. What is lambda? What are Lambda Functions ? +## Q. When would I use Python? -A function which doesn\'t contain any name is known as a anonymous function lambda function, Lambda function we can assign to the variable & we can call the lambda function through the variable. +Python excels in domains where developer productivity, readability, and ecosystem richness matter more than raw execution speed: -Syntax: -`Lambda arguments:expression` +| Domain | Why Python | Key Libraries | +|--------|-----------|--------------| +| **Data science / ML** | NumPy/Pandas, rich ecosystem | numpy, pandas, scikit-learn, PyTorch, TensorFlow | +| **Web development** | Rapid development, batteries included | Django, FastAPI, Flask | +| **Scripting / automation** | Readable syntax, OS integration | subprocess, pathlib, fabric | +| **DevOps / infrastructure** | Cloud SDKs, IaC tools | boto3, Ansible, Terraform CDK | +| **Data engineering / ETL** | Spark PySpark API, Airflow, connectors | pyspark, apache-airflow, dbt | +| **API / microservices** | Async support, fast iteration | FastAPI, aiohttp, httpx | +| **Testing** | Expressive assertions, fixtures | pytest, hypothesis | +| **CLI tools** | Simple argument parsing | click, typer, argparse | +| **Scientific computing** | SciPy, symbolic math | scipy, sympy, matplotlib | +```py +# Example: Python in all these roles in a single data pipeline -It is a single expression anonymous function often used as inline function. A lambda form in python does not have statements as it is used to make new function object and then return them at runtime. +# 1. Scripting — read config +import pathlib, json +config = json.loads(pathlib.Path("config.json").read_text()) -The lambda operator is used to create anonymous functions. It is mostly used in cases where one wishes to pass functions as parameters. or assign them to variable names. +# 2. Data engineering — transform +import csv +from io import StringIO +raw = "name,score\nAlice,95\nBob,87" +reader = csv.DictReader(StringIO(raw)) +records = [{"name": r["name"], "score": int(r["score"])} for r in reader] -When we want a function with a single expression, we can define it anonymously. A lambda expression may take input and returns a value. To define the above function as a lambda expression, we type the following code in the interpreter: +# 3. Data science — analyse +scores = [r["score"] for r in records] +mean = sum(scores) / len(scores) +print(f"Mean score: {mean}") # 91.0 -```py -(lambda a,b:a if a>b else b)(3,3.5) +# 4. Web API — serve results (FastAPI pattern) +# from fastapi import FastAPI +# app = FastAPI() +# @app.get("/scores") +# def get_scores() -> list[dict]: return records + +# 5. Testing +assert mean == 91.0, "Unexpected mean" +print("All assertions passed") ``` -`3.5` +**Use Case:** A startup uses Python end-to-end: Airflow for ETL orchestration, FastAPI for the REST backend, pandas for analytics, pytest for CI testing, and boto3 for AWS infrastructure — a single language team covers the full stack. -Here, a and b are the inputs. -`a if a > b else b` is the expression to return. -The arguments are 3 and 3.5. + -It is possible to not have any inputs here. +## Q. Give examples of Python frameworks. -`(lambda :print("Hi"))()` +| Category | Framework | Key Strengths | +|----------|-----------|--------------| +| **Full-stack web** | Django | ORM, admin panel, auth, batteries-included | +| **Micro/async web** | FastAPI | Auto OpenAPI, type hints, async-first, high performance | +| **Micro web** | Flask | Lightweight, flexible, easy to learn | +| **Async web** | aiohttp | Full async HTTP client + server | +| **Data pipeline** | Apache Airflow | DAG-based scheduling, rich operator ecosystem | +| **ML / Deep learning** | PyTorch, TensorFlow | Neural network training, GPU acceleration | +| **Data manipulation** | pandas | DataFrame API for tabular data | +| **Scientific** | SciPy / NumPy | Numerical computation, signal processing | +| **Testing** | pytest | Fixtures, parametrize, plugin ecosystem | +| **CLI** | Click / Typer | Decorator-based CLI with type hints | +| **ORM** | SQLAlchemy | Database-agnostic ORM + core SQL | +| **Task queue** | Celery | Distributed task execution | -Hi +```py +# FastAPI — modern async REST API +from fastapi import FastAPI, HTTPException +from pydantic import BaseModel -Example: +app = FastAPI(title="User API", version="1.0.0") -```py -myfunction = lambda x:x*x -a = myfunction(10) -print(a) -``` +class User(BaseModel): + id: int + name: str + email: str -Output: 100 +users_db: dict[int, User] = { + 1: User(id=1, name="Alice", email="alice@example.com"), +} -- Why can\'t lambda forms in Python contain statements? +@app.get("/users/{user_id}", response_model=User) +async def get_user(user_id: int) -> User: + if user_id not in users_db: + raise HTTPException(status_code=404, detail="User not found") + return users_db[user_id] -Lambdas evaluates at run time and these do not need statements -Lambda is a anonymous function, which does not have a name and no fixed number of arguments. Represented by keyword lambda followed by statement. +# Run with: uvicorn main:app --reload +# Auto-generated docs at: http://localhost:8000/docs -Ex: +# pytest — expressive testing +import pytest -```py -add = lambda a,b: a+b -add(2,3) +def add(a: int, b: int) -> int: + return a + b + +@pytest.mark.parametrize("a,b,expected", [ + (1, 2, 3), + (-1, 1, 0), + (0, 0, 0), +]) +def test_add(a: int, b: int, expected: int) -> None: + assert add(a, b) == expected ``` -output: -`5` +**Use Case:** A company\'s production stack uses Django for the customer-facing portal (ORM, admin, auth), FastAPI for internal microservices (high-throughput async endpoints), Airflow for nightly ETL jobs, and Celery for async email/notification tasks — all Python, all interoperating. -## Q. How do you create your own package in Python? +## Q. How is Python interpreted? -It overrides the any initialization from an inherited class and is called when the class is instantiated. +Python\'s interpretation process has distinct stages. "Interpreted" means source code is never compiled to native machine code — it is instead compiled to **bytecode** which a software VM (the CPython interpreter loop) executes instruction by instruction. -We know that a package may contain sub-packages and modules. A module is nothing but Python code. -To create a package of our own, we create a directory and create a file `__init__.py` in it. We leave it empty. Then, in that package, we create a module(s) with whatever code we want. For a detailed explanation with pictures, refer to Python Packages. +``` +Source code (.py) + ↓ 1. Tokenizer — splits text into tokens (keywords, operators, literals) +Token stream + ↓ 2. Parser — builds Abstract Syntax Tree (PEG grammar, Python 3.9+) +AST + ↓ 3. Compiler — converts AST to bytecode (list of stack-machine opcodes) +Code object (.pyc cached in __pycache__) + ↓ 4. Eval loop (ceval.c) — CPython VM executes opcode by opcode +Result +``` -## Q. Explain the use "with" statement in python? +```py +import ast +import dis +import tokenize +import io -- In python generally "with" statement is used to open a file, process the data present in the file, and also to close the file without calling a close() method. "with" statement makes the exception handling simpler by providing cleanup activities. +source = "result = (2 + 3) * 10" -General form of with: +# Stage 1: Tokenize +tokens = list(tokenize.generate_tokens(io.StringIO(source).readline)) +for tok in tokens: + print(tok) +# TokenInfo(type=1 (NAME), string='result', ...) +# TokenInfo(type=54 (OP), string='=', ...) etc. -```py -with open("filename", "mode") as file-var: +# Stage 2: Parse to AST +tree = ast.parse(source) +print(ast.dump(tree, indent=2)) + +# Stage 3: Compile to bytecode +code = compile(source, "", "exec") +dis.dis(code) +# LOAD_CONST 1 (2) +# LOAD_CONST 2 (3) +# BINARY_OP 0 (+) +# LOAD_CONST 3 (10) +# BINARY_OP 5 (*) +# STORE_NAME 0 (result) + +# Stage 4: Execute +exec(code) +# result = 50 in the executed namespace ``` -processing statements -Note: no need to close the file by calling close() upon file-var.close() +**Key CPython details:** +- The eval loop in `Python/ceval.c` dispatches on each opcode (switch table) +- CPython 3.11+ uses a **specializing adaptive interpreter** — frequently executed opcodes get specialized variants (e.g., `LOAD_ATTR_MODULE` instead of generic `LOAD_ATTR`) +- `.pyc` files cache stages 1–3; only stage 4 runs on subsequent imports +- PyPy replaces the eval loop with a **tracing JIT** that compiles hot paths to machine code -## Q. What is Monkey patching ? Give example ? +**Use Case:** A platform engineering team sets `PYTHONDONTWRITEBYTECODE=0` in their Docker builds and precompiles all `.py` files with `python -m compileall` during image build, shaving 400ms off cold-start time for serverless Lambda functions. -Dynamically modifying a class or module at run-time. + + +## Q. What is Dynamic Typing? + +Dynamic typing means **type information is attached to objects at runtime, not to variables at compile time**. A variable name is just a label that can be rebound to any object of any type at any point. ```py -class A: - def func(self): - print("Hi") - def monkey(self): - print "Hi, monkey" - m.A.func = monkey - a = m.A() - a.func() -``` +# The variable 'x' has no type — only the object it points to has a type +x = 42 +print(type(x).__name__) # int -Hi, monkey +x = "hello" # rebind — no error, no type declaration +print(type(x).__name__) # str - +x = [1, 2, 3] +print(type(x).__name__) # list -## Q. Explain serialization and deserialization / Pickling and unpicking? +# Type is checked AT RUNTIME, not before execution +def add(a, b): + return a + b # works for int, float, str, list... -Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling. +print(add(1, 2)) # 3 +print(add("foo", "bar")) # foobar +print(add([1], [2])) # [1, 2] -To create portable serialized representations of Python objects, we have the module 'pickle'. It accepts a Python object (remember, everything in Python is an object). It then converts it into a string representation and uses the dump() function to dump it into a file. We call this pickling. In contrast, retrieving objects from this stored string representation is termed 'unpickling'. +# Runtime type error — only caught when the bad line is actually reached +def risky(value): + if value > 0: + return value * 2 + else: + return value + "oops" # TypeError only raised if value <= 0 -The pickle module implements binary protocols for serializing and deserializing a Python object structure. "Pickling" is the process whereby a Python object hierarchy is converted into a byte stream, and "unpickling" is the inverse operation, whereby a byte stream (from a binary file or bytes-like object) is converted back into an object hierarchy. Pickling (and unpickling) is alternatively known as `serialization`, `marshalling`, or `flattening`; however, to avoid confusion, the terms used here are `pickling` and `unpickling`. +print(risky(5)) # 10 — fine +# print(risky(-1)) # TypeError: unsupported operand type(s) for +: 'int' and 'str' -```py -import json -json_string = json.dumps([1, 2, 3, "a", "b"]) -print(json_string) - -import pickle -pickled_string = pickle.dumps([1, 2, 3, "a", "b"]) -print(pickle.loads(pickled_string)) +# Optional static typing via mypy — adds compile-time checking WITHOUT +# changing runtime behaviour +def typed_add(a: int, b: int) -> int: + return a + b + +# mypy would flag: typed_add("x", "y") — but Python still runs it ``` -Reference: +**Benefits:** Flexibility, rapid prototyping, less boilerplate. +**Drawbacks:** Type errors surface at runtime; harder to refactor large codebases (use `mypy` / `pyright` to compensate). -[1] https://www.sanfoundry.com/python-questions-answers-pickle-module/ -[2] https://docs.python-guide.org/scenarios/serialization/ +**Use Case:** A data science notebook exploits dynamic typing to reuse the same variable name across exploration phases (`data = load_csv()` → `data = clean(data)` → `data = aggregate(data)`) without ceremony — then `mypy` is added to the production export path for safety. -## Q. What are higher ordered functions? +## Q. Justify this statement: Everything is an object in Python. -You have two choices: you can use nested scopes or you can use callable objects. For example, suppose you wanted to define linear(a,b) which returns a function f(x) that computes the value a*x+b. -`Using nested scopes:` +In Python, **every value — integers, strings, functions, classes, modules, `None`, `True`** — is a first-class object. This means everything has: a type (`type()`), an identity (`id()`), attributes, and can be assigned to a variable, passed to a function, or stored in a collection. ```py -def linear(a,b): - def result(x): - return a*x + b - return result -``` +import inspect -Or - -`using a callable object:` - -```py -class linear: - def __init__(self, a, b): - self.a, self.b = a,b - def __call__(self, x): - return self.a * x + self.b -``` +# Integers are objects +x = 42 +print(type(x)) # +print(id(x)) # memory address +print(x.bit_length()) # 6 — integers have methods! -In both cases: +# Functions are objects +def greet(name: str) -> str: + return f"Hello, {name}" -`taxes = linear(0.3,2)` gives a callable object where +print(type(greet)) # +print(greet.__name__) # 'greet' +greet_alias = greet # assign function to variable +print(greet_alias("Alice")) # Hello, Alice -`taxes(10e6) == 0.3 * 10e6 + 2. ` +# Functions can be stored in collections +operations = [abs, str, type, len] +print([op(42) for op in operations]) # [42, '42', , ...] -The callable object approach has the disadvantage that it is a bit slower and results in slightly longer code. However, note that a collection of callables can share their signature via inheritance: +# Classes are objects (instances of 'type') +class Dog: + pass -```py -class exponential(linear): - __init__ inherited - def __call__(self, x): - return self.a * (x ** self.b) -``` +print(type(Dog)) # — Dog itself is an object +print(id(Dog)) # memory address of the class object -Object can encapsulate state for several methods: +# None is an object +print(type(None)) # +print(id(None)) # always the same — singleton -```py -class counter: - value = 0 - def set(self, x): - self.value = x - def up(self): - self.value=self.value+1 - def down(self): - self.value=self.value-1 - count = counter() +# Modules are objects +import os +print(type(os)) # +print(os.__file__) # path to os.py -inc, dec, reset = count.up, count.down, count.set +# Even types (classes) have methods inherited from object +print(dir(42)) # list of all methods on int +print(isinstance(42, object)) # True +print(isinstance(greet, object)) # True +print(isinstance(int, object)) # True — class itself is an object ``` -Here inc(), dec() and reset() act like functions which share the same counting variable. +**Implication:** Because everything is an object, Python enables true first-class functions, decorators, metaclasses, introspection, and dynamic behaviour that would require special syntax in other languages. + +**Use Case:** A plugin framework stores user-provided callables (functions, class instances with `__call__`, lambdas) in a `dict[str, Callable]` registry — possible only because Python treats all of them as first-class objects with a uniform interface. -## Q. How do I copy a file? How to copy object in Python? Diff between shallow copy and deep copy? - -The shutil module contains a `copyfile()` function. +## Q. What is the difference between Python and Java? -A deep copy copies an object into another. This means that if you make a change to a copy of an object, it won\'t affect the original object. In Python, we use the function deepcopy() for this, and we import the module copy. We use it like: +| Aspect | Python | Java | +|--------|--------|------| +| **Typing** | Dynamic (optional static via mypy) | Static (compile-time) | +| **Compilation** | Bytecode (.pyc) + VM | Bytecode (.class) + JVM with JIT | +| **Syntax** | Concise, indentation-based | Verbose, brace-based | +| **OOP** | Multi-paradigm (OOP, FP, procedural) | Primarily OOP | +| **Memory** | GC (ref counting + cyclic) | GC (generational) | +| **Concurrency** | GIL limits CPU threads; asyncio for I/O | True multi-threading, no GIL | +| **Performance** | Slower (CPython); PyPy/numba for speed | Faster (JIT compilation) | +| **Primary use** | Data science, scripting, web, automation | Enterprise backend, Android, big data | +| **Null safety** | `None` + `Optional[T]` typing | `null` — NullPointerException risk | +| **Entry point** | Top-level script or `if __name__ == "__main__"` | `public static void main(String[] args)` | ```py -import copy -b = copy.deepcopy (a) -``` +# Python: concise — less boilerplate +from dataclasses import dataclass -A shallow copy, however, copies one object\'s reference to another. So, if we make a change in the copy, it will affect the original object. For this, we have the function `copy()`, we use it like: +@dataclass +class Person: + name: str + age: int -```py -b = copy.copy(a) + def greet(self) -> str: + return f"Hello, I'm {self.name}, age {self.age}" + +p = Person("Alice", 30) +print(p.greet()) ``` -- Differentiate between lists and tuples. +```java +// Java equivalent — much more verbose +public class Person { + private String name; + private int age; -The major difference is that a list is mutable, but a tuple is immutable. Examples: + public Person(String name, int age) { + this.name = name; + this.age = age; + } -```py + public String greet() { + return "Hello, I'm " + name + ", age " + age; + } + // + getters, setters, equals, hashCode, toString... +} ``` -Traceback (most recent call last): -File "", line 1, in mytuple[1]=2 +**When to choose Python:** Prototyping, ML/DS, scripting, small-to-medium web APIs. +**When to choose Java:** High-throughput enterprise services, Android, large team type-safety requirements. -TypeError: 'tuple' object does not support item assignment +**Use Case:** A fintech firm writes its ML risk models in Python (pandas, scikit-learn) but the core transaction processing engine in Java (Spring Boot) for JIT performance and strict type safety — connecting them via a REST/gRPC boundary. -## Q. What is the purpose of PYTHONSTARTUP, PYTHONCASEOK, PYTHONHOME & PYTHONPATH environment variables? +## # 2. Data Types -- PYTHONSTARTUP − It contains the path of an initialization file containing Python source code. It is executed every time you start the interpreter. It is named as .pythonrc.py in Unix and it contains commands that load utilities or modify PYTHONPATH. - -- PYTHONCASEOK − It is used in Windows to instruct Python to find the first case-insensitive match in an import statement. Set this variable to any value to activate it. - -- PYTHONHOME − It is an alternative module search path. It is usually embedded in the PYTHONSTARTUP or PYTHONPATH directories to make switching module libraries easy. - -- PYTHONPATH − It has a role similar to PATH. This variable tells the Python interpreter where to locate the module files imported into a program. It should include the Python source library directory and the directories containing Python source code. PYTHONPATH is sometimes preset by the Python installer. +
- +## Q. How do I convert a number to a string? -## Q. Explain Inheritance in Python with an example? +To convert, e.g., the number 144 to the string '144', use the built-in function str(). If you want a hexadecimal or octal representation, use the built-in functions hex() or oct(). For fancy formatting, use the % operator on strings, e.g. "%04d" % 144 yields '0144' and "%.3f" % (1/3.0) yields '0.333'. See the library reference manual for details. -When one class inherits from another, it is said to be the child/ derived/sub class inheriting from the parent/base/super class. It inherits/gains all members (attributes and methods). Inheritance lets us reuse our code, and also makes it easier to create and maintain applications. + -Inheritance allows One class to gain all the members(say attributes and methods) of another class. Inheritance provides code reusability,makes it easier to create and maintain an application. The class from which we are inheriting is called super-class and the class that is inherited is called a derived/child class. +## Q. Does Python support strongly for regular expressions? -- They are different types of inheritance supported by Python: +Yes, Python Supports Regular Expressions Well. `re` is an in-buit library for the same. There is a lot of other languages that have good support to RegEx- Perl, Awk, Sed, Java etc. - - Single Inheritance – where a derived class acquires the members of a single super class. - - _OR_ - - - Single Inheritance- A class inherits from a single base class. - - - Multi-level inheritance – a derived class d1 in inherited from base class base1, and d2 is inherited from base2. +Regular expressions (called REs, or regexes, or regex patterns) are essentially a tiny, highly specialized programming language embedded inside Python and made available through the re module. Using this little language, you specify the rules for the set of possible strings that you want to match; this set might contain English sentences, or e-mail addresses, or TeX commands, or anything you like. You can then ask questions such as "Does this string match the pattern?", or "Is there a match for the pattern anywhere in this string?". You can also use REs to modify a string or to split it apart in various ways. - _OR_ - - Multilevel Inheritance- A class inherits from a base class, which in turn, inherits from another base class. - - - Hierarchical inheritance – from one base class you can inherit any number of child classes +Regular expression patterns are compiled into a series of bytecodes which are then executed by a matching engine written in C. For advanced use, it may be necessary to pay careful attention to how the engine will execute a given RE, and write the RE in a certain way in order to produce bytecode that runs faster. Optimization isn\'t covered in this document, because it requires that you have a good understanding of the matching engine\'s internals. - _OR_ - - Hierarchical Inheritance- Multiple classes inherit from a single base class. + - - Multiple inheritance – a derived class is inherited from more than one base class. +## Q. How do you perform pattern matching in Python? Explain. - _OR_ - - Multiple Inheritance- A class inherits from multiple base classes. - - - Hybrid Inheritance- Hybrid inheritance is a combination of two or more types of inheritance. +Regular Expressions/REs/ regexes enable us to specify expressions that can match specific "parts" of a given string. For instance, we can define a regular expression to match a single character or a digit, a telephone number, or an email address, etc. The Python\'s "re" module provides regular expression patterns and was introduce from later versions of Python 2.5. "re" module is providing methods for search text strings, or replacing text strings along with methods for splitting text strings based on the pattern defined. -## Q. What is Hierarchical Inheritance? - -The concept of inheriting the properties from one class into multiple classes separately is known as hierarchical inheritance. +## Q. Write a regular expression that will accept an email id. Use the `re` module. -Example: +Ans. ```py -class x(object): - def m1(self): - print("in m1 of x") - -class y(x): - def m2(self): - print("in m2 of y") - -class z(x): - def m3(self): - print("in m3 of z") -y1=y() -y1.m1() -y1.m2() -a=y1.--hash--() -print(a) -z1=z() -z1.m1() -z1.m3() -b=z1.hash--() -print(b) +import re +e = re.search(r'[0-9a-zA-Z.]+@[a-zA-Z]+\.(com|co\.in)$' 'JaiRameshwar@gmail.com') +e.group() ``` -Output: +'Ramayanwashere@gmail.com' -```py -M m1 of X -In m2 of Y -2337815 -In m1 of X -In m3 of Z -2099735 -``` +To brush up on regular expressions, check Regular Expressions in Python. -## Q. Suppose class C inherits from classes A and B as class C(A,B).Classes A and B both have their own versions of method func(). If we call func() from an object of class C, which version gets invoked? +### _Garbage Collector & Memory Manager_ -Ans. In our article on Multiple Inheritance in Python, we discussed Method Resolution Order (MRO). C does not contain its own version of func(). Since the interpreter searches in a left-to-right fashion, it finds the method in A, and does not go to look for it in B. +## Q. Explain split(), sub(), subn() methods of `re` module in Python. -## Q. Which methods/functions do we use to determine the type of instance and inheritance? +- To modify the strings, Python\'s "re" module is providing 3 methods. They are: + - split() – uses a regex pattern to "split" a given string into a list. + - sub() – finds all substrings where the regex pattern matches and then replace them with a different string. + - subn() – it is similar to sub() and also returns the new string along with the no. of replacements. -Ans. Here, we talk about three methods/functions- `type(), isinstance() and issubclass()`. +## Q. Which of the following is an invalid statement? -a. `type()`: This tells us the type of object we\'re working with. +a) abc = 1,000,000 +b) a b c = 1000 2000 3000 +c) a,b,c = 1000, 2000, 3000 +d) a_b_c = 1,000,000 +Answer: b - type(3) - +## Q. What is the output of the following? - type(False) +```py +try: + if '1' != 1: + raise +``` - +a) some Error has occured +b) some Error has not occured +c) invalid code +d) none of the above - type(lambda :print("Hi")) +Answer: C - +## Q. What is the output of the following? - type(type) +```py +x = ['ab', 'cd'] +print(len(map(list, x))) +``` - +A TypeError occurs as map has no len(). -b. isinstance() +## Q. What is the output of the following? -This takes in two arguments- a value and a type. If the value is of the kind of the specified type, it returns True. Else, it returns False. +```py +x = ['ab', 'cd'] +print(len(list(map(list, x)))) +``` - isinstance(3,int) -True +The length of each string is 2. - isinstance((1),tuple) -False +## Q. Write a Python function that checks whether a passed string is palindrome Or not? - isinstance((1,),tuple) -True +Note: A palindrome is a word, phrase, or sequence that reads the same backward as forward, e.g., madam , saas, nun. -c. issubclass() +```py +def isPalindrome(string): + left_pos = 0 + right_pos = len(string) – 1 -This takes two classes as arguments. If the first one inherits from the second, it returns True. Else, it returns False. + while right_pos >= left_pos: + if not string[left_pos] == string[right_pos]: + return False - class A: pass + left_pos += 1 + right_pos -= 1 + return True +print(isPalindrome('aza')) +``` + + + +## Q. Explain join() and split() in Python? + +`.join([])` It takes any iterables into this method. Join method is used to concatenate the elements of any list. `join()` lets us join characters from a string together by a character we specify. + +```py +','.join('12345') +``` + +'1,2,3,4,5' + +`split()` lets us split a string around the character we specify. + +```py +'1,2,3,4,5'.split(',') +``` + +['1', '2', '3', '4', '5'] + +## Q. Is Python case-sensitive? + +A language is case-sensitive if it distinguishes between identifiers like myname and Myname. In other words, it cares about case- lowercase or uppercase. Let\'s try this with Python. + +```py +myname='Ramayan' +Myname +``` + +Traceback (most recent call last): +File "", line 1, in Myname +NameError: name 'Myname' is not defined + +As you can see, this raised a NameError. This means that Python is indeed case-sensitive. + +- How long can an identifier be in Python? + +In Python, an identifier can be of any length. Apart from that, there are certain rules we must follow to name one: +> + - It can only begin with an underscore or a character from A-Z or a-z. + - The rest of it can contain anything from the following: A-Z/a-z/_/0-9. + - Python is case-sensitive, as we discussed in the previous question. + - Keywords cannot be used as identifiers. + +Python has the following keywords: +`and def False import not True` +`as del finally in or try` +`assert elif for is pass while` +`break else from lambda print with ` +`class except global None raise yield` +`continue exec if nonlocal return` + + + +## Q. How do you remove the leading whitespace in a string? + +Leading whitespace in a string is the whitespace in a string before the first non-whitespace character. To remove it from a string, we use the method `lstrip()`. + +```py +' Ram '.lstrip() +``` + +'Ram ' + +As you can see, this string had both leading and trailing whitespaces. lstrip() stripped the string of the leading whitespace. If we want to strip the trailing whitespace instead, we use rstrip(). + +```py +' Ram '.rstrip() +``` + +' Ram' + +- How would you convert a string into lowercase? + +We use the lower() method for this. + +```py +'Ramayan'.lower() +``` + +'ramayan' + +To convert it into uppercase, then, we use upper(). + +```py +'Ramayan'.upper() +``` + +'RAMAYAN' + +Also, to check if a string is in all uppercase or all lowercase, we use the methods isupper() and islower(). + +```py +'Ramayan'.isupper() +``` + +False + +```py +'Ramayan'.isupper() +``` + +True + +```py +'Ramayan'.islower() +``` + +True + +```py +'$hrir@m'.islower() +``` + +True + +```py +'$HRIR@M'.isupper() +``` + +True + +So, characters like @ and $ will suffice for both cases. + +Also, istitle() will tell us if a string is in title case. + +```py +'Arrested Development'.istitle() +``` + +True + + + +## Q. What data types does Python support? + +Python provides us with five kinds of data types: + +```py + a=7.0 + title="Ramayan\'s Book" + colors=['red','green','blue'] + type(colors) + + name=('Ramayan','Sharma') + name[0]='Avery' + Traceback (most recent call last): + File ", line 1, in name[0]='Avery' + TypeError: 'tuple' object does not support item assignment + squares={1:1,2:4,3:9,4:16,5:25} + type(squares) + + type({}) + + squares={x:x**2 for x in range(1,6)} + squares + {1: 1, 2: 4, 3: 9, 4: 16, 5: 25} +``` + +## Q. What is a docstring? + +A docstring is a documentation string that we use to explain what a construct does. We place it as the first thing under a function, class, or a method, to describe what it does. We declare a docstring using three sets of single or double quotes. + +```py +def sayhi(): + """ + The function prints Hi + """ + print("Hi") + +sayhi() +``` + +Hi + +To get a function\'s docstring, we use its `__doc__` attribute. + +```py +sayhi.__doc__ +``` + +'\n\tThis function prints Hi\n\t' + +A docstring, unlike a comment, is retained at runtime. + +## Q. What is slicing? + +These are the types of basic Python interview questions for freshers. + +Slicing is a technique that allows us to retrieve only a part of a list, tuple, or string. For this, we use the slicing operator []. + +```py + (1,2,3,4,5)[2:4] + (3, 4) + [7,6,8,5,9][2:] + [8, 5, 9] + 'Hello'[:-1] + 'Hell' +``` + +## Q. What is a namedtuple? + +A namedtuple will let us access a tuple\'s elements using a name/label. We use the function namedtuple() for this, and import it from collections. + +```py + from collections import namedtuple + result=namedtuple('result','Physics Chemistry Maths') #format + Ramayan=result(Physics=86,Chemistry=95,Maths=86) #declaring the tuple + Ramayan.Chemistry +``` +`95` + +As you can see, it let us access the marks in Chemistry using the Chemistry attribute of object Ramayan. + +## Q. How would you declare a comment in Python? + +Unlike languages like C++, Python does not have multiline comments. All it has is octothorpe (#). Anything following a hash is considered a comment, and the interpreter ignores it. + + #line 1 of comment + #line 2 of comment + +In fact, you can place a comment anywhere in your code. You can use it to explain your code. + + + +## Q. How would you convert a string into an int in Python? + +If a string contains only numerical characters, you can convert it into an integer using the int() function. + + int('227') + +227 + +Let\'s check the types: + + type('227') + + + + type(int('227')) + + + + + +## Q. How do you take input in Python? + +For taking input from user, we have the function input(). In Python 2, we had another function raw_input(). + +The input() function takes, as an argument, the text to be displayed for the task: + + a=input('Enter a number') + +Enter a number7 + +But if you have paid attention, you know that it takes input in the form of a string. + + type(a) + + + +Multiplying this by 2 gives us this: + + a*=2 + a + +'77' + +So, what if we need to work on an integer instead? + +We use the int() function for this. + + a=int(input('Enter a number')) + +Enter a number7 + +Now when we multiply it by 2, we get this: + + a*=2 + a + +`14` + + + +## Q. What is a frozen set in Python? + +Answer these type of Python Interview Questions with Examples. + +First, let\'s discuss what a set is. A set is a collection of items, where there cannot be any duplicates. A set is also unordered. + + myset={1,3,2,2} + myset + +`{1, 2, 3}` + +This means that we cannot index it. + + myset[0] + +Traceback (most recent call last): +File "", line 1, in myset[0] +TypeError: \'set' object does not support indexing + +However, a set is mutable. A frozen set is immutable. This means we cannot change its values. This also makes it eligible to be used as a key for a dictionary. + + myset=frozenset([1,3,2,2]) + myset + + frozenset({1, 2, 3}) + + type(myset) + + + + +## Q. How will you capitalize the first letter of a string? + +Simply using the method capitalize(). + + 'Ramayan'.capitalize() + +'Ramayan' + + type(str.capitalize) + + + +However, it will let other characters be. + + '$hrir@m'.capitalize() + +'$HRIR@M' + + + +## Q. How will you check if all characters in a string are alphanumeric? + +For this, we use the method isalnum(). + + 'Ramayan123'.isalnum() + +True + + 'Ramayan123!'.isalnum() + +False + +Other methods that we have include: + + '123.3'.isdigit() + +False + + '123'.isnumeric() + +True + + 'Ramayan'.islower() + +True + + 'Ramayan'.isupper() + +False + + 'Ramayan'.istitle() + +True + + ' '.isspace() + +True + + '123F'.isdecimal() + +False + + + +## Q. What is the concatenation? + +This is very basic Python Interview Question, try not to make any mistake in this. + +Concatenation is joining two sequences. We use the + operator for this. + + '32'+'32' + '3232' + + [1,2,3]+[4,5,6] + [1, 2, 3, 4, 5, 6] + + (2,3)+(4) + `Traceback (most recent call last): ` + `File "", line 1, in (2,3)+(4)` + `TypeError: can only concatenate tuple (not "int") to tuple` + +Here, 4 is considered an int. Let\'s do this again. + + (2,3)+(4,) # (obj,) is way to declare single empty + (2, 3, 4) + + + +## Q. How will you find, in a string, the first word that rhymes with 'cake'? + +For our purpose, we will use the function search(), and then use group() to get the output. + + import re + rhyme=re.search('.ake','I would make a cake, but I hate to bake') + rhyme.group() + +'make' + +And as we know, the function search() stops at the first match. Hence, we have our first rhyme to 'cake'. + +## Q. How do you calculate the length of a string? + +This is simple. We call the function len() on the string we want to calculate the length of. + + len('Adi Shakara') + + + +## Q. What does the following code output? + + def extendList(val, list=[]): + list.append(val) + return list + list1 = extendList(10) + list2 = extendList(123,[]) + list3 = extendList('a') + list1,list2,list3 + +Ans. ([10, 'a'], [123], [10, 'a']) + +You'd expect the output to be something like this: + +([10],[123],['a']) + +Well, this is because the list argument does not initialize to its default value ([]) every time we make a call to the function. Once we define the function, it creates a new list. Then, whenever we call it again without a list argument, it uses the same list. This is because it calculates the expressions in the default arguments when we define the function, not when we call it. + +Let\'s revise the Basis of Python Programming + + + +## Q. What are The Data Types Supports in Python Language? + +- Numbers- Numbers use to hold numerical values. +- Strings- A string is a sequence of characters. We declare it using single or double quotes. +- Lists- A list is an ordered collection of values, and we declare it using square brackets. +- Tuples- A tuple, like a list, is an ordered collection of values. The difference. However, is that a tupleis immutable. This means that we cannot change a value in it. +- Dictionary- A dictionary is a data structure that holds key-value pairs. We declare it using curly braces. +- We can also use a dictionary comprehension: + +Numbers: + Int + Float + Complex +Boolean +Operational: + Strings + List + Tuple + Set + Dictionary + +Every data type in python language is internally implemented as a class. Python language data types are categorized into two types. + +They are: + +Fundamental Types +Collection Types + + + +### _Control Flow_ + +## Q. In Python what is slicing? + +A mechanism to select a range of items from sequence types like list, tuple, strings etc. is known as slicing. + + + +## Q. How you can convert a number to a string? + +In order to convert a number into a string, use the inbuilt function str(). If you want a octal or hexadecimal representation, use the inbuilt function oct() or hex(). + +## Q. What are some built-in types in Python? + +```py +import sys + +# Numeric types +i: int = 42 # arbitrary precision integer +f: float = 3.14159 # IEEE 754 double (64-bit) +c: complex = 2 + 3j # complex number +b: bool = True # subclass of int (True==1, False==0) + +# Text / Bytes +s: str = "hello" # immutable Unicode sequence (PEP 393) +by: bytes = b"hello" # immutable byte sequence +ba: bytearray = bytearray(b"x")# mutable byte sequence + +# Collections +lst: list = [1, "a", 3.0] # ordered, mutable, heterogeneous +tpl: tuple = (1, "a", 3.0) # ordered, immutable +dct: dict = {"key": "value"} # hash map, insertion-ordered (3.7+) +st: set = {1, 2, 3} # unordered, unique, mutable +fs: frozenset = frozenset({1}) # unordered, unique, immutable/hashable + +# Singleton types +n = None # NoneType — single instance +e = ... # Ellipsis — used in type hints & numpy +ni = NotImplemented # returned by comparison dunder methods + +print(type(True).__mro__) # (, , ) +print(sys.getsizeof(True)) # 28 bytes (bool is int subclass) +print(isinstance(True, int)) # True +``` + +**Use Case:** Understanding that `bool` is a subclass of `int` prevents subtle bugs — a function accepting `int` will silently accept `True`/`False`. Teams enforcing strict APIs use `isinstance(val, int) and not isinstance(val, bool)` for explicit integer validation. + + + +## Q. What are bindings — what does it mean for a value to be bound to a variable? + +In Python, **variables are names (bindings) that reference objects** — they are not containers that hold values. Assignment (`=`) creates a binding between a name and an object in the current namespace (a dictionary). The object itself exists independently; multiple names can reference the same object. + +```py +import sys + +# Binding: name 'x' points to the integer object 42 +x = 42 +print(id(x)) # memory address of the int object 42 + +# Rebinding: x now points to a new string object; 42 is unchanged +x = "hello" +print(id(x)) # different address + +# Aliasing: both names reference the SAME list object +a = [1, 2, 3] +b = a # b is an alias for a, not a copy +b.append(4) +print(a) # [1, 2, 3, 4] — mutated through b + +# Check if two names point to the same object +print(a is b) # True (same identity) +print(a == b) # True (same value — would also be True for equal copies) + +# Multiple bindings to same small integer (cache proof) +x = 100; y = 100 +print(x is y) # True — same cached int object + +# Checking refcount +sys.getrefcount(a) # number of bindings pointing to the list object + +# del unbinds the name, not necessarily the object +del b +print(a) # still [1, 2, 3, 4] — object lives on via 'a' +``` + +**Use Case:** Understanding bindings vs copies prevents data corruption in distributed processing — when passing a `dict` config to multiple worker functions, each worker receives a binding to the same object. Modifying it in one worker unexpectedly affects others; a `copy.deepcopy()` creates an independent object instead. + + + +## Q. What is a Unicode string? + +A **Unicode string** is a sequence of **Unicode code points** — an international standard covering 149,000+ characters across all writing systems, symbols, and emoji. In Python 3, **all `str` objects are Unicode** by default. The encoding (e.g., UTF-8, UTF-16) only matters when converting to/from `bytes`. + +```py +# Python 3: str IS Unicode +greeting = "こんにちは" # Japanese — 5 code points +emoji = "Python 🐍" +arabic = "مرحبا" + +print(len(greeting)) # 5 — counts code points, not bytes +print(len(greeting.encode("utf-8"))) # 15 — 3 bytes per CJK character + +# Code point inspection +for ch in "café": + print(f"{ch!r} U+{ord(ch):04X} {ch.encode('utf-8').hex()}") +# 'c' U+0063 63 +# 'a' U+0061 61 +# 'f' U+0066 66 +# 'é' U+00E9 c3a9 (2-byte UTF-8 sequence) + +# Encoding: str → bytes +text = "résumé" +utf8_bytes = text.encode("utf-8") +latin_bytes = text.encode("latin-1") +print(utf8_bytes) # b'r\xc3\xa9sum\xc3\xa9' +print(latin_bytes) # b'r\xe9sum\xe9' + +# Decoding: bytes → str +print(utf8_bytes.decode("utf-8")) # résumé + +# Normalization (NFC vs NFD) +import unicodedata +e_composed = "\u00e9" # é as single code point (NFC) +e_decomposed = "e\u0301" # e + combining accent (NFD) +print(e_composed == e_decomposed) # False! +print(unicodedata.normalize("NFC", e_decomposed) == e_composed) # True +``` + +**Use Case:** A global SaaS application normalizes all user-submitted strings to NFC Unicode before storing them, preventing duplicate user records where `café` (composed form) and `cafe\u0301` (decomposed form) would compare as unequal strings despite looking identical. + + + +## Q. Explain indexing and slicing. + +**Indexing** accesses a single element by position (0-based; negative indices count from the end). **Slicing** extracts a sub-sequence using `[start:stop:step]`. + +```py +lst = [10, 20, 30, 40, 50, 60, 70, 80, 90] +# 0 1 2 3 4 5 6 7 8 +# -9 -8 -7 -6 -5 -4 -3 -2 -1 + +# Indexing +print(lst[0]) # 10 — first element +print(lst[-1]) # 90 — last element +print(lst[-3]) # 70 — third from end + +# Slicing: [start:stop:step] — stop is EXCLUSIVE +print(lst[2:5]) # [30, 40, 50] start=2, stop=5 +print(lst[:3]) # [10, 20, 30] from beginning +print(lst[6:]) # [70, 80, 90] to end +print(lst[::2]) # [10, 30, 50, 70, 90] every 2nd +print(lst[::-1]) # [90, 80, 70, 60, 50, 40, 30, 20, 10] reversed +print(lst[1:8:3]) # [20, 50, 80] start=1, stop=8, step=3 + +# Strings — same rules +s = "Python" +print(s[0]) # 'P' +print(s[-1]) # 'n' +print(s[1:4]) # 'yth' +print(s[::-1]) # 'nohtyP' + +# Slice assignment (mutable sequences only) +lst[1:3] = [200, 300] +print(lst) # [10, 200, 300, 40, 50, 60, 70, 80, 90] + +lst[::2] = [0] * 5 # replace every even index +print(lst) + +# slice object — reusable named slice +HEADER = slice(None, 3) +DATA = slice(3, -1) +row = [1, 2, 3, 4, 5, 6, 7] +print(row[HEADER]) # [1, 2, 3] +print(row[DATA]) # [4, 5, 6] +``` + +**Use Case:** A data ingestion service parses fixed-width records using named `slice` objects (`ACCOUNT_NO = slice(0, 8)`, `AMOUNT = slice(8, 16)`) — replacing magic index numbers with readable identifiers and making format changes a one-line update. + + + +## # 3. Dictionary + +
+ +## Q. How is the Implementation of Python\'s dictionaries done? + +Python dictionary needs to be declared first: +`dict = {}` + +Key value pair can be added as: +`dict[key] = value` +or +`objDict.update({key:value})` + +Remove element by: +`dict.pop(key)` + +Remove all: +`objDict.clear()` + +A hash value of the key is computed using a hash function, The hash value addresses a location in an array of "buckets" or "collision lists" which contains the (key , value) pair. + + + +## Q. What is a dictionary in Python? + +The built-in datatypes in Python is called dictionary. It defines one-to-one relationship between keys and values. Dictionaries contain pair of keys and their corresponding values. Dictionaries are indexed by keys. + +Let\'s take an example: + +The following example contains some keys. Country, Capital & PM. Their corresponding values are India, Delhi and Modi respectively. + +`dict={'Country':'India','Capital':'Delhi','PM':'Modi'}` + +`print dict[Country]` + +```py +roots={25:5,16:4,9:3,4:2,1:1} +type(roots) +``` + + + +```py +roots[9] +``` + +3 + +A dictionary is mutable, and we can also use a comprehension to create it. + +```py +roots={x**2:x for x in range(5,0,-1)} +roots +``` + +{25: 5, 16: 4, 9: 3, 4: 2, 1: 1} + +## Q. How do you get a list of all the keys in a dictionary? + +Be specific in these type of Python Interview Questions and Answers. + +For this, we use the function keys(). + +```py +mydict={'a':1,'b':2,'c':3,'e':5} +mydict.keys() +print(dict_keys) +``` + +(['a', 'b', 'c', 'e']) + + + +## Q. What is the Dictionary? + +- Dictionary objects can be created by using curly braces{} or by calling dictionary function. + +- Dictionary objects are mutable objects. + +- Dictionary represents key value base. + +- Each key value pair of Dictionary is known as a item. + +- Dictionary keys must be immutable. + +- Dictionary values can be mutable or immutable. + +- Duplicate keys are not allowed but values can be duplicate. + +- Insertion order is not preserved. + +- Heterogeneous keys and heterogeneous values are allowed. + + + +## Q. How do you create a dictionary? + +```py +# Method 1: Dict literal +user: dict[str, Any] = {"name": "Alice", "age": 30, "active": True} + +# Method 2: dict() constructor with keyword arguments +config = dict(host="localhost", port=5432, db="prod") + +# Method 3: dict() from list of (key, value) pairs +pairs = dict([("a", 1), ("b", 2), ("c", 3)]) + +# Method 4: Dict comprehension +word_len: dict[str, int] = {w: len(w) for w in ["python", "java", "rust"]} + +# Method 5: dict.fromkeys() — initialize all keys to same value +counters: dict[str, int] = dict.fromkeys(["hits", "misses", "errors"], 0) +print(counters) # {'hits': 0, 'misses': 0, 'errors': 0} + +# Method 6: Merging dicts (Python 3.9+ | operator) +defaults = {"timeout": 30, "retries": 3} +overrides = {"timeout": 60} +merged = defaults | overrides # {'timeout': 60, 'retries': 3} + +# Key operations +d = {"a": 1, "b": 2, "c": 3} +print(d.get("z", 0)) # 0 — safe get with default +d.setdefault("d", 4) # inserts only if key absent +print(list(d.keys())) # dict_keys view — O(1) iteration +print("b" in d) # O(1) hash lookup + +# Nested access safely +from collections import defaultdict +nested: defaultdict = defaultdict(lambda: defaultdict(int)) +nested["user_1"]["clicks"] += 1 +``` + +**Use Case:** A caching layer in a web service uses a `dict` as an in-memory LRU cache (backed by `collections.OrderedDict` or `functools.lru_cache`) for O(1) average-case lookups of pre-computed API responses, reducing database round-trips by 80%. + + + +## Q. Explain `dict`. + +A Python `dict` is a **hash map** — a mutable, unordered (insertion-ordered since Python 3.7+) collection of key-value pairs with O(1) average-case lookup, insertion, and deletion. + +```py +from collections import defaultdict, Counter, OrderedDict +from typing import Any + +# Creation +d: dict[str, Any] = {"name": "Alice", "age": 30, "active": True} + +# Access patterns +print(d["name"]) # 'Alice' — KeyError if missing +print(d.get("city", "N/A")) # 'N/A' — safe get with default +d.setdefault("score", 0) # insert only if key absent +print(d) + +# Iteration — all O(n) +for key in d: print(key) # keys +for key, val in d.items(): print(key, val) # key-value pairs +for val in d.values(): print(val) # values + +# Comprehension +squares = {x: x**2 for x in range(5)} # {0:0, 1:1, 2:4, 3:9, 4:16} + +# Merging (Python 3.9+) +defaults = {"timeout": 30, "retries": 3} +overrides = {"timeout": 60} +merged = defaults | overrides # {'timeout': 60, 'retries': 3} + +# Useful dict subclasses +word_count: defaultdict[str, int] = defaultdict(int) +for word in "the cat sat on the mat".split(): + word_count[word] += 1 +print(dict(word_count)) # {'the': 2, 'cat': 1, 'sat': 1, 'on': 1, 'mat': 1} + +counter = Counter("abracadabra") +print(counter.most_common(3)) # [('a', 5), ('b', 2), ('r', 2)] + +# Time complexity +import timeit +n = 100_000 +lst = list(range(n)); s = set(range(n)); d2 = dict.fromkeys(range(n)) +t_list = timeit.timeit(lambda: 99999 in lst, number=10000) +t_set = timeit.timeit(lambda: 99999 in s, number=10000) +t_dict = timeit.timeit(lambda: 99999 in d2, number=10000) +print(f"list: {t_list:.4f}s set: {t_set:.6f}s dict: {t_dict:.6f}s") +# dict and set are ~100-1000x faster than list for membership +``` + +**Internal mechanics:** +- Uses open addressing with pseudorandom probing +- Load factor ~2/3 — resizes (doubles) when exceeded +- Keys must be **hashable** (immutable): `str`, `int`, `tuple`, `frozenset` +- `dict` remembers insertion order (CPython 3.6+ impl detail, 3.7+ language spec) + +**Use Case:** An API gateway caches authentication tokens in a `dict` keyed by `user_id`, providing O(1) token validation for every request without hitting the database — reducing auth latency from 50ms to <1ms. + + + +## Q. How do you create a dictionary that preserves the order of pairs? + +Since **Python 3.7+**, the built-in `dict` **preserves insertion order** as part of the language specification. For Python 3.6 (CPython implementation detail) and older, use `collections.OrderedDict`. + +```py +from collections import OrderedDict + +# Python 3.7+ — plain dict preserves insertion order +d = {} +d["z"] = 1 +d["a"] = 2 +d["m"] = 3 +print(list(d.keys())) # ['z', 'a', 'm'] — insertion order preserved + +# Dict literal also preserves order +config = { + "host": "localhost", + "port": 5432, + "database": "prod", + "user": "app", +} +print(list(config.keys())) # ['host', 'port', 'database', 'user'] + +# OrderedDict — useful for equality-by-order comparison +od1 = OrderedDict([("a", 1), ("b", 2)]) +od2 = OrderedDict([("b", 2), ("a", 1)]) +print(od1 == od2) # False — order matters for OrderedDict + +d1 = {"a": 1, "b": 2} +d2 = {"b": 2, "a": 1} +print(d1 == d2) # True — order doesn't matter for plain dict equality + +# OrderedDict.move_to_end() — LRU cache implementation +from collections import OrderedDict + +class LRUCache: + def __init__(self, capacity: int) -> None: + self._cache: OrderedDict = OrderedDict() + self._cap = capacity + + def get(self, key: str): + if key not in self._cache: + return None + self._cache.move_to_end(key) # mark as recently used + return self._cache[key] + + def put(self, key: str, value) -> None: + if key in self._cache: + self._cache.move_to_end(key) + self._cache[key] = value + if len(self._cache) > self._cap: + self._cache.popitem(last=False) # evict least recently used + +cache = LRUCache(3) +cache.put("a", 1); cache.put("b", 2); cache.put("c", 3) +cache.get("a") # moves 'a' to end +cache.put("d", 4) # evicts 'b' (least recently used) +print(list(cache._cache.keys())) # ['c', 'a', 'd'] +``` + +**Use Case:** A configuration loader builds an `OrderedDict` from a YAML file to preserve the human-authored key order — when the config is serialised back to YAML for diff review, keys appear in the same order as the original, making change reviews readable. + + + +## Q. Can you use a mutable data structure as a key in a dictionary? + +**No.** Dictionary keys must be **hashable**, and all mutable built-in types (`list`, `dict`, `set`) are **not hashable**. Attempting to use them raises `TypeError`. + +```py +# Mutable types — NOT hashable, cannot be dict keys +try: + d = {[1, 2, 3]: "value"} # list key +except TypeError as e: + print(f"TypeError: {e}") # unhashable type: 'list' + +try: + d = {{1: "a"}: "value"} # dict key +except TypeError as e: + print(f"TypeError: {e}") # unhashable type: 'dict' + +# Immutable types — hashable, valid dict keys +valid_keys = { + "string": 1, + 42: 2, + 3.14: 3, + True: 4, + (1, 2, 3): 5, # tuple of immutables — hashable + frozenset({1, 2}): 6, # frozenset — hashable +} +print(valid_keys[(1, 2, 3)]) # 5 + +# A tuple is only hashable if ALL its elements are hashable +try: + d = {(1, [2, 3]): "value"} # tuple containing a list +except TypeError as e: + print(f"TypeError: {e}") # unhashable type: 'list' + +# Making a custom object hashable +class Point: + def __init__(self, x: float, y: float) -> None: + self.x, self.y = x, y + + def __eq__(self, other: object) -> bool: + if not isinstance(other, Point): return NotImplemented + return self.x == other.x and self.y == other.y + + def __hash__(self) -> int: + return hash((self.x, self.y)) # hash based on immutable tuple + +grid: dict[Point, str] = {} +grid[Point(0, 0)] = "origin" +grid[Point(1, 2)] = "marker" +print(grid[Point(0, 0)]) # origin + +# Rule: if __eq__ is defined, __hash__ MUST also be defined +# Python sets __hash__ = None if __eq__ is defined without __hash__ +``` + +**Rule:** Hashable = immutable structure + consistent `__hash__`. `hash(x) == hash(y)` whenever `x == y`. + +**Use Case:** A graph algorithm caches computed shortest paths using `frozenset({source, destination})` as a dict key — symmetric pairs `(A→B)` and `(B→A)` map to the same cache entry, halving cache misses. + + + +## # 4. Operators + +
+ +## Q. Explain the //, %, and ** operators in Python. + +The // operator performs floor division. It will return the integer part of the result on division. + +```py +7//2 +``` +`3` +Normal division would return 3.5 here. + +Similarly, **performs exponentiation. a**b returns the value of a raised to the power b. + +```py +2**10 +``` + +1024 + +Finally, % is for modulus. This gives us the value left after the highest achievable division. + +```py +13 % 7 +``` + +6 + +```py +3.5 % 1.5 +``` + +0.5 + +## Q. How many kinds of operators do we have in Python? Explain arithmetic operators. + +This type of Python Interview Questions and Answers can decide your knowledge in Python. Answer the Python Interview Questions with some good Examples. + +Here in Python, we have 7 kinds of operators: arithmetic, relational assignment, logical, membership, identity, and bitwise. + +We have seven arithmetic operators. These allow us to perform arithmetic operations on values: + +Addition (+) This adds two values. + +```py +7+8 +7-8 +7*8 +7/8 # 0.875 +7//8 # 0 +``` + + + +## Q. Explain relational operators in Python? + +Relational operators compare values. + +> - Less than (<) If the value on the left is lesser, it returns True. + +```py +'hi'<'Hi' +``` + +False + + Greater than (>) If the value on the left is greater, it returns True. + + 1.1 + 2.2 > 3.3 + +True + +This is because of the flawed floating-point arithmetic in Python, due to hardware dependencies. + + Less than or equal to (<=) If the value on the left is lesser than or equal to, it returns True. + + 3.0 <= 3 + +True + + Greater than or equal to (>=) If the value on the left is greater than or equal to, it returns True. + + True >= False + +True + + Equal to (==) If the two values are equal, it returns True. + + {1,3,2,2} == {1,2,3} + +True + + Not equal to (!=) If the two values are unequal, it returns True. + + True!=0.1 + +True + + False!=0.1 + +True + + + +## Q. What are assignment operators in Python? + +This one is an Important Interview question in Python Interview. + +We can combine all arithmetic operators with the assignment symbol. + + a = 7 + a += 1 + a + +8 + + a -= 1 + a + +7 + + a*=2 + a + +14 + + a/=2 + a + +7.0 + + a**=2 + a + +49.0 + + a//=3 + a + +16.0 + + a%=4 + a + +0.0 + + + +## Q. Explain logical operators in Python. + +We have three logical operators- and, or, not. + + False and True + +False + + 7<7 or True + +True + + not 2==2 + +False + +## Q. What are membership, operators? + +With the operators 'in' and 'not in', we can confirm if a value is a member in another. + + 'me' in 'disappointment' +True + + 'us' not in 'disappointment' +True + +## Q. Explain identity operators in Python. + +This is one of the very commonly asked Python Interview Questions and answers it with examples. +The operators 'is' and 'is not' tell us if two values have the same identity. + + 10 is '10' +False + + True is not False + +True + +## Q. Finally, tell us about bitwise operators in Python. + +These operate on values bit by bit. + + AND (&) This performs & on each bit pair. + + 0b110 & 0b010 + +2 + + OR (|) This performs | on each bit pair. + + 3|2 + +3 + + XOR (^) This performs an exclusive-OR operation on each bit pair. + + 3^2 + +1 + + Binary One\'s Complement (~) This returns the one\'s complement of a value. + + ~2 + +-3 + + Binary Left-Shift (<<) This shifts the bits to the left by the specified amount. + + 1<<2 + +4 + +Here, 001 was shifted to the left by two places to get 100, which is binary for 4. + + Binary Right-Shift (>>) + + 4>>2 + +1 + +For more insight on operators, refer to Operators in Python. + + + +## Q. How would you work with numbers other than those in the decimal number system? + +With Python, it is possible to type numbers in binary, octal, and hexadecimal. + + Binary numbers are made of 0 and 1. To type in binary, we use the prefix 0b or 0B. + + int(0b1010) + +10 + +To convert a number into its binary form, we use bin(). + + bin(0xf) + +'0b1111' + + Octal numbers may have digits from 0 to 7. We use the prefix 0o or 0O. + + oct(8) + +'0o10' + + Hexadecimal numbers may have digits from 0 to 15. We use the prefix 0x or 0X. + + hex(16) + +'0x10' + + hex(15) + +'0xf' + +## Q. Mention the use of // operator in Python? + +It is a Floor Divisionoperator , which is used for dividing two operands with the result as quotient showing only digits before the decimal point. For instance, 10//5 = 2 and 10.0//5.0 = 2.0. + + + +## # 5. Control Flow Statements + +
+ +## Q. What is the built-in function used in Python to iterate over a sequence of numbers? + +Syntax: `range(start,end,step count)` + +Ex: + +```py +a = range(1,10,2) +print (a) +``` +Output: +`[1, 3, 5, 7, 9]` + +If using to iterate + +```py +for i in range(1,10): + print (i) +``` + +Output: + +> + 1 + 2 + 3 + 4 + 5 + 6 + 7 + 8 + 9 + + + +## Q. What is the statement that can be used in Python if a statement is required syntactically but the program requires no action? + +`pass` keyword is used to do nothing but it fulfill the syntactical requirements. + +```py +try x[10]: + print(x) +except: + pass +``` + +Use `pass` keyword over there like: + +```py +if a > 0: + print("Hello") +else: + pass +``` + + + +## Q. Explain the ternary operator in Python? + +Unlike C++, we don\'t have ?: in Python, but we have this: + +[on true] if [expression] else [on false] + +If the expression is True, the statement under [on true] is executed. Else, that under [on false] is executed. + +Below is how you would use it: +ex 1. + +```py +a,b=2,3 +min=a if a + ↥ back to top + + +## Q. What is the pass statement in Python? + +There may be times in our code when we haven\'t decided what to do yet, but we must type something for it to be syntactically correct. In such a case, we use the pass statement. + +```py +def func(*args): + pass +``` + +Similarly, the break statement breaks out of a loop. + +```py +for i in range(7): + if i==3: break + print(i) +``` + +0 +1 +2 + +Finally, the continue statement skips to the next iteration. + +```py +for i in range(7): + if i==3: continue + print(i) +``` +0 +1 +2 +4 +5 +6 + + + +## Q. If you are ever stuck in an infinite loop, how will you break out of it? + +For this, we press Ctrl+C. This interrupts the execution. Let\'s create an infinite loop to demonstrate this. + + def counterfunc(n): + while(n==7):print(n) + counterfunc(7) + +7 +7 +7 +7 +. +. +. +. +. +Traceback (most recent call last): +File "", line 1, in counterfunc(7) +File "", line 2, in counterfunc +while(n==7):print(n) +KeyboardInterrupt + + + +## Q. How many arguments can the range() function take? + +Ans. The range() function in Python can take up to 3 arguments. Let\'s see this one by one. + +a. One argument + +When we pass only one argument, it takes it as the stop value. Here, the start value is 0, and the step value is +1. + + list(range(5)) + +[0, 1, 2, 3, 4] + + list(range(-5)) + +[] + + list(range(0)) + +[] + +b. Two arguments + +When we pass two arguments, the first one is the start value, and the second is the stop value. + + list(range(2,7)) + +[2, 3, 4, 5, 6] + + list(range(7,2)) + +[] + + list(range(-3,4)) + +[-3, -2, -1, 0, 1, 2, 3] + +c. Three arguments + +Here, the first argument is the start value, the second is the stop value, and the third is the step value. + + list(range(2,9,2)) + +[2, 4, 6, 8] + + list(range(9,2,-1)) + +[9, 8, 7, 6, 5, 4, 3] + + + +## Q. Explain Control flow statements. + +By default python program execution starts from first line, execute each and every statements only once and transactions the program if the last statement of the program execution is over. +Control flow statements are used to disturb the normal flow of the execution of the program. + +## Q. What are the two major loop statements? + +`for and while` + +## Q. Under what circumstances would one use a `while` statement rather than `for`? + +The while statement is used for simple repetitive looping and the for statement is used when one wishes to iterate through a list of items, such as database records, characters in a string, etc. + +## Q. What happens if output an `else` statement after after block? + +The code in the else block is executed after the for loop completes, unless a break is encountered in the for loop execution. in which case the else block is not executed. + +## Q. Explain the use of break and continue in Python looping. + +The break statement stops execution of the current loop. and transfers control to the next block. The continue statement ends the current block\'s execution and jumps to the next iteration of the loop. + +## Q. When would you use a continue statement in a for loop? + +When processing a particular item was complete; to move on to the next, without executing further processing in the block. The continue statement says, "I'm done processing this item, move on to the next item." + +## Q. When would you use a break statement in a for loop? + +When the loop has served its purpose. As an example. after finding the item in a list searched for, there is no need to keep looping. The break statement says, I'm done in this loop; move on to the next block of code." + +## Q. What is the structure of a for loop? + +for in : … The ellipsis represents a code block to be executed, once for each item in the sequence. Within the block the item is available as the current item from the entire list. + +## Q. What is the structure of a while loop? + +while : … The ellipsis represents a code block to be executed. until the condition becomes false. The condition is an expression that is considered true unless it evaluates to o, null or false. + +## Q. Use a for loop and illustrate how you would define and print the characters in a string out, one per line. + +```py +myString = "I Love Python" +for myChar hi myString: + print(myChar) +``` + + + +## Q. Given the string "I LoveQPython" use afor loop and illustrate printing each character tip to, but not including the Q. + +```py +inyString = "I Love Pijtlzon" +for myCizar in myString: + fmyC'har == + break +print(myChar) +``` + + + +## Q. Given the string "I Love Python" print out each character except for the spaces, using a for loop. + +```py +inyString = I Love Python" +for myCizar in myString: +fmyChar == '' '': +continue +print myChar +``` + + + +### _Data Types_ + +## Q. for loop is implemented in python language as follows: + +```py +for element in iterable: + iter-obj=iter(iterable) + while true: + try: + element=next(iter_obj) + except(slop iteration) + break +``` +For loop takes the given object, convert that object in the form of iterable object & gets the one by one element form the iterable object. + +While getting the one by value element from the iterable object if stop iteration exception is raised then for loop internally handle that exception + + + +## Q. What is pass in Python? + +Pass means, no-operation Python statement, or in other words it is a place holder in compound statement, where there should be a blank left and nothing has to be written there. + +## # 6. Core Data Structures + +
+ +## Q. Is it possible to assign multiple var to values in list? + +The multiple assignment trick is a shortcut that lets you assign multiple variables with the values in a list in one line of code. So instead of doing this: + +```py +cat = ['fat', 'orange', 'loud'] +size = cat[0] +color = cat[1] +disposition = cat[2] +``` + +Do this: + +```js +cat = ['fat', 'orange', 'loud'] +size, color, disposition = cat +``` + + + +## Q. Write a sorting algorithm for a numerical dataset in Python? + +The following code can be used to sort a list in Python: + +```py +list = ["1", "4", "0", "6", "9"] +list = [int(i) for i in list] +list.sort() +print(list) +``` + +## Q. How will you remove last object from a list? + +`list.pop(obj=list[-1])` − Removes and returns last object or obj from list. + +## Q. What are negative indexes and why are they used? + +Python sequences can be index in positive and negative numbers. For positive index, 0 is the first index, 1 is the second index and so forth. For negative index, (-1) is the last index and (-2) is the second last index and so forth. + +The sequences in Python are indexed and it consists of the positive as well as negative numbers. The numbers that are positive uses '0' that is uses as first index and '1' as the second index and the process goes on like that. + +The index for the negative number starts from '-1' that represents the last index in the sequence and '-2' as the penultimate index and the sequence carries forward like the positive number. + +The negative index is used to remove any new-line spaces from the string and allow the string to except the last character that is given as S[:-1]. The negative index is also used to show the index to represent the string in correct order. + +Let\'s take a list for this. + +```py +mylist=[0,1,2,3,4,5,6,7,8] +``` + +A negative index, unlike a positive one, begins searching from the right. + +```py +mylist[-3] +``` + +6 + +This also helps with slicing from the back: + +```py +mylist[-6:-1] +``` + +[3, 4, 5, 6, 7] + + + +## Q. How many kinds of sequences are supported by Python? What are they? + +Python supports 7 sequence types. They are str, list, tuple, unicode, byte array, xrange, and buffer. where xrange is deprecated in python 3.5.X. + + + +## Q. Suppose list1 is [2, 33, 222, 14, 25], What is list1[-1] ? + +25 + +## Q. Differentiate between append() and extend() methods.? + +Both append() and extend() methods are the methods of list. These methods are used to add the elements at the end of the list. + +`append(element)` – adds the given element at the end of the list which has called this method. + `extend(another-list)` – adds the elements of another-list at the end of the list which is called the extend method. + +## Q. Which of the following is not the correct syntax for creating a set? + +a) set([[1,2],[3,4]]) +b) set([1,2,2,3,4]) +c) set((1,2,3,4)) +d) {1,2,3,4} + +A. +Explanation : The argument given for the set must be an iterable. + +## Q. Write a Python program to calculate the sum of a list of numbers? + +```py +def list_sum(num_List): + if len(num_List) == 1: + return num_List[0] + else: + return num_List[0] + list_sum(num_List[1:]) +print(list_sum([2, 4, 5, 6, 7])) +``` + +Sample Output: 24 + +## Q. How would you randomize the contents of a list in-place? + +For this, we'll import the function `shuffle()` from the module `random`. + +```py +from random import shuffle +shuffle(mylist) +mylist +``` + +[3, 4, 8, 0, 5, 7, 6, 2, 1] + +## Q. What is tuple unpacking? + +First, let\'s discuss tuple packing. It is a way to pack a set of values into a tuple. + +```py +mytuple=3,4,5 +mytuple +``` + +(3, 4, 5) + +This packs 3, 4, and 5 into mytuple. + +Now, we will unpack the values from the tuple into variables x, y, and z. + +```py +x,y,z=mytuple +x+y+z +``` + +## Q. What is a Tuple? + +- Tuple Objects can be created by using parenthesis or by calling tuple function or by assigning multiple values to a single variable +- Tuple objects are immutable objects +- Incision order is preserved + +- Duplicate elements are allowed + +- Heterogeneous elements are allowed + +- Tuple supports both positive and negative indexing + +- The elements of the tuple can be mutable or immutable + +```py +#Example: +x=() +print(x) +print(type(x)) +print(len(x)) +y-tuple() +print(y) +print(type(y)) +print(len(y)) +z=10,20 +print(z) +print(type(z)) +print(len(z)) +p=(10,20,30,40,50,10,20,10) Insertion & duplicate +print(p) +q=(100, 123.123, True, "mindmajix") Heterogeneous +print(q) +Output: +``` + + + +## Q. What is List Comprehensions feature of Python used for? + +List comprehensions help to create and manage lists in a simpler and clearer way than using `map()`, `filter()` and `lambda`. Each list comprehension consists of an expression followed by a for clause, then zero or more for or if clauses. + + + +## Q. What are lambda expressions, list comprehensions and generator expressions? + +**Lambda expressions** + +are a shorthand technique for creating single line, anonymous functions. Their simple, inline nature often – though not always – leads to more readable and concise code than the alternative of formal function declarations. On the other hand, their terse inline nature, by definition, very much limits what they are capable of doing and their applicability. Being anonymous and inline, the only way to use the same lambda function in multiple locations in your code is to specify it redundantly. + +**List comprehensions** + +provide a concise syntax for creating lists. List comprehensions are commonly used to make lists where each element is the result of some operation(s) applied to each member of another sequence or iterable. They can also be used to create a subsequence of those elements whose members satisfy a certain condition. In Python, list comprehensions provide an alternative to using the built-in map() and filter() functions. + +As the applied usage of lambda expressions and list comprehensions can overlap, opinions vary widely as to when and where to use one vs. the other. One point to bear in mind, though, is that a list comprehension executes somewhat faster than a comparable solution using map and lambda (some quick tests yielded a performance difference of roughly 10%). This is because calling a lambda function creates a new stack frame while the expression in the list comprehension is evaluated without doing so. + +**Generator expressions** + +are syntactically and functionally similar to list comprehensions but there are some fairly significant differences between the ways the two operate and, accordingly, when each should be used. In a nutshell, iterating over a generator expression or list comprehension will essentially do the same thing, but the list comprehension will create the entire list in memory first while the generator expression will create the items on the fly as needed. Generator expressions can therefore be used for very large (and even infinite) sequences and their lazy (i.e., on demand) generation of values results in improved performance and lower memory usage. It is worth noting, though, that the standard Python list methods can be used on the result of a list comprehension, but not directly on that of a generator expression. + +- Consider the two approaches below for initializing an array and the arrays that will result. How will the resulting arrays differ and why should you use one initialization approach vs. the other? + +```py + # INITIALIZING AN ARRAY -- METHOD 1 +... + x = [[1,2,3,4]] * 3 + x +[[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]] + + + # INITIALIZING AN ARRAY -- METHOD 2 +... + y = [[1,2,3,4] for _ in range(3)] + y +[[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]] + + # WHICH METHOD SHOULD YOU USE AND WHY? +``` + +**Ans:** + +While both methods appear at first blush to produce the same result, there is an extremely significant difference between the two. Method 2 produces, as you would expect, an array of 3 elements, each of which is itself an independent 4-element array. In method 1, however, the members of the array all point to the same object. This can lead to what is most likely unanticipated and undesired behavior as shown below. + +```py +# MODIFYING THE x ARRAY FROM THE PRIOR CODE SNIPPET: +x[0][3] = 99 +x +[[1, 2, 3, 99], [1, 2, 3, 99], [1, 2, 3, 99]] + +# UH-OH, DON'T THINK YOU WANTED THAT TO HAPPEN! +... +# MODIFYING THE y ARRAY FROM THE PRIOR CODE SNIPPET: +y[0][3] = 99 +y +[[1, 2, 3, 99], [1, 2, 3, 4], [1, 2, 3, 4]] +# THAT\'s MORE LIKE WHAT YOU EXPECTED! +... +``` + + + +## Q. What will be printed out by the second append() statement below? + +```py +def append(list=[]): + # append the length of a list to the list + list.append(len(list)) + return list + +append(['a','b']) +['a', 'b', 2] +``` +append() # calling with no arg uses default list value of [][0] + +append() # but what happens when we AGAIN call append with no arg? + +**Ans:** + +When the default value for a function argument is an expression, the expression is evaluated only once, not every time the function is called. Thus, once the list argument has been initialized to an empty array, subsequent calls to append without any argument specified will continue to use the same array to which list was originally initialized. This will therefore yield the following, presumably unexpected, behavior: + +append() # first call with no arg uses default list value of [][0] +append() # but then look what happens...[0, 1] +append() # successive calls keep extending the same default list! +[0, 1, 2] +append() # and so on, and so on, and so on... +[0, 1, 2, 3] + +- How might one modify the implementation of the `append` method in the previous question to avoid the undesirable behavior described there? + +The following alternative implementation of the append method would be one of a number of ways to avoid the undesirable behavior described in the answer to the previous question: + +```py +def append(list=None): + if list is None: + list = [] + # append the length of a list to the list + list.append(len(list)) + return list +append() +[0] +append() +[0] +``` +Q: How can you swap the values of two variables with a single line of Python code? + +Consider this simple example: +``` + x = 'X' + y = 'Y' +``` +In many other languages, swapping the values of x and y requires that you to do the following: +``` +tmp = x +x = y +y = tmp +x, y +('Y', 'X') +``` + +But in Python, makes it possible to do the swap with a single line of code (thanks to implicit tuple packing and unpacking) as follows: +``` +x,y = y,x +x,y +('Y', 'X') +``` + + + +## Q. What will be printed out by the last statement below? + +```py +flist = [] +for i in range(3): + flist.append(lambda: i) + +[f() for f in flist] # what will this print out? +``` +In any closure in Python, variables are bound by name. Thus, the above line of code will print out the following: +[2, 2, 2] +Presumably not what the author of the above code intended? + +A workaround is to either create a separate function or to pass the args by name; e.g. + +```py +flist = [] +for i in range(3): + flist.append(lambda i = i : i) + +[f() for f in flist] +[0, 1, 2] +``` + + + +## Q. How do you reverse a list? + +There are three main approaches, each with different trade-offs: + +| Method | In-place | Returns new list | Time | Space | +|--------|----------|-----------------|------|-------| +| `list.reverse()` | Yes | No (`None`) | O(n) | O(1) | +| `reversed()` | No | Iterator | O(1) | O(1) | +| Slice `[::-1]` | No | New list | O(n) | O(n) | + +```py +from typing import TypeVar + +T = TypeVar("T") + +numbers: list[int] = [1, 2, 3, 4, 5] + +# Method 1: In-place mutation (most memory-efficient for large lists) +numbers.reverse() +print(numbers) # [5, 4, 3, 2, 1] + +# Method 2: Slice — creates a new reversed copy +original = [1, 2, 3, 4, 5] +reversed_copy = original[::-1] +print(reversed_copy) # [5, 4, 3, 2, 1] +print(original) # [1, 2, 3, 4, 5] (unchanged) + +# Method 3: reversed() iterator — lazy, best for one-pass iteration +for item in reversed([1, 2, 3, 4, 5]): + print(item, end=" ") # 5 4 3 2 1 +``` + +**Use Case:** In a data pipeline processing time-series records, `reversed()` is used to iterate over rows newest-first without copying the entire dataset into memory — critical when processing millions of records. + + + +## Q. How would you merge two sorted lists? + +The optimal approach uses `heapq.merge()` which runs in O(n + m) time and O(1) extra space (lazy iterator). A manual two-pointer approach is also O(n + m) but builds the result eagerly. + +```py +import heapq +from typing import Iterator + +# Method 1: heapq.merge — lazy O(1) space iterator (preferred for large lists) +list_a: list[int] = [1, 3, 5, 7, 9] +list_b: list[int] = [2, 4, 6, 8, 10] + +merged: Iterator[int] = heapq.merge(list_a, list_b) +print(list(merged)) # [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + + +# Method 2: Two-pointer — O(n + m) time, O(n + m) space +def merge_sorted(a: list[int], b: list[int]) -> list[int]: + result: list[int] = [] + i = j = 0 + while i < len(a) and j < len(b): + if a[i] <= b[j]: + result.append(a[i]) + i += 1 + else: + result.append(b[j]) + j += 1 + result.extend(a[i:]) + result.extend(b[j:]) + return result + +print(merge_sorted([1, 3, 5], [2, 4, 6])) # [1, 2, 3, 4, 5, 6] +``` + +**Use Case:** In a distributed search engine, each shard returns a sorted list of ranked results. `heapq.merge()` lazily merges all shard results to produce a globally sorted feed without loading all results into memory. + + + +## Q. Tell me some data structures in Python? + +Python\'s built-in and standard-library data structures cover most algorithmic needs: + +| Structure | Module | Ordered | Mutable | Unique | Avg Lookup | +|-----------|--------|---------|---------|--------|------------| +| `list` | built-in | Yes | Yes | No | O(n) | +| `tuple` | built-in | Yes | No | No | O(n) | +| `dict` | built-in | Yes (3.7+) | Yes | Keys only | O(1) | +| `set` | built-in | No | Yes | Yes | O(1) | +| `frozenset` | built-in | No | No | Yes | O(1) | +| `deque` | `collections` | Yes | Yes | No | O(1) ends | +| `defaultdict` | `collections` | Yes | Yes | Keys only | O(1) | +| `OrderedDict` | `collections` | Yes | Yes | Keys only | O(1) | +| `namedtuple` | `collections` | Yes | No | No | O(1) | +| `Counter` | `collections` | Yes | Yes | Keys only | O(1) | +| `heapq` (min-heap) | `heapq` | Partial | Yes | No | O(log n) push/pop | + +```py +from collections import deque, defaultdict, Counter, namedtuple +import heapq + +# deque — O(1) append/pop from both ends (unlike list's O(n) left operations) +queue: deque[int] = deque([1, 2, 3]) +queue.appendleft(0) +queue.append(4) +print(queue) # deque([0, 1, 2, 3, 4]) + +# Counter — frequency map +word_freq = Counter(["python", "java", "python", "rust", "python"]) +print(word_freq.most_common(2)) # [('python', 3), ('java', 1)] + +# namedtuple — immutable record with named fields (memory-efficient vs class) +Point = namedtuple("Point", ["x", "y"]) +p = Point(3, 4) +print(p.x, p.y) # 3 4 + +# heapq — min-heap for priority queue +tasks: list[tuple[int, str]] = [] +heapq.heappush(tasks, (2, "medium priority")) +heapq.heappush(tasks, (1, "high priority")) +heapq.heappush(tasks, (3, "low priority")) +print(heapq.heappop(tasks)) # (1, 'high priority') +``` + +**Use Case:** A real-time leaderboard service uses `heapq` to maintain the top-K scores efficiently (O(log K) per insert), `Counter` for aggregating player event frequencies, and `deque` as a circular buffer for a sliding-window activity feed. + + + +## Q. What is List Comprehension? Show with example. + +A list comprehension is a concise, readable syntax for creating a new list by applying an expression to each item in an iterable, with an optional filter condition. It is faster than an equivalent `for` loop because the iteration happens internally in C (CPython). + +**Syntax:** `[expression for item in iterable if condition]` + +```py +# Basic: square numbers 0–9 +squares: list[int] = [x ** 2 for x in range(10)] +print(squares) # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] + +# With filter: even squares only +even_squares: list[int] = [x ** 2 for x in range(10) if x % 2 == 0] +print(even_squares) # [0, 4, 16, 36, 64] + +# Nested: flatten a 2D matrix +matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] +flat: list[int] = [cell for row in matrix for cell in row] +print(flat) # [1, 2, 3, 4, 5, 6, 7, 8, 9] + +# String processing: extract valid emails from raw strings +raw_data = ["alice@example.com", "not-an-email", "bob@corp.io", "bad@@test"] +valid_emails: list[str] = [ + addr.strip().lower() + for addr in raw_data + if "@" in addr and addr.count("@") == 1 +] +print(valid_emails) # ['alice@example.com', 'bob@corp.io'] + +# Dict comprehension variant +word_lengths: dict[str, int] = {word: len(word) for word in ["python", "java", "rust"]} +print(word_lengths) # {'python': 6, 'java': 4, 'rust': 4} + +# Set comprehension — unique first letters +first_letters: set[str] = {word[0] for word in ["apple", "ant", "banana", "avocado"]} +print(first_letters) # {'a', 'b'} +``` + +**Performance:** List comprehensions are typically **1.5–2× faster** than equivalent `for` loops because they avoid repeated attribute lookups for `list.append`. + +**Use Case:** A data ingestion pipeline uses list comprehensions to transform and filter 100K raw JSON records into clean domain objects in a single readable expression, replacing a 15-line `for` loop and reducing code review friction. + + + +## Q. How do you create a list? + +```py +from typing import Any + +# Method 1: List literal (most common) +numbers: list[int] = [1, 2, 3, 4, 5] + +# Method 2: list() constructor +from_tuple: list[int] = list((10, 20, 30)) +from_range: list[int] = list(range(5)) # [0, 1, 2, 3, 4] +from_str: list[str] = list("abc") # ['a', 'b', 'c'] + +# Method 3: List comprehension (preferred for transformations) +squares: list[int] = [x ** 2 for x in range(10)] + +# Method 4: Multiplication (shallow copy — beware with mutable elements) +zeros: list[int] = [0] * 5 # [0, 0, 0, 0, 0] + +# Method 5: Empty list and append +result: list[Any] = [] +result.append("a") +result.extend([1, 2]) +print(result) # ['a', 1, 2] + +# Key operations and complexity +lst = [3, 1, 4, 1, 5, 9, 2, 6] +lst.sort() # O(n log n) in-place Timsort +print(lst[2:5]) # O(k) slicing — [3, 4, 5] +print(4 in lst) # O(n) linear search +lst.insert(0, 0) # O(n) — shifts all elements +lst.pop() # O(1) — remove from end +lst.pop(0) # O(n) — remove from front (use deque instead) +``` + +**Use Case:** A recommendation engine collects candidate items into a `list`, sorts them by score with a custom `key` function (`list.sort(key=lambda x: x.score, reverse=True)`), and slices the top-K results — leveraging Python\'s Timsort which is O(n log n) worst-case but O(n) for nearly-sorted data. + + + +## Q. What is a list comprehension? Why would you use one? + +A list comprehension creates a list by evaluating an expression for each element of an iterable, with an optional filter. It is more readable and ~1.5–2× faster than an equivalent `for` loop because the loop runs in C internally and avoids `list.append` attribute lookup overhead. + +**Use it when:** creating a new list from a transformation/filter in one readable line. +**Avoid it when:** logic is complex (multiple conditions, side effects, nested 3+ levels). + +```py +# Why faster: no append() call overhead per iteration +import timeit + +# For loop version +def squares_loop(n: int) -> list[int]: + result = [] + for x in range(n): + result.append(x ** 2) + return result + +# List comprehension version +def squares_comp(n: int) -> list[int]: + return [x ** 2 for x in range(n)] + +t_loop = timeit.timeit(lambda: squares_loop(1000), number=10000) +t_comp = timeit.timeit(lambda: squares_comp(1000), number=10000) +print(f"Loop: {t_loop:.3f}s Comprehension: {t_comp:.3f}s") +# Comprehension is typically ~40% faster + +# Real-world example: parse and clean CSV-like data +raw_records = [ + " alice , 30 , engineer ", + " bob , 25 , designer ", + " , , ", # invalid row +] + +cleaned: list[dict[str, str]] = [ + {"name": parts[0], "age": parts[1], "role": parts[2]} + for record in raw_records + if (parts := [f.strip() for f in record.split(",")]) and all(parts) +] +print(cleaned) +# [{'name': 'alice', 'age': '30', 'role': 'engineer'}, {'name': 'bob', ...}] +``` + +**Use Case:** An ETL pipeline processes 500K JSON records from S3 with a single list comprehension to extract, validate, and normalize fields, making the transformation self-documenting and testable as a pure function. + + + +## Q. What is list/dict comprehension? + +**Comprehensions** are concise, readable expressions that create a new collection by applying a transformation and optional filter to an iterable — all in one line. They run faster than equivalent `for` loops because the loop body executes in C. + +```py +# List comprehension: [expression for item in iterable if condition] +squares = [x ** 2 for x in range(10)] +evens = [x for x in range(20) if x % 2 == 0] +cleaned = [s.strip().lower() for s in [" Alice ", "BOB", " Charlie"]] + +print(squares) # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] +print(evens) # [0, 2, 4, 6, 8, 10, 12, 14, 16, 18] +print(cleaned) # ['alice', 'bob', 'charlie'] + +# Nested list comprehension (flatten 2D → 1D) +matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] +flat = [val for row in matrix for val in row] +print(flat) # [1, 2, 3, 4, 5, 6, 7, 8, 9] + +# Dict comprehension: {key_expr: val_expr for item in iterable if condition} +word_lengths = {word: len(word) for word in ["python", "java", "rust"]} +inverted = {v: k for k, v in {"a": 1, "b": 2, "c": 3}.items()} +print(word_lengths) # {'python': 6, 'java': 4, 'rust': 4} +print(inverted) # {1: 'a', 2: 'b', 3: 'c'} + +# Set comprehension (unique values) +unique_lengths = {len(w) for w in ["hi", "hello", "hey", "howdy"]} +print(unique_lengths) # {2, 5} + +# Generator expression (lazy — use when you don't need a list) +total = sum(x ** 2 for x in range(1_000_000)) # O(1) memory + +# When to AVOID comprehensions +# BAD — multiple conditions + side effects: use a regular for loop +# result = [ +# (process(item), log(item)) # side effect inside comprehension +# for item in items +# if validate(item) and item.active and item.score > threshold +# ] +``` + +**Performance tip:** List comprehensions are ~1.3–2× faster than `for`+`append` loops. + +**Use Case:** A Kafka consumer normalizes incoming JSON messages with a dict comprehension: `{k.lower().replace("-", "_"): str(v).strip() for k, v in raw.items()}` — transforming keys and values in one readable expression before schema validation. + + + +## Q. How to make an array in Python? + +Python has three main ways to create array-like structures, each with different trade-offs: + +```py +# 1. Built-in list — flexible, heterogeneous, dynamic size +lst: list[int] = [1, 2, 3, 4, 5] +lst.append(6) +print(lst) # [1, 2, 3, 4, 5, 6] + +# 2. array module — typed, memory-efficient, homogeneous (like C arrays) +import array +int_arr = array.array("i", [1, 2, 3, 4, 5]) # 'i' = signed int +dbl_arr = array.array("d", [1.1, 2.2, 3.3]) # 'd' = double +print(int_arr) # array('i', [1, 2, 3, 4, 5]) +print(int_arr.itemsize) # 4 bytes per element (vs 28 for Python int) +int_arr.append(6) +print(int_arr.tolist()) # [1, 2, 3, 4, 5, 6] + +# 3. numpy array — vectorized math, multi-dimensional, C-speed +import numpy as np +np_arr = np.array([1, 2, 3, 4, 5], dtype=np.int32) +print(np_arr * 2) # [2 4 6 8 10] — element-wise, no loop needed +print(np_arr.mean()) # 3.0 +print(np_arr.sum()) # 15 + +# 2D array (matrix) +matrix = np.zeros((3, 4), dtype=np.float64) # 3 rows × 4 cols +print(matrix.shape) # (3, 4) + +# numpy operations (no Python loop) +a = np.arange(1_000_000, dtype=np.float64) +b = np.arange(1_000_000, dtype=np.float64) +c = a + b # vectorized — ~100× faster than list + for loop + +# Type codes for array.array +# 'b' = signed char, 'B' = unsigned char +# 'h' = short, 'i' = int, 'l' = long +# 'f' = float, 'd' = double +``` + +| | `list` | `array.array` | `numpy.ndarray` | +|-|--------|--------------|----------------| +| Typed | No | Yes | Yes | +| Multi-dim | No | No | Yes | +| Math ops | Manual | Manual | Vectorized | +| Memory | High | Low | Low–Medium | +| Speed | Slow | Medium | Fast (C) | + +**Use Case:** A signal processing service stores 10M ADC samples in `numpy.ndarray(dtype=np.int16)` — 20 MB instead of ~280 MB for a Python list — and applies FFT with `np.fft.fft()` in milliseconds using BLAS/LAPACK internally. + + + +## Q. When to use `list` / `tuple` / `set` / `dict`? + +| Structure | Ordered | Mutable | Duplicates | Key-value | Use when | +|-----------|---------|---------|------------|-----------|----------| +| `list` | Yes | Yes | Yes | No | Ordered, changeable sequence | +| `tuple` | Yes | No | Yes | No | Fixed record, dict key, return multiple values | +| `set` | No | Yes | No | No | Membership test, deduplication, set operations | +| `frozenset` | No | No | No | No | Immutable set, hashable (dict key) | +| `dict` | Yes (3.7+) | Yes | Keys: No | Yes | Key→value mapping, O(1) lookup | + +```py +# list — ordered, mutable sequence +pipeline_steps: list[str] = ["extract", "transform", "load"] +pipeline_steps.append("validate") # add at end — O(1) + +# tuple — immutable record; can be a dict key +point = (3.0, 4.0) # coordinates — immutable pair +rgb = (255, 128, 0) # fixed-size record +cache: dict[tuple, int] = {point: 1} # tuple as dict key (hashable!) + +# Multiple return values (naturally a tuple) +def min_max(values: list[float]) -> tuple[float, float]: + return min(values), max(values) + +lo, hi = min_max([3, 1, 4, 1, 5, 9]) +print(lo, hi) # 1 9 + +# set — fast membership, deduplication, set algebra +seen: set[str] = set() +visited: set[str] = {"a", "b", "c"} +new: set[str] = {"b", "c", "d", "e"} + +print("a" in visited) # True — O(1) hash lookup +print(visited & new) # {'b', 'c'} — intersection +print(visited | new) # {'a', 'b', 'c', 'd', 'e'} — union +print(visited - new) # {'a'} — difference + +# Remove duplicates from a list (preserving order via dict trick) +dupes = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3] +unique = list(dict.fromkeys(dupes)) +print(unique) # [3, 1, 4, 5, 9, 2, 6] + +# dict — named fields, O(1) lookup +user: dict[str, object] = {"id": 1, "name": "Alice", "active": True} +print(user.get("role", "viewer")) # safe get with default +``` + +**Use Case:** A URL deduplication stage uses a `set` to track seen URLs (O(1) lookup), stores extracted data as named `dict` records, passes immutable `(source_id, record_id)` tuples as task identifiers to Celery (hashable, safe to use as cache keys), and collects ordered results in a `list`. + + + +## Q. When to use list comprehensions and when to avoid them? + +**Use list comprehensions when:** +- Creating a new list from a transformation or filter in one line +- The expression is simple enough to read at a glance +- You want the performance benefit (~1.5–2× faster than `for`+`append`) + +**Avoid list comprehensions when:** +- Logic is complex (nested 3+ levels, multiple conditions, side effects) +- The result is not needed as a list (use a generator expression instead) +- Debugging is hard (comprehensions can\'t have breakpoints mid-expression) + +```py +import timeit + +data = range(1000) + +# GOOD: simple, readable transformation +squares = [x ** 2 for x in data] + +# GOOD: filter + transform +even_squares = [x ** 2 for x in data if x % 2 == 0] + +# GOOD: replace map/filter +cleaned = [s.strip().lower() for s in [" Alice ", "BOB", " Carol "]] + +# GOOD: flatten one level +matrix = [[1, 2], [3, 4], [5, 6]] +flat = [v for row in matrix for v in row] + +# AVOID: deeply nested — unreadable +# BAD: +nested = [ + (i, j, k) + for i in range(3) + for j in range(3) + for k in range(3) + if i != j != k +] +# Use itertools.product() or a regular for loop instead + +# AVOID: side effects inside comprehension +import logging +# BAD: +# results = [process(item) for item in items if log_and_validate(item)] +# Use a for loop when you need side effects + +# AVOID: large intermediate list you'll only iterate once — use generator +# BAD: +total = sum([x ** 2 for x in range(1_000_000)]) # builds full list in memory +# GOOD: +total = sum(x ** 2 for x in range(1_000_000)) # generator expression, O(1) memory + +# Performance proof +t_loop = timeit.timeit( + "r=[]; [r.append(x**2) for x in range(1000)]", number=10000 +) +t_comp = timeit.timeit( + "[x**2 for x in range(1000)]", number=10000 +) +print(f"loop: {t_loop:.3f}s comp: {t_comp:.3f}s") +# comprehension is ~40% faster +``` + +**Rule of thumb:** If it fits naturally on one line and is easy to read, use a comprehension. If you need `if/elif/else` chains, multiple side effects, or more than two nested `for` clauses — write a regular `for` loop. + +**Use Case:** A data pipeline uses list comprehensions for field extraction (`[r["id"] for r in rows]`) but switches to explicit `for` loops for multi-step transformations that require logging, error handling, and conditional branching per record. + + + +## Q. What is the difference between a `tuple` and a `list`? Where will you use each? + +| | `list` | `tuple` | +|--|--------|---------| +| Mutability | Mutable | Immutable | +| Syntax | `[1, 2, 3]` | `(1, 2, 3)` | +| Memory | Larger (over-allocates) | Smaller (fixed) | +| Hashable | No | Yes (if all elements are) | +| Can be dict key | No | Yes | +| Iteration speed | Slightly slower | Slightly faster | +| Intended semantics | Homogeneous sequence | Heterogeneous record | + +```py +import sys + +# Memory comparison +lst = list(range(10)) +tpl = tuple(range(10)) +print(sys.getsizeof(lst)) # 184 bytes (over-allocated) +print(sys.getsizeof(tpl)) # 136 bytes (exact) + +# list — ordered, changeable sequence (homogeneous items) +tasks: list[str] = ["extract", "transform", "load"] +tasks.append("validate") +tasks[0] = "fetch" + +# tuple — fixed record (heterogeneous, positional meaning) +point = (3.0, 4.0) # 2D coordinate — positions have meaning +person = ("Alice", 30, "SWE") # (name, age, role) — named positions + +# Named tuple for readability +from collections import namedtuple +from typing import NamedTuple + +class Employee(NamedTuple): + name: str + age: int + role: str + +emp = Employee("Bob", 28, "DevOps") +print(emp.name) # Bob +print(emp[0]) # Bob (also index-accessible) + +# Tuple as dict key — list cannot be +location_cache: dict[tuple[float, float], str] = {} +location_cache[(51.5074, -0.1278)] = "London" +location_cache[(48.8566, 2.3522)] = "Paris" + +# Tuple unpacking — clean multiple assignment +x, y, z = (1, 2, 3) +first, *rest = (10, 20, 30, 40) +print(first, rest) # 10 [20, 30, 40] + +# Swap using tuple +a, b = 1, 2 +a, b = b, a # (b, a) tuple — no temp variable needed +print(a, b) # 2 1 +``` + +**When to use:** +- `list` — when you need to add, remove, or sort items (shopping cart, pipeline steps) +- `tuple` — when the structure is fixed (coordinates, database rows, function return values, dict keys) + +**Use Case:** A geospatial service stores cached reverse-geocoding results in `dict[tuple[float, float], str]` — using `(lat, lon)` tuples as hashable keys — and returns coordinate results as `tuple[float, float]` to signal immutability to callers. + + + +## # 7. Functions + +
+ +## Q. What are `*args`, `**kwargs` ? + +In cases when we don\'t know how many arguments will be passed to a function, like when we want to pass a list or a tuple of values, we use `*args`. + +```py +def func(*args): + for i in args: + print(i) + +func(3,2,1,4,7) +``` +3 +2 +1 +4 +7 + +`**kwargs` takes keyword arguments when we don\'t know how many there will be: + +```py +def func(**kwargs): + for i in kwargs: + print(i,kwargs[i]) + +func(a=1,b=2,c=7) +``` +a.1 +b.2 +c.7 + +The words `args` and `kwargs` are a convention, and we can use anything in their place. + + + +## Q. How can I pass optional or keyword parameters from one function to another? + +Collect the arguments using the * and ** specifier in the function\'s parameter list; this gives you the positional arguments as a tuple and the keyword arguments as a dictionary. You can then pass these arguments when calling another function by using `*` and `**` : + +```py +def f(x, *tup, **kwargs): + kwargs['width']='14.3c' + g(x, *tup, **kwargs) +``` + +In the unlikely case that you care about Python versions older than 2.0, use 'apply': + +```python +def f(x, *tup, **kwargs): + kwargs['width']='14.3c' + apply(g, (x,)+tup, kwargs) +``` + + + +## Q. What are higher ordered functions? + +You have two choices: you can use nested scopes or you can use callable objects. For example, suppose you wanted to define linear(a,b) which returns a function f(x) that computes the value a*x+b. +`Using nested scopes:` + +```py +def linear(a,b): + def result(x): + return a*x + b + return result +``` + +Or + +`using a callable object:` + +```py +class linear: + def __init__(self, a, b): + self.a, self.b = a,b + def __call__(self, x): + return self.a * x + self.b +``` + +In both cases: + +`taxes = linear(0.3,2)` gives a callable object where + +`taxes(10e6) == 0.3 * 10e6 + 2. ` + +The callable object approach has the disadvantage that it is a bit slower and results in slightly longer code. However, note that a collection of callables can share their signature via inheritance: + +```py +class exponential(linear): + __init__ inherited + def __call__(self, x): + return self.a * (x ** self.b) +``` + +Object can encapsulate state for several methods: + +```py +class counter: + value = 0 + def set(self, x): + self.value = x + def up(self): + self.value=self.value+1 + def down(self): + self.value=self.value-1 + count = counter() + +inc, dec, reset = count.up, count.down, count.set +``` + +Here inc(), dec() and reset() act like functions which share the same counting variable. + + + +## Q. Explain help() and dir() functions in Python? + +The help() function displays the documentation string and help for its argument. + +```py +import copy +help(copy.copy) +``` + +Help on function copy in module copy: copy(x) +Shallow copy operation on arbitrary Python objects. +See the module\'s __doc__ string for more info. +The dir() function displays all the members of an object(any kind). + +```py +dir(copy.copy) + +['__annotations__', '__call__', '__class__', '__closure__', '__code__', '__defaults__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__get__', '__getattribute__', '__globals__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__kwdefaults__', '__le__', '__lt__', '__module__', '__name__', '__ne__', '__new__', '__qualname__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__'] +``` + + + +### _**Dictionary**_ + +## Q. What is a closure in Python? + +In Python, a closure is a function object that has access to variables in its enclosing lexical scope, even when the function is called outside that scope. In other words, a closure allows a function to remember and access the values of the variables in the environment where it was created, even if those variables are no longer in scope when the function is called. + +Closures are created when a nested function references a value from its enclosing function. The enclosing function returns the nested function, which maintains a reference to the enclosed value. The enclosed value is stored in the closure, which is attached to the nested function object. + +Here\'s an example of a closure in Python: + +```py +def outer_function(x): + def inner_function(y): + return x + y + return inner_function + +closure = outer_function(10) +result = closure(5) +print(result) # Output: 15 +``` + +```py +def A(x): + def B(): + print(x) + return B + +A(7)() +``` + +7 + +## Q. Why are identifier names with a leading underscore disparaged? + +Since Python does not have a concept of private variables, it is a convention to use leading underscores to declare a variable private. This is why we mustn\'t do that to variables we do not want to make private. + +## Q. How can you declare multiple assignments in one statement? + +There are two ways to do this: + +```py + a,b,c=3,4,5 #This assigns 3, 4, and 5 to a, b, and c resp. + a = b = c =3 #This assigns 3 to a, b, and c +``` + +## Q. What is a function? + +When we want to execute a sequence of statements, we can give it a name. Let\'s define a function to take two numbers and return the greater number. + +```py +def greater(a,b): + return a is a>b else b +greater(3,3.5) +``` + +`3.5` + +You can create your own function or use one of Python\'s many built-in functions. + + + +## Q. What is recursion? + +Recursion is a programming technique in which a function calls itself to solve a problem. The idea is to break down a complex problem into smaller, simpler problems and solve them in a recursive manner until the base case is reached. The base case is a condition that stops the recursion and returns a value. + +A common example of a recursive function is the factorial function, which calculates the product of all positive integers up to a given number. Here\'s an example of a recursive factorial function in Python: + +```py +def factorial(n): + if n == 0: + return 1 + else: + return n * factorial(n-1) +``` + +```py +def facto(n): + if n==1: return 1 + return n*facto(n-1) + +facto(4) +``` +`24` + +## Q. What does the function zip() do? + +One of the less common functions with beginners, zip() returns an iterator of tuples. + + list(zip(['a','b','c'],[1,2,3])) + +[('a', 1), ('b', 2), ('c', 3)] + +Here, it pairs items from the two lists, and creates tuples with those. But it doesn\'t have to be lists. + + list(zip(('a','b','c'),(1,2,3))) + +[('a', 1), ('b', 2), ('c', 3)] + + + +## Q. What is a Counter in Python? + +Ans. The function Counter() from the module 'collections'. It counts the number of occurrences of the elements of a container. + + from collections import Counter + Counter([1,3,2,1,4,2,1,3,1]) + +Counter({1: 4, 3: 2, 2: 2, 4: 1}) + +Python provides us with a range of ways and methods to work with a Counter. Read Python Counter. + +## Q. Explain the use of the 'nonlocal' keyword in Python. + +Ans. First, let\'s discuss the local and global scope. By example, a variable defined inside a function is local to that function. Another variable defined outside any other scope is global to the function. + +Suppose we have nested functions. We can read a variable in an enclosing scope from inside he inner function, but cannot make a change to it. For that, we must declare it nonlocal inside the function. First, let\'s see this without the nonlocal keyword. + + def outer(): + a=7 + def inner(): + print(a) + inner() + outer() + +7 + + def outer(): + a=7 + def inner(): + print(a) + a+=1 + print(a) + inner() + + outer() + +Traceback (most recent call last): +File "", line 1, in outer() +File "", line 7, in outer inner() +File "", line 4, in inner print(a) +UnboundLocalError: local variable 'a' referenced before assignment + +So now, let\'s try doing this with the 'nonlocal' keyword: + + def outer(): + a=7 + def inner(): + nonlocal a + print(a) + a+=1 + print(a) + +inner() + + outer() + + + +## Q. What is the global keyword? + +Ans. Like we saw in the previous question, the global keyword lets us deal with, inside any scope, the global version of a variable. + +The problem: + + a=7 + def func(): + print(a) + a+=1 + print(a) + +The solution: + + a=7 + def func(): + global a + print(a) + a+=1 + print(a) + func() + + + +## Q. Mention what are the rules for local and global variables in Python? + +Local variables: If a variable is assigned a new value anywhere within the function\'s body, it\'s assumed to be local. + +Global variables: Those variables that are only referenced inside a function are implicitly global. + +## Q. How can you share global variables across modules? + +To share global variables across modules within a single program, create a special module. Import the config module in all modules of your application. The module will be available as a global variable across modules. + + + +## Q. How can you return multiple values from a function? + +In Python, you can return multiple values from a function by packing them into a tuple or a list. Here\'s an example using a tuple: + +```py +def get_user_info(user_id): + # ... do some work to retrieve user info ... + name = "John Doe" + email = "johndoe@example.com" + age = 35 + return name, email, age + +# call the function and unpack the returned values +user_name, user_email, user_age = get_user_info(123) + +# print the values +print(user_name) # Output: John Doe +print(user_email) # Output: johndoe@example.com +print(user_age) # Output: 35 +``` + +In this example, the `get_user_info` function returns three values (`name`, `email`, and `age`) as a tuple. When the function is called, the returned tuple is unpacked into individual variables (`user_name`, `user_email`, and `user_age`) using tuple unpacking syntax. The variables can then be used as needed. + +You can also return multiple values as a list or any other iterable. For example: + +```py +def get_user_info(user_id): + # ... do some work to retrieve user info ... + name = "John Doe" + email = "johndoe@example.com" + age = 35 + return [name, email, age] + +# call the function and unpack the returned values +user_info = get_user_info(123) +user_name, user_email, user_age = user_info + +# print the values +print(user_name) # Output: John Doe +print(user_email) # Output: johndoe@example.com +print(user_age) # Output: 35 +``` + +In this example, the get_user_info function returns a list instead of a tuple, and the returned values are unpacked into variables using list unpacking syntax. + +## Q. What is the fastest way to swap the values bound to two variables? + +In Python, the fastest way to swap the values bound to two variables is to use tuple unpacking, like this: + +```py +a, b = b, a +``` + +In this code, the values of a and b are swapped by creating a tuple of the values in reverse order ((`b`, `a`)) and unpacking them into the variables a and b. The assignment is performed simultaneously, which makes this method faster than using a temporary variable, which requires an additional assignment. + +For example: + +```py +a = 5 +b = 10 + +# swap the values using tuple unpacking +a, b = b, a + +print(a) # Output: 10 +print(b) # Output: 5 +``` + +In this example, the values of `a` and `b` are swapped using tuple unpacking, and the final output shows that the values have indeed been swapped. + +## Q. Do functions return something even if there is not a `return` statement? + +Yes. In Python, every function returns a value. If no `return` statement is present (or `return` is used without a value), the function implicitly returns `None`. + +```py +def greet(name: str) -> None: + print(f"Hello, {name}") + +result = greet("Alice") +print(result) # Output: None +print(type(result)) # Output: +``` + +**Use Case:** This behaviour is relied upon in API validation helpers — a function that validates input and raises exceptions on failure is called for its side effects, not its return value. The caller checks `if validate(data) is None` to confirm successful validation. + + + +## Q. How to pass optional or keyword arguments. + +Python supports four argument types: positional, default (optional), `*args` (variadic positional), and `**kwargs` (variadic keyword). + +```py +from typing import Any + +# Default arguments — make parameter optional +def greet(name: str, greeting: str = "Hello", punct: str = "!") -> str: + return f"{greeting}, {name}{punct}" + +print(greet("Alice")) # Hello, Alice! +print(greet("Bob", greeting="Hi")) # Hi, Bob! +print(greet("Eve", "Hey", ".")) # Hey, Eve. + +# *args — variable number of positional arguments +def total(*values: float) -> float: + return sum(values) + +print(total(1, 2, 3, 4)) # 10.0 + +# **kwargs — variable number of keyword arguments +def build_url(base: str, **params: Any) -> str: + query = "&".join(f"{k}={v}" for k, v in params.items()) + return f"{base}?{query}" if query else base + +print(build_url("https://api.example.com", page=1, limit=20, sort="desc")) +# https://api.example.com?page=1&limit=20&sort=desc + +# Combining all types (order matters: positional, *args, keyword-only, **kwargs) +def create_record( + table: str, # positional + *fields: str, # variadic positional + schema: str = "public", # keyword-only (after *) + **metadata: Any, # variadic keyword +) -> dict: + return {"table": table, "fields": fields, "schema": schema, **metadata} + +rec = create_record("users", "id", "name", "email", schema="app", version=2) +print(rec) +# {'table': 'users', 'fields': ('id', 'name', 'email'), 'schema': 'app', 'version': 2} + +# Unpacking when calling +def add(a: int, b: int, c: int) -> int: + return a + b + c + +args = [1, 2, 3] +kwargs = {"a": 10, "b": 20, "c": 30} +print(add(*args)) # 6 — unpack list as positional args +print(add(**kwargs)) # 60 — unpack dict as keyword args +``` + +**Use Case:** A REST client wrapper accepts `**query_params` so callers can pass any API-supported filter without updating the function signature for every new parameter — future-proofing the interface while keeping calls readable. + + + +## Q. What is the use of `enumerate()` in Python? + +`enumerate()` wraps an iterable and yields `(index, value)` pairs, eliminating the need for a manual counter variable. It is O(1) memory (returns a lazy iterator) and supports a `start` parameter to control the starting index. + +```py +fruits = ["apple", "banana", "cherry", "date"] + +# Without enumerate — error-prone manual counter +for i in range(len(fruits)): + print(i, fruits[i]) + +# With enumerate — Pythonic, cleaner +for i, fruit in enumerate(fruits): + print(i, fruit) +# 0 apple +# 1 banana +# 2 cherry +# 3 date + +# Custom start index +for i, fruit in enumerate(fruits, start=1): + print(f"{i}. {fruit}") +# 1. apple +# 2. banana +# 3. cherry +# 4. date + +# Building indexed structures +index_map = {fruit: i for i, fruit in enumerate(fruits)} +print(index_map) # {'apple': 0, 'banana': 1, 'cherry': 2, 'date': 3} + +# Tracking position while modifying (safe pattern) +data = [3, -1, 4, -1, 5, 9, -2] +for i, val in enumerate(data): + if val < 0: + data[i] = 0 # replace negatives in-place using index + +print(data) # [3, 0, 4, 0, 5, 9, 0] + +# Works with any iterable +for line_no, line in enumerate(open("README.md"), start=1): + if "TODO" in line: + print(f"Line {line_no}: {line.rstrip()}") + break +``` + +**Use Case:** A CSV validator uses `enumerate(rows, start=2)` (header is row 1) to report validation errors with their exact spreadsheet line number: `"Row 47: missing required field 'email'"` — without any manual counter. + + + +## Q. Namespace vs scope? + +**Namespace:** A mapping (dictionary) from names to objects. Examples: `locals()`, `globals()`, a class body, a module, `builtins`. + +**Scope:** The region of source code where a namespace is *directly accessible* without qualification. Python resolves names using the **LEGB rule**: Local → Enclosing → Global → Built-in. + +```py +# Global namespace +MODULE_CONST = "global" + +def outer(): + # Enclosing namespace + enclosing_var = "enclosing" + + def inner(): + # Local namespace + local_var = "local" + + # LEGB lookup order: + print(local_var) # L — local + print(enclosing_var) # E — enclosing + print(MODULE_CONST) # G — global + print(len) # B — built-in + + inner() + +outer() + +# global and nonlocal keywords modify outer scopes +counter = 0 + +def increment(): + global counter # modify global, not create a local shadow + counter += 1 + +increment() +print(counter) # 1 + +def make_counter(): + count = 0 + + def inc(): + nonlocal count # modify enclosing scope variable + count += 1 + return count + + return inc + +c = make_counter() +print(c()) # 1 +print(c()) # 2 + +# Inspect namespaces at runtime +def show_namespaces(): + x = 10 + print("locals:", locals()) + print("globals keys:", list(globals().keys())[:5]) + +show_namespaces() + +# Class body has its own namespace (but NOT a scope for nested functions) +class Config: + DEBUG = True + DB_URL = "postgres://..." + # Inside a method, Config.DEBUG — must qualify with class name +``` + +**Key distinction:** +- **Namespace** = the dictionary itself (data structure) +- **Scope** = the textual region where a namespace is searched automatically + +**Use Case:** A plugin system uses `globals()` to dynamically register handler functions by name at import time, and `locals()` to safely evaluate template expressions in a restricted namespace — separating the framework\'s global namespace from per-render local context. + + + +## Q. How do you create a Python function? + +```py +from typing import Optional + +# 1. Basic function +def greet(name: str) -> str: + return f"Hello, {name}!" + +print(greet("Alice")) # Hello, Alice! + +# 2. Default arguments +def power(base: float, exponent: int = 2) -> float: + return base ** exponent + +print(power(3)) # 9.0 +print(power(3, 3)) # 27.0 + +# 3. *args and **kwargs +def summarize(*args: float, label: str = "Total", **meta) -> dict: + return {"label": label, "sum": sum(args), "count": len(args), **meta} + +print(summarize(1, 2, 3, label="Score", unit="pts")) +# {'label': 'Score', 'sum': 6, 'count': 3, 'unit': 'pts'} + +# 4. Return multiple values (returns a tuple) +def min_max(values: list[float]) -> tuple[float, float]: + return min(values), max(values) + +lo, hi = min_max([3, 1, 4, 1, 5, 9]) +print(lo, hi) # 1 9 + +# 5. Lambda — anonymous single-expression function +square = lambda x: x ** 2 +print(square(7)) # 49 + +# 6. Nested functions and closures +def make_multiplier(factor: int): + def multiply(x: float) -> float: + return x * factor # closes over 'factor' + return multiply + +triple = make_multiplier(3) +print(triple(10)) # 30.0 + +# 7. Decorator +from functools import wraps + +def log_call(func): + @wraps(func) + def wrapper(*args, **kwargs): + print(f"Calling {func.__name__}") + result = func(*args, **kwargs) + print(f"{func.__name__} returned {result!r}") + return result + return wrapper + +@log_call +def add(a: int, b: int) -> int: + return a + b + +add(2, 3) +# Calling add +# add returned 5 + +# 8. Generator function +def fibonacci(): + a, b = 0, 1 + while True: + yield a + a, b = b, a + b +``` + +**Use Case:** A data transformation library exposes its API entirely as plain functions with type annotations, enabling `mypy` static analysis, easy unit testing (`assert transform(input) == expected`), and simple composition via `functools.partial` and `compose`. + + + +## Q. How does a function return values? + +A function returns a value using the `return` statement. Python allows returning multiple values (as a tuple), returning early, and using generators to yield values lazily. + +```py +# 1. Single return value +def square(n: float) -> float: + return n ** 2 + +# 2. Multiple return values — actually returns a tuple +def divmod_custom(a: int, b: int) -> tuple[int, int]: + return a // b, a % b # quotient and remainder + +quotient, remainder = divmod_custom(17, 5) +print(quotient, remainder) # 3, 2 + +raw = divmod_custom(17, 5) +print(type(raw)) # + +# 3. Early return (guard clauses) +def safe_sqrt(n: float) -> float | None: + if n < 0: + return None # early exit + return n ** 0.5 + +# 4. Return a function (closure / higher-order) +def make_adder(n: int): + def adder(x: int) -> int: + return x + n + return adder # return a function object + +add5 = make_adder(5) +print(add5(10)) # 15 + +# 5. Return a complex object +from dataclasses import dataclass + +@dataclass +class ParseResult: + success: bool + value: float | None + error: str | None = None + +def parse_number(s: str) -> ParseResult: + try: + return ParseResult(success=True, value=float(s)) + except ValueError as e: + return ParseResult(success=False, value=None, error=str(e)) + +r = parse_number("3.14") +print(r) # ParseResult(success=True, value=3.14, error=None) + +# 6. Generator — yield instead of return (lazy values) +def even_range(n: int): + for i in range(n): + if i % 2 == 0: + yield i # yields one at a time, not all at once + +print(list(even_range(10))) # [0, 2, 4, 6, 8] +``` + +**Use Case:** An API handler returns a `dataclass` `Result(ok, data, error)` from every service call — callers pattern-match on `result.ok` rather than catching exceptions, making error handling explicit and the happy path obvious. + + + +## Q. What happens when a function does not have a `return` statement? + +A function without a `return` statement (or with a bare `return`) implicitly returns `None`. This is by design — Python always returns an object; `None` is the sentinel for "no meaningful value". + +```py +def greet(name: str) -> None: + print(f"Hello, {name}!") + +result = greet("Alice") # prints "Hello, Alice!" +print(result) # None +print(type(result)) # + +# Bare return also returns None +def process(value: int) -> int | None: + if value < 0: + return # same as: return None + return value * 2 + +print(process(-1)) # None +print(process(5)) # 10 + +# Common pitfall — accidentally using the return value of a mutating method +lst = [3, 1, 2] +sorted_lst = lst.sort() # list.sort() returns None (in-place) +print(sorted_lst) # None ← BUG! should use sorted(lst) + +# Fix: +sorted_lst = sorted(lst) # sorted() returns a new list +print(sorted_lst) # [1, 2, 3] + +# Type checkers (mypy) will warn if you assign None to a typed variable +def no_return(): + x = 1 + 1 # computes but discards + +val = no_return() +# mypy: error: "no_return" does not return a value (it only ever returns None) + +# None check pattern +def find(items: list, target) -> int | None: + for i, item in enumerate(items): + if item == target: + return i + # falls off end — returns None + +idx = find([1, 2, 3], 99) +if idx is None: + print("Not found") +``` + +**Use Case:** A data validation function returns `None` on success and a `ValidationError` (or error string) on failure — callers check `if error := validate(data): handle(error)`, making the "no return = success" contract explicit. + + + +## # 8. Lambda Functions + +
+ +## Q. What is lambda? What are Lambda Functions ? + +A function which doesn\'t contain any name is known as a anonymous function lambda function, Lambda function we can assign to the variable & we can call the lambda function through the variable. + +Syntax: +`Lambda arguments:expression` + + +It is a single expression anonymous function often used as inline function. A lambda form in python does not have statements as it is used to make new function object and then return them at runtime. + +The lambda operator is used to create anonymous functions. It is mostly used in cases where one wishes to pass functions as parameters. or assign them to variable names. + +When we want a function with a single expression, we can define it anonymously. A lambda expression may take input and returns a value. To define the above function as a lambda expression, we type the following code in the interpreter: + +```py +(lambda a,b:a if a>b else b)(3,3.5) +``` + +`3.5` + +Here, a and b are the inputs. +`a if a > b else b` is the expression to return. +The arguments are 3 and 3.5. + +It is possible to not have any inputs here. + +`(lambda :print("Hi"))()` + +Hi + +Example: + +```py +myfunction = lambda x:x*x +a = myfunction(10) +print(a) +``` + +Output: 100 + +- Why can\'t lambda forms in Python contain statements? + +Lambdas evaluates at run time and these do not need statements +Lambda is a anonymous function, which does not have a name and no fixed number of arguments. Represented by keyword lambda followed by statement. + +Ex: + +```py +add = lambda a,b: a+b +add(2,3) +``` + +output: +`5` + + + +## Q. What is map function in Python? + +Map function executes the function given as the first argument on all the elements of the iterable given as the second argument. If the function given takes in more than 1 arguments, then many iterables are given. #Follow the link to know more similar functions + + + +## Q. Name few methods that are used to implement Functionally Oriented Programming in Python? + +Python supports methods (called iterators in Python3), such as filter(), map(), and reduce(), that are very useful when you need to iterate over the items in a list, create a dictionary, or extract a subset of a list. + +- `filter()` – enables you to extract a subset of values based on conditional logic. +- `map()` – it is a built-in function that applies the function to each item in an iterable. +- `reduce()` – repeatedly performs a pair-wise reduction on a sequence until a single value is computed. + +## Q. Explain a few methods to implement Functionally Oriented Programming in Python. + +Sometimes, when we want to iterate over a list, a few methods come in handy. + + `filter()`: Filter lets us filter in some values based on conditional logic. + +```py +list(filter(lambda x:x>5,range(8))) +``` + + Ans: [6, 7] + `map()`: Map applies a function to every element in an iterable. + +```py +list(map(lambda x:x**2,range(8))) +``` + + Ans: [0, 1, 4, 9, 16, 25, 36, 49] + +`reduce()`: Reduce repeatedly reduces a sequence pair-wise until we reach a single value + +```py + from functools import reduce + reduce(lambda x,y:x-y,[1,2,3,4,5]) +``` + + Ans: -13 + + + +## Q. Explain the difference between `map()`, `reduce()`, and `filter()`? + +All three are higher-order functions from functional programming that operate on iterables: + +| Function | Operation | Returns | Python 3 | +|----------|-----------|---------|----------| +| `map(f, iterable)` | Apply `f` to each element | `map` iterator | Built-in | +| `filter(pred, iterable)` | Keep elements where `pred` is truthy | `filter` iterator | Built-in | +| `reduce(f, iterable)` | Fold elements into single value using `f` | single value | `functools.reduce` | + +```py +from functools import reduce +from typing import Callable + +numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + +# map — transform each element (lazy iterator) +doubled = list(map(lambda x: x * 2, numbers)) +print(doubled) # [2, 4, 6, 8, 10, 12, 14, 16, 18, 20] + +# filter — select elements matching predicate (lazy iterator) +evens = list(filter(lambda x: x % 2 == 0, numbers)) +print(evens) # [2, 4, 6, 8, 10] + +# reduce — aggregate to single value +total = reduce(lambda acc, x: acc + x, numbers, 0) +print(total) # 55 + +product = reduce(lambda acc, x: acc * x, numbers, 1) +print(product) # 3628800 + +# Chained pipeline (functional composition) +result = reduce( + lambda acc, x: acc + x, + filter(lambda x: x % 2 == 0, + map(lambda x: x ** 2, numbers)) +) +print(result) # sum of squares of evens: 4+16+36+64+100 = 220 + +# Modern Pythonic equivalent (often more readable) +result2 = sum(x ** 2 for x in numbers if x % 2 == 0) +print(result2) # 220 +``` + +**Performance note:** `map()` and `filter()` return lazy iterators in Python 3 — no intermediate list is allocated. `reduce()` is O(n) regardless. + +**Use Case:** A data transformation service uses `map()` to normalize 1M user records (lowercase, strip), `filter()` to remove invalid entries, and `reduce()` to compute aggregate statistics — all in a single memory-efficient pipeline without materializing intermediate collections. + + + +## Q. How can you implement functional programming and why would you? + +Functional programming (FP) treats computation as the evaluation of pure functions, avoiding shared state and mutable data. Python supports FP through `map`, `filter`, `functools.reduce`, closures, higher-order functions, and immutable data. + +**Why use it?** +- Predictable, testable pure functions (no side effects) +- Composable pipelines +- Easier parallelization (no shared state) +- Concise transformations on collections + +```py +from functools import reduce, partial +from itertools import chain, starmap +from typing import Callable, TypeVar + +T = TypeVar("T") + +# 1. Pure functions — no side effects, same input → same output +def add(a: int, b: int) -> int: + return a + b + +# 2. Higher-order functions +def compose(*funcs: Callable) -> Callable: + """Compose functions right to left: compose(f, g)(x) == f(g(x))""" + return reduce(lambda f, g: lambda x: f(g(x)), funcs) + +double = lambda x: x * 2 +add_one = lambda x: x + 1 +square = lambda x: x ** 2 + +transform = compose(double, add_one, square) # double(add_one(square(x))) +print(transform(3)) # double(add_one(9)) = double(10) = 20 + +# 3. Partial application +multiply = lambda x, y: x * y +triple = partial(multiply, 3) +print(list(map(triple, [1, 2, 3, 4]))) # [3, 6, 9, 12] + +# 4. Functional pipeline using generators +orders = [ + {"product": "A", "qty": 5, "price": 10.0, "active": True}, + {"product": "B", "qty": 2, "price": 50.0, "active": False}, + {"product": "C", "qty": 10, "price": 5.0, "active": True}, +] + +total_revenue = reduce( + lambda acc, order: acc + order["qty"] * order["price"], + filter(lambda o: o["active"], orders), + 0.0, +) +print(f"Total: £{total_revenue:.2f}") # £100.00 +``` + +**Use Case:** A billing engine implements its pricing logic as pure functions composed into a pipeline: `apply_discount → add_tax → round_to_pence`. Each step is independently unit-testable, and the whole pipeline can be parallelized with `concurrent.futures.ProcessPoolExecutor` safely because there is no shared mutable state. + + + +## Q. Explain `map()`, `filter()`, `reduce()`, and `lambda`. + +```py +from functools import reduce + +# lambda — anonymous function: lambda args: expression +square = lambda x: x ** 2 +add = lambda x, y: x + y +print(square(5)) # 25 +print(add(3, 4)) # 7 + +numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + +# map(func, iterable) — apply func to each element → lazy iterator +doubled = list(map(lambda x: x * 2, numbers)) +str_nums = list(map(str, numbers)) +print(doubled) # [2, 4, 6, 8, 10, 12, 14, 16, 18, 20] + +# filter(predicate, iterable) — keep elements where predicate is True → lazy +evens = list(filter(lambda x: x % 2 == 0, numbers)) +positives = list(filter(None, [-1, 0, 2, 0, 5])) # None = truthiness check +print(evens) # [2, 4, 6, 8, 10] +print(positives) # [-1, 2, 5] wait — [-1 is truthy!] → [-1, 2, 5] + +# reduce(func, iterable, initial) — fold to single value +total = reduce(lambda acc, x: acc + x, numbers, 0) +product = reduce(lambda acc, x: acc * x, numbers, 1) +maximum = reduce(lambda a, b: a if a > b else b, numbers) +print(total) # 55 +print(product) # 3628800 +print(maximum) # 10 + +# Chained functional pipeline +result = reduce( + lambda acc, x: acc + x, # sum + filter(lambda x: x % 2 == 0, # only evens + map(lambda x: x ** 2, numbers)) # squared +) +print(result) # 4+16+36+64+100 = 220 + +# Pythonic equivalent (often more readable) +result2 = sum(x ** 2 for x in numbers if x % 2 == 0) +print(result2) # 220 + +# When to use lambda vs def +# lambda: single expression, used inline (sort key, map/filter callbacks) +data = [{"name": "Bob", "age": 30}, {"name": "Alice", "age": 25}] +data.sort(key=lambda d: d["age"]) # lambda as sort key +print([d["name"] for d in data]) # ['Alice', 'Bob'] +``` + +**Use Case:** A recommendation engine scores items with `map()` (compute scores), filters with `filter()` (remove below threshold), and reduces with `reduce()` (aggregate final ranking score) — three composable, testable steps replacing a monolithic scoring loop. + + + +## Q. What are `map()`, `filter()`, and `reduce()` functions? + +All three are higher-order functions that operate on iterables without explicit `for` loops: + +| Function | What it does | Returns | Module | +|----------|-------------|---------|--------| +| `map(f, it)` | Apply `f` to every element | lazy `map` iterator | built-in | +| `filter(pred, it)` | Keep elements where `pred` is `True` | lazy `filter` iterator | built-in | +| `reduce(f, it, init)` | Fold elements into a single value | single value | `functools` | + +```py +from functools import reduce + +numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + +# map — transform each element +doubled = list(map(lambda x: x * 2, numbers)) +as_str = list(map(str, numbers)) +print(doubled) # [2, 4, 6, 8, 10, 12, 14, 16, 18, 20] + +# filter — select elements matching predicate +evens = list(filter(lambda x: x % 2 == 0, numbers)) +non_zero = list(filter(None, [0, 1, 2, 0, 3])) # None = truthiness +print(evens) # [2, 4, 6, 8, 10] + +# reduce — accumulate to single value +total = reduce(lambda acc, x: acc + x, numbers, 0) # 55 +product = reduce(lambda acc, x: acc * x, numbers, 1) # 3628800 +maximum = reduce(lambda a, b: a if a > b else b, numbers) # 10 + +# Chained pipeline — sum of squares of even numbers +result = reduce( + lambda acc, x: acc + x, + filter(lambda x: x % 2 == 0, + map(lambda x: x ** 2, numbers)) +) +print(result) # 220 (4+16+36+64+100) + +# Pythonic equivalent (generator expression — often more readable) +result2 = sum(x ** 2 for x in numbers if x % 2 == 0) +print(result2) # 220 +``` + +**Use Case:** A pricing engine uses `map()` to apply a discount function to all products, `filter()` to remove items below minimum margin, and `reduce()` to compute the cart total — three composable, independently testable steps. + + + +## # 9. Modules and Packages + +
+ +## Q. How do I find the current module name? + +A module can find out its own module name by looking at the predefined global variable `__name__`. If this has the value `'__main__'`, the program is running as a script. Many modules that are usually used by importing them also provide a command-line interface or a self-test, and only execute this code after checking `__name__`: + +```py +def main(): + print('Running test...') +if __name__ == '__main__': + main() +``` + +`__import__('x.y.z')` +returns Try: `__import__('x.y.z').y.z` + +```py +# For more realistic situations, you may have to do something like: +m = __import__(s) + for i in s.split(".")[1:]: m = getattr(m, i) +``` + + + +## Q. How do I access a module written in Python from C? + +You can get a pointer to the module object as follows: + +`module = PyImport_ImportModule("");` + +If the module hasn\'t been imported yet (i.e. it is not yet present in sys.modules), this initializes the module; otherwise it simply returns the value of ``sys.modules[""]``. Note that it doesn\'t enter the module into any namespace -- it only ensures it has been initialized and is stored in sys.modules. You can then access the module\'s attributes (i.e. any name defined in the module) as follows: attr = PyObject_GetAttrString(module, ""); Calling PyObject_SetAttrString() to assign to variables in the module also works. + + + +## Q. How do you create your own package in Python? + +It overrides the any initialization from an inherited class and is called when the class is instantiated. + +We know that a package may contain sub-packages and modules. A module is nothing but Python code. +To create a package of our own, we create a directory and create a file `__init__.py` in it. We leave it empty. Then, in that package, we create a module(s) with whatever code we want. For a detailed explanation with pictures, refer to Python Packages. + +## Q. What is the purpose of PYTHONSTARTUP, PYTHONCASEOK, PYTHONHOME & PYTHONPATH environment variables? + +- PYTHONSTARTUP − It contains the path of an initialization file containing Python source code. It is executed every time you start the interpreter. It is named as .pythonrc.py in Unix and it contains commands that load utilities or modify PYTHONPATH. + +- PYTHONCASEOK − It is used in Windows to instruct Python to find the first case-insensitive match in an import statement. Set this variable to any value to activate it. + +- PYTHONHOME − It is an alternative module search path. It is usually embedded in the PYTHONSTARTUP or PYTHONPATH directories to make switching module libraries easy. + +- PYTHONPATH − It has a role similar to PATH. This variable tells the Python interpreter where to locate the module files imported into a program. It should include the Python source library directory and the directories containing Python source code. PYTHONPATH is sometimes preset by the Python installer. + + + +## Q. What is a Python module? + +A module is a Python script that generally contains import statements, functions, classes and variable definitions, and Python runnable code and it "lives" file with a '.py' extension. zip files and DLL files can also be modules.Inside the module, you can refer to the module name as a string that is stored in the global variable name . + +## Q. Name the File-related modules in Python? + +Python provides libraries / modules with functions that enable you to manipulate text files and binary fileson file system. Using them you can create files, update their contents, copy, and delete files. The librariesare : os, os.path, and shutil. + +Here, +`os` and `os.path` – modules include functions for accessing the filesystem +`shutil` – module enables you to copy and delete the files. + + + +## Q. What is the PYTHONPATH variable? + +PYTHONPATH is the variable that tells the interpreter where to locate the module files imported into a program. Hence, it must include the Python source library directory and the directories containing Python source code. You can manually set PYTHONPATH, but usually, the Python installer will preset it. + +## Q. Where is math.py (socket.py, regex.py, etc.) source file? + +If you can\'t find a source file for a module, it may be a built-in or dynamically loaded module implemented in C, C++ or other compiled language. In this case you may not have the source file or it may be something like mathmodule.c, somewhere in a C source directory (not on the Python Path). There are (at least) three kinds of modules in Python: + +- Modules written in Python (.py); +- Modules written in C and dynamically loaded (.dll, .pyd, .so, .sl, etc); +- Modules written in C and linked with the interpreter; to get a list of these, type; +Import sys print sys.builtin_module_names; + + + +## Q. How to Search Path of Modules? + +By default python interpreter search for the imported modules in the following locations: + +- Current directory (main module location) +- Environment variable path +- Installation dependent directory +- If the imported module is not found in the any one of the above locations. Then python interpreter giveserror. +- Built-in attributes of a module: +- By default for each and every python module some properties are added internally and we call thoseproperties as a built-in-attribute of a module + + + +## Q. What are the Packages? + +Package is nothing but a folder or dictionary which represents collection of modules. + +A package can also contain sub packages. + +We can import the modules of the package by using package +name.module name or name.subpackage name.module name + +## Q. OS Module + +OS Module is a predefined module and which provides various functions and methods to perform the operating system related activities, such as creating the files, removing the files, creating the directories removing the directories, executing the operating system related commands, etc. +Example: + +```py +import os +cwd=os.getwd() +print("1", cwd) +os.chdir("samples") +print("2", os.getcwd()) +os.chdir(os.pardir) +print("3",os.getcwd()) +``` +Output: + + + +## Q. What is module and package in Python? + +In Python, module is the way to structure program. Each Python program file is a module, which imports other modules like objects and attributes. + +The folder of Python program is a package of modules. A package can have modules or subfolders. + + + +## Q. Should I import an entire module? + +It depends on the use case. The two forms have different trade-offs: + +| Form | Memory | Namespace | Lazy load | Recommendation | +|------|--------|-----------|-----------|----------------| +| `import module` | Full module loaded | Explicit: `module.func()` | No | Preferred for clarity | +| `from module import name` | Full module still loaded | Direct: `func()` | No | Good for frequently used names | +| `from module import *` | Full module loaded | Pollutes namespace | No | **Avoid** (except `__init__.py` re-exports) | + +```py +# Recommended — explicit, avoids name collisions +import os +import json +import pathlib + +path = pathlib.Path("/tmp/data.json") +data = json.loads(path.read_text()) + +# Good — importing specific names you use many times +from datetime import datetime, timezone +from collections import defaultdict + +ts = datetime.now(tz=timezone.utc) + +# Bad — wildcard import pollutes namespace and hides dependencies +# from os.path import * # now 'join', 'exists', 'split' are in scope — unclear origin + +# Conditional / lazy import — for optional heavy dependencies +def load_data(path: str): + try: + import pandas as pd # deferred — only imported when needed + except ImportError: + raise ImportError("pandas is required: pip install pandas") + return pd.read_csv(path) + +# Aliased import — standard for well-known libraries +import numpy as np +import pandas as pd + +arr = np.array([1, 2, 3]) + +# Relative import inside a package +# from .models import User # import from sibling module +# from ..utils import helpers # import from parent package +``` + +**Best practices:** +- `import module` keeps origin clear (`os.path.join` vs `join`) +- `from module import name` is fine for commonly used symbols (`datetime`, `Path`) +- Never use `from module import *` in application code +- Defer heavy imports (`tensorflow`, `pandas`) to avoid slow startup when the feature is optional + +**Use Case:** A microservice with optional ML inference defers `import torch` inside the prediction function — the service starts in <200ms for health checks, and only pays the 4-second PyTorch load cost when an inference request actually arrives. + + + +## Q. How are modules used in a Python program? + +A **module** is any `.py` file. Python finds modules on `sys.path`. You import modules to reuse code across files. + +```py +# ── Creating a module ────────────────────────────────────────────── +# math_utils.py +PI = 3.14159265358979 + +def circle_area(r: float) -> float: + return PI * r * r + +def circle_perimeter(r: float) -> float: + return 2 * PI * r + +class Vector2D: + def __init__(self, x: float, y: float): + self.x, self.y = x, y + def magnitude(self) -> float: + return (self.x**2 + self.y**2) ** 0.5 + +# ── Importing ────────────────────────────────────────────────────── +import math_utils # whole module — use math_utils.PI +from math_utils import circle_area # single name +from math_utils import PI, Vector2D # multiple names +import math_utils as mu # alias + +print(math_utils.circle_area(5)) +print(circle_area(5)) # same result +print(mu.PI) + +# ── Standard library modules ─────────────────────────────────────── +import os, sys, json, pathlib, datetime, collections, itertools, functools + +# os — OS interaction +print(os.getcwd()) +print(os.environ.get("PATH", "")) + +# pathlib — file system +p = pathlib.Path("README.md") +if p.exists(): + print(p.stat().st_size, "bytes") + +# json — serialization +data = {"key": "value", "num": 42} +print(json.dumps(data, indent=2)) + +# ── Package (directory with __init__.py) ────────────────────────── +# mypackage/ +# __init__.py ← makes it a package +# database.py +# models/ +# __init__.py +# user.py + +# from mypackage import database +# from mypackage.models.user import User + +# ── Inspecting modules ──────────────────────────────────────────── +import math +print(dir(math)) # list all names in module +print(math.__file__) # path to module file +print(math.__doc__[:50]) # module docstring +``` + +**Use Case:** A microservice organises code into `src/api/`, `src/services/`, and `src/repositories/` packages — each layer imports only what it needs via explicit relative imports, enforcing architectural boundaries and making unit testing straightforward. + + + +## # 10. Object-Oriented Programming + +
+ +## Q. How do I call a method defined in a base class from a derived class that overrides it? + +If you\'re using new-style classes, use the built-in `super()` function: + +```py +class Derived(Base): + def meth (self): + super(Derived, self).meth() +``` + +If you\'re using classic classes: For a class definition such as +`class Derived(Base):` ... you can call method meth() defined in Base (or one of Base\'s base classes) as Base.meth(self,arguments). Here, Base.meth is an unbound method, so you need to provide the self argument. + + + +## Q. How can I organize my code to make it easier to change the base class? + +You could define an alias for the base class, assign the real base class to it before your class definition, and use the alias throughout your class. Then all you have to change is the value assigned to the alias. Incidentally, this trick is also handy if you want to decide dynamically (e.g. depending on availability of resources) which base class to use. + +Example: BaseAlias = class Derived(BaseAlias): def meth(self): BaseAlias.meth(self). + + + +## Q. Explain Inheritance in Python with an example? + +When one class inherits from another, it is said to be the child/ derived/sub class inheriting from the parent/base/super class. It inherits/gains all members (attributes and methods). Inheritance lets us reuse our code, and also makes it easier to create and maintain applications. + +Inheritance allows One class to gain all the members(say attributes and methods) of another class. Inheritance provides code reusability,makes it easier to create and maintain an application. The class from which we are inheriting is called super-class and the class that is inherited is called a derived/child class. + +- They are different types of inheritance supported by Python: + + - Single Inheritance – where a derived class acquires the members of a single super class. + + _OR_ + + - Single Inheritance- A class inherits from a single base class. + + - Multi-level inheritance – a derived class d1 in inherited from base class base1, and d2 is inherited from base2. + + _OR_ + - Multilevel Inheritance- A class inherits from a base class, which in turn, inherits from another base class. + + - Hierarchical inheritance – from one base class you can inherit any number of child classes + + _OR_ + - Hierarchical Inheritance- Multiple classes inherit from a single base class. + + - Multiple inheritance – a derived class is inherited from more than one base class. + + _OR_ + - Multiple Inheritance- A class inherits from multiple base classes. + + - Hybrid Inheritance- Hybrid inheritance is a combination of two or more types of inheritance. + + + +## Q. What is Hierarchical Inheritance? + +The concept of inheriting the properties from one class into multiple classes separately is known as hierarchical inheritance. + +Example: + +```py +class x(object): + def m1(self): + print("in m1 of x") + +class y(x): + def m2(self): + print("in m2 of y") + +class z(x): + def m3(self): + print("in m3 of z") +y1=y() +y1.m1() +y1.m2() +a=y1.--hash--() +print(a) +z1=z() +z1.m1() +z1.m3() +b=z1.hash--() +print(b) +``` + +Output: + +```py +M m1 of X +In m2 of Y +2337815 +In m1 of X +In m3 of Z +2099735 +``` + + + +## Q. Suppose class C inherits from classes A and B as class C(A,B).Classes A and B both have their own versions of method func(). If we call func() from an object of class C, which version gets invoked? + +Ans. In our article on Multiple Inheritance in Python, we discussed Method Resolution Order (MRO). C does not contain its own version of func(). Since the interpreter searches in a left-to-right fashion, it finds the method in A, and does not go to look for it in B. + +## Q. Which methods/functions do we use to determine the type of instance and inheritance? + +Ans. Here, we talk about three methods/functions- `type(), isinstance() and issubclass()`. + +a. `type()`: This tells us the type of object we\'re working with. + + type(3) + + + type(False) + + + + type(lambda :print("Hi")) + + + + type(type) + + + +b. isinstance() + +This takes in two arguments- a value and a type. If the value is of the kind of the specified type, it returns True. Else, it returns False. + + isinstance(3,int) +True + + isinstance((1),tuple) +False + + isinstance((1,),tuple) +True + +c. issubclass() + +This takes two classes as arguments. If the first one inherits from the second, it returns True. Else, it returns False. + + class A: pass class B(A): pass issubclass(B,A) True - issubclass(A,B) -False + issubclass(A,B) +False + +## Q. Is Python object oriented? what is object oriented programming? + +Yes. Python is Object Oriented Programming language. OOP is the programming paradigm based on classes and instances of those classes called objects. The features of OOP are: Encapsulation, Data Abstraction, Inheritance, Polymorphism. + +## Q. Does Python supports interfaces like in Java? Discuss? + +Python does not provide interfaces like in Java. Abstract Base Class (ABC) and its feature are provided by the Python\'s "abc" module. Abstract Base Class is a mechanism for specifying what methods must be implemented by its implementation subclasses. The use of ABC'c provides a sort of "understanding" about methods and their expected behaviour. This module was made available from Python 2.7 version onwards. + +## Q. What are Accessors, mutators, @property? -## Q. Write a one-liner that will count the number of capital letters in a file. Your code should work even if the file is too big to fit in memory? +Accessors and mutators are often called getters and setters in languages like "Java". For example, if x is a property of a user-defined class, then the class would have methods called setX() and getX(). Python has an @property 'decorator' that allows you to ad getters and setters in order to access the attribute of the class. -Let us first write a multiple line solution and then convert it to one liner code. +## Q. What are accessors, mutators, and @property? - ```py -with open(SOME_LARGE_FILE) as fh: - count = 0 - text = fh.read() -for character in text: - if character.isupper(): - count += 1 -``` +Ans. What we call getters and setters in languages like Java, we term accessors and mutators in Python. In Java, if we have a user-defined class with a property 'x', we have methods like getX() and setX(). In Python, we have @property, which is syntactic sugar for property(). This lets us get and set variables without compromising on the conventions. For a detailed explanation on property, refer to Python property. -## Q. Write a sorting algorithm for a numerical dataset in Python? + -The following code can be used to sort a list in Python: +## Q. What do you mean by overriding methods? + +Ans. Suppose class B inherits from class A. Both have the method sayhello()- to each, their own version. B overrides the sayhello() of class A. So, when we create an object of class B, it calls the version that class B has. ```py -list = ["1", "4", "0", "6", "9"] -list = [int(i) for i in list] -list.sort() -print(list) +class A: + def sayhello(self): + print("Hello, I'm A") +class B(A): + def sayhello(self): + print("Hello, I'm B") +a=A() +b=B() +a.sayhello() ``` +Hello, I\'m A -## Q. How will you remove last object from a list? +```py +b.sayhello() +``` +Hello, I'm B -`list.pop(obj=list[-1])` − Removes and returns last object or obj from list. + -## Q. What are negative indexes and why are they used? +## Q. What is the Difference Between Methods & Constructors? -Python sequences can be index in positive and negative numbers. For positive index, 0 is the first index, 1 is the second index and so forth. For negative index, (-1) is the last index and (-2) is the second last index and so forth. +Methods +- Method name can be any name. +- With respect to one object one method can be called for 'n' members of lines +- Methods are used to represent business logic to perform the operations -The sequences in Python are indexed and it consists of the positive as well as negative numbers. The numbers that are positive uses '0' that is uses as first index and '1' as the second index and the process goes on like that. +Constructor +- Constructor will be executed automatically whenever we create a object. +- With respect to one object one constructor can be executed only once +- Constructors are used to define & initialize the non static variable -The index for the negative number starts from '-1' that represents the last index in the sequence and '-2' as the penultimate index and the sequence carries forward like the positive number. + -The negative index is used to remove any new-line spaces from the string and allow the string to except the last character that is given as S[:-1]. The negative index is also used to show the index to represent the string in correct order. +## Q. What is the Encapsulation? -Let\'s take a list for this. +The concept of binding or grouping related data members along with its related functionalities is known as a Encapsulation. -```py -mylist=[0,1,2,3,4,5,6,7,8] -``` +## Q. How are instance variables different from class variables? -A negative index, unlike a positive one, begins searching from the right. +| Aspect | Instance Variable | Class Variable | +|--------|------------------|----------------| +| Defined in | `__init__` (or any method) via `self.x` | Class body, outside methods | +| Scope | Per-object — each instance has its own copy | Shared across all instances | +| Access | `self.x` or `instance.x` | `ClassName.x` or `self.x` (via MRO) | +| Mutation | Affects only that instance | Affects all instances (if mutated on the class) | ```py -mylist[-3] -``` +class Employee: + company: str = "Acme Corp" # class variable — shared + _headcount: int = 0 # class variable — shared counter -6 + def __init__(self, name: str, salary: float) -> None: + self.name = name # instance variable + self.salary = salary # instance variable + Employee._headcount += 1 -This also helps with slicing from the back: + @classmethod + def headcount(cls) -> int: + return cls._headcount -```py -mylist[-6:-1] +emp1 = Employee("Alice", 90_000) +emp2 = Employee("Bob", 75_000) + +print(Employee.company) # "Acme Corp" +print(emp1.company) # "Acme Corp" — reads class variable via MRO +print(Employee.headcount()) # 2 + +# DANGER: assigning via instance SHADOWS the class variable, not mutates it +emp1.company = "New Corp" +print(emp1.company) # "New Corp" — instance now has its own copy +print(emp2.company) # "Acme Corp" — class variable untouched +print(Employee.company) # "Acme Corp" + +# DANGER with mutable class variables — all instances share the SAME list +class BadExample: + items: list = [] # shared mutable default — antipattern! + +a = BadExample() +b = BadExample() +a.items.append(1) +print(b.items) # [1] — b was mutated too! + +# FIX: use None default and initialize in __init__ +class GoodExample: + def __init__(self) -> None: + self.items: list = [] # each instance gets its own list ``` -[3, 4, 5, 6, 7] +**Use Case:** A request-counting middleware uses a class variable `RequestCounter.total` as an atomic counter shared across all instances, while each instance tracks its own `self.request_id` — demonstrating the correct split between shared state and per-request state. -## Q. Explain split(), sub(), subn() methods of `re` module in Python. +## Q. What is Method Resolution Order (MRO)? -- To modify the strings, Python\'s "re" module is providing 3 methods. They are: - - split() – uses a regex pattern to "split" a given string into a list. - - sub() – finds all substrings where the regex pattern matches and then replace them with a different string. - - subn() – it is similar to sub() and also returns the new string along with the no. of replacements. +MRO is the **deterministic order** in which Python searches for a method or attribute through a class hierarchy. Python 3 uses the **C3 Linearization algorithm** (also called C3 superclass linearization), which guarantees: +- A class always appears before its parents +- Parent order from the class definition is preserved +- Monotonicity (no class appears before a class it inherits from) + +```py +# Diamond inheritance problem +class A: + def method(self) -> str: + return "A" +class B(A): + def method(self) -> str: + return "B" -## Q. What is map function in Python? +class C(A): + def method(self) -> str: + return "C" -Map function executes the function given as the first argument on all the elements of the iterable given as the second argument. If the function given takes in more than 1 arguments, then many iterables are given. #Follow the link to know more similar functions +class D(B, C): # multiple inheritance + pass + +d = D() +print(d.method()) # "B" — follows MRO + +# View the MRO +print(D.__mro__) +# (, , , , ) +print([cls.__name__ for cls in D.__mro__]) +# ['D', 'B', 'C', 'A', 'object'] + +# super() follows MRO — not just the immediate parent +class Base: + def greet(self) -> str: + return "Hello from Base" + +class Left(Base): + def greet(self) -> str: + return f"Left -> {super().greet()}" + +class Right(Base): + def greet(self) -> str: + return f"Right -> {super().greet()}" + +class Child(Left, Right): + def greet(self) -> str: + return f"Child -> {super().greet()}" + +print(Child().greet()) +# Child -> Left -> Right -> Hello from Base +# super() in Left calls Right (not Base directly) — cooperative MRO +``` + +**Use Case:** Django\'s class-based views (e.g., `CreateView(LoginRequiredMixin, CreateView)`) rely on C3 MRO to ensure mixins are applied in the correct order — `LoginRequiredMixin.dispatch()` runs before `CreateView.dispatch()`, enforcing authentication. -## Q. How to get indices of N maximum values in a NumPy array? +## Q. What is multiple inheritance and when should you use it? -We can get the indices of N maximum values in a NumPy array using the below code: +**Multiple inheritance** allows a class to inherit from more than one parent class, acquiring all their attributes and methods. Python resolves method lookup order using the **C3 MRO** algorithm, and `super()` cooperatively chains calls through the full MRO. + +**Use it when:** +- Composing orthogonal behaviours (mixins): `class View(LoginRequiredMixin, PermissionMixin, BaseView)` +- The "mixin" pattern — small, focused classes that add a single capability +- A class genuinely *is-a* multiple types + +**Avoid it when:** +- Classes share overlapping state (use composition instead) +- Hierarchy becomes a diamond of unrelated classes ```py -import numpy as np -arr = np.array([1, 3, 2, 4, 5]) -print(arr.argsort()[-3:][::-1]) +# Mixin pattern — the idiomatic use of multiple inheritance +class LogMixin: + def log(self, msg: str) -> None: + print(f"[{self.__class__.__name__}] {msg}") + +class SerializeMixin: + def to_dict(self) -> dict: + return {k: v for k, v in self.__dict__.items() if not k.startswith("_")} + +class TimestampMixin: + from datetime import datetime + created_at: str = "" + + def set_timestamp(self) -> None: + from datetime import datetime + self.created_at = datetime.utcnow().isoformat() + +# Combine mixins into a concrete class +class Order(LogMixin, SerializeMixin, TimestampMixin): + def __init__(self, order_id: str, amount: float) -> None: + self.order_id = order_id + self.amount = amount + self.set_timestamp() + + def process(self) -> None: + self.log(f"Processing order {self.order_id}") + +o = Order("ORD-001", 99.99) +o.process() +print(o.to_dict()) +# {'order_id': 'ORD-001', 'amount': 99.99, 'created_at': '2025-...'} + +# MRO shows resolution order +print([cls.__name__ for cls in Order.__mro__]) +# ['Order', 'LogMixin', 'SerializeMixin', 'TimestampMixin', 'object'] ``` +**Use Case:** Django\'s class-based views compose `LoginRequiredMixin`, `PermissionRequiredMixin`, and `generic.ListView` via multiple inheritance — each mixin adds one cross-cutting concern (auth, permissions, rendering) without modifying the base class. + -## Q. What is a Python module? +## Q. What is a metaclass? -A module is a Python script that generally contains import statements, functions, classes and variable definitions, and Python runnable code and it "lives" file with a '.py' extension. zip files and DLL files can also be modules.Inside the module, you can refer to the module name as a string that is stored in the global variable name . +A **metaclass** is the class of a class — it controls how classes themselves are created. When Python executes a `class` statement, it calls the metaclass to construct the class object. `type` is the default metaclass for all Python classes. -## Q. Name the File-related modules in Python? +The metaclass receives: `(name, bases, namespace)` and returns a new class object. Custom metaclasses allow you to intercept and modify class creation. -Python provides libraries / modules with functions that enable you to manipulate text files and binary fileson file system. Using them you can create files, update their contents, copy, and delete files. The librariesare : os, os.path, and shutil. +```py +# type IS the metaclass of all classes +print(type(int)) # +print(type(str)) # +print(type(type)) # — type is its own metaclass + +# Custom metaclass +class SingletonMeta(type): + """Metaclass that enforces the Singleton pattern on any class.""" + _instances: dict = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super().__call__(*args, **kwargs) + return cls._instances[cls] + +class DatabasePool(metaclass=SingletonMeta): + def __init__(self, size: int = 5) -> None: + self.size = size + print(f"Pool created with size {size}") + +pool1 = DatabasePool(5) # "Pool created with size 5" +pool2 = DatabasePool(10) # silent — returns existing instance +print(pool1 is pool2) # True + +# Metaclass that auto-registers subclasses +class PluginMeta(type): + registry: dict[str, type] = {} + + def __new__(mcs, name, bases, namespace): + cls = super().__new__(mcs, name, bases, namespace) + if bases: # skip the base class itself + PluginMeta.registry[name] = cls + return cls + +class Plugin(metaclass=PluginMeta): + pass -Here, -`os` and `os.path` – modules include functions for accessing the filesystem -`shutil` – module enables you to copy and delete the files. +class CSVPlugin(Plugin): pass +class JSONPlugin(Plugin): pass - +print(PluginMeta.registry) +# {'CSVPlugin': , 'JSONPlugin': } +``` -## Q. How many kinds of sequences are supported by Python? What are they? +> **When to use:** Metaclasses are rarely needed. Prefer class decorators or `__init_subclass__` for most cases. Use metaclasses only when you need to intercept *class creation itself* (ORMs, plugin systems, API schema generation). -Python supports 7 sequence types. They are str, list, tuple, unicode, byte array, xrange, and buffer. where xrange is deprecated in python 3.5.X. +**Use Case:** SQLAlchemy\'s `DeclarativeMeta` metaclass scans class attributes at definition time to build the ORM mapping, table schema, and column metadata — enabling `class User(Base): id = Column(Integer)` syntax without any explicit registration call. -## Q. How to display the contents of text file in reverse order?How will you reverse a list? +## Q. What are properties and what is the point? - `list.reverse()` − Reverses objects of list in place, convert the given file into a list. Reverse the list by using `reversed()`. - E.g.: - ```py - for line in reversed(list(open("file-name","r"))): - print(line) - ``` +A **property** is a descriptor that turns method calls into attribute access syntax. It lets you add validation, computed values, or lazy loading **without breaking the public API** — callers use `obj.x` regardless of whether `x` is a plain attribute or a property. - +`@property` adds a getter; `@x.setter` / `@x.deleter` add write/delete access. -## Q. What is the difference between NumPy and SciPy? +```py +class Temperature: + def __init__(self, celsius: float) -> None: + self._celsius = celsius # private backing attribute -In an ideal world, NumPy would contain nothing but the array data type and the most basic operations: indexing, sorting, reshaping, basic element wise functions, et cetera. All numerical code would reside in SciPy. However, one of NumPy\'s important goals is compatibility, so NumPy tries to retain all features supported by either of its predecessors. Thus NumPy contains some linear algebra functions, even though these more properly belong in SciPy. In any case, SciPy contains more fully-featured versions of the linear algebra modules, as well as many other numerical algorithms. If you are doing scientific computing with python, you should probably install both NumPy and SciPy. Most new features belong in SciPy rather than NumPy. + @property + def celsius(self) -> float: + """Getter — called on `obj.celsius`""" + return self._celsius -## Q. Which of the following is an invalid statement? + @celsius.setter + def celsius(self, value: float) -> None: + """Setter — called on `obj.celsius = value`""" + if value < -273.15: + raise ValueError(f"Temperature {value}°C below absolute zero") + self._celsius = value -a) abc = 1,000,000 -b) a b c = 1000 2000 3000 -c) a,b,c = 1000, 2000, 3000 -d) a_b_c = 1,000,000 -Answer: b + @celsius.deleter + def celsius(self) -> None: + del self._celsius -## Q. What is the output of the following? + @property + def fahrenheit(self) -> float: + """Computed / read-only property — no setter""" + return self._celsius * 9 / 5 + 32 -```py -try: - if '1' != 1: - raise + @property + def kelvin(self) -> float: + return self._celsius + 273.15 + + +t = Temperature(100) +print(t.celsius) # 100 (getter) +print(t.fahrenheit) # 212.0 (computed, no backing attribute) +t.celsius = 0 # setter with validation +print(t.kelvin) # 273.15 + +try: + t.celsius = -300 # raises ValueError +except ValueError as e: + print(e) + +# t.fahrenheit = 500 # AttributeError: can't set attribute (no setter) ``` -a) some Error has occured -b) some Error has not occured -c) invalid code -d) none of the above +**Why not just make it a plain attribute?** +- Plain attributes expose internal representation +- Changing from attribute to method later *breaks* all callers (`obj.x` → `obj.x()`) +- Properties maintain `obj.x` syntax while adding logic invisibly -Answer: C +**Use Case:** A REST API model class uses `@property` for `full_name = f"{first} {last}"` and a `password` setter that automatically hashes input with `bcrypt` — the API surface remains clean attribute access while security logic is enforced transparently. -## Q. Suppose list1 is [2, 33, 222, 14, 25], What is list1[-1] ? - -25 + -## Q. How to open a file c:\scores.txt for writing? +## Q. What is polymorphism, and when would I use it? -``fileWriter = open("c:\\scores.txt", "w")`` +**Polymorphism** means "many forms" — the same interface (method name / operator) behaves differently depending on the object\'s type. Python supports two kinds: -## Q. Name few Python modules for Statistical, Numerical and scientific computations ? +- **Duck typing polymorphism:** Any object with the right method works, regardless of class hierarchy +- **Subtype polymorphism:** Subclasses override a parent method — callers use the parent type -`numPy` – this module provides an array/matrix type, and it is useful for doing computations on arrays. -`scipy` – this module provides methods for doing numeric integrals, solving differential equations, etc -`pylab` – is a module for generating and saving plots +```py +from abc import ABC, abstractmethod +from typing import Protocol -## Q. What is TkInter? +# 1. Subtype polymorphism — classic OOP +class Shape(ABC): + @abstractmethod + def area(self) -> float: ... -TkInter is Python library. It is a toolkit for GUI development. It provides support for various GUI tools or widgets (such as buttons, labels, text boxes, radio buttons, etc) that are used in GUI applications. The common attributes of them include Dimensions, Colors, Fonts, Cursors, etc. + @abstractmethod + def perimeter(self) -> float: ... -## Q. Is Python object oriented? what is object oriented programming? +class Circle(Shape): + def __init__(self, radius: float) -> None: + self.radius = radius + def area(self) -> float: + import math + return math.pi * self.radius ** 2 + def perimeter(self) -> float: + import math + return 2 * math.pi * self.radius -Yes. Python is Object Oriented Programming language. OOP is the programming paradigm based on classes and instances of those classes called objects. The features of OOP are: Encapsulation, Data Abstraction, Inheritance, Polymorphism. +class Rectangle(Shape): + def __init__(self, w: float, h: float) -> None: + self.w, self.h = w, h + def area(self) -> float: + return self.w * self.h + def perimeter(self) -> float: + return 2 * (self.w + self.h) -## Q. Does Python supports interfaces like in Java? Discuss? +def print_shape_info(shape: Shape) -> None: + print(f"{type(shape).__name__}: area={shape.area():.2f}, " + f"perimeter={shape.perimeter():.2f}") -Python does not provide interfaces like in Java. Abstract Base Class (ABC) and its feature are provided by the Python\'s "abc" module. Abstract Base Class is a mechanism for specifying what methods must be implemented by its implementation subclasses. The use of ABC'c provides a sort of "understanding" about methods and their expected behaviour. This module was made available from Python 2.7 version onwards. +shapes: list[Shape] = [Circle(5), Rectangle(4, 6), Circle(2)] +for s in shapes: + print_shape_info(s) # same call, different behaviour -## Q. What are Accessors, mutators, @property? +# 2. Duck typing polymorphism — no inheritance needed +class Renderer: + def render(self, template: str) -> str: + return f"{template}" -Accessors and mutators are often called getters and setters in languages like "Java". For example, if x is a property of a user-defined class, then the class would have methods called setX() and getX(). Python has an @property 'decorator' that allows you to ad getters and setters in order to access the attribute of the class. +class EmailRenderer: + def render(self, template: str) -> str: + return f"Subject: {template}" -## Q. Differentiate between append() and extend() methods.? +def send_notification(renderer, msg: str) -> None: + print(renderer.render(msg)) # works for any object with .render() -Both append() and extend() methods are the methods of list. These methods are used to add the elements at the end of the list. +send_notification(Renderer(), "Hello") +send_notification(EmailRenderer(), "Hello") # no common base class needed +``` -`append(element)` – adds the given element at the end of the list which has called this method. - `extend(another-list)` – adds the elements of another-list at the end of the list which is called the extend method. +**When to use:** +- Processing collections of mixed-but-related types (shapes, payment methods, file handlers) +- Plugin/strategy patterns where behaviour varies at runtime +- Any time you want to add new types without modifying existing code (Open/Closed Principle) -## Q. Name few methods that are used to implement Functionally Oriented Programming in Python? +**Use Case:** A report generation service stores a list of `ReportSection` objects (charts, tables, text blocks). Calling `section.render(canvas)` on each produces correct output without the caller knowing the concrete type — adding a new `MapSection` requires zero changes to the rendering loop. -Python supports methods (called iterators in Python3), such as filter(), map(), and reduce(), that are very useful when you need to iterate over the items in a list, create a dictionary, or extract a subset of a list. + -- `filter()` – enables you to extract a subset of values based on conditional logic. -- `map()` – it is a built-in function that applies the function to each item in an iterable. -- `reduce()` – repeatedly performs a pair-wise reduction on a sequence until a single value is computed. +## Q. What is a class and what is `self`? -## Q. What is the output of the following? +A **class** is a blueprint for creating objects, bundling data (attributes) and behaviour (methods) together. An **instance** is a concrete object created from a class. + +`self` is a **reference to the current instance** — the first parameter of every instance method. Python passes it automatically when you call `instance.method()`; you name it `self` by convention (not a keyword). ```py -x = ['ab', 'cd'] -print(len(map(list, x))) +class BankAccount: + # Class variable — shared by all instances + bank_name: str = "PyBank" + _total_accounts: int = 0 + + def __init__(self, owner: str, balance: float = 0.0) -> None: + # self refers to the new instance being constructed + self.owner = owner # instance variable + self._balance = balance # private instance variable + BankAccount._total_accounts += 1 + + def deposit(self, amount: float) -> None: + if amount <= 0: + raise ValueError("Deposit amount must be positive") + self._balance += amount # self refers to THIS account + + def withdraw(self, amount: float) -> None: + if amount > self._balance: + raise ValueError("Insufficient funds") + self._balance -= amount + + def get_balance(self) -> float: + return self._balance + + def __repr__(self) -> str: + return f"BankAccount(owner={self.owner!r}, balance={self._balance:.2f})" + + @classmethod + def total_accounts(cls) -> int: # cls = the class itself + return cls._total_accounts + + @staticmethod + def validate_amount(amount: float) -> bool: + return amount > 0 + +# Creating instances — __init__ called automatically +alice = BankAccount("Alice", 1000.0) +bob = BankAccount("Bob") # balance defaults to 0.0 + +# self is passed automatically — alice is 'self' in deposit() +alice.deposit(500) +print(alice.get_balance()) # 1500.0 + +# Both instances are independent +bob.deposit(200) +print(bob.get_balance()) # 200.0 + +print(BankAccount.total_accounts()) # 2 +print(repr(alice)) # BankAccount(owner='Alice', balance=1500.00) ``` -A TypeError occurs as map has no len(). +**Why `self` is explicit:** Unlike Java/C++ `this`, Python\'s explicit `self` makes it immediately clear that a method accesses instance state — improving readability and enabling the method-as-function duality (`BankAccount.deposit(alice, 500)` is valid). -## Q. What is the output of the following? +**Use Case:** An e-commerce order system models `Order` as a class — each instance holds its own `items`, `status`, and `customer_id`, while shared business rules (tax rate, currency) live as class variables or static methods. + + + +## Q. Explain `isinstance()` + +`isinstance(object, classinfo)` returns `True` if `object` is an instance of `classinfo` or any of its subclasses. It is **type-hierarchy aware** — unlike `type(x) == SomeClass` which only matches the exact type. ```py -x = ['ab', 'cd'] -print(len(list(map(list, x)))) +# Basic usage +print(isinstance(42, int)) # True +print(isinstance(42, float)) # False +print(isinstance(True, int)) # True — bool IS a subclass of int +print(isinstance("hi", (int, str))) # True — second arg can be a tuple of types + +# Hierarchy awareness +class Animal: pass +class Dog(Animal): pass +class GoldenRetriever(Dog): pass + +gr = GoldenRetriever() +print(isinstance(gr, GoldenRetriever)) # True +print(isinstance(gr, Dog)) # True — subclass check +print(isinstance(gr, Animal)) # True — transitive +print(isinstance(gr, str)) # False + +# type() vs isinstance() — key difference +print(type(True) == int) # False — exact type match only +print(isinstance(True, int)) # True — hierarchy-aware + +# Using with Abstract Base Classes +from collections.abc import Mapping, Sequence, Iterable + +print(isinstance({}, Mapping)) # True +print(isinstance([], Sequence)) # True +print(isinstance("abc", Sequence)) # True +print(isinstance(range(5), Iterable)) # True + +# Practical: safe attribute access +def process(value: object) -> str: + if isinstance(value, str): + return value.upper() + elif isinstance(value, (int, float)): + return f"{value:.2f}" + elif isinstance(value, list): + return ", ".join(str(v) for v in value) + else: + return repr(value) + +print(process("hello")) # HELLO +print(process(3.14)) # 3.14 +print(process([1, 2, 3])) # 1, 2, 3 ``` -The length of each string is 2. +**Use Case:** A serialization library uses `isinstance(value, Mapping)` rather than `isinstance(value, dict)` so it correctly handles `OrderedDict`, `defaultdict`, and custom `Mapping` subclasses — making the serializer work with any dict-like object. -## Q. Which of the following is not the correct syntax for creating a set? + -a) set([[1,2],[3,4]]) -b) set([1,2,2,3,4]) -c) set((1,2,3,4)) -d) {1,2,3,4} +## Q. What are static methods, class methods, and instance methods? -A. -Explanation : The argument given for the set must be an iterable. +| Method type | First param | Access | Decorator | Use when | +|-------------|-------------|--------|-----------|----------| +| Instance method | `self` (instance) | Instance + class state | (none) | Needs access to instance data | +| Class method | `cls` (class) | Class state only | `@classmethod` | Alternative constructors, factory methods | +| Static method | (none) | Neither | `@staticmethod` | Utility functions logically grouped with the class | -## Q. Explain a few methods to implement Functionally Oriented Programming in Python. +```py +from datetime import datetime -Sometimes, when we want to iterate over a list, a few methods come in handy. +class User: + _count: int = 0 - `filter()`: Filter lets us filter in some values based on conditional logic. + def __init__(self, name: str, email: str) -> None: + self.name = name + self.email = email + self.created_at = datetime.utcnow() + User._count += 1 -```py -list(filter(lambda x:x>5,range(8))) -``` + # Instance method — has access to self (instance attributes) + def greet(self) -> str: + return f"Hi, I'm {self.name} <{self.email}>" - Ans: [6, 7] - `map()`: Map applies a function to every element in an iterable. + def is_active(self) -> bool: + age_days = (datetime.utcnow() - self.created_at).days + return age_days < 365 -```py -list(map(lambda x:x**2,range(8))) -``` + # Class method — receives class, not instance + @classmethod + def from_dict(cls, data: dict) -> "User": + """Alternative constructor — factory pattern.""" + return cls(name=data["name"], email=data["email"]) - Ans: [0, 1, 4, 9, 16, 25, 36, 49] - -`reduce()`: Reduce repeatedly reduces a sequence pair-wise until we reach a single value - -```py - from functools import reduce - reduce(lambda x,y:x-y,[1,2,3,4,5]) + @classmethod + def count(cls) -> int: + return cls._count + + # Static method — no self or cls; logically belongs here + @staticmethod + def validate_email(email: str) -> bool: + return "@" in email and "." in email.split("@")[-1] + +# Usage +u1 = User("Alice", "alice@example.com") +u2 = User.from_dict({"name": "Bob", "email": "bob@example.com"}) + +print(u1.greet()) # Hi, I'm Alice +print(User.count()) # 2 +print(User.validate_email("bad")) # False +print(User.validate_email("a@b.com")) # True + +# Static methods can also be called on instances (but class is cleaner) +print(u1.validate_email("test@x.io")) # True ``` - Ans: -13 +**Use Case:** An ORM `Model` class uses `@classmethod` `from_row(cls, row)` as a factory to construct model instances from database cursor rows — enabling `User.from_row(row)` without exposing the internal column mapping to callers. -## Q. Write a Python function that checks whether a passed string is palindrome Or not? +## Q. Difference between new-style and old-style classes. + +This distinction only matters for **Python 2**. In Python 3 all classes are new-style. -Note: A palindrome is a word, phrase, or sequence that reads the same backward as forward, e.g., madam , saas, nun. +| Feature | Old-style (Python 2 only) | New-style (Python 2.2+ / Python 3 all) | +|---------|--------------------------|---------------------------------------| +| Declaration | `class Foo:` | `class Foo(object):` or just `class Foo:` in Py3 | +| Base class | No implicit base | Implicitly inherits from `object` | +| MRO algorithm | Depth-first (DFS), left to right | C3 Linearization | +| `super()` | Broken in diamond inheritance | Works correctly | +| `type(instance)` | `` | `` | +| Descriptors, `__slots__`, properties | Not fully supported | Fully supported | ```py -def isPalindrome(string): - left_pos = 0 - right_pos = len(string) – 1 +# Python 3 — ALL classes are new-style automatically +class Animal: # implicitly: class Animal(object): + def speak(self) -> str: + return "..." - while right_pos >= left_pos: - if not string[left_pos] == string[right_pos]: - return False +class Dog(Animal): + def speak(self) -> str: + return "Woof" - left_pos += 1 - right_pos -= 1 - return True -print(isPalindrome('aza')) +class Cat(Animal): + def speak(self) -> str: + return "Meow" + +class PoliceDog(Dog): + def speak(self) -> str: + return super().speak() + "! (police)" # super() works correctly + +# Verify new-style +print(PoliceDog.__bases__) # (,) +print(PoliceDog.__mro__) # correct C3 order +print(isinstance(PoliceDog(), object)) # True — inherits from object + +# object gives all classes built-in methods +d = Dog() +print(dir(d)) # __init__, __str__, __repr__, __eq__, __hash__, etc. + +# In Python 2 (for reference — do NOT write this) +# class OldStyle: pass # old-style — no object base +# class NewStyle(object): pass # new-style (must be explicit in Py2) ``` +**Practical implication today:** Always use `class Foo:` in Python 3 — it\'s new-style. If maintaining Python 2 legacy code, add `(object)` explicitly to ensure new-style behaviour. + +**Use Case:** A Django migration from Python 2 to 3 required removing redundant `(object)` from hundreds of model class definitions — a safe, automated change since Python 3 makes them equivalent. + -## Q. Write a Python program to calculate the sum of a list of numbers? +## Q. What are Metaclasses? + +A **metaclass** is the class of a class — it controls how class objects are created and can intercept attribute access, method definition, and subclass registration at class creation time. `type` is the default metaclass for all Python classes. ```py -def list_sum(num_List): - if len(num_List) == 1: - return num_List[0] - else: - return num_List[0] + list_sum(num_List[1:]) -print(list_sum([2, 4, 5, 6, 7])) -``` +# type is the metaclass of all classes +print(type(int)) # +print(type(list)) # +print(type(type)) # — type is its own metaclass -Sample Output: 24 +# Custom metaclass — intercepts class creation +class ValidatedMeta(type): + """Ensure all public methods have type annotations.""" -## Q. How to retrieve data from a table in MySQL database through Python code? + def __new__(mcs, name: str, bases: tuple, namespace: dict): + for attr_name, attr_val in namespace.items(): + if callable(attr_val) and not attr_name.startswith("_"): + annotations = getattr(attr_val, "__annotations__", {}) + if "return" not in annotations: + raise TypeError( + f"{name}.{attr_name}() is missing a return type annotation" + ) + return super().__new__(mcs, name, bases, namespace) -```py -#import MySQLdb module as : -import MySQLdb +class MyService(metaclass=ValidatedMeta): + def greet(self, name: str) -> str: # OK — has return annotation + return f"Hello, {name}" -#establish a connection to the database. -db = MySQLdb.connect("host"="local host", "database-user"="user-name", "password"="password","database-name"="database") + # def bad_method(self): pass # Would raise TypeError at class definition! -#initialize the cursor variable upon the established connection: -c1 = db.cursor() +# __init_subclass__ — simpler alternative for subclass hooks (Python 3.6+) +class PluginBase: + _registry: dict[str, type] = {} -#retrieve the information by defining a required query string. -s = "Select * from dept" + def __init_subclass__(cls, plugin_name: str = "", **kwargs): + super().__init_subclass__(**kwargs) + if plugin_name: + PluginBase._registry[plugin_name] = cls -#fetch the data using fetch() methods and print it. -data = c1.fetch(s) +class CSVPlugin(PluginBase, plugin_name="csv"): + def process(self): pass -#close the database connection. -db.close() +class JSONPlugin(PluginBase, plugin_name="json"): + def process(self): pass + +print(PluginBase._registry) +# {'csv': , 'json': } ``` +> **When to use:** Metaclasses are for framework authors. For application code, prefer `__init_subclass__`, class decorators, or `dataclasses`. Use metaclasses only when you need to control *how class objects themselves are constructed*. + +**Use Case:** SQLAlchemy\'s `DeclarativeMeta` scans each model class body at definition time to register column descriptors, build table metadata, and wire up the ORM mapper — all happening before any instance is ever created. + -## Q. Write a Python program to read a random line from a file. +## Q. What is a descriptor? + +A **descriptor** is any object that defines `__get__`, `__set__`, or `__delete__` and is assigned as a class attribute. Python calls these methods automatically when the attribute is accessed on an instance, enabling properties, methods, `classmethod`, `staticmethod`, and custom validation attributes. ```py -import random -def random_line(fname): - lines = open(fname).read().splitlines() - return random.choice(lines) - print(random_line('test.txt')) -``` +from typing import Any -## Q. Write a Python program to count the number of lines in a text file? +# Data descriptor — defines both __get__ and __set__ +class TypedAttribute: + """Descriptor that enforces a type on assignment.""" -```py -def file_lengthy(fname): - with open(fname) as f: - for i, l in enumerate(f): - pass - return i + 1 + def __init__(self, name: str, expected_type: type) -> None: + self._name = name + self._type = expected_type -print("Number of lines in the file: ",file_lengthy("test.txt")) -``` + def __set_name__(self, owner: type, name: str) -> None: + self._name = name # called automatically at class creation -## Q. What are the key features of Python? + def __get__(self, obj: Any, objtype: type = None) -> Any: + if obj is None: + return self # accessed on class, not instance + return obj.__dict__.get(self._name) -If it makes for an introductory language to programming, Python must mean something. These are its qualities: -> -- Interpreted. -- Dynamically-typed. -- Object-oriented -- Concise and simple -- Free -- Has a large community + def __set__(self, obj: Any, value: Any) -> None: + if not isinstance(value, self._type): + raise TypeError( + f"{self._name} must be {self._type.__name__}, " + f"got {type(value).__name__}" + ) + obj.__dict__[self._name] = value -## Q. Explain the ternary operator in Python? +class Product: + name = TypedAttribute("name", str) + price = TypedAttribute("price", float) + stock = TypedAttribute("stock", int) -Unlike C++, we don\'t have ?: in Python, but we have this: + def __init__(self, name: str, price: float, stock: int) -> None: + self.name = name # triggers TypedAttribute.__set__ + self.price = price + self.stock = stock -[on true] if [expression] else [on false] +p = Product("Widget", 9.99, 100) +print(p.name) # Widget -If the expression is True, the statement under [on true] is executed. Else, that under [on false] is executed. +try: + p.price = "expensive" # raises TypeError +except TypeError as e: + print(e) # price must be float, got str -Below is how you would use it: -ex 1. +# Non-data descriptor — only __get__ (e.g., functions / methods) +class LazyProperty: + """Compute once, cache on instance.""" + def __init__(self, func): + self._func = func + self.__doc__ = func.__doc__ -```py -a,b=2,3 -min=a if a None: + self.radius = radius -```py -print("Hi") if a float: + import math + print("computing area...") + return math.pi * self.radius ** 2 + +c = Circle(5) +print(c.area) # "computing area..." then 78.54... +print(c.area) # no print — cached in instance __dict__ ``` -Ans: Hi +**Use Case:** Django\'s ORM `Field` objects (e.g., `CharField`, `IntegerField`) are descriptors — `model.field_name` triggers `__get__` which queries the deferred value, and assignment triggers `__set__` which validates and stores the value before `save()`. -## Q. What is multithreading? - -It means running several different programs at the same time concurrently by invoking multiple threads. Multiple threads within a process refer the data space with main thread and they can communicate with each other to share information more easily.Threads are light-weight processes and have less memory overhead. Threads can be used just for quick task like calculating results and also running other processes in the background while the main program is running. +## Q. What is MRO (Method Resolution Order)? -Thread Is a functionality or logic which can execute simultaneously along with the other part of the program. -Thread is a light weight process. -Any program which is under execution is known as process. -We can define the threads in python by overwriting run method of thread class. -Thread class is a predefined class which is defined in threading module. -Thread in module is a predefined module. -If we call the run method directly the logic of the run method will be executed as a normal method logic. -In order to execute the logic of the run method as a we use start method of thread class. -Example +**MRO** is the deterministic order in which Python searches a class hierarchy to resolve a method or attribute lookup. Python 3 uses the **C3 Linearization algorithm**, which guarantees: +1. A class always appears before its parents +2. Left-to-right order of parents in the class definition is preserved +3. Monotonicity — no class appears after one of its superclasses ```py -import threading -class x (threading.Thread): - def run(self): - for p in range(1, 101): - print(p) -class y (threading.Thread): - def run(self): - for q in range(1, 101): - print(q) -x1=x() -y1=y() -x1.start() -y1.start() -``` +# Diamond inheritance — the classic MRO challenge +class A: + def method(self) -> str: return "A" -A thread is a lightweight process and multithreading allows us to execute multiple threads at once. As you know, Python is a multithreaded language. It has a multithreading package. The GIL (Global Interpreter Lock) ensures that a single thread executes at a time. A thread holds the GIL and does a little work before passing it on to the next thread. This makes for an illusion of parallel execution. But in reality, it is just threaded taking turns at the CPU. Of course, all the passing around adds overhead to the execution. +class B(A): + def method(self) -> str: return "B" -## Q. Explain help() and dir() functions in Python? +class C(A): + def method(self) -> str: return "C" -The help() function displays the documentation string and help for its argument. +class D(B, C): # diamond: D → B → C → A → object + pass -```py -import copy -help(copy.copy) -``` +print(D().method()) # "B" — first in MRO -Help on function copy in module copy: copy(x) -Shallow copy operation on arbitrary Python objects. -See the module\'s __doc__ string for more info. -The dir() function displays all the members of an object(any kind). +# Inspect MRO +print([cls.__name__ for cls in D.__mro__]) +# ['D', 'B', 'C', 'A', 'object'] -```py -dir(copy.copy) +# super() cooperatively follows MRO +class Base: + def process(self) -> list: + return ["Base"] -['__annotations__', '__call__', '__class__', '__closure__', '__code__', '__defaults__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__get__', '__getattribute__', '__globals__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__kwdefaults__', '__le__', '__lt__', '__module__', '__name__', '__ne__', '__new__', '__qualname__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__'] -``` +class Left(Base): + def process(self) -> list: + return ["Left"] + super().process() # calls Right, not Base directly - +class Right(Base): + def process(self) -> list: + return ["Right"] + super().process() -### _**Dictionary**_ +class Child(Left, Right): + def process(self) -> list: + return ["Child"] + super().process() -## Q. What is a dictionary in Python? +print(Child().process()) +# ['Child', 'Left', 'Right', 'Base'] +# MRO: Child → Left → Right → Base → object -The built-in datatypes in Python is called dictionary. It defines one-to-one relationship between keys and values. Dictionaries contain pair of keys and their corresponding values. Dictionaries are indexed by keys. +# Mixin pattern relying on MRO +class LogMixin: + def save(self) -> None: + print(f"[LOG] Saving {self.__class__.__name__}") + super().save() # calls next in MRO -Let\'s take an example: +class Model: + def save(self) -> None: + print("Model.save()") -The following example contains some keys. Country, Capital & PM. Their corresponding values are India, Delhi and Modi respectively. +class User(LogMixin, Model): # LogMixin.save() → Model.save() + pass -`dict={'Country':'India','Capital':'Delhi','PM':'Modi'}` +User().save() +# [LOG] Saving User +# Model.save() +``` -`print dict[Country]` +**Use Case:** Django\'s class-based views depend on MRO so that `LoginRequiredMixin` (placed first) has its `dispatch()` called before the view\'s `dispatch()`, intercepting unauthenticated requests without modifying `DetailView` at all. -```py -roots={25:5,16:4,9:3,4:2,1:1} -type(roots) -``` + - +## Q. How is a Python class created? ```py -roots[9] -``` +from __future__ import annotations +from dataclasses import dataclass, field +from typing import ClassVar -3 +# 1. Basic class with __init__ +class Rectangle: + # Class variable — shared by all instances + shape_type: ClassVar[str] = "rectangle" -A dictionary is mutable, and we can also use a comprehension to create it. + def __init__(self, width: float, height: float) -> None: + self.width = width # instance variable + self.height = height -```py -roots={x**2:x for x in range(5,0,-1)} -roots -``` + def area(self) -> float: + return self.width * self.height -{25: 5, 16: 4, 9: 3, 4: 2, 1: 1} + def perimeter(self) -> float: + return 2 * (self.width + self.height) -## Q. How do you get a list of all the keys in a dictionary? + def __repr__(self) -> str: + return f"Rectangle(width={self.width}, height={self.height})" -Be specific in these type of Python Interview Questions and Answers. + def __eq__(self, other: object) -> bool: + if not isinstance(other, Rectangle): return NotImplemented + return self.width == other.width and self.height == other.height -For this, we use the function keys(). +r = Rectangle(4, 6) +print(r.area()) # 24.0 +print(repr(r)) # Rectangle(width=4, height=6) -```py -mydict={'a':1,'b':2,'c':3,'e':5} -mydict.keys() -print(dict_keys) +# 2. Inheritance +class Square(Rectangle): + def __init__(self, side: float) -> None: + super().__init__(side, side) # delegate to parent + + def __repr__(self) -> str: + return f"Square(side={self.width})" + +s = Square(5) +print(s.area()) # 25.0 +print(isinstance(s, Rectangle)) # True + +# 3. dataclass — auto-generates __init__, __repr__, __eq__ +@dataclass +class Point: + x: float + y: float + label: str = "origin" + _history: list = field(default_factory=list, repr=False) + + def distance_to(self, other: Point) -> float: + return ((self.x - other.x)**2 + (self.y - other.y)**2) ** 0.5 + +p1 = Point(0, 0) +p2 = Point(3, 4) +print(p1.distance_to(p2)) # 5.0 +print(p2) # Point(x=3, y=4, label='origin') ``` -(['a', 'b', 'c', 'e']) +**Use Case:** A domain model layer defines `@dataclass` classes (`Order`, `LineItem`, `Customer`) with type annotations — getting `__repr__`, `__eq__`, and `__init__` for free while keeping the model readable and `mypy`-checked. -## Q. Write Python logic to count the number of capital letters in a file? +## Q. How is a Python class instantiated? + +Instantiation calls the **metaclass** (`type.__call__`) which first calls `__new__` (allocate memory, return the new object) then `__init__` (initialize the object). The result is the fully constructed instance. ```py -import os -os.chdir('C:\\Users\\lifei\\Desktop') -with open('Today.txt') as today: - count = 0 - for i in today.read(): - if i.isupper(): - count+=1 - print(count) -``` +class Connection: + def __new__(cls, *args, **kwargs): + print(f"__new__: allocating {cls.__name__}") + instance = super().__new__(cls) # allocate memory + return instance -26 + def __init__(self, host: str, port: int) -> None: + print(f"__init__: initializing with {host}:{port}") + self.host = host + self.port = port -## Q. How would you randomize the contents of a list in-place? + def __repr__(self) -> str: + return f"Connection({self.host!r}, {self.port})" -For this, we'll import the function `shuffle()` from the module `random`. +# Instantiation — calls __new__ then __init__ +conn = Connection("localhost", 5432) +# __new__: allocating Connection +# __init__: initializing with localhost:5432 +print(conn) # Connection('localhost', 5432) -```py -from random import shuffle -shuffle(mylist) -mylist -``` +# Multiple instances are independent objects +conn2 = Connection("db.prod", 5432) +print(conn is conn2) # False — different objects +print(id(conn), id(conn2)) -[3, 4, 8, 0, 5, 7, 6, 2, 1] +# Singleton via __new__ +class Config: + _instance = None -## Q. Explain join() and split() in Python? + def __new__(cls): + if cls._instance is None: + cls._instance = super().__new__(cls) + return cls._instance -`.join([])` It takes any iterables into this method. Join method is used to concatenate the elements of any list. `join()` lets us join characters from a string together by a character we specify. + def __init__(self) -> None: + if not hasattr(self, "_initialized"): + self.debug = False + self._initialized = True -```py -','.join('12345') -``` +cfg1 = Config() +cfg2 = Config() +print(cfg1 is cfg2) # True — same object -'1,2,3,4,5' +# Named constructors (alternative instantiation patterns) +from datetime import date -`split()` lets us split a string around the character we specify. +d1 = date(2025, 6, 15) # standard __init__ +d2 = date.today() # classmethod factory +d3 = date.fromisoformat("2025-06-15") # another classmethod -```py -'1,2,3,4,5'.split(',') +print(type(d1) is type(d2) is type(d3)) # True — all date instances ``` -['1', '2', '3', '4', '5'] +**Use Case:** An ORM `Session` factory uses `__new__` to return an existing session from the pool instead of creating a new object, making `Session()` syntax act as a transparent pool checkout without callers knowing about pooling. -## Q. Is Python case-sensitive? + -A language is case-sensitive if it distinguishes between identifiers like myname and Myname. In other words, it cares about case- lowercase or uppercase. Let\'s try this with Python. +## # 11. Exception Handling -```py -myname='Ramayan' -Myname -``` +
-Traceback (most recent call last): -File "", line 1, in Myname -NameError: name 'Myname' is not defined +## Q. Explain the use of try: except: raise, and finally: -As you can see, this raised a NameError. This means that Python is indeed case-sensitive. +Try, except and finally blocks are used in Python error handling. Code is executed in the try block until an error occurs. One can use a generic except block, which will receive control after all errors, or one can use specific exception handling blocks for various error types. Control is transferred to the appropriate except block. In all cases, the finally block is executed. Raise may be used to raise your own exceptions. -- How long can an identifier be in Python? +## Q. Illustrate the proper use of Python error handling. + +Code Example: + + try: + ….#This can be any code + except: + …# error handling code goes here + finally: + …# code that will be executed regardless of exception handling goes here. + + + +## Q. What are the Runtime Errors? + +The errors which occurs after starting the execution of the programs are known as runtime errors. Runtime errors can occur because of: + + Invalid Input + Invalid Logic + Memory issues + Hardware failures and so on + +With respect to every reason which causes to runtime error correspoing runtime error representation class is available +Runtime error representation classes technically we call as a exception classes. +While executing the program if any runtime error is occur corresponding runtime error representation class object is created +Creating runtime error representation class object is technically known as a rising exception +While executing the program if any exception is raised, then internally python interpreter verify any code is implemented to handle raised exception or not +If code is not implemented to handle raised exception then program will be terminated abnormally + + + +## Q. What is Abnormal Termination? + +The concept of terminating the program in the middle of its execution without executing last statement of the main module is known as a abnormal termination +Abnormal termination is undesirable situation in programming languages. + +## Q. What is `try` Block? -In Python, an identifier can be of any length. Apart from that, there are certain rules we must follow to name one: -> - - It can only begin with an underscore or a character from A-Z or a-z. - - The rest of it can contain anything from the following: A-Z/a-z/_/0-9. - - Python is case-sensitive, as we discussed in the previous question. - - Keywords cannot be used as identifiers. +A block which is preceded by the try keyword is known as a try block +Syntax: +try{ + //statements that may cause an exception +} -Python has the following keywords: -`and def False import not True` -`as del finally in or try` -`assert elif for is pass while` -`break else from lambda print with ` -`class except global None raise yield` -`continue exec if nonlocal return` +The statements which causes to run time errors and other statements which depends on the execution of run time errors statements are recommended to represent in try block +While executing try block statement if any exception is raised then immediately try block identifies that exception, receive that exception and forward that exception to except block without executing remaining statements to try block. -## Q. How do you remove the leading whitespace in a string? +## Q. What happens if you have an error in an `__init__` statement? -Leading whitespace in a string is the whitespace in a string before the first non-whitespace character. To remove it from a string, we use the method `lstrip()`. +If `__init__` raises an exception, the object construction **fails**. `__new__` has already allocated memory for the object, but since `__init__` did not complete, the partially-constructed object is not returned to the caller — the exception propagates instead. Python\'s garbage collector will eventually reclaim the allocated memory via reference counting. ```py -' Ram '.lstrip() -``` +class DatabaseConnection: + _instances: list["DatabaseConnection"] = [] -'Ram ' - -As you can see, this string had both leading and trailing whitespaces. lstrip() stripped the string of the leading whitespace. If we want to strip the trailing whitespace instead, we use rstrip(). + def __init__(self, host: str, port: int) -> None: + if not (1 <= port <= 65535): + raise ValueError(f"Invalid port: {port}") + if not host: + raise ValueError("Host cannot be empty") -```py -' Ram '.rstrip() -``` + # If an error occurs here, __init__ partially ran + self.host = host + self.port = port + self._connection = self._connect() # might raise ConnectionError + DatabaseConnection._instances.append(self) # only reached if no error -' Ram' + def _connect(self): + # Simulate connection failure + raise ConnectionError(f"Cannot reach {self.host}:{self.port}") -- How would you convert a string into lowercase? +# Test: error in validation +try: + conn = DatabaseConnection("localhost", 99999) +except ValueError as e: + print(f"ValueError: {e}") # Invalid port: 99999 + # conn is never bound — the object does not exist -We use the lower() method for this. +# Test: error mid-init (after self.host assigned but before append) +try: + conn2 = DatabaseConnection("db.prod", 5432) +except ConnectionError as e: + print(f"ConnectionError: {e}") + # DatabaseConnection._instances is still empty — append never ran -```py -'Ramayan'.lower() +print(DatabaseConnection._instances) # [] ``` -'ramayan' +**Key takeaway:** Always use `try/except` or `contextlib.ExitStack` in `__init__` to clean up resources (file handles, sockets) if later steps fail — Python does not call `__del__` on the partially-constructed object reliably. -To convert it into uppercase, then, we use upper(). +**Use Case:** A connection pool factory wraps `__init__` errors to ensure that failed connection attempts are logged and retried without leaving zombie objects registered in the pool registry. -```py -'Ramayan'.upper() -``` + -'RAMAYAN' +## Q. What happens in Python if you try to divide by zero? -Also, to check if a string is in all uppercase or all lowercase, we use the methods isupper() and islower(). +Integer or float division by zero raises `ZeroDivisionError`. The `//` (floor division) and `%` (modulo) operators also raise it. The only exception: `float('inf') / 0` does not raise — it follows IEEE 754 and returns `inf`. ```py -'Ramayan'.isupper() +# Integer division by zero +try: + result = 10 // 0 +except ZeroDivisionError as e: + print(f"ZeroDivisionError: {e}") # integer division or modulo by zero + +# Float division by zero +try: + result = 10.0 / 0.0 +except ZeroDivisionError as e: + print(f"ZeroDivisionError: {e}") # float division by zero + +# Modulo by zero +try: + result = 7 % 0 +except ZeroDivisionError as e: + print(f"ZeroDivisionError: {e}") # integer division or modulo by zero + +# Safe division utility +def safe_divide(numerator: float, denominator: float, default: float = 0.0) -> float: + """Return numerator/denominator, or default if denominator is zero.""" + if denominator == 0: + return default + return numerator / denominator + +print(safe_divide(10, 2)) # 5.0 +print(safe_divide(10, 0)) # 0.0 + +# In numpy, division by zero produces inf/nan (not an exception) +import numpy as np +arr = np.array([1.0, 2.0, 0.0]) +with np.errstate(divide="ignore", invalid="ignore"): + result = np.array([1.0, 2.0]) / arr[:2] ``` -False +**Use Case:** A financial analytics system wraps all division operations in `safe_divide()` to handle missing or zero-denominator data gracefully, returning `None` instead of crashing — critical for ratio metrics like conversion rate where denominators can be zero. -```py -'Ramayan'.isupper() -``` + -True +## Q. How to handle exceptions? + +Python uses `try / except / else / finally` blocks. The `else` clause runs only if no exception was raised; `finally` always runs (cleanup). ```py -'Ramayan'.islower() -``` +import logging +from pathlib import Path + +logger = logging.getLogger(__name__) + +# Basic exception handling +try: + result = 10 / 0 +except ZeroDivisionError as e: + print(f"Caught: {e}") + +# Multiple except clauses (most specific first) +def parse_config(path: str) -> dict: + try: + text = Path(path).read_text(encoding="utf-8") + import json + return json.loads(text) + except FileNotFoundError: + logger.error("Config file not found: %s", path) + return {} + except json.JSONDecodeError as e: + logger.error("Invalid JSON in %s: %s", path, e) + return {} + except PermissionError as e: + raise RuntimeError(f"Cannot read {path}") from e # chained exception + +# else — runs only when no exception occurred +def divide(a: float, b: float) -> float | None: + try: + result = a / b + except ZeroDivisionError: + return None + else: + print("Division succeeded") # only runs if no exception + return result + finally: + print("Always runs — cleanup here") # always executes -True +# Custom exception hierarchy +class AppError(Exception): + """Base exception for this application.""" -```py -'$hrir@m'.islower() -``` +class ValidationError(AppError): + def __init__(self, field: str, message: str) -> None: + self.field = field + super().__init__(f"{field}: {message}") -True +class DatabaseError(AppError): + pass -```py -'$HRIR@M'.isupper() +# Catching base class catches all subclasses +try: + raise ValidationError("email", "invalid format") +except AppError as e: + print(f"App error: {e}") # email: invalid format + +# Re-raising +try: + int("not a number") +except ValueError: + logger.warning("Bad input received") + raise # re-raise the same exception with original traceback + +# Exception groups (Python 3.11+) +# try: +# ... +# except* ValueError as eg: +# for exc in eg.exceptions: handle(exc) +``` + +**Use Case:** A payment service wraps all third-party API calls with specific `except` clauses: `RequestTimeout` triggers a retry, `AuthError` raises a `PaymentDeclinedError` for the caller, and `finally` always releases the connection back to the pool. + + + +## Q. What are the different types of exceptions in Python? + +Python exceptions form a hierarchy rooted at `BaseException`. Most user-facing exceptions inherit from `Exception`. + +``` +BaseException +├── SystemExit # sys.exit() +├── KeyboardInterrupt # Ctrl+C +├── GeneratorExit # generator.close() +└── Exception + ├── ArithmeticError + │ ├── ZeroDivisionError + │ ├── OverflowError + │ └── FloatingPointError + ├── LookupError + │ ├── IndexError # list[99] out of range + │ └── KeyError # dict["missing"] + ├── ValueError # right type, wrong value: int("abc") + ├── TypeError # wrong type: 1 + "a" + ├── AttributeError # obj.no_such_attr + ├── NameError # undefined variable + │ └── UnboundLocalError + ├── OSError (IOError, EnvironmentError) + │ ├── FileNotFoundError + │ ├── PermissionError + │ ├── TimeoutError + │ └── ConnectionError + │ ├── ConnectionRefusedError + │ └── ConnectionResetError + ├── RuntimeError + │ └── RecursionError + ├── StopIteration + ├── ImportError + │ └── ModuleNotFoundError + ├── MemoryError + ├── NotImplementedError + └── AssertionError # failed assert statement ``` -True +```py +# Triggering common exceptions +try: + x = 1 / 0 +except ZeroDivisionError as e: + print(f"ZeroDivisionError: {e}") -So, characters like @ and $ will suffice for both cases. +try: + lst = [1, 2, 3]; _ = lst[10] +except IndexError as e: + print(f"IndexError: {e}") -Also, istitle() will tell us if a string is in title case. +try: + d = {"a": 1}; _ = d["z"] +except KeyError as e: + print(f"KeyError: {e}") -```py -'Arrested Development'.istitle() +try: + int("not a number") +except ValueError as e: + print(f"ValueError: {e}") + +try: + open("/etc/shadow") +except PermissionError as e: + print(f"PermissionError: {e}") + +# Catching base class catches all subclasses +try: + {}["missing"] +except LookupError as e: # catches both KeyError and IndexError + print(f"LookupError: {type(e).__name__}: {e}") ``` -True +**Use Case:** A data ingestion service catches `LookupError` for missing fields, `ValueError` for bad formats, and `OSError` for file access problems — each mapped to a specific retry or dead-letter strategy in the error handler. -## Q. What is the pass statement in Python? +## Q. How to write your own custom exception handling? -There may be times in our code when we haven\'t decided what to do yet, but we must type something for it to be syntactically correct. In such a case, we use the pass statement. +Define a class hierarchy inheriting from `Exception` (or a more specific built-in), add context attributes, and catch at the appropriate granularity. ```py -def func(*args): - pass -``` +# Custom exception hierarchy +class AppError(Exception): + """Base exception — catch-all for this application.""" + +class ValidationError(AppError): + def __init__(self, field: str, message: str, value=None) -> None: + self.field = field + self.value = value + super().__init__(f"[{field}] {message} (got: {value!r})") + +class DatabaseError(AppError): + def __init__(self, operation: str, cause: Exception) -> None: + self.operation = operation + super().__init__(f"DB error during '{operation}'") + self.__cause__ = cause # explicit exception chaining + +class NotFoundError(AppError): + def __init__(self, resource: str, identifier) -> None: + self.resource = resource + self.identifier = identifier + super().__init__(f"{resource} not found: {identifier!r}") + +# Using the custom exceptions +def get_user(user_id: int) -> dict: + if not isinstance(user_id, int) or user_id <= 0: + raise ValidationError("user_id", "Must be a positive integer", user_id) + users = {1: {"name": "Alice"}, 2: {"name": "Bob"}} + if user_id not in users: + raise NotFoundError("User", user_id) + return users[user_id] + +def fetch_and_save(user_id: int) -> None: + try: + user = get_user(user_id) + print(f"Found: {user}") + except ValidationError as e: + print(f"Validation failed — field={e.field}, value={e.value}") + except NotFoundError as e: + print(f"Not found — {e.resource} id={e.identifier}") + except AppError as e: + print(f"General app error: {e}") + finally: + print("Cleanup always runs") -Similarly, the break statement breaks out of a loop. +fetch_and_save(1) # Found: {'name': 'Alice'} +fetch_and_save(99) # Not found — User id=99 +fetch_and_save(-5) # Validation failed — field=user_id, value=-5 -```py -for i in range(7): - if i==3: break - print(i) +# Exception chaining (raise X from Y) +try: + try: + int("bad") + except ValueError as e: + raise DatabaseError("insert", e) from e +except DatabaseError as e: + print(e) + print(f"Caused by: {e.__cause__}") ``` -0 -1 -2 - -Finally, the continue statement skips to the next iteration. - -```py -for i in range(7): - if i==3: continue - print(i) -``` -0 -1 -2 -4 -5 -6 +**Use Case:** A REST API defines `ValidationError`, `AuthorizationError`, and `NotFoundError` — each mapped in an exception handler middleware to HTTP 400, 403, and 404 responses with structured JSON error bodies, keeping business logic clean of HTTP concerns. -## Q. What is a closure in Python? - -In Python, a closure is a function object that has access to variables in its enclosing lexical scope, even when the function is called outside that scope. In other words, a closure allows a function to remember and access the values of the variables in the environment where it was created, even if those variables are no longer in scope when the function is called. - -Closures are created when a nested function references a value from its enclosing function. The enclosing function returns the nested function, which maintains a reference to the enclosed value. The enclosed value is stored in the closure, which is attached to the nested function object. +## Q. Why does exception handling have a `finally` block? -Here's an example of a closure in Python: +The `finally` block **always executes** — whether the `try` block succeeded, raised an exception that was caught, raised one that wasn\'t caught, or even executed a `return`/`break`/`continue`. Its purpose is **guaranteed cleanup** of resources (file handles, database connections, locks). ```py -def outer_function(x): - def inner_function(y): - return x + y - return inner_function +import threading -closure = outer_function(10) -result = closure(5) -print(result) # Output: 15 +# finally runs even when an exception is raised and NOT caught +def risky_operation(): + lock = threading.Lock() + lock.acquire() + try: + print("Doing work...") + result = 10 / 2 # success path + return result + except ZeroDivisionError: + print("Caught division error") + return None + finally: + lock.release() # ALWAYS runs — prevents deadlock + print("Lock released (finally)") + +print(risky_operation()) +# Doing work... +# Lock released (finally) +# 5.0 + +# finally runs even with return in try +def demo_return(): + try: + return "from try" # return is delayed until finally finishes + finally: + print("finally ran before returning") + # return "from finally" # this would OVERRIDE the try return! + +print(demo_return()) +# finally ran before returning +# from try + +# Practical: file I/O cleanup (the manual way — use `with` in production) +f = None +try: + f = open("data.txt", "w") + f.write("hello") + int("bad") # raises ValueError +except ValueError as e: + print(f"Error: {e}") +finally: + if f: + f.close() # file is closed regardless of exception + print("File closed") + +# else + finally together +def read_file(path: str) -> str: + try: + text = open(path).read() + except FileNotFoundError: + return "" + else: + print("Read succeeded") # only if no exception + return text + finally: + print("Cleanup (always)") # always ``` -```py -def A(x): - def B(): - print(x) - return B +**Rule:** +- `else` — runs only when **no exception** occurred +- `finally` — runs **always**, exception or not -A(7)() -``` +**Use Case:** A database transaction wrapper uses `finally` to always call `connection.close()` — even if the application raises an unhandled exception mid-transaction, the connection is returned to the pool and no resources leak. -7 + -## Q. Explain the //, %, and ** operators in Python. +## Q. What happens if an error occurs that is not handled in the `except` block? -The // operator performs floor division. It will return the integer part of the result on division. +If an exception is raised and no matching `except` clause handles it, it **propagates up the call stack** frame by frame until either a handler is found or it reaches the top level. At the top level, Python prints the full **traceback** and terminates the program (or thread). ```py -7//2 -``` -`3` -Normal division would return 3.5 here. - -Similarly, **performs exponentiation. a**b returns the value of a raised to the power b. +import traceback +import sys + +def level_3() -> None: + raise ValueError("Something went wrong at level 3") + +def level_2() -> None: + level_3() # no try/except here — exception propagates up + +def level_1() -> None: + try: + level_2() + except TypeError: + print("Caught TypeError") # won't match — exception is ValueError + # ValueError is NOT caught — propagates further + +# Unhandled exception terminates the program with a traceback: +# Traceback (most recent call last): +# File "...", line X, in +# File "...", line X, in level_1 +# File "...", line X, in level_2 +# File "...", line X, in level_3 +# ValueError: Something went wrong at level 3 + +# Install a global exception handler to log before crash +def global_handler(exc_type, exc_value, exc_traceback): + if issubclass(exc_type, KeyboardInterrupt): + sys.__excepthook__(exc_type, exc_value, exc_traceback) + return + print("UNHANDLED EXCEPTION — logging before exit:") + traceback.print_exception(exc_type, exc_value, exc_traceback) + +sys.excepthook = global_handler + +# In threads, unhandled exceptions terminate only THAT thread (not the process) +import threading -```py -2**10 -``` +def bad_thread(): + raise RuntimeError("Thread crashed") -1024 +t = threading.Thread(target=bad_thread) +t.start() +t.join() +print("Main thread continues") # still runs — thread crash is isolated -Finally, % is for modulus. This gives us the value left after the highest achievable division. +# Threading.excepthook (Python 3.8+) — catch unhandled thread exceptions +def thread_exception_handler(args): + print(f"Thread exception: {args.exc_type.__name__}: {args.exc_value}") -```py -13 % 7 +threading.excepthook = thread_exception_handler ``` -6 +**Propagation rules:** +1. Exception travels up the call stack looking for a matching `except` +2. `finally` blocks run at each frame during unwinding (cleanup is guaranteed) +3. If no handler found → `sys.excepthook` → traceback printed → process exits with code 1 +4. In threads → only that thread terminates; `threading.excepthook` catches it -```py -3.5 % 1.5 -``` +**Use Case:** A web framework installs a global `sys.excepthook` that serializes the full traceback to a structured JSON log and sends it to Sentry — ensuring every unhandled exception is captured even when the server crashes. -0.5 + -## Q. How many kinds of operators do we have in Python? Explain arithmetic operators. +## # 12. File Handling -This type of Python Interview Questions and Answers can decide your knowledge in Python. Answer the Python Interview Questions with some good Examples. +
-Here in Python, we have 7 kinds of operators: arithmetic, relational assignment, logical, membership, identity, and bitwise. +## Q. How do I copy a file? How to copy object in Python? Diff between shallow copy and deep copy? -We have seven arithmetic operators. These allow us to perform arithmetic operations on values: +The shutil module contains a `copyfile()` function. -Addition (+) This adds two values. +A deep copy copies an object into another. This means that if you make a change to a copy of an object, it won\'t affect the original object. In Python, we use the function deepcopy() for this, and we import the module copy. We use it like: ```py -7+8 -7-8 -7*8 -7/8 # 0.875 -7//8 # 0 +import copy +b = copy.deepcopy (a) ``` - - -## Q. Explain relational operators in Python? - -Relational operators compare values. - -> - Less than (<) If the value on the left is lesser, it returns True. +A shallow copy, however, copies one object\'s reference to another. So, if we make a change in the copy, it will affect the original object. For this, we have the function `copy()`, we use it like: ```py -'hi'<'Hi' +b = copy.copy(a) ``` -False - - Greater than (>) If the value on the left is greater, it returns True. - - 1.1 + 2.2 > 3.3 - -True - -This is because of the flawed floating-point arithmetic in Python, due to hardware dependencies. - - Less than or equal to (<=) If the value on the left is lesser than or equal to, it returns True. - - 3.0 <= 3 - -True +- Differentiate between lists and tuples. - Greater than or equal to (>=) If the value on the left is greater than or equal to, it returns True. +The major difference is that a list is mutable, but a tuple is immutable. Examples: - True >= False +```py -True +``` - Equal to (==) If the two values are equal, it returns True. +Traceback (most recent call last): +File "", line 1, in mytuple[1]=2 - {1,3,2,2} == {1,2,3} +TypeError: \'tuple' object does not support item assignment -True + - Not equal to (!=) If the two values are unequal, it returns True. +## Q. Write a one-liner that will count the number of capital letters in a file. Your code should work even if the file is too big to fit in memory? - True!=0.1 +Let us first write a multiple line solution and then convert it to one liner code. -True + ```py +with open(SOME_LARGE_FILE) as fh: + count = 0 + text = fh.read() +for character in text: + if character.isupper(): + count += 1 +``` - False!=0.1 +## Q. How to display the contents of text file in reverse order?How will you reverse a list? -True + `list.reverse()` − Reverses objects of list in place, convert the given file into a list. Reverse the list by using `reversed()`. + E.g.: + ```py + for line in reversed(list(open("file-name","r"))): + print(line) + ``` -## Q. What are assignment operators in Python? - -This one is an Important Interview question in Python Interview. - -We can combine all arithmetic operators with the assignment symbol. - - a = 7 - a += 1 - a +## Q. Write a Python program to read a random line from a file. -8 +```py +import random +def random_line(fname): + lines = open(fname).read().splitlines() + return random.choice(lines) + print(random_line('test.txt')) +``` - a -= 1 - a +## Q. Write a Python program to count the number of lines in a text file? -7 +```py +def file_lengthy(fname): + with open(fname) as f: + for i, l in enumerate(f): + pass + return i + 1 - a*=2 - a +print("Number of lines in the file: ",file_lengthy("test.txt")) +``` -14 +## Q. Write Python logic to count the number of capital letters in a file? - a/=2 - a +```py +import os +os.chdir('C:\\Users\\lifei\\Desktop') +with open('Today.txt') as today: + count = 0 + for i in today.read(): + if i.isupper(): + count+=1 + print(count) +``` -7.0 +26 - a**=2 - a +## Q. What functions or methods will you use to delete a file in Python? -49.0 +Ans. For this, we may use remove() or unlink(). - a//=3 - a + import os + os.chdir('C:\\Users\\lifei\\Desktop') + os.remove(\'try.py') + -16.0 +When we go and check our Desktop, the file is gone. Let\'s go make it again so we can delete it again using unlink(). - a%=4 - a + os.unlink(\'try.py') + -0.0 +Both functions are the same, but unlink is the traditional Unix name for it. -## Q. Explain logical operators in Python. +## Q. What is File Handling? -We have three logical operators- and, or, not. +File is a named location on the disk, which stores the data in permanent manner. +Python language provides various functions and methods to provide the communication between python programs and files. +Python programs can open the file, perform the read or write operations on the file and close the file +We can open the files by calling open function of built-in-modules +At the time of opening the file, we have to specify the mode of the file +Mode of the file indicates for what purpose the file is going to be opened(r,w,a,b) - False and True + -False +## Q. Explain how to delete a file in Python? - 7<7 or True +By using a command os.remove (filename) or os.unlink(filename) -True + - not 2==2 +## Q. How would you count the lines in a file? -False +```py +from pathlib import Path -## Q. What are membership, operators? +file_path = Path("data.txt") -With the operators 'in' and 'not in', we can confirm if a value is a member in another. +# Method 1: Generator expression — O(1) memory, reads line by line +with open(file_path, "r", encoding="utf-8") as f: + line_count = sum(1 for _ in f) +print(f"Lines: {line_count}") - 'me' in 'disappointment' -True - - 'us' not in 'disappointment' -True +# Method 2: len(readlines()) — loads entire file into memory (avoid for large files) +with open(file_path, "r", encoding="utf-8") as f: + line_count = len(f.readlines()) -## Q. Explain identity operators in Python. +# Method 3: enumerate — useful when you also need the last line number +with open(file_path, "r", encoding="utf-8") as f: + for line_count, _ in enumerate(f, start=1): + pass +print(f"Lines: {line_count}") +``` -This is one of the very commonly asked Python Interview Questions and answers it with examples. -The operators 'is' and 'is not' tell us if two values have the same identity. +**Use Case:** A log monitoring service counts lines in rotating log files every minute using the generator approach to check ingestion rate (lines/sec) without loading multi-GB log files into memory. - 10 is '10' -False + - True is not False +## Q. How to access a file on a Linux server using Python? -True +There are three main approaches depending on protocol: -## Q. Finally, tell us about bitwise operators in Python. +```py +# Method 1: Paramiko (SSH/SFTP) — most common for secure remote file access +import paramiko +from pathlib import Path + +def download_remote_file( + hostname: str, + username: str, + key_path: str, + remote_path: str, + local_path: str, +) -> None: + """Download a file from a remote Linux server over SFTP.""" + client = paramiko.SSHClient() + client.set_missing_host_key_policy(paramiko.RejectPolicy()) # secure: reject unknown hosts + try: + client.connect( + hostname=hostname, + username=username, + key_filename=key_path, + timeout=10, + ) + with client.open_sftp() as sftp: + sftp.get(remote_path, local_path) + print(f"Downloaded {remote_path} → {local_path}") + finally: + client.close() + +# Usage +download_remote_file( + hostname="10.0.0.5", + username="deploy", + key_path="/home/user/.ssh/id_rsa", + remote_path="/var/log/app/app.log", + local_path="/tmp/app.log", +) + + +# Method 2: Execute remote commands and capture output +def read_remote_file_content(hostname: str, username: str, key_path: str, remote_path: str) -> str: + client = paramiko.SSHClient() + client.set_missing_host_key_policy(paramiko.RejectPolicy()) + client.connect(hostname=hostname, username=username, key_filename=key_path) + try: + _, stdout, stderr = client.exec_command(f"cat {remote_path}") + return stdout.read().decode("utf-8") + finally: + client.close() -These operate on values bit by bit. - AND (&) This performs & on each bit pair. +# Method 3: Mount via sshfs (OS-level), then use normal file I/O +# $ sshfs user@host:/remote/path /mnt/remote +with open("/mnt/remote/data.csv", "r", encoding="utf-8") as f: + for line in f: + print(line.strip()) +``` - 0b110 & 0b010 +> **Security Note:** Always use `RejectPolicy` (not `AutoAddPolicy`) for SSH host key verification to prevent man-in-the-middle attacks. -2 +**Use Case:** A DevOps automation script uses `paramiko` to pull the last 1000 lines of application logs from 50 production servers in parallel using `concurrent.futures.ThreadPoolExecutor`, aggregating them for a centralized error dashboard. - OR (|) This performs | on each bit pair. + - 3|2 +## Q. Explain all the file processing modes supported by Python. -3 +```py +from pathlib import Path - XOR (^) This performs an exclusive-OR operation on each bit pair. +# Mode reference +# ┌──────┬────────┬────────┬────────┬─────────────────────────────┐ +# │ Mode │ Read │ Write │ Create │ Notes │ +# ├──────┼────────┼────────┼────────┼─────────────────────────────┤ +# │ 'r' │ Yes │ No │ No │ Default; error if not exists │ +# │ 'w' │ No │ Yes │ Yes │ Truncates existing file │ +# │ 'a' │ No │ Yes │ Yes │ Appends; preserves content │ +# │ 'x' │ No │ Yes │ Yes │ Exclusive create; error if │ +# │ │ │ │ │ file exists │ +# │ 'r+' │ Yes │ Yes │ No │ Read+write; no truncate │ +# │ 'w+' │ Yes │ Yes │ Yes │ Read+write; truncates │ +# │ 'a+' │ Yes │ Yes │ Yes │ Read+append │ +# └──────┴────────┴────────┴────────┴─────────────────────────────┘ +# Append 'b' for binary mode: 'rb', 'wb', 'ab', 'rb+', 'wb+' - 3^2 +# 'r' — read text (default) +# with open("data.txt", "r", encoding="utf-8") as f: +# content = f.read() # whole file +# line = f.readline() # one line +# lines = f.readlines() # list of lines -1 +# 'w' — write text (creates or truncates) +with open("output.txt", "w", encoding="utf-8") as f: + f.write("Line 1\n") + f.writelines(["Line 2\n", "Line 3\n"]) - Binary One\'s Complement (~) This returns the one\'s complement of a value. +# 'a' — append (safe for log files) +with open("app.log", "a", encoding="utf-8") as f: + f.write("2025-06-15 INFO: service started\n") - ~2 +# 'x' — exclusive create (fails if file exists — safe for atomic create) +try: + with open("new_file.txt", "x", encoding="utf-8") as f: + f.write("Created fresh") +except FileExistsError: + print("File already exists — not overwritten") --3 +# 'r+' — read and write without truncating +with open("output.txt", "r+", encoding="utf-8") as f: + content = f.read() + f.seek(0) # rewind to start + f.write(content.upper()) - Binary Left-Shift (<<) This shifts the bits to the left by the specified amount. +# 'rb' / 'wb' — binary mode (images, PDFs, pickles) +with open("output.txt", "rb") as f: + raw: bytes = f.read() + print(raw[:20]) - 1<<2 +# Pathlib equivalents (preferred in modern Python) +p = Path("data.json") +p.write_text('{"key": "value"}', encoding="utf-8") # 'w' +text = p.read_text(encoding="utf-8") # 'r' +p.write_bytes(b"\x89PNG\r\n") # 'wb' +``` -4 +**Use Case:** A log rotation service opens log files in `'a'` mode so concurrent writers can safely append without overwriting each other, and `'x'` mode to atomically create a new rotation lock file — failing fast if another process already claimed it. -Here, 001 was shifted to the left by two places to get 100, which is binary for 4. + - Binary Right-Shift (>>) +## # 13. Memory Management - 4>>2 +
-1 +## Q. How is memory managed in Python? -For more insight on operators, refer to Operators in Python. +Python memory is managed by Python private heap space. All Python objects and data structures are located in a private heap. The programmer does not have an access to this private heap and interpreter. Like other programming language python also has garbage collector which will take care of memory management in python.Python also have an inbuilt garbage collector, which recycle all the unused memory and frees the memory and makes it available to the heap space. The allocation of Python heap space for Python objects is done by Python memory manager. The core API gives access to some tools for the programmer to code. + +Python has a private heap space to hold all objects and data structures. Being programmers, we cannot access it; it is the interpreter that manages it. But with the core API, we can access some tools. The Python memory manager controls the allocation. -## Q. How would you work with numbers other than those in the decimal number system? - -With Python, it is possible to type numbers in binary, octal, and hexadecimal. +## Q. How will you check memory leak on Linux? - Binary numbers are made of 0 and 1. To type in binary, we use the prefix 0b or 0B. +There are several tools and techniques that can be used to check for memory leaks on Linux: - int(0b1010) +- Valgrind: Valgrind is a powerful memory debugging tool that can be used to detect memory leaks, among other issues. It works by running the program in a simulated environment that tracks all memory allocations and deallocations, and generates a detailed report of any errors or leaks that it finds. -10 +- LeakSanitizer (LSan): LSan is a lightweight tool that is built into the Clang and GCC compilers. It can be used to detect memory leaks at runtime by instrumenting the code with memory tracking code. When a leak is detected, LSan generates a report that includes the stack trace of the leaking allocation. -To convert a number into its binary form, we use bin(). +- AddressSanitizer (ASan): ASan is another tool built into the Clang and GCC compilers. It can be used to detect a wide range of memory errors, including memory leaks, buffer overflows, and use-after-free errors. Like LSan, ASan instruments the code with memory tracking code and generates a report when an error is detected. - bin(0xf) +- /proc/meminfo: The /proc/meminfo file contains information about the system\'s memory usage, including the total amount of memory, the amount of free memory, and the amount of memory used by each process. By monitoring the values in this file over time, it may be possible to detect a memory leak by looking for a steady increase in the amount of memory used by a particular process. -'0b1111' +- ps and top: The ps and top commands can be used to monitor the system\'s memory usage and identify processes that are consuming large amounts of memory. By monitoring the memory usage of a particular process over time, it may be possible to detect a memory leak by looking for a steady increase in the amount of memory used by that process. - Octal numbers may have digits from 0 to 7. We use the prefix 0o or 0O. +In general, detecting and diagnosing memory leaks can be a complex and time-consuming process. It often requires a combination of tools and techniques, as well as a deep understanding of the code and the system\'s memory usage patterns. - oct(8) + -'0o10' +## Q. What is the importance of reference counting? - Hexadecimal numbers may have digits from 0 to 15. We use the prefix 0x or 0X. +In Python, reference counting is a technique used for automatic memory management. It involves keeping track of the number of references to an object and automatically deallocating the object when there are no more references to it. - hex(16) +The importance of reference counting in Python can be summarized as follows: -'0x10' +- Automatic memory management: Reference counting allows Python to automatically manage memory without requiring explicit memory allocation or deallocation by the programmer. This simplifies programming and reduces the risk of memory leaks and other memory-related bugs. - hex(15) +- Efficient memory management: By deallocating objects as soon as they are no longer needed, reference counting helps to minimize the amount of memory used by a program. This is especially important for long-running programs or programs that work with large data sets. -'0xf' +- Improved performance: Because Python uses reference counting to manage memory, it can often achieve better performance than other languages that use garbage collection or other memory management techniques. This is because reference counting can be faster and more predictable than other methods, especially for small or short-lived objects. -## Q. Why are identifier names with a leading underscore disparaged? +- Object lifetime management: Reference counting ensures that objects are deallocated as soon as they are no longer needed, which helps to avoid problems such as dangling pointers or use-after-free errors. This helps to ensure the correctness and reliability of Python programs. -Since Python does not have a concept of private variables, it is a convention to use leading underscores to declare a variable private. This is why we mustn\'t do that to variables we do not want to make private. +Overall, reference counting is a key feature of Python\'s automatic memory management system, and it plays an important role in ensuring that Python programs are both efficient and reliable. -## Q. How can you declare multiple assignments in one statement? +## Q. What is the size of an integer in Python? -There are two ways to do this: +Unlike C/Java where `int` is fixed at 4 bytes, Python integers have **arbitrary precision** — they grow as needed. CPython uses a `PyLongObject` struct. A small integer takes **28 bytes** minimum (`sys.getsizeof(0)`), and size grows with magnitude. CPython also maintains a **small integer cache** for values in `[-5, 256]`, reusing the same object rather than allocating new ones. ```py - a,b,c=3,4,5 #This assigns 3, 4, and 5 to a, b, and c resp. - a = b = c =3 #This assigns 3 to a, b, and c -``` +import sys -## Q. What is tuple unpacking? +print(sys.getsizeof(0)) # 24 bytes (Python 3.11+) +print(sys.getsizeof(1)) # 28 bytes +print(sys.getsizeof(2**30)) # 32 bytes +print(sys.getsizeof(2**60)) # 36 bytes +print(sys.getsizeof(2**90)) # 40 bytes — grows with digit count -First, let\'s discuss tuple packing. It is a way to pack a set of values into a tuple. +# Small integer cache demo +a = 256 +b = 256 +print(a is b) # True — same cached object -```py -mytuple=3,4,5 -mytuple -``` +a = 257 +b = 257 +print(a is b) # False — different objects (outside cache range) -(3, 4, 5) +# Python handles arbitrarily large integers natively +big = 2 ** 10000 +print(type(big)) # +``` -This packs 3, 4, and 5 into mytuple. +**Use Case:** Cryptographic libraries (e.g., RSA key generation) rely on Python\'s arbitrary-precision integers to perform modular exponentiation with 2048-bit or 4096-bit numbers natively, without any overflow concerns. -Now, we will unpack the values from the tuple into variables x, y, and z. + -```py -x,y,z=mytuple -x+y+z -``` +## Q. How does Python manage memory? -## Q. What data types does Python support? +CPython uses a three-layer memory management system: -Python provides us with five kinds of data types: +1. **Reference Counting** — Every object has a `ob_refcnt` field. When it drops to zero, memory is freed immediately. Handles ~99% of objects. +2. **Cyclic Garbage Collector** (`gc` module) — Handles reference cycles (e.g., `a.ref = b; b.ref = a`). Uses generational collection across 3 generations (thresholds: 700, 10, 10 by default). +3. **PyMalloc Arena Allocator** — For objects ≤ 512 bytes, CPython uses its own allocator (bypassing `malloc`) organized as: Arenas (256 KB) → Pools (4 KB) → Blocks (8–512 bytes in 8-byte increments). ```py - a=7.0 - title="Ramayan\'s Book" - colors=['red','green','blue'] - type(colors) - - name=('Ramayan','Sharma') - name[0]='Avery' - Traceback (most recent call last): - File ", line 1, in name[0]='Avery' - TypeError: 'tuple' object does not support item assignment - squares={1:1,2:4,3:9,4:16,5:25} - type(squares) - - type({}) - - squares={x:x**2 for x in range(1,6)} - squares - {1: 1, 2: 4, 3: 9, 4: 16, 5: 25} -``` +import sys +import gc -## Q. What is a docstring? +# Check reference count +x = [1, 2, 3] +print(sys.getrefcount(x)) # 2 (one for x, one for getrefcount arg) -A docstring is a documentation string that we use to explain what a construct does. We place it as the first thing under a function, class, or a method, to describe what it does. We declare a docstring using three sets of single or double quotes. +y = x +print(sys.getrefcount(x)) # 3 -```py -def sayhi(): - """ - The function prints Hi - """ - print("Hi") +del y +print(sys.getrefcount(x)) # 2 again -sayhi() -``` +# Force garbage collection of cyclic references +class Node: + def __init__(self) -> None: + self.next: "Node | None" = None -Hi +a = Node() +b = Node() +a.next = b +b.next = a # cycle! +del a, b -To get a function\'s docstring, we use its `__doc__` attribute. +collected = gc.collect() # manually trigger GC +print(f"Collected {collected} objects") -```py -sayhi.__doc__ +# Inspect GC thresholds +print(gc.get_threshold()) # (700, 10, 10) ``` -'\n\tThis function prints Hi\n\t' +**Use Case:** A long-running data processing service tuned `gc.set_threshold(1000, 15, 15)` to reduce GC pauses on throughput-sensitive workloads, and used `__slots__` on hot objects to reduce per-instance memory from ~264 bytes to ~56 bytes. -A docstring, unlike a comment, is retained at runtime. + -## Q. What is the PYTHONPATH variable? +## Q. How will you measure memory on a Linux server? -PYTHONPATH is the variable that tells the interpreter where to locate the module files imported into a program. Hence, it must include the Python source library directory and the directories containing Python source code. You can manually set PYTHONPATH, but usually, the Python installer will preset it. +```py +import subprocess +import pathlib +import os -## Q. What is slicing? +# 1. /proc/meminfo — kernel memory counters +def read_meminfo() -> dict[str, int]: + """Parse /proc/meminfo into a dict of kilobyte values.""" + info = {} + for line in pathlib.Path("/proc/meminfo").read_text().splitlines(): + parts = line.split() + if len(parts) >= 2: + info[parts[0].rstrip(":")] = int(parts[1]) + return info + +# mem = read_meminfo() +# print(f"Total: {mem['MemTotal'] // 1024} MB") +# print(f"Free: {mem['MemFree'] // 1024} MB") +# print(f"Available: {mem['MemAvailable'] // 1024} MB") +# print(f"Cached: {mem['Cached'] // 1024} MB") +# print(f"Swap used: {(mem['SwapTotal'] - mem['SwapFree']) // 1024} MB") + +# 2. /proc/self/status — memory of the current process +def process_memory_kb() -> dict[str, int]: + info = {} + for line in pathlib.Path("/proc/self/status").read_text().splitlines(): + if line.startswith(("VmRSS:", "VmPeak:", "VmSize:")): + key, val = line.split(":", 1) + info[key.strip()] = int(val.strip().split()[0]) + return info + +# 3. psutil — cross-platform, most Pythonic +import psutil + +def memory_report() -> dict: + vm = psutil.virtual_memory() + swap = psutil.swap_memory() + proc = psutil.Process(os.getpid()) + + return { + "system": { + "total_gb": round(vm.total / 1e9, 2), + "available_gb": round(vm.available / 1e9, 2), + "used_pct": vm.percent, + }, + "swap": { + "total_gb": round(swap.total / 1e9, 2), + "used_pct": swap.percent, + }, + "this_process": { + "rss_mb": round(proc.memory_info().rss / 1e6, 1), + "vms_mb": round(proc.memory_info().vms / 1e6, 1), + "pct": round(proc.memory_percent(), 2), + }, + } -These are the types of basic Python interview questions for freshers. +# print(memory_report()) -Slicing is a technique that allows us to retrieve only a part of a list, tuple, or string. For this, we use the slicing operator []. +# 4. tracemalloc — trace Python object allocations +import tracemalloc -```py - (1,2,3,4,5)[2:4] - (3, 4) - [7,6,8,5,9][2:] - [8, 5, 9] - 'Hello'[:-1] - 'Hell' -``` +tracemalloc.start() -## Q. What is a namedtuple? +# Code to profile +data = [list(range(1000)) for _ in range(100)] -A namedtuple will let us access a tuple\'s elements using a name/label. We use the function namedtuple() for this, and import it from collections. +snapshot = tracemalloc.take_snapshot() +top = snapshot.statistics("lineno")[:5] +for stat in top: + print(stat) # file:line: size KB -```py - from collections import namedtuple - result=namedtuple('result','Physics Chemistry Maths') #format - Ramayan=result(Physics=86,Chemistry=95,Maths=86) #declaring the tuple - Ramayan.Chemistry +# 5. Shell commands (via subprocess) +commands = { + "free -h": "human-readable RAM + swap summary", + "vmstat -s": "virtual memory stats", + "cat /proc/meminfo": "kernel memory breakdown", + "ps aux --sort=-%mem | head -10": "top 10 memory processes", + "smem -r -k | head -10": "proportional set size (PSS) per process", +} ``` -`95` - -As you can see, it let us access the marks in Chemistry using the Chemistry attribute of object Ramayan. - -## Q. How would you declare a comment in Python? -Unlike languages like C++, Python does not have multiline comments. All it has is octothorpe (#). Anything following a hash is considered a comment, and the interpreter ignores it. +**Memory metrics to monitor:** - #line 1 of comment - #line 2 of comment +| Metric | What it means | +|--------|--------------| +| `MemTotal` | Physical RAM | +| `MemAvailable` | RAM available without swapping | +| `Cached` | Disk cache (reclaimable) | +| `SwapUsed` | Swap in use — indicates memory pressure | +| `VmRSS` | Resident Set Size — actual RAM used by process | +| `VmSize` | Virtual memory size (includes mapped files) | -In fact, you can place a comment anywhere in your code. You can use it to explain your code. +**Use Case:** A production ML inference service runs `psutil.Process().memory_info().rss` as a Prometheus gauge — alerting when RSS exceeds 80% of the container memory limit, triggering a graceful restart before the OOM killer terminates the pod. -## Q. How would you convert a string into an int in Python? +## # 14. Garbage Collector -If a string contains only numerical characters, you can convert it into an integer using the int() function. +
- int('227') +## Q. What is Garbage Collection? -227 +The concept of removing unused or unreferenced objects from the memory location is known as a Garbage Collection. While executing the program, if garbage collection takes place then more memory space is available for the program and rest of the program execution becomes faster. -Let\'s check the types: +Garbage collector is a predefined program, which removes the unused or unreferenced objects from the memory location. + +Any object reference count becomes zero then we call that object as a unused or unreferenced object Then no.of reference variables which are pointing the object is known as a reference count of the object. + +While executing the python program if any object reference count becomes zero, then internally python interpreter calls the garbage collector and garbage collector will remove that object from memory location. + + + +## Q. Why isn\'t all memory freed when Python exits? - type('227') +Objects referenced from the global namespaces of Python modules are not always deallocated when Python exits. This may happen if there are circular references. There are also certain bits of memory ... - +## Q. Whenever you exit Python, is all memory de-allocated? - type(int('227')) +The answer here is no. The modules with circular references to other objects, or to objects referenced from global namespaces, aren\'t always freed on exiting Python. +Plus, it is impossible to de-allocate portions of memory reserved by the C library. - +Whenever Python exits, especially those Python modules which are having circular references to other objects or the objects that are referenced from the global namespaces are not always de-allocated or freed.It is impossible to de-allocate those portions of memory that are reserved by the C library.On exit, because of having its own efficient clean up mechanism, Python would try to de-allocate/destroy every other object. -## Q. How do you take input in Python? +## # 15. Mutable vs Immutable -For taking input from user, we have the function input(). In Python 2, we had another function raw_input(). +
-The input() function takes, as an argument, the text to be displayed for the task: +## Q. What Are The Types of Objects Support in Python Language? - a=input('Enter a number') +Python supports are two types are of objects. They are: -Enter a number7 +Immutable built-in types: -But if you have paid attention, you know that it takes input in the form of a string. + Strings + Tuples + Numbers - type(a) +Mutable built-in types: - + List + Sets + Dictionaries -Multiplying this by 2 gives us this: +- Immutable Objects - a*=2 - a + The objects which doesn\'t allow to modify the contents of those objects are known as 'Immutable Objects' + + Before creating immutable objects with some content python interpreter verifies is already any object is available. In memory location with same content or not. + + If already object is not available then python interpreter creates new objects with that content and store that object address two reference variable. + + If already object is present in memory location with the same content creating new objects already existing object address will be given to the reference variable. -'77' + _Program:_ -So, what if we need to work on an integer instead? +```py +i=1000 +print(i) +print(type(i)) +print(id(i)) +j=2000 +print(j) +print(type(j)) +print(id(j)) +x=3000 +print(x) +print(type(x)) +print(id(x)) +y=3000 +print(y) +print(type(y)) +print(id(y)) +``` -We use the int() function for this. + `int, float, complex, bool, str, tuple` are immutable objects - a=int(input('Enter a number')) + Immutable objects performance is high. -Enter a number7 + Applying iterations on Immutable objects takes less time. + + All fundamentals types represented classes objects and tuple class objects are immutable objects. -Now when we multiply it by 2, we get this: +- Mutable Objects: + 1. The Objects which allows to modify the contents of those objects are known as 'Mutable Objects' + 2. We can create two different mutable objects with same content - a*=2 - a + Program: -`14` + ```py + x=[10,20,30] + print(x) + print(type(x)) + print(id(x)) + y=[10,20,30] + print(y) + print(type(y)) + print(id(y)) + ``` + + Output: + + `List, set, dict` classes objects are mutable objects + + Mutable objects performance is low when compared to immutable objects + Applying Iterations mutable objects takes huge time -## Q. What is a frozen set in Python? - -Answer these type of Python Interview Questions with Examples. +## Q. Python is Call by Value or Call by Reference? How are arguments passed by value or by reference? -First, let\'s discuss what a set is. A set is a collection of items, where there cannot be any duplicates. A set is also unordered. +Everything in Python is an object and all variables hold references to the objects. The references values are according to the functions; as a result you cannot change the value of the references. However, you can change the objects if it is mutable. - myset={1,3,2,2} - myset +## Q. Explain parameter-passing mechanism in python? -`{1, 2, 3}` +- To pass its parameters to a function, Python uses pass-by-reference. If you change a parameter within a function, the change reflects in the calling function. This is its default behavior. -This means that we cannot index it. +- However, when we pass literal arguments like strings, numbers, or tuples, they pass by value. This is because they are immutable. - myset[0] +## Q. How can you copy an object in Python? -Traceback (most recent call last): -File "", line 1, in myset[0] -TypeError: 'set' object does not support indexing +To copy an object in Python, you can try copy.copy () or copy.deepcopy() for the general case. You cannot copy all objects but most of them. -However, a set is mutable. A frozen set is immutable. This means we cannot change its values. This also makes it eligible to be used as a key for a dictionary. +## Q. Is a tuple mutable or immutable? - myset=frozenset([1,3,2,2]) - myset - - frozenset({1, 2, 3}) - - type(myset) - +Tuples are **immutable** — you cannot add, remove, or replace elements after creation. However, if a tuple *contains* a mutable object (like a list), that inner object can be mutated. - +```py +# Immutable — cannot reassign elements +point = (10, 20) +try: + point[0] = 99 # raises TypeError +except TypeError as e: + print(e) # 'tuple' object does not support item assignment -## Q. How would you generate a random number in Python? +# But a tuple holding a mutable object: the container is immutable, contents are not +record = (1, [2, 3], "hello") +record[1].append(4) # allowed — mutating the list inside +print(record) # (1, [2, 3, 4], 'hello') -This kind of Python interview Questions and Answers can Prove your depth of knowledge. +# Tuples are hashable ONLY when all elements are hashable +hashable_tuple = (1, 2, 3) +print(hash(hashable_tuple)) # valid -To generate a random number, we import the function random() from the module random. +unhashable_tuple = (1, [2, 3]) +try: + hash(unhashable_tuple) # raises TypeError +except TypeError as e: + print(e) # unhashable type: 'list' - from random import random - random() +# Performance: tuple creation is faster than list +import timeit +print(timeit.timeit("(1,2,3,4,5)", number=10_000_000)) # ~0.07s +print(timeit.timeit("[1,2,3,4,5]", number=10_000_000)) # ~0.45s +``` -`0.7931961644126482` +**Use Case:** Database record rows returned by `psycopg2` are tuples — immutability guarantees that row data is not accidentally mutated between the fetch and the business logic layer, and their hashability allows rows to be used as dictionary keys for deduplication. -Let\'s call for help on this. + - help(random) +## # 16. Iterators and Generators -Help on built-in function random: +
-`random(…)` method of random.Random instance +## Q. In Python what are iterators? -`random() -> x` in the interval [0, 1). +In Python, iterators are used to iterate a group of elements, containers like list. -This means that it will return a random number equal to or greater than 0, and less than 1. +## Q. What are generators in Python? -We can also use the function randint(). It takes two arguments to indicate a range from which to return a random integer. -``` - from random import randint - randint(2,7) -``` -6 +The way of implementing iterators are known as generators. It is a normal function except that it yields expression in the function. +Python generator produces a sequence of values to iterate on. This way, it is kind of an iterable. We define a function that 'yields' values one by one, and then use a for loop to iterate on it. ``` - randint(2,7) + def squares(n): + i=1 + while(i<=n): + yield i**2 + i+=1 + for i in squares(7): + print(i) ``` -5 - - randint(2,7) - -7 - - randint(2,7) -6 - - randint(2,7) - -2 +1 +4 +9 +16 +25 +36 +49 -## Q. How will you capitalize the first letter of a string? +## Q. So, what is an iterator, then? -Simply using the method capitalize(). +An iterator returns one object at a time to iterate on. To create an iterator, we use the iter() function. - 'Ramayan'.capitalize() + odds=iter([1,3,5]) -'Ramayan' +Then, we call the next() function on it every time we want an object. - type(str.capitalize) + next(odds) +1 - + next(odds) +3 -However, it will let other characters be. + next(odds) +5 - '$hrir@m'.capitalize() +And now, when we call it again, it raises a StopIteration exception. This is because it has reached the end of the values to iterate on. -'$HRIR@M' + next(odds) + + Traceback (most recent call last): + File "", line 1, in next(odds) + StopIteration -## Q. How will you check if all characters in a string are alphanumeric? - -For this, we use the method isalnum(). - - 'Ramayan123'.isalnum() - -True - - 'Ramayan123!'.isalnum() - -False +## Q. Explain generators and iterators in python? -Other methods that we have include: +- They do, but there are subtle differences: - '123.3'.isdigit() +- For a generator, we create a function. For an iterator, we use in-built functions `iter()` and `next()`. +- For a generator, we use the keyword 'yield' to yield/return an object at a time. +- A generator may have as many 'yield' statements as you want. +- A generator will save the states of the local variables every time 'yield' will pause the loop. +- An iterator does not use local variables; it only needs an iterable to iterate on. +- Using a class, you can implement your own iterator, but not a generator. +- Generators are fast, compact, and simpler. +- Iterators are more memory-efficient. -False + - '123'.isnumeric() +## Q. What is a generator? What can it be used for? -True +A generator is a function that uses `yield` to produce values **lazily** — one at a time, on demand — rather than computing and storing all values at once. It implements the iterator protocol automatically (provides `__iter__` and `__next__`). Execution is **suspended** at each `yield` and **resumed** on the next `next()` call, preserving local state. - 'Ramayan'.islower() +```py +from typing import Generator, Iterator +import sys -True +# Generator function — produces Fibonacci numbers lazily +def fibonacci() -> Generator[int, None, None]: + a, b = 0, 1 + while True: # infinite sequence — memory safe! + yield a + a, b = b, a + b - 'Ramayan'.isupper() +# Only the current value is in memory at any time +gen = fibonacci() +first_10 = [next(gen) for _ in range(10)] +print(first_10) # [0, 1, 1, 2, 3, 5, 8, 13, 21, 34] -False +# Memory comparison +list_version = [x ** 2 for x in range(1_000_000)] # ~8 MB +gen_version = (x ** 2 for x in range(1_000_000)) # ~120 bytes - 'Ramayan'.istitle() +print(sys.getsizeof(list_version)) # ~8,697,456 bytes +print(sys.getsizeof(gen_version)) # 120 bytes -True +# Generator pipeline — process without loading everything into memory +def read_large_file(path: str) -> Generator[str, None, None]: + with open(path, encoding="utf-8") as f: + for line in f: + yield line.rstrip() - ' '.isspace() +def filter_errors(lines: Iterator[str]) -> Generator[str, None, None]: + for line in lines: + if "ERROR" in line: + yield line -True +def parse_log_entry(lines: Iterator[str]) -> Generator[dict, None, None]: + for line in lines: + parts = line.split(" ", 3) + if len(parts) == 4: + yield {"timestamp": parts[0], "level": parts[1], "msg": parts[3]} - '123F'.isdecimal() +# Chained pipeline — O(1) memory regardless of file size +# pipeline = parse_log_entry(filter_errors(read_large_file("app.log"))) +``` -False +**Use Case:** A log aggregation service uses a generator pipeline to process 10 GB log files line-by-line through filter → parse → enrich stages, keeping memory usage constant at ~50 MB regardless of file size — impossible with a list-based approach. -## Q. What is the concatenation? - -This is very basic Python Interview Question, try not to make any mistake in this. - -Concatenation is joining two sequences. We use the + operator for this. +## Q. What are Generators? - '32'+'32' - '3232' +Generators are functions that produce a sequence of values **lazily** using the `yield` keyword. When called, they return a **generator object** (which is both an iterator and an iterable) without executing the function body. Each call to `next()` resumes execution until the next `yield`. - [1,2,3]+[4,5,6] - [1, 2, 3, 4, 5, 6] +```py +from typing import Generator +import sys + +# Simple generator +def countdown(n: int) -> Generator[int, None, None]: + while n > 0: + yield n + n -= 1 + +gen = countdown(3) +print(next(gen)) # 3 +print(next(gen)) # 2 +print(next(gen)) # 1 +# next(gen) would raise StopIteration + +# yield also receives values via send() +def accumulator() -> Generator[float, float, str]: + total = 0.0 + while True: + value = yield total # yield current total, receive next value + if value is None: + break + total += value + return f"Final: {total}" - (2,3)+(4) - `Traceback (most recent call last): ` - `File "", line 1, in (2,3)+(4)` - `TypeError: can only concatenate tuple (not "int") to tuple` +acc = accumulator() +next(acc) # prime the generator +print(acc.send(10)) # 10.0 +print(acc.send(20)) # 30.0 +print(acc.send(5)) # 35.0 -Here, 4 is considered an int. Let\'s do this again. +# Memory efficiency +list_1m = [x for x in range(1_000_000)] # ~8.5 MB +gen_1m = (x for x in range(1_000_000)) # 120 bytes +print(sys.getsizeof(list_1m)) # ~8,697,456 +print(sys.getsizeof(gen_1m)) # 120 +``` - (2,3)+(4,) # (obj,) is way to declare single empty - (2, 3, 4) +**Use Case:** A streaming data processor uses generators to read records from S3 in chunks, parse them, and yield validated domain objects — the entire pipeline runs in constant memory regardless of dataset size. -## Q. What is a function? +## Q. What are Iterators? -When we want to execute a sequence of statements, we can give it a name. Let\'s define a function to take two numbers and return the greater number. +An **iterator** is any object that implements the **iterator protocol**: `__iter__()` (returns `self`) and `__next__()` (returns the next value or raises `StopIteration`). Iterators maintain their position in a sequence between calls. ```py -def greater(a,b): - return a is a>b else b -greater(3,3.5) -``` +from typing import Iterator -`3.5` +# Custom iterator class +class NumberRange: + """Yields integers from start to stop (exclusive), step by step.""" -You can create your own function or use one of Python\'s many built-in functions. + def __init__(self, start: int, stop: int, step: int = 1) -> None: + self._current = start + self._stop = stop + self._step = step - + def __iter__(self) -> "NumberRange": + return self # iterator returns itself -## Q. What is recursion? + def __next__(self) -> int: + if self._current >= self._stop: + raise StopIteration + value = self._current + self._current += self._step + return value -Recursion is a programming technique in which a function calls itself to solve a problem. The idea is to break down a complex problem into smaller, simpler problems and solve them in a recursive manner until the base case is reached. The base case is a condition that stops the recursion and returns a value. +# Usage +rng = NumberRange(0, 10, 2) +print(list(rng)) # [0, 2, 4, 6, 8] -A common example of a recursive function is the factorial function, which calculates the product of all positive integers up to a given number. Here's an example of a recursive factorial function in Python: +# All for loops use the iterator protocol internally +for n in NumberRange(1, 4): + print(n) # 1, 2, 3 -```py -def factorial(n): - if n == 0: - return 1 - else: - return n * factorial(n-1) +# Built-in iterators +it: Iterator[int] = iter([10, 20, 30]) +print(next(it)) # 10 +print(next(it)) # 20 + +# Key distinction: iterable vs iterator +lst = [1, 2, 3] # iterable — has __iter__ but NOT __next__ +it = iter(lst) # iterator — has both __iter__ and __next__ +print(hasattr(lst, "__next__")) # False +print(hasattr(it, "__next__")) # True ``` +**Use Case:** A paginated API client implements an iterator that fetches the next page lazily on each `next()` call, enabling `for record in api_client:` syntax while deferring HTTP requests until needed. + + + +## Q. Can a generator be used to create iterators? Give an example. + +Yes. **Every generator is an iterator** — generators automatically implement both `__iter__` and `__next__`, satisfying the iterator protocol. A generator function is the most concise way to create a custom iterator. + ```py -def facto(n): - if n==1: return 1 - return n*facto(n-1) +from typing import Iterator, Generator -facto(4) -``` -`24` +# Generator automatically IS an iterator +def even_numbers(limit: int) -> Generator[int, None, None]: + n = 0 + while n < limit: + yield n + n += 2 -## Q. What does the function zip() do? +gen = even_numbers(10) -One of the less common functions with beginners, zip() returns an iterator of tuples. +# __iter__ returns self — it IS an iterator +print(gen.__iter__() is gen) # True - list(zip(['a','b','c'],[1,2,3])) +# __next__ advances the generator +print(next(gen)) # 0 +print(next(gen)) # 2 +print(next(gen)) # 4 -[('a', 1), ('b', 2), ('c', 3)] +# Equivalent class-based iterator (more verbose, same behaviour) +class EvenNumbers: + def __init__(self, limit: int) -> None: + self._n = 0 + self._limit = limit -Here, it pairs items from the two lists, and creates tuples with those. But it doesn\'t have to be lists. + def __iter__(self) -> "EvenNumbers": + return self - list(zip(('a','b','c'),(1,2,3))) + def __next__(self) -> int: + if self._n >= self._limit: + raise StopIteration + value = self._n + self._n += 2 + return value -[('a', 1), ('b', 2), ('c', 3)] +# Both behave identically in a for loop +for n in even_numbers(10): + print(n, end=" ") # 0 2 4 6 8 + +for n in EvenNumbers(10): + print(n, end=" ") # 0 2 4 6 8 +``` + +**Use Case:** A data pipeline replaces verbose iterator classes with generator functions to stream database query results row-by-row, reducing boilerplate from ~20 lines of iterator class code to 3 lines of generator code with identical memory behaviour. -## Q. If you are ever stuck in an infinite loop, how will you break out of it? +## Q. Can iterators be used to create generators? -For this, we press Ctrl+C. This interrupts the execution. Let\'s create an infinite loop to demonstrate this. +No — iterators and generators are **different** constructs. You can consume an iterator inside a generator (using `yield` in a loop), but an iterator class itself is not a generator. Generators are a special type of iterator created via `yield`. - def counterfunc(n): - while(n==7):print(n) - counterfunc(7) +```py +# An iterator cannot become a generator just by being used inside one +class Counter: + def __init__(self, stop: int) -> None: + self._val = 0; self._stop = stop + def __iter__(self): return self + def __next__(self) -> int: + if self._val >= self._stop: raise StopIteration + v = self._val; self._val += 1; return v -7 -7 -7 -7 -. -. -. -. -. -Traceback (most recent call last): -File "", line 1, in counterfunc(7) -File "", line 2, in counterfunc -while(n==7):print(n) -KeyboardInterrupt +# But you CAN wrap an iterator inside a generator function +def filtered_counter(stop: int, predicate) -> "Generator": + for value in Counter(stop): # consuming iterator inside generator + if predicate(value): + yield value + +for n in filtered_counter(10, lambda x: x % 3 == 0): + print(n, end=" ") # 0 3 6 9 + +# Key differences +counter = Counter(3) +print(type(counter)) # +print(hasattr(counter, "send")) # False — not a generator +print(hasattr(counter, "throw")) # False — not a generator +print(hasattr(counter, "gi_frame")) # False — no generator frame + +gen = filtered_counter(3, lambda x: True) +print(type(gen)) # +print(hasattr(gen, "send")) # True +print(hasattr(gen, "gi_frame")) # True +``` + +**Key distinction:** Generators have additional methods (`send()`, `throw()`, `close()`) that iterators don\'t — enabling coroutine-style communication and cooperative cancellation. -## Q. With Python, how do you find out which directory you are currently in? - -To find this, we use the function/method getcwd(). We import it from the module os. +## Q. What are iterators and generators? - import os - os.getcwd() +**Iterator:** An object implementing `__iter__()` and `__next__()`. Maintains traversal state. Raises `StopIteration` when exhausted. -'C:\\Users\\lifei\\AppData\\Local\\Programs\\Python\\Python36-32' +**Generator:** A special iterator created with a `yield` statement. Suspends and resumes execution, preserving local variable state between `yield` points. - type(os.getcwd) +| Aspect | Iterator (class) | Generator (function) | +|--------|-----------------|---------------------| +| Creation | Class with `__iter__`/`__next__` | Function with `yield` | +| State storage | Instance attributes | Execution frame (automatic) | +| `send()` / coroutine | No | Yes | +| Memory for infinite seq | Manual | Automatic | +| Code size | Verbose | Concise | - +```py +# Both solve the same problem — generator is simpler +def fib_gen(): # generator + a, b = 0, 1 + while True: + yield a + a, b = b, a + b -We can also change the current working directory with chdir(). +class FibIter: # iterator class equivalent + def __init__(self): + self.a, self.b = 0, 1 + def __iter__(self): return self + def __next__(self): + value = self.a + self.a, self.b = self.b, self.a + self.b + return value - os.chdir('C:\\Users\\lifei\\Desktop') - os.getcwd() +from itertools import islice +print(list(islice(fib_gen(), 8))) # [0, 1, 1, 2, 3, 5, 8, 13] +print(list(islice(FibIter(), 8))) # [0, 1, 1, 2, 3, 5, 8, 13] +``` -'C:\\Users\\lifei\\Desktop' +**Use Case:** An ETL system uses generator pipelines for memory-efficient processing and class-based iterators for external pagination APIs where explicit state reset (`iter(obj)`) is needed. -## Q. How will you find, in a string, the first word that rhymes with 'cake'? - -For our purpose, we will use the function search(), and then use group() to get the output. +## Q. What does the `yield` statement do? - import re - rhyme=re.search('.ake','I would make a cake, but I hate to bake') - rhyme.group() +`yield` **suspends** a generator function\'s execution and **returns a value** to the caller. The function\'s local state (variables, instruction pointer) is preserved. The next call to `next()` (or iteration) **resumes** from immediately after the `yield`. -'make' +```py +from typing import Generator -And as we know, the function search() stops at the first match. Hence, we have our first rhyme to 'cake'. +def step_by_step() -> Generator[str, None, None]: + print("Step 1") + yield "first value" # suspend here, return "first value" + print("Step 2") + yield "second value" # suspend again + print("Step 3") + # function ends → StopIteration raised automatically -## Q. What is Tkinter? +gen = step_by_step() -Tkinter is a famous Python library with which you can craft a GUI. It provides support for different GUI tools and widgets like buttons, labels, text boxes, radio buttons, and more. These tools and widgets have attributes like dimensions, colors, fonts, colors, and more. +val = next(gen) # prints "Step 1", val = "first value" +print(f"Got: {val}") +val = next(gen) # prints "Step 2", val = "second value" +print(f"Got: {val}") +# next(gen) would print "Step 3" then raise StopIteration -You can also import the tkinter module. +# yield with send() — bidirectional communication +def echo_double() -> Generator[float, float, None]: + while True: + received = yield # receive a value from send() + yield received * 2 # yield the doubled result - import tkinter - top=tkinter.Tk() +gen2 = echo_double() +next(gen2) # prime the generator to the first yield +print(gen2.send(5)) # 10.0 +next(gen2) # advance to the next receive yield +print(gen2.send(7)) # 14.0 -This will create a new window for you: +# yield from — delegate to sub-generator +def chain(*iterables): + for it in iterables: + yield from it # yields each element of it directly -This creates a window with the title 'My Game'. You can position your widgets on this. +print(list(chain([1, 2], [3, 4], [5]))) # [1, 2, 3, 4, 5] +``` -Follow this link to know more about Python Libraries +**Use Case:** An async web framework (like `asyncio`) builds coroutines on top of `yield`/`yield from` — each `await` suspension point is ultimately a `yield` that hands control back to the event loop, enabling thousands of concurrent I/O operations on a single thread. -## Q. How is a .pyc file different from a .py file? +## Q. What is a generator? Why would I use one? -While both files hold bytecode, .pyc is the compiled version of a Python file. It has platform-independent bytecode. Hence, we can execute it on any platform that supports the .pyc format. Python automatically generates it to improve performance(in terms of load time, not speed). +A **generator** is a function that uses `yield` to produce values **lazily** — one at a time, on demand. Calling a generator function returns a **generator object** (an iterator); execution only starts on the first `next()` call. -## Q. How do you calculate the length of a string? +**Why use a generator?** -This is simple. We call the function len() on the string we want to calculate the length of. +| Reason | Benefit | +|--------|---------| +| **Memory efficiency** | Constant memory regardless of sequence length | +| **Infinite sequences** | Can model streams with no defined end | +| **Pipeline composition** | Chain generators without intermediate lists | +| **Lazy evaluation** | Compute only what is consumed | +| **Coroutines** | `send()` enables bidirectional data flow | - len('Adi Shakara') +```py +import sys +from typing import Generator, Iterator + +# Problem: process a 10 GB log file +# BAD — loads everything into memory +def bad_approach(path: str) -> list[str]: + with open(path) as f: + return [line for line in f if "ERROR" in line] # may OOM + +# GOOD — constant memory generator pipeline +def read_lines(path: str) -> Generator[str, None, None]: + with open(path, encoding="utf-8") as f: + yield from f + +def filter_errors(lines: Iterator[str]) -> Generator[str, None, None]: + for line in lines: + if "ERROR" in line: + yield line.strip() + +def parse_error(lines: Iterator[str]) -> Generator[dict, None, None]: + for line in lines: + parts = line.split(" ", 2) + if len(parts) >= 3: + yield {"ts": parts[0], "svc": parts[1], "msg": parts[2]} + +# Entire pipeline: O(1) memory regardless of file size +# pipeline = parse_error(filter_errors(read_lines("app.log"))) +# for record in pipeline: +# send_to_alert_system(record) + +# Memory comparison +list_million = [x ** 2 for x in range(1_000_000)] +gen_million = (x ** 2 for x in range(1_000_000)) +print(sys.getsizeof(list_million)) # ~8,697,456 bytes +print(sys.getsizeof(gen_million)) # 120 bytes +``` + +**Use Case:** A Kafka consumer service uses a generator to yield deserialized messages from a partition indefinitely, feeding them into a processing pipeline without ever accumulating a backlog in memory — critical for high-throughput event streaming. -## Q. What does the following code output? - - def extendList(val, list=[]): - list.append(val) - return list - list1 = extendList(10) - list2 = extendList(123,[]) - list3 = extendList('a') - list1,list2,list3 +## Q. What is `yield`? -Ans. ([10, 'a'], [123], [10, 'a']) +`yield` pauses a generator function\'s execution, returns the yielded value to the caller, and **preserves all local state** so execution can resume from the same point on the next `next()` call. It transforms a regular function into a **generator function**. -You'd expect the output to be something like this: +```py +from typing import Generator + +# yield — suspends execution, returns value, resumes on next() +def countdown(n: int) -> Generator[int, None, None]: + print(f"Starting at {n}") + while n > 0: + yield n # pause here, return n + n -= 1 # resume here on next call + print("Done!") + +gen = countdown(3) +print(next(gen)) # prints "Starting at 3", returns 3 +print(next(gen)) # returns 2 +print(next(gen)) # returns 1 +# next(gen) → prints "Done!", raises StopIteration + +# yield from — delegate to another iterable +def chain_iters(*iterables): + for it in iterables: + yield from it # yields each item from it directly + +print(list(chain_iters([1, 2], [3, 4], [5]))) # [1, 2, 3, 4, 5] + +# yield with send() — bidirectional coroutine +def running_average() -> Generator[float, float, None]: + total, count = 0.0, 0 + while True: + value = yield (total / count if count else 0.0) + total += value + count += 1 -([10],[123],['a']) +avg = running_average() +next(avg) # prime — advance to first yield +print(avg.send(10)) # 10.0 +print(avg.send(20)) # 15.0 +print(avg.send(30)) # 20.0 +``` -Well, this is because the list argument does not initialize to its default value ([]) every time we make a call to the function. Once we define the function, it creates a new list. Then, whenever we call it again without a list argument, it uses the same list. This is because it calculates the expressions in the default arguments when we define the function, not when we call it. +**`yield` vs `return`:** +| | `return` | `yield` | +|--|----------|---------| +| Terminates function | Yes | No (suspends) | +| State preserved | No | Yes | +| Creates generator | No | Yes | +| Can call multiple times | Yes (restart) | Yes (resume) | -Let\'s revise the Basis of Python Programming +**Use Case:** A streaming CSV writer uses `yield` to produce one formatted row at a time, allowing an HTTP response to stream to the client incrementally rather than buffering all rows in memory before sending. +## # 17. Decorators + +
+ ## Q. What is a decorator? How do I define my own? Ans. A decorator is a function that adds functionality to another function without modifying it. It wraps another function to add functionality to it. A Python decorator is a specific change that we make in Python syntax to alter functions easily. @@ -2648,81 +8330,364 @@ $$$$$$$$$$$$$$$$$ Hi $$$$$$$$$$$$$$$$$ -Decorators are an example of metaprogramming, where one part of the code tries to change another. For more on decorators, read Python Decorators. +Decorators are an example of metaprogramming, where one part of the code tries to change another. For more on decorators, read Python Decorators. + + + +## Q. Why use function decorators? Give an example. + +A decorator is essentially a callable Python object that is used to modify or extend a function or class definition. + +One of the beauties of decorators is that a single decorator definition can be applied to multiple functions (or classes). Much can thereby be accomplished with decorators that would otherwise require lots of boilerplate (or even worse redundant!) code. + +Flask, for example, uses decorators as the mechanism for adding new endpoints to a web application. Examples of some of the more common uses of decorators include adding synchronization, type enforcement,logging, or pre/post conditions to a class or function. + +2. Basic Python Programming Interview Questions + +Below are some Basic Python Programming Interview Questions and answers for freshers. + + + +## # 18. Context Managers + +
+ +## Q. Explain the use "with" statement in python? + +- In python generally "with" statement is used to open a file, process the data present in the file, and also to close the file without calling a close() method. "with" statement makes the exception handling simpler by providing cleanup activities. + +General form of with: + +```py +with open("filename", "mode") as file-var: +``` + +processing statements +Note: no need to close the file by calling close() upon file-var.close() + +## Q. What is the `with` statement and its usage? + +The `with` statement is Python\'s **context manager** protocol. It guarantees setup and teardown code runs correctly — even if an exception is raised — replacing verbose `try/finally` blocks. + +An object used with `with` must implement `__enter__` (setup, returns resource) and `__exit__` (cleanup, receives exception info). + +```py +from contextlib import contextmanager, suppress +import threading + +# 1. File I/O — file always closed, even on exception +with open("data.txt", "w", encoding="utf-8") as f: + f.write("Hello, world") +# f.close() called automatically + +# 2. Database transaction pattern +class Transaction: + def __enter__(self): + print("BEGIN TRANSACTION") + return self + + def __exit__(self, exc_type, exc_val, exc_tb) -> bool: + if exc_type is None: + print("COMMIT") + else: + print(f"ROLLBACK — {exc_val}") + return False # False = don't suppress exceptions + +with Transaction() as tx: + print("Doing work...") + # raise ValueError("oops") # would trigger ROLLBACK + +# 3. Multiple context managers on one line +with open("a.txt", "w") as a, open("b.txt", "w") as b: + a.write("file A"); b.write("file B") + +# 4. @contextmanager — create context manager from a generator +@contextmanager +def timer(label: str): + import time + start = time.perf_counter() + try: + yield # body of the with block runs here + finally: + elapsed = time.perf_counter() - start + print(f"{label}: {elapsed * 1000:.2f}ms") + +with timer("heavy computation"): + total = sum(range(10_000_000)) + +# 5. Suppress specific exceptions +with suppress(FileNotFoundError): + import os + os.remove("nonexistent.txt") # silently ignored + +# 6. Threading lock +lock = threading.Lock() +with lock: + print("thread-safe section") # lock released even on exception +``` + +**Use Case:** A database connection pool uses a `with` context manager to check out a connection, run the query, and return the connection to the pool in `__exit__` — ensuring no connections leak even if a query raises an unhandled exception. + + + +## # 19. Concurrency and Parallelism + +
+ +## Q. What is multithreading? + +It means running several different programs at the same time concurrently by invoking multiple threads. Multiple threads within a process refer the data space with main thread and they can communicate with each other to share information more easily.Threads are light-weight processes and have less memory overhead. Threads can be used just for quick task like calculating results and also running other processes in the background while the main program is running. + +Thread Is a functionality or logic which can execute simultaneously along with the other part of the program. +Thread is a light weight process. +Any program which is under execution is known as process. +We can define the threads in python by overwriting run method of thread class. +Thread class is a predefined class which is defined in threading module. +Thread in module is a predefined module. +If we call the run method directly the logic of the run method will be executed as a normal method logic. +In order to execute the logic of the run method as a we use start method of thread class. +Example + +```py +import threading +class x (threading.Thread): + def run(self): + for p in range(1, 101): + print(p) +class y (threading.Thread): + def run(self): + for q in range(1, 101): + print(q) +x1=x() +y1=y() +x1.start() +y1.start() +``` + +A thread is a lightweight process and multithreading allows us to execute multiple threads at once. As you know, Python is a multithreaded language. It has a multithreading package. The GIL (Global Interpreter Lock) ensures that a single thread executes at a time. A thread holds the GIL and does a little work before passing it on to the next thread. This makes for an illusion of parallel execution. But in reality, it is just threaded taking turns at the CPU. Of course, all the passing around adds overhead to the execution. + +## Q. Why do not my signal handlers work? + +The most common problem is that the signal handler is declared with the wrong argument list. It is called as: +handler (signum, frame) +So it should be declared with two arguments: +def handler(signum, frame): + +## Q. Why is that none of my threads are not running? How can I make it work? + +As soon as the main thread exits, all threads are killed. Your main thread is running too quickly, giving the threads no time to do any work. +A simple fix is to add a sleep to the end of the program that\'s long enough for all the threads to finish: + +```py +import threading, time +def thread_task(name, n): +for i in range(n): print name, i +for i in range(10) +``` + + + +## Q. What is Threads Life Cycle? + +Threads Life Cycle + +- Creating the object of a class which is overwriting run method of thread class is known as a creating thread. +- Whenever thread is created then we call thread is in new state or birth state thread. +- Whenever we call the start method on the new state threads then those threads will be forwarded for scheduling. +- The threads which are forwarded for scheduling are known as ready state threads +- Whenever scheduling time occurs, ready state thread starts execution. +- The threads which are executing are known as running state threads +Whenever sleep fun or join methods are called on the running state threads then immediately those threads will wait. +- The threads which are waiting are known as waiting state threads +Whenever waiting time is over or specified thread execution is over - then immediately waiting state threads are forwarded for scheduling. +- If running state threads execution is over then immediately those threads execution will be terminated + -The threads which execution is terminated are known as dead state threads. + + + +## Q. What is scheduling? + +Among multiple threads: +- which thread as to start the execution first, +- How much time the thread as to execute after allocated time + is over, +- which thread as to continue the execution next this comes under scheduling. Scheduling is highly dynamic -## Q. Why use function decorators? Give an example. +## Q. Does Python provide thread-safe multi-threading? -A decorator is essentially a callable Python object that is used to modify or extend a function or class definition. +Python\'s threading module provides OS-level threads, but the **Global Interpreter Lock (GIL)** in CPython ensures only **one thread executes Python bytecode at a time**. This makes individual built-in operations thread-safe but does **not** protect compound operations. -One of the beauties of decorators is that a single decorator definition can be applied to multiple functions (or classes). Much can thereby be accomplished with decorators that would otherwise require lots of boilerplate (or even worse redundant!) code. +```py +import threading +import time -Flask, for example, uses decorators as the mechanism for adding new endpoints to a web application. Examples of some of the more common uses of decorators include adding synchronization, type enforcement,logging, or pre/post conditions to a class or function. +# GIL: threads don't speed up CPU-bound work +counter = 0 -2. Basic Python Programming Interview Questions +def increment(n: int) -> None: + global counter + for _ in range(n): + counter += 1 # NOT atomic — read-modify-write can interleave! -Below are some Basic Python Programming Interview Questions and answers for freshers. +# Race condition demonstration +counter = 0 +threads = [threading.Thread(target=increment, args=(100_000,)) for _ in range(5)] +[t.start() for t in threads] +[t.join() for t in threads] +print(f"Expected: 500000 Got: {counter}") # likely < 500000! - +# Fix: use a Lock +counter = 0 +lock = threading.Lock() +def safe_increment(n: int) -> None: + global counter + for _ in range(n): + with lock: + counter += 1 -## Q. How many arguments can the range() function take? +counter = 0 +threads = [threading.Thread(target=safe_increment, args=(100_000,)) for _ in range(5)] +[t.start() for t in threads] +[t.join() for t in threads] +print(f"Safe result: {counter}") # always 500000 -Ans. The range() function in Python can take up to 3 arguments. Let\'s see this one by one. +# Fix: use threading.local() for per-thread state +local_data = threading.local() -a. One argument +def worker(value: int) -> None: + local_data.result = value * 2 # per-thread — no sharing + time.sleep(0.01) + print(f"Thread result: {local_data.result}") -When we pass only one argument, it takes it as the stop value. Here, the start value is 0, and the step value is +1. +# GIL is released during I/O — threads ARE useful for I/O-bound tasks +import urllib.request - list(range(5)) +def fetch(url: str) -> None: + with urllib.request.urlopen(url, timeout=5) as resp: + print(f"{url}: {resp.status}") -[0, 1, 2, 3, 4] +# For CPU-bound parallelism, use multiprocessing (bypasses GIL) +from multiprocessing import Pool +with Pool(4) as pool: + results = pool.map(lambda x: x ** 2, range(10)) +``` - list(range(-5)) +**Thread-safe primitives:** +- `threading.Lock`, `RLock`, `Semaphore` — mutual exclusion +- `threading.Event`, `Condition` — coordination +- `queue.Queue` — thread-safe FIFO (use this for producer-consumer) -[] +**Use Case:** A web scraper uses `threading.Thread` with `queue.Queue` to fetch 1000 URLs concurrently (I/O-bound — GIL released during network wait), cutting total time from 500s to ~3s, while `Lock` protects the shared results dictionary. - list(range(0)) + -[] +## Q. What do you mean by non-blocking I/O? -b. Two arguments +**Blocking I/O** pauses the current thread until an operation completes (read from socket, write to file). **Non-blocking I/O** returns immediately — either with data or with an indication that the operation is not yet ready — allowing the same thread to handle other tasks in the meantime. -When we pass two arguments, the first one is the start value, and the second is the stop value. +Python\'s `asyncio` module implements non-blocking I/O via an **event loop** and `async/await` coroutines. - list(range(2,7)) +```py +import asyncio +import time -[2, 3, 4, 5, 6] +# Blocking (synchronous) — each request waits before starting the next +def blocking_fetch(url: str) -> str: + import urllib.request + with urllib.request.urlopen(url, timeout=5) as resp: + return resp.read(100).decode() - list(range(7,2)) +# Sequential — total time ≈ sum of individual times +# start = time.perf_counter() +# for url in urls: blocking_fetch(url) +# print(time.perf_counter() - start) # ~N × latency -[] +# Non-blocking (async) — event loop multiplexes I/O waits +import aiohttp # pip install aiohttp - list(range(-3,4)) +async def async_fetch(session: aiohttp.ClientSession, url: str) -> str: + async with session.get(url) as response: + return await response.text() # yields control to event loop while waiting -[-3, -2, -1, 0, 1, 2, 3] +async def fetch_all(urls: list[str]) -> list[str]: + async with aiohttp.ClientSession() as session: + tasks = [async_fetch(session, url) for url in urls] + return await asyncio.gather(*tasks) # all run concurrently -c. Three arguments +# asyncio basics +async def producer() -> None: + for i in range(3): + await asyncio.sleep(0.1) # non-blocking sleep — yields to event loop + print(f"produced {i}") -Here, the first argument is the start value, the second is the stop value, and the third is the step value. +async def consumer() -> None: + for i in range(3): + await asyncio.sleep(0.15) + print(f"consumed {i}") - list(range(2,9,2)) +async def main() -> None: + # Both coroutines interleave on a SINGLE thread + await asyncio.gather(producer(), consumer()) -[2, 4, 6, 8] +asyncio.run(main()) - list(range(9,2,-1)) +# asyncio.Queue for async producer-consumer +async def async_pipeline() -> None: + queue: asyncio.Queue[int] = asyncio.Queue(maxsize=10) -[9, 8, 7, 6, 5, 4, 3] + async def produce(): + for i in range(5): + await queue.put(i) + await queue.put(None) # sentinel + + async def consume(): + while True: + item = await queue.get() + if item is None: + break + print(f"Processing {item}") + + await asyncio.gather(produce(), consume()) + +asyncio.run(async_pipeline()) +``` + +**Blocking vs Non-blocking:** + +| | Blocking | Non-blocking (asyncio) | +|--|---------|----------------------| +| Thread per request | Yes | No — single thread | +| Memory | High (thread stacks) | Low | +| CPU-bound | Use `multiprocessing` | Not suitable | +| I/O-bound | Thread pool (limited) | Best choice | + +**Use Case:** A FastAPI microservice handles 10,000 concurrent WebSocket connections on a single thread using `asyncio` — each `await` on a database query or HTTP call yields to the event loop, which services other connections during the wait. +## # 20. Testing and Debugging + +
+ ## Q. How do you debug a program in Python? Answer in brief. Ans. To debug a Python program, we use the @@ -2740,7 +8705,7 @@ Some pdb commands include-

— Print an expression

## Q. What command do we use to debug a Python program? @@ -2770,174 +8735,351 @@ C:\Users\lifei\Desktop>python -m pdb try.py Then, we can start debugging. -## Q. What is a Counter in Python? +## Q. What are the tools that help to find bugs or perform static analysis? -Ans. The function Counter() from the module 'collections'. It counts the number of occurrences of the elements of a container. +PyChecker is a static analysis tool that detects the bugs in Python source code and warns about the style and complexity of the bug. Pylint is another tool that verifies whether the module meets the coding standard. - from collections import Counter - Counter([1,3,2,1,4,2,1,3,1]) +## Q. What is unittest in Python? What\'s your approach to unit testing in Python? + +A unit testing framework in Python is known as unittest. It supports sharing of setups, automation testing, shutdown code for tests, aggregation of tests into collections etc. + +The most fundamental answer to this question centers around Python\'s unittest testing framework. Basically, if a candidate doesn\'t mention unittest when answering this question, that should be a huge red flag. + +`unittest` supports test automation, sharing of setup and shutdown code for tests, aggregation of tests into collections, and independence of the tests from the reporting framework. The unittest module provides classes that make it easy to support these qualities for a set of tests. + +Assuming that the candidate does mention unittest (if they don\'t, you may just want to end the interview right then and there!), you should also ask them to describe the key elements of the unittest framework; namely, test fixtures, test cases, test suites and test runners. + +A more recent addition to the unittest framework is mock. mock allows you to replace parts of your system under test with mock objects and make assertions about how they are to be used. mock is now part of the Python standard library, available as unittest.mock in Python 3.3 onwards. + +The value and power of mock are well explained in An Introduction to Mocking in Python. As noted therein, system calls are prime candidates for mocking: whether writing a script to eject a CD drive, a web server which removes antiquated cache files from /tmp, or a socket server which binds to a TCP port, these calls all feature undesired side-effects in the context of unit tests. Similarly, keeping your unit-tests efficient and performant means keeping as much "slow code" as possible out of the automated test runs, namely filesystem and network access. + +[Note: This question is for Python developers who are also experienced in Java.] + + + +## Q. How would you perform unit-testing on your Python code? + +Ans. For this purpose, we have the module unittest in Python. It has the following members: +> +- FunctionTestCase +- SkipTest +- TestCase +- TestLoader +- TestResult +- TestSuite +- TextTestResult +- TextTestRunner +- defaultTestLoader +- expectedFailure +- findTestCases +- getTestCaseNames +- installHandler +- main +- makeSuite +- registerResult +- removeHandler +- removeResult +- skip +- skipIf +- skipUnless + +Below are some Advanced Python Programming Interview Questions For Experienced. I recommend freshers to also refer these interview questions for advanced knowledge. + + + +## Q. How do I test a Python program or component? + +Python comes with two testing frameworks: +The documentation test module finds examples in the documentation strings for a module and runs them, comparing the output with the expected output given in the documentation string. + +The unittest moduleis a fancier testing framework modeled on Java and Smalltalk testing frameworks. + +For testing, it helps to write the program so that it may be easily tested by using good modular design. Your program should have almost all functionality encapsulated in either functions or class methods. And this sometimes has the surprising and delightful effect of making the program run faster because local variable accesses are faster than global accesses. + +Furthermore the program should avoid depending on mutating global variables, since this makes testing much more difficult to do. +The "global main logic" of your program may be as simple as: + +if __name__=="__main__": + main_logic() + +at the bottom of the main module of your program. +Once your program is organized as a tractable collection of functions and class behaviors, you should write test functions that exercise the behaviors. + +A test suite can be associated with each module which automates a sequence of tests. + +You can make coding much more pleasant by writing your test functions in parallel with the "production code", since this makes it easy to find bugs and even design flaws earlier. + +"Support modules" that are not intended to be the main module of a program may include a self-test of the module. + +if __name__ == "__main__": + self_test() + +Even programs that interact with complex external interfaces may be tested when the external interfaces are unavailable by using "fake" interfaces implemented in Python. + + + +### FLASK + +## # 21. Miscellaneous + +
+ +## Q. What will be the output of the code below? + +```py +List = ['a', 'b', 'c', 'd', 'e'] +print(list[10:]) +``` + +- TypeError: \'type' object is not subscriptable +if proper name given,it will print []. + + + +## Q. What is a method? + +A method is a function on some object x that you normally call as x.name(arguments...). Methods are defined as functions inside the class definition: + +```py +class C: + def meth (self, arg): + return arg*2 + self.attribute +``` + +## Q. What is `__slots__` and when is it useful? + +In Python, every class can have instance attributes. By default Python uses a `dict` to store an object\'s instance attributes. This is really helpful as it allows setting arbitrary new attributes at runtime. + +However, for small classes with known attributes it might be a bottleneck. The `dict` wastes a lot of RAM. Python can\'t just allocate a static amount of memory at object creation to store all the attributes. Therefore it sucks a lot of RAM if you create a lot of objects. The usage of `__slots__` to tell Python not to use a `dict`, and only allocate space for a fixed set of attributes. + +**Example:** + +**1. Object without slots:** + +```py +class MyClass(object): + def __init__(self, *args, **kwargs): + self.a = 1 + self.b = 2 + +if __name__ == "__main__": + instance = MyClass() + print(instance.__dict__) +``` + +**2. Object with slots:** + +```py +class MyClass(object): + __slots__=['a', 'b'] + def __init__(self, *args, **kwargs): + self.a = 1 + self.b = 2 + +if __name__ == "__main__": + instance = MyClass() + print(instance.__slots__) +``` + + + +## Q. What is Monkey patching ? Give example ? + +Dynamically modifying a class or module at run-time. + +```py +class A: + def func(self): + print("Hi") + def monkey(self): + print "Hi, monkey" + m.A.func = monkey + a = m.A() + a.func() +``` + +Hi, monkey + + + +## Q. Explain serialization and deserialization / Pickling and unpicking? + +Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling. + +To create portable serialized representations of Python objects, we have the module 'pickle'. It accepts a Python object (remember, everything in Python is an object). It then converts it into a string representation and uses the dump() function to dump it into a file. We call this pickling. In contrast, retrieving objects from this stored string representation is termed 'unpickling'. + +The pickle module implements binary protocols for serializing and deserializing a Python object structure. "Pickling" is the process whereby a Python object hierarchy is converted into a byte stream, and "unpickling" is the inverse operation, whereby a byte stream (from a binary file or bytes-like object) is converted back into an object hierarchy. Pickling (and unpickling) is alternatively known as `serialization`, `marshalling`, or `flattening`; however, to avoid confusion, the terms used here are `pickling` and `unpickling`. + +```py +import json +json_string = json.dumps([1, 2, 3, "a", "b"]) +print(json_string) + +import pickle +pickled_string = pickle.dumps([1, 2, 3, "a", "b"]) +print(pickle.loads(pickled_string)) +``` + +Reference: + +[1] https://www.sanfoundry.com/python-questions-answers-pickle-module/ +[2] https://docs.python-guide.org/scenarios/serialization/ + + + +## Q. How to open a file c:\scores.txt for writing? + +``fileWriter = open("c:\\scores.txt", "w")`` -Counter({1: 4, 3: 2, 2: 2, 4: 1}) +## Q. What is TkInter? -Python provides us with a range of ways and methods to work with a Counter. Read Python Counter. +TkInter is Python library. It is a toolkit for GUI development. It provides support for various GUI tools or widgets (such as buttons, labels, text boxes, radio buttons, etc) that are used in GUI applications. The common attributes of them include Dimensions, Colors, Fonts, Cursors, etc. -## Q. What is NumPy? Is it better than a list? +## Q. How to retrieve data from a table in MySQL database through Python code? -Python Programming Interview Questions - Numpy vs List +```py +#import MySQLdb module as : +import MySQLdb -Python Programming Interview Questions – Numpy vs List +#establish a connection to the database. +db = MySQLdb.connect("host"="local host", "database-user"="user-name", "password"="password","database-name"="database") -Ans. NumPy, a Python package, has made its place in the world of scientific computing. It can deal with large data sizes, and also has a powerful N-dimensional array object along with a set of advanced functions. +#initialize the cursor variable upon the established connection: +c1 = db.cursor() -Yes, a NumPy array is better than a Python list. This is in the following ways: +#retrieve the information by defining a required query string. +s = "Select * from dept" - It is more compact. - It is more convenient. - It Is more efficiently. - It is easier to read and write items with NumPy. +#fetch the data using fetch() methods and print it. +data = c1.fetch(s) -Read our latest tutorial on Python NumPy +#close the database connection. +db.close() +``` -## Q. How would you create an empty NumPy array? - -Ans. To create an empty array with NumPy, we have two options: - -a. Option 1 - - import numpy - numpy.array([]) +## Q. How would you generate a random number in Python? -array([], dtype=float64) +This kind of Python interview Questions and Answers can Prove your depth of knowledge. -b. Option 2 +To generate a random number, we import the function random() from the module random. - numpy.empty(shape=(0,0)) + from random import random + random() -array([], shape=(0, 0), dtype=float64) +`0.7931961644126482` - +Let\'s call for help on this. -## Q. Explain the use of the 'nonlocal' keyword in Python. + help(random) -Ans. First, let\'s discuss the local and global scope. By example, a variable defined inside a function is local to that function. Another variable defined outside any other scope is global to the function. +Help on built-in function random: -Suppose we have nested functions. We can read a variable in an enclosing scope from inside he inner function, but cannot make a change to it. For that, we must declare it nonlocal inside the function. First, let\'s see this without the nonlocal keyword. +`random(…)` method of random.Random instance - def outer(): - a=7 - def inner(): - print(a) - inner() - outer() +`random() -> x` in the interval [0, 1). -7 +This means that it will return a random number equal to or greater than 0, and less than 1. - def outer(): - a=7 - def inner(): - print(a) - a+=1 - print(a) - inner() +We can also use the function randint(). It takes two arguments to indicate a range from which to return a random integer. +``` + from random import randint + randint(2,7) +``` +6 +``` + randint(2,7) +``` +5 - outer() + randint(2,7) -Traceback (most recent call last): -File "", line 1, in outer() -File "", line 7, in outer inner() -File "", line 4, in inner print(a) -UnboundLocalError: local variable 'a' referenced before assignment +7 -So now, let\'s try doing this with the 'nonlocal' keyword: + randint(2,7) - def outer(): - a=7 - def inner(): - nonlocal a - print(a) - a+=1 - print(a) +6 -inner() + randint(2,7) - outer() +2 -## Q. What is the global keyword? - -Ans. Like we saw in the previous question, the global keyword lets us deal with, inside any scope, the global version of a variable. +## Q. With Python, how do you find out which directory you are currently in? -The problem: +To find this, we use the function/method getcwd(). We import it from the module os. - a=7 - def func(): - print(a) - a+=1 - print(a) + import os + os.getcwd() -The solution: +'C:\\Users\\lifei\\AppData\\Local\\Programs\\Python\\Python36-32' - a=7 - def func(): - global a - print(a) - a+=1 - print(a) - func() + type(os.getcwd) - + -## Q. How would you make a Python script executable on Unix? +We can also change the current working directory with chdir(). -Ans. For this to happen, two conditions must be met: + os.chdir('C:\\Users\\lifei\\Desktop') + os.getcwd() -The script file\'s mode must be executable -The first line must begin with a hash(#). An example of this will be: #!/usr/local/bin/python +'C:\\Users\\lifei\\Desktop' -## Q. What functions or methods will you use to delete a file in Python? +## Q. What is Tkinter? -Ans. For this, we may use remove() or unlink(). +Tkinter is a famous Python library with which you can craft a GUI. It provides support for different GUI tools and widgets like buttons, labels, text boxes, radio buttons, and more. These tools and widgets have attributes like dimensions, colors, fonts, colors, and more. - import os - os.chdir('C:\\Users\\lifei\\Desktop') - os.remove('try.py') - +You can also import the tkinter module. -When we go and check our Desktop, the file is gone. Let\'s go make it again so we can delete it again using unlink(). + import tkinter + top=tkinter.Tk() - os.unlink('try.py') - +This will create a new window for you: -Both functions are the same, but unlink is the traditional Unix name for it. +This creates a window with the title 'My Game'. You can position your widgets on this. + +Follow this link to know more about Python Libraries -## Q. What are accessors, mutators, and @property? +## Q. How would you make a Python script executable on Unix? -Ans. What we call getters and setters in languages like Java, we term accessors and mutators in Python. In Java, if we have a user-defined class with a property 'x', we have methods like getX() and setX(). In Python, we have @property, which is syntactic sugar for property(). This lets us get and set variables without compromising on the conventions. For a detailed explanation on property, refer to Python property. +Ans. For this to happen, two conditions must be met: + +The script file\'s mode must be executable +The first line must begin with a hash(#). An example of this will be: #!/usr/local/bin/python ## Q. Differentiate between the append() and extend() methods of a list. @@ -2963,34 +9105,7 @@ And this is how extend() works: [1, 2, 3, 4, 5, 6, 7, 8] - - -## Q. What do you mean by overriding methods? - -Ans. Suppose class B inherits from class A. Both have the method sayhello()- to each, their own version. B overrides the sayhello() of class A. So, when we create an object of class B, it calls the version that class B has. - -```py -class A: - def sayhello(self): - print("Hello, I'm A") -class B(A): - def sayhello(self): - print("Hello, I'm B") -a=A() -b=B() -a.sayhello() -``` -Hello, I\'m A - -```py -b.sayhello() -``` -Hello, I'm B - - ## Q. What is JSON? Describe in brief how you\'d convert JSON data into Python data? @@ -3003,7 +9118,7 @@ Ans. JSON stands for JavaScript Object Notation. It is a highly popular data for Python supports JSON parsers. In fact, JSON-based data is internally represented as a dictionary in Python. To convert JSON data into Python data, we use the load() function from the JSON module. ## Q. How do you execute a Python Script? @@ -3013,26 +9128,7 @@ From the command line, type python .py or pythonx.y Learn how to use Python, from beginner basics to advanced techniques, with online video tutorials taught by industry experts. Enroll for Free Python Training Demo! - -## Q. Explain the use of try: except: raise, and finally: - -Try, except and finally blocks are used in Python error handling. Code is executed in the try block until an error occurs. One can use a generic except block, which will receive control after all errors, or one can use specific exception handling blocks for various error types. Control is transferred to the appropriate except block. In all cases, the finally block is executed. Raise may be used to raise your own exceptions. - -## Q. Illustrate the proper use of Python error handling. - -Code Example: - - try: - ….#This can be any code - except: - …# error handling code goes here - finally: - …# code that will be executed regardless of exception handling goes here. - - ## Q. What is a namespace in Python? @@ -3044,7 +9140,7 @@ A namespace is a collection of names. It maps names to corresponding objects. Wh On starting the interpreter, it creates a namespace for as long as we don\'t exit. We have local namespaces, global namespaces, and a built-in namespace. ## Q. Explain the differences between local and global namespaces. @@ -3053,14 +9149,14 @@ Local namespaces are created within a function. when that function is called. Gl ## Q. Name the four main types of namespaces in Python? -Global, Local, Module and Class namespaces. +Global, Local, Module and Class namespaces. ## Q. When would you use triple quotes as a delimiter? Triple quotes ''"" or '" are string delimiters that can span multiple lines in Python. Triple quotes are usually used when spanning multiple lines, or enclosing a string that has a mix of single and double quotes contained therein. ## Q. How to use GUI that comes with Python to test your code? @@ -3069,60 +9165,14 @@ That is just an editor and a graphical version of the interactive shell. You wri There is no automated testing. - -## Q. How does the Python version numbering scheme work? - -Python versions are numbered A.B.C or A.B. - -A is the major version number. It is only incremented for major changes in the language. - -B is the minor version number, incremented for less earth-shattering changes. - -C is the micro-level. It is incremented for each bug fix release. -Not all releases are bug fix releases. - -In the run-up to a new major release, 'A' series of development releases are made denoted as alpha, beta, or release candidate. - -Alphas are early releases in which interfaces aren\'t finalized yet; it\'s not unexpected to see an interface change between two alpha releases. - -Betas are more stable, preserving existing interfaces but possibly adding new modules, and release candidates are frozen, making no changes except as needed to fix critical bugs. - -Alpha, beta and release candidate versions have an additional suffix. - -The suffix for an alpha version is "aN" for some small number N, - -The suffix for a beta version is "bN" for some small number N, - -And the suffix for a release candidate version is "cN" for some small number N. - -In other words, all versions labeled 2.0aN precede the versions labeled 2.0bN, which precede versions labeled 2.0cN, and those precede 2.0. - -You may also find version numbers with a "+" suffix, e.g. "2.2+". These are unreleased versions, built directly from the subversion trunk. In practice, after a final minor release is made, the subversion trunk is incremented to the next minor version, which becomes the "a0" version, e.g. "2.4a0". - - - -## Q. Where is math.py (socket.py, regex.py, etc.) source file? - -If you can\'t find a source file for a module, it may be a built-in or dynamically loaded module implemented in C, C++ or other compiled language. In this case you may not have the source file or it may be something like mathmodule.c, somewhere in a C source directory (not on the Python Path). There are (at least) three kinds of modules in Python: - -- Modules written in Python (.py); -- Modules written in C and dynamically loaded (.dll, .pyd, .so, .sl, etc); -- Modules written in C and linked with the interpreter; to get a list of these, type; -Import sys print sys.builtin_module_names; - - ## Q. How do I make a Python script executable on UNIX? You need to do two things: The script file\'s mode must be executable and the first line must begin with "#!" followed by the path of the Python interpreter. -- The first is done by executing chmod +x scriptfile or perhaps chmod 755 'script' file. +- The first is done by executing chmod +x scriptfile or perhaps chmod 755 \'script' file. - The second can be done in a number of ways. The most straightforward way is to write: @@ -3143,16 +9193,9 @@ exec python $0 ${1+"$@"} The minor disadvantage is that this defines the script\'s `__doc__`string. However, you can fix that by adding: `__doc__ = """…Whatever…"""` - - -## Q. Why do not my signal handlers work? - -The most common problem is that the signal handler is declared with the wrong argument list. It is called as: -handler (signum, frame) -So it should be declared with two arguments: -def handler(signum, frame): + ## Q. How do I find undefined g++ symbols __builtin_new or __pure_virtual? @@ -3162,7 +9205,7 @@ Re-link it using g++ (change LINKCC in the python Modules Makefile) Link your extension module using g++ (e.g., "g++ -shared -o mymodule.so mymodule.o"). ## Q. How do I send mail from a Python script? @@ -3204,7 +9247,7 @@ if sts != 0: ``` ## Q. How can I mimic CGI form submission (METHOD=POST)? I would like to retrieve web pages that are the result of posting a form. @@ -3248,1331 +9291,1652 @@ print(x) ``` - -## Q. Why is that none of my threads are not running? How can I make it work? - -As soon as the main thread exits, all threads are killed. Your main thread is running too quickly, giving the threads no time to do any work. -A simple fix is to add a sleep to the end of the program that\'s long enough for all the threads to finish: - -```py -import threading, time -def thread_task(name, n): -for i in range(n): print name, i -for i in range(10) -``` - - -## Q. What Are The Implementation In Python Program? - -Python program can be implemented by two ways -> - 1. Interactive Mode (Submit statement by statement explicitly) - 2. Batch Mode (Writing all statements and submit all statements) - -In Interactive mode python command shell is required. It is available in installation of python cell. - -In Interactive mode is not suitable for developing the projects & Applications - -Interactive mode is used for predefined function and programs. -Example: - -```py -X=1000 -Y=2000 -X+Y -3000 -Quit(X+Y) -``` -X, Y is not find. -Interactive mode is unfit for looping purpose. - -Interactive Mode: -The concept of submitting one by one python statements explicitly in the python interpreter is known as "Interactive Mode" -In Order to submit the one by one python statements explicitly to the python interpreter we use python command line shell. -Python command line shell is present in python software -We can open the python command line shell by executing python command on command prompt or terminal -Example: -c:/users/mindmajix>python -`3+4` -7 - -`'mindmajix'*3` -'mindmajix mindmajix mindmajix' - -`x=1000` -`y=2000` -`x+y` -3000 -Quit -x+y -c:/users/sailu>python -Error: name 'X' not defined - -Batch Mode: -In the concept of writing the group of python statements in a file, save the file with extension .py and submit that entire file to the python interpreter is known as Batch Mode. - -In Order to develop the python files we use editors or IDE\'s -Different editors are notepad, notepad++, edit+,nano, VI, gedil and so on. -Open the notepad and write the following code: - -Example: - -```py -X=1000 -Y=2000 -print(x+y, x-y, x*y) -``` -Save the file in D drive mindmajix python folder with the demo.py -Open command prompt and execute following commands: - -Python D:/mindmajix python/Demo.py - -3000 - --1000 - -2000.000 - -Save another method if we correctly set the path - -D: - -D:/>cd "mindmajix python" - -D:/mindmajix Python>python Demo.py -`3000` -`-1000` -`2000.000` - - +## Q. Executing DML Commands Through Python Programs? -## Q. What are The Data Types Supports in Python Language? +DML (Data Modification Language) Commands are used to modify the data of the database objects +Whenever we execute DML Commands the records are going to be modified temporarily. -- Numbers- Numbers use to hold numerical values. -- Strings- A string is a sequence of characters. We declare it using single or double quotes. -- Lists- A list is an ordered collection of values, and we declare it using square brackets. -- Tuples- A tuple, like a list, is an ordered collection of values. The difference. However, is that a tupleis immutable. This means that we cannot change a value in it. -- Dictionary- A dictionary is a data structure that holds key-value pairs. We declare it using curly braces. -- We can also use a dictionary comprehension: +Whenever we run "rollback" command the modified records will come back to its original state. -Numbers: - Int - Float - Complex -Boolean -Operational: - Strings - List - Tuple - Set - Dictionary +To modify the records of the database objects permanently we use "commit" command -Every data type in python language is internally implemented as a class. Python language data types are categorized into two types. +After executing the commit command even though we execute "rollback" command, the modified records will not come back to its original state. -They are: +Create the emp1 table in the database by using following command +`Create table emp1 as select * from emp;` -Fundamental Types -Collection Types +Whenever we run the DML commands through the python program, then the no.of records which are modified because of that command will be stored into the rowcount attribute of cursor object. +After executing the DML Command through the python program we have to call commit method of cursor object. -### _Control Flow_ - -## Q. Explain Control flow statements. - -By default python program execution starts from first line, execute each and every statements only once and transactions the program if the last statement of the program execution is over. -Control flow statements are used to disturb the normal flow of the execution of the program. - -## Q. What are the two major loop statements? - -`for and while` - -## Q. Under what circumstances would one use a `while` statement rather than `for`? - -The while statement is used for simple repetitive looping and the for statement is used when one wishes to iterate through a list of items, such as database records, characters in a string, etc. - -## Q. What happens if output an `else` statement after after block? - -The code in the else block is executed after the for loop completes, unless a break is encountered in the for loop execution. in which case the else block is not executed. - -## Q. Explain the use of break and continue in Python looping. - -The break statement stops execution of the current loop. and transfers control to the next block. The continue statement ends the current block\'s execution and jumps to the next iteration of the loop. - -## Q. When would you use a continue statement in a for loop? - -When processing a particular item was complete; to move on to the next, without executing further processing in the block. The continue statement says, "I'm done processing this item, move on to the next item." +### _Multithreading_ -## Q. When would you use a break statement in a for loop? +## Q. How Python is interpreted? -When the loop has served its purpose. As an example. after finding the item in a list searched for, there is no need to keep looping. The break statement says, I'm done in this loop; move on to the next block of code." +Python language is an interpreted language. Python program runs directly from the source code. It converts the source code that is written by the programmer into an intermediate language, which is again translated into machine language that has to be executed. -## Q. What is the structure of a for loop? +## Q. Explain how can you make a Python Script executable on Unix? -for in : … The ellipsis represents a code block to be executed, once for each item in the sequence. Within the block the item is available as the current item from the entire list. +To make a Python Script executable on Unix, you need to do two things, -## Q. What is the structure of a while loop? + Script file\'s mode must be executable and + the first line must begin with # ( #!/usr/local/bin/python) -while : … The ellipsis represents a code block to be executed. until the condition becomes false. The condition is an expression that is considered true unless it evaluates to o, null or false. +## Q. Explain how can you generate random numbers in Python? -## Q. Use a for loop and illustrate how you would define and print the characters in a string out, one per line. +To generate random numbers in Python, you need to import command as ```py -myString = "I Love Python" -for myChar hi myString: - print(myChar) +import random +random.random() ``` - - -## Q. Given the string "I LoveQPython" use afor loop and illustrate printing each character tip to, but not including the Q. - -```py -inyString = "I Love Pijtlzon" -for myCizar in myString: - fmyC'har == - break -print(myChar) -``` +This returns a random floating point number in the range (0,1) - +- Explain how can you access a module written in Python from C? -## Q. Given the string "I Love Python" print out each character except for the spaces, using a for loop. +You can access a module written in Python from C by following method, -```py -inyString = I Love Python" -for myCizar in myString: -fmyChar == '' '': -continue -print myChar -``` +Module = =PyImport_ImportModule(""); -### _Data Types_ - -## Q. What is a Tuple? - -- Tuple Objects can be created by using parenthesis or by calling tuple function or by assigning multiple values to a single variable -- Tuple objects are immutable objects -- Incision order is preserved - -- Duplicate elements are allowed - -- Heterogeneous elements are allowed - -- Tuple supports both positive and negative indexing - -- The elements of the tuple can be mutable or immutable +## Q. Mention the use of the split function in Python? -```py -#Example: -x=() -print(x) -print(type(x)) -print(len(x)) -y-tuple() -print(y) -print(type(y)) -print(len(y)) -z=10,20 -print(z) -print(type(z)) -print(len(z)) -p=(10,20,30,40,50,10,20,10) Insertion & duplicate -print(p) -q=(100, 123.123, True, "mindmajix") Heterogeneous -print(q) -Output: -``` +The use of the split function in Python is that it breaks a string into shorter strings using the defined separator. It gives a list of all words present in the string. - -## Q. What is the Dictionary? - -- Dictionary objects can be created by using curly braces{} or by calling dictionary function. - -- Dictionary objects are mutable objects. - -- Dictionary represents key value base. + ↥ back to top + -- Each key value pair of Dictionary is known as a item. +## Q. Mention what is the difference between Django, Pyramid, and Flask? -- Dictionary keys must be immutable. +Flask is a "microframework" primarily build for a small application with simpler requirements. In flask, you have to use external libraries. Flask is ready to use. -- Dictionary values can be mutable or immutable. +Pyramid are build for larger applications. It provides flexibility and lets the developer use the right tools for their project. The developer can choose the database, URL structure, templating style and more. Pyramid is heavy configurable. -- Duplicate keys are not allowed but values can be duplicate. +Like Pyramid, Django can also used for larger applications. It includes an ORM. -- Insertion order is not preserved. +- You are having multiple Memcache servers running Python, in which one of the memcacher server fails, and it has your data, will it ever try to get key data from that one failed server? -- Heterogeneous keys and heterogeneous values are allowed. +The data in the failed server won\'t get removed, but there is a provision for auto-failure, which you can configure for multiple nodes. Fail-over can be triggered during any kind of socket or Memcached server level errors and not during normal client errors like adding an existing key, etc. -## Q. How to Search Path of Modules? - -By default python interpreter search for the imported modules in the following locations: +## Q. Explain how you can minimize the Memcached server outages in your Python Development? -- Current directory (main module location) -- Environment variable path -- Installation dependent directory -- If the imported module is not found in the any one of the above locations. Then python interpreter giveserror. -- Built-in attributes of a module: -- By default for each and every python module some properties are added internally and we call thoseproperties as a built-in-attribute of a module +- When one instance fails, several of them goes down, this will put larger load on the database server when lost data is reloaded as client make a request. To avoid this, if your code has been written to minimize cache stampedes then it will leave a minimal impact +- Another way is to bring up an instance of Memcached on a new machine using the lost machines IP address +- Code is another option to minimize server outages as it gives you the liberty to change the Memcached server list with minimal work +- Setting timeout value is another option that some Memcached clients implement for Memcached server outage. When your Memcached server goes down, the client will keep trying to send a request till the time-out limit is reached -## Q. What are the Packages? +## Q. Explain what is Dogpile effect? How can you prevent this effect? -Package is nothing but a folder or dictionary which represents collection of modules. +Dogpile effect is referred to the event when cache expires, and websites are hit by the multiple requests made by the client at the same time. +This effect can be prevented by using semaphore lock. In this system when value expires, first process acquires the lock and starts generating new value. -A package can also contain sub packages. + -We can import the modules of the package by using package -name.module name or name.subpackage name.module name +## Q. Explain how Memcached should not be used in your Python project? -## Q. What is File Handling? +Memcached common misuse is to use it as a data store, and not as a cache. Never use Memcached as the only source of the information you need to run your application. Data should always be available through another source as well. Memcached is just a key or value store and cannot perform query over the data or iterate over the contents to extract information. -File is a named location on the disk, which stores the data in permanent manner. -Python language provides various functions and methods to provide the communication between python programs and files. -Python programs can open the file, perform the read or write operations on the file and close the file -We can open the files by calling open function of built-in-modules -At the time of opening the file, we have to specify the mode of the file -Mode of the file indicates for what purpose the file is going to be opened(r,w,a,b) +Memcached does not offer any form of security either in encryption or authentication -## Q. What are the Runtime Errors? +## Q. What is Flask & its benefits? -The errors which occurs after starting the execution of the programs are known as runtime errors. Runtime errors can occur because of: +Python Flask, as we've previously discussed, is a web microframework for Python. It is based on the '`Werkzeug`, `Jinja 2` and good intentions' BSD license. Two of its dependencies are Werkzeug and Jinja2. This means that it has around no dependencies on external libraries. Due to this, we can call it a light framework. A session uses a signed cookie to allow the user to look at and modify session contents. It will remember information from one request to another. However, to modify a session, the user must have the secret key `Flask.secret_key`. - Invalid Input - Invalid Logic - Memory issues - Hardware failures and so on +Flask is a web micro framework for Python based on "Werkzeug, Jinja 2 and good intentions" BSD licensed. Werkzeug and jingja are two of its dependencies. -With respect to every reason which causes to runtime error correspoing runtime error representation class is available -Runtime error representation classes technically we call as a exception classes. -While executing the program if any runtime error is occur corresponding runtime error representation class object is created -Creating runtime error representation class object is technically known as a rising exception -While executing the program if any exception is raised, then internally python interpreter verify any code is implemented to handle raised exception or not -If code is not implemented to handle raised exception then program will be terminated abnormally +Flask is part of the micro-framework. Which means it will have little to no dependencies on external libraries. It makes the framework light while there is little dependency to update and less security bugs. -## Q. What is Abnormal Termination? - -The concept of terminating the program in the middle of its execution without executing last statement of the main module is known as a abnormal termination -Abnormal termination is undesirable situation in programming languages. - -## Q. What is `try` Block? - -A block which is preceded by the try keyword is known as a try block -Syntax: -try{ - //statements that may cause an exception -} +## Q. Mention what is Flask-WTF and what are their features? -The statements which causes to run time errors and other statements which depends on the execution of run time errors statements are recommended to represent in try block -While executing try block statement if any exception is raised then immediately try block identifies that exception, receive that exception and forward that exception to except block without executing remaining statements to try block. +Flask-WTF offers simple integration with WTForms. Features include for Flask WTF are + > + Integration with wtforms + Secure form with csrf token + Global csrf protection + Internationalization integration + Recaptcha supporting + File upload that works with Flask Uploads -## Q. What is the Difference Between Methods & Constructors? +## Q. Explain what is the common way for the Flask script to work? -Methods -- Method name can be any name. -- With respect to one object one method can be called for 'n' members of lines -- Methods are used to represent business logic to perform the operations +The common way for the flask script to work is -Constructor -- Constructor will be executed automatically whenever we create a object. -- With respect to one object one constructor can be executed only once -- Constructors are used to define & initialize the non static variable + Either it should be the import path for your application + Or the path to a Python file + +## Q. Explain how you can access sessions in Flask? + +A session basically allows you to remember information from one request to another. In a flask, it uses a signed cookie so the user can look at the session contents and modify. The user can modify the session if only it has the secret key Flask.secret_key. -## Q. What is the Encapsulation? +## Q. Is Flask an MVC model and if yes give an example showing MVC pattern for your application? -The concept of binding or grouping related data members along with its related functionalities is known as a Encapsulation. +Basically, Flask is a minimalistic framework which behaves same as MVC framework. So MVC is a perfect fit for Flask, and the pattern for MVC we will consider for the following example -## Q. Executing DML Commands Through Python Programs? +from flask import Flask +app = Flask(_name_) +@app.route("/") +def hello(): + return "Hello World" +app.run(debug = True) + +In this code your, -DML (Data Modification Language) Commands are used to modify the data of the database objects -Whenever we execute DML Commands the records are going to be modified temporarily. +Configuration part will be -Whenever we run "rollback" command the modified records will come back to its original state. + from flask import Flask + app = Flask(_name_) -To modify the records of the database objects permanently we use "commit" command +View part will be -After executing the commit command even though we execute "rollback" command, the modified records will not come back to its original state. + @app.route("/") + def hello(): + return "Hello World" -Create the emp1 table in the database by using following command -`Create table emp1 as select * from emp;` +While your model or main part will be -Whenever we run the DML commands through the python program, then the no.of records which are modified because of that command will be stored into the rowcount attribute of cursor object. -After executing the DML Command through the python program we have to call commit method of cursor object. + app.run(debug = True) -### _Multithreading_ - -## Q. What is Threads Life Cycle? +## Q. Explain database connection in Python Flask? -Threads Life Cycle +Best database for flask is MySQL. Flask supports database powered application (RDBS). Such system requires creating a schema, which requires piping the shema.sql file into a sqlite3 command. So you need to install `sqlite3` command in order to create or initiate the database in Flask. Flask allows to request database in three ways -- Creating the object of a class which is overwriting run method of thread class is known as a creating thread. -- Whenever thread is created then we call thread is in new state or birth state thread. -- Whenever we call the start method on the new state threads then those threads will be forwarded for scheduling. -- The threads which are forwarded for scheduling are known as ready state threads -- Whenever scheduling time occurs, ready state thread starts execution. -- The threads which are executing are known as running state threads -Whenever sleep fun or join methods are called on the running state threads then immediately those threads will wait. -- The threads which are waiting are known as waiting state threads -Whenever waiting time is over or specified thread execution is over - then immediately waiting state threads are forwarded for scheduling. -- If running state threads execution is over then immediately those threads execution will be terminated - -The threads which execution is terminated are known as dead state threads. + - `before_request()`: They are called before a request and pass no arguments. + - `after_request()`: They are called after a request and pass the response that will be sent to the client. + - `teardown_request()`: They are called in situation when exception is raised, and response are not guaranteed. They are called after the response been constructed. They are not allowed to modify the request, and their values are ignored. -## Q. What is scheduling? - -Among multiple threads: -- which thread as to start the execution first, -- How much time the thread as to execute after allocated time - is over, -- which thread as to continue the execution next this comes under scheduling. Scheduling is highly dynamic +## How will you sort result of student whose marks are unknown to you based on their roll numbers? - +Using bubble sort. -## Q. for loop is implemented in python language as follows: +## Q. What are the standard Python libraries commonly used? + +| Category | Libraries | +|----------|-----------| +| OS & File System | `os`, `sys`, `pathlib`, `shutil`, `glob` | +| Data Structures | `collections`, `heapq`, `bisect`, `array` | +| Functional | `functools`, `itertools`, `operator` | +| Serialization | `json`, `pickle`, `csv`, `xml`, `configparser` | +| Networking | `socket`, `http`, `urllib`, `ftplib`, `smtplib` | +| Concurrency | `threading`, `multiprocessing`, `asyncio`, `concurrent.futures` | +| Date & Time | `datetime`, `time`, `calendar`, `zoneinfo` | +| Logging | `logging` | +| Testing | `unittest`, `unittest.mock`, `doctest` | +| Regex | `re` | +| Math | `math`, `statistics`, `decimal`, `fractions`, `random` | ```py -for element in iterable: - iter-obj=iter(iterable) - while true: - try: - element=next(iter_obj) - except(slop iteration) - break +from collections import defaultdict, Counter +from itertools import groupby +from functools import lru_cache +import pathlib, logging, json + +# defaultdict — avoids KeyError on missing keys +word_groups: defaultdict[str, list[str]] = defaultdict(list) +for word in ["apple", "ant", "ball", "banana"]: + word_groups[word[0]].append(word) +print(dict(word_groups)) # {'a': ['apple', 'ant'], 'b': ['ball', 'banana']} + +# lru_cache — memoization for expensive pure functions +@lru_cache(maxsize=128) +def fibonacci(n: int) -> int: + return n if n < 2 else fibonacci(n - 1) + fibonacci(n - 2) ``` -For loop takes the given object, convert that object in the form of iterable object & gets the one by one element form the iterable object. -While getting the one by value element from the iterable object if stop iteration exception is raised then for loop internally handle that exception +**Use Case:** A data engineering team uses `collections.defaultdict` + `itertools.groupby` to aggregate streaming event logs by user session without third-party dependencies, keeping the Lambda function package under 10 MB. -## Q. OS Module +## Q. What are serialization formats in Python? -OS Module is a predefined module and which provides various functions and methods to perform the operating system related activities, such as creating the files, removing the files, creating the directories removing the directories, executing the operating system related commands, etc. -Example: +Serialization converts a Python object into a byte stream or string for storage or transmission. Deserialization reverses this. + +| Format | Module | Human-readable | Cross-language | Speed | +|--------|--------|---------------|---------------|-------| +| JSON | `json` | Yes | Yes | Fast | +| Pickle | `pickle` | No | Python only | Fast | +| CSV | `csv` | Yes | Yes | Medium | +| XML | `xml`, `lxml` | Yes | Yes | Slow | +| MessagePack | `msgpack` | No | Yes | Very fast | +| Protocol Buffers | `protobuf` | No | Yes | Very fast | +| YAML | `yaml` (PyYAML) | Yes | Yes | Slow | ```py -import os -cwd=os.getwd() -print("1", cwd) -os.chdir("samples") -print("2", os.getcwd()) -os.chdir(os.pardir) -print("3",os.getcwd()) +import json +import pickle +import csv +from io import StringIO + +# JSON — best for APIs and config files +data = {"user": "alice", "score": 98, "tags": ["python", "backend"]} +json_str = json.dumps(data, indent=2) +restored = json.loads(json_str) +print(restored["user"]) # alice + +# Pickle — Python-native, supports any object (NEVER unpickle untrusted data) +with open("model.pkl", "wb") as f: + pickle.dump(data, f) +with open("model.pkl", "rb") as f: + loaded = pickle.load(f) + +# CSV — for tabular data +output = StringIO() +writer = csv.DictWriter(output, fieldnames=["user", "score"]) +writer.writeheader() +writer.writerow({"user": "alice", "score": 98}) +print(output.getvalue()) ``` -Output: + +> **Security Warning:** Never use `pickle.load()` on data from untrusted sources — it can execute arbitrary code during deserialization. + +**Use Case:** A machine learning platform serializes trained `scikit-learn` models with `pickle` for internal storage, but exposes model metadata via JSON for the REST API, keeping concerns separated by format suitability. -## Q. What Are Applications of Python? +## Q. What version of Azure Data Factory are you using? -Applications of Python +Azure Data Factory (ADF) **V2** is the current and only actively supported version (V1 reached end-of-support in 2018). ADF V2 key capabilities include: -- Automation App -- Data Analytics -- Scientific App -- Web App -- Web Scrapping -- Test Cases -- Network with IOT -- Admin Script -- GUI -- Gaming -- Animation +- **Data Flows** (Mapping & Wrangling) — visual ETL with Spark-based execution +- **Triggers** — Schedule, Tumbling Window, Storage Event, Custom Event +- **Integration Runtimes** — Azure IR, Self-Hosted IR, SSIS IR +- **ARM/Bicep templates** for CI/CD deployments +- **Git integration** — Azure DevOps or GitHub for source control +- **Managed Virtual Network** — private endpoint connectivity + +```py +# Interacting with ADF V2 via Python SDK (azure-mgmt-datafactory) +from azure.identity import DefaultAzureCredential +from azure.mgmt.datafactory import DataFactoryManagementClient +from azure.mgmt.datafactory.models import CreateRunResponse + +subscription_id = "your-subscription-id" +resource_group = "my-rg" +factory_name = "my-adf" +pipeline_name = "IngestPipeline" + +credential = DefaultAzureCredential() +adf_client = DataFactoryManagementClient(credential, subscription_id) + +# Trigger a pipeline run programmatically +run_response: CreateRunResponse = adf_client.pipelines.create_run( + resource_group_name=resource_group, + factory_name=factory_name, + pipeline_name=pipeline_name, + parameters={"source_date": "2026-05-17"}, +) +print(f"Pipeline run ID: {run_response.run_id}") + +# Poll run status +import time +while True: + run = adf_client.pipeline_runs.get(resource_group, factory_name, run_response.run_id) + print(f"Status: {run.status}") + if run.status in ("Succeeded", "Failed", "Cancelled"): + break + time.sleep(15) +``` + +**Use Case:** A data platform team uses ADF V2 with Self-Hosted IR to move data from on-premises SQL Server to Azure Data Lake Gen2, triggered by Azure Event Grid file-arrival events, with Python SDK used in CI/CD pipelines to deploy and monitor ADF runs automatically. -## Q. How Python is interpreted? - -Python language is an interpreted language. Python program runs directly from the source code. It converts the source code that is written by the programmer into an intermediate language, which is again translated into machine language that has to be executed. +## Q. How to execute Java code from Python? -## Q. What are the tools that help to find bugs or perform static analysis? +```py +import subprocess +import os +from pathlib import Path -PyChecker is a static analysis tool that detects the bugs in Python source code and warns about the style and complexity of the bug. Pylint is another tool that verifies whether the module meets the coding standard. +def compile_and_run_java(java_file: Path, *args: str) -> tuple[str, str, int]: + """ + Compile and run a Java source file, returning (stdout, stderr, return_code). + Requires JDK installed and `javac`/`java` on PATH. + """ + class_dir = java_file.parent -## Q. What is pass in Python? + # Step 1: Compile + compile_result = subprocess.run( + ["javac", "-d", str(class_dir), str(java_file)], + capture_output=True, + text=True, + timeout=30, + ) + if compile_result.returncode != 0: + return "", compile_result.stderr, compile_result.returncode -Pass means, no-operation Python statement, or in other words it is a place holder in compound statement, where there should be a blank left and nothing has to be written there. + # Step 2: Run — derive class name from filename (Java convention) + class_name = java_file.stem + run_result = subprocess.run( + ["java", "-cp", str(class_dir), class_name, *args], + capture_output=True, + text=True, + timeout=60, + ) + return run_result.stdout, run_result.stderr, run_result.returncode -## Q. In Python what are iterators? -In Python, iterators are used to iterate a group of elements, containers like list. +# Example usage +java_source = Path("/tmp/Hello.java") +java_source.write_text( + 'public class Hello {\n' + ' public static void main(String[] args) {\n' + ' System.out.println("Hello from Java: " + args[0]);\n' + ' }\n' + '}\n' +) +stdout, stderr, code = compile_and_run_java(java_source, "PythonCaller") +print(f"Output: {stdout.strip()}") # Hello from Java: PythonCaller +print(f"Return code: {code}") # 0 +``` -## Q. In Python what is slicing? +> **Security Note:** Never pass user-provided strings directly to `subprocess` commands — use argument lists (not `shell=True`) to prevent shell injection. -A mechanism to select a range of items from sequence types like list, tuple, strings etc. is known as slicing. +**Use Case:** A polyglot CI/CD pipeline uses Python as the orchestration layer to compile, test, and execute Java microservices during integration testing, capturing their stdout/stderr for structured test reporting. -## Q. What are generators in Python? +## Q. How to find PID of a process and how much resources it is taking on Linux? -The way of implementing iterators are known as generators. It is a normal function except that it yields expression in the function. -Python generator produces a sequence of values to iterate on. This way, it is kind of an iterable. We define a function that 'yields' values one by one, and then use a for loop to iterate on it. -``` - def squares(n): - i=1 - while(i<=n): - yield i**2 - i+=1 - for i in squares(7): - print(i) -``` +```py +import os +import psutil # pip install psutil +from typing import Iterator + +# Method 1: Find PID of current process +current_pid: int = os.getpid() +print(f"Current PID: {current_pid}") + +# Method 2: Find PID(s) by process name using psutil +def find_pids_by_name(process_name: str) -> list[int]: + """Return all PIDs whose name matches the given string.""" + return [ + proc.pid + for proc in psutil.process_iter(["pid", "name"]) + if process_name.lower() in proc.info["name"].lower() + ] + +pids = find_pids_by_name("python") +print(f"Python PIDs: {pids}") + +# Method 3: Detailed resource usage for a specific PID +def get_process_resources(pid: int) -> dict: + """Return CPU%, memory, open files, threads for a given PID.""" + try: + proc = psutil.Process(pid) + proc.cpu_percent(interval=None) # prime the counter + import time; time.sleep(0.1) + + mem_info = proc.memory_info() + return { + "pid": pid, + "name": proc.name(), + "status": proc.status(), + "cpu_percent": proc.cpu_percent(interval=0.1), + "memory_rss_mb": mem_info.rss / (1024 ** 2), + "memory_vms_mb": mem_info.vms / (1024 ** 2), + "memory_percent": proc.memory_percent(), + "num_threads": proc.num_threads(), + "open_files": len(proc.open_files()), + "connections": len(proc.net_connections()), + } + except psutil.NoSuchProcess: + return {} + +stats = get_process_resources(current_pid) +for key, value in stats.items(): + print(f" {key}: {value}") + +# Method 4: Top N processes by memory consumption +def top_processes_by_memory(n: int = 5) -> list[dict]: + procs = [ + proc.as_dict(attrs=["pid", "name", "memory_percent", "cpu_percent"]) + for proc in psutil.process_iter(["pid", "name", "memory_percent", "cpu_percent"]) + if proc.info["memory_percent"] is not None + ] + return sorted(procs, key=lambda p: p["memory_percent"], reverse=True)[:n] + +print(top_processes_by_memory()) +``` + +**Linux CLI equivalents:** `ps aux | grep `, `top -p `, `cat /proc//status`, `/proc//smaps` + +**Use Case:** A production monitoring agent runs as a sidecar container, using `psutil` to emit process-level CPU and RSS memory metrics to Prometheus every 15 seconds, alerting when any single process exceeds 80% of available memory. + + + +## Q. What are tools for data ingestion? + +Data ingestion tools move data from sources into storage/processing systems. They fall into several categories: + +| Category | Tools | Best For | +|----------|-------|----------| +| **Message Queues / Streaming** | Apache Kafka, AWS Kinesis, Azure Event Hubs, Google Pub/Sub | Real-time event streams | +| **Batch ETL / Pipeline** | Apache Airflow, Azure Data Factory, AWS Glue, dbt | Scheduled batch ingestion | +| **Log / Event Collection** | Logstash (ELK), Fluentd, Filebeat, Vector | Log aggregation | +| **Change Data Capture (CDC)** | Debezium, AWS DMS, Striim | DB replication / CDC | +| **File Transfer** | Apache NiFi, Airbyte, Fivetran | File-based & API connectors | +| **Python Libraries** | `kafka-python`, `boto3`, `azure-eventhub`, `apache-beam` | Custom pipelines | -1 -4 -9 -16 -25 -36 -49 +```py +# Example: Producing messages to Apache Kafka using kafka-python +from kafka import KafkaProducer +import json + +producer = KafkaProducer( + bootstrap_servers=["localhost:9092"], + value_serializer=lambda v: json.dumps(v).encode("utf-8"), + acks="all", # strongest durability guarantee + retries=3, + max_in_flight_requests_per_connection=1, # ensure ordering +) + +event = {"user_id": "u123", "action": "purchase", "amount": 49.99} +future = producer.send("user-events", value=event, key=b"u123") +record_metadata = future.get(timeout=10) +print(f"Sent to {record_metadata.topic}:{record_metadata.partition} @ {record_metadata.offset}") +producer.flush() +producer.close() +``` + +**Use Case:** A ride-sharing platform ingests 2M GPS location updates per minute using Apache Kafka as the ingestion backbone, with Kafka Streams for real-time ETA computation and a Spark Structured Streaming job writing aggregated trip data to Delta Lake every 30 seconds. -## Q. So, what is an iterator, then? +## Q. What is inheritance? -An iterator returns one object at a time to iterate on. To create an iterator, we use the iter() function. +Inheritance is an OOP mechanism where a **child class** acquires the attributes and methods of one or more **parent classes**, enabling code reuse, specialization, and the Liskov Substitution Principle. Python supports single, multiple, and multilevel inheritance. - odds=iter([1,3,5]) +```py +from abc import ABC, abstractmethod +from typing import ClassVar -Then, we call the next() function on it every time we want an object. +# Base class (abstract) +class Animal(ABC): + species_count: ClassVar[int] = 0 - next(odds) -1 + def __init__(self, name: str, sound: str) -> None: + self.name = name + self._sound = sound + Animal.species_count += 1 - next(odds) -3 + @abstractmethod + def speak(self) -> str: + ... - next(odds) -5 + def describe(self) -> str: + return f"I am {self.name}" -And now, when we call it again, it raises a StopIteration exception. This is because it has reached the end of the values to iterate on. +# Single inheritance +class Dog(Animal): + def __init__(self, name: str, breed: str) -> None: + super().__init__(name, "woof") # call parent __init__ + self.breed = breed - next(odds) + def speak(self) -> str: # implement abstract method + return f"{self.name} says {self._sound}!" - Traceback (most recent call last): - File "", line 1, in next(odds) - StopIteration + def fetch(self, item: str) -> str: + return f"{self.name} fetches the {item}" - +# Multilevel inheritance +class GuideDog(Dog): + def guide(self) -> str: + return f"{self.name} guides its owner safely" -## Q. Explain generators and iterators in python? +d = Dog("Rex", "Labrador") +print(d.speak()) # Rex says woof! +print(d.describe()) # I am Rex (inherited from Animal) -- They do, but there are subtle differences: +g = GuideDog("Buddy", "Golden Retriever") +print(g.guide()) # Buddy guides its owner safely +print(g.fetch("ball")) # Buddy fetches the ball (inherited from Dog) -- For a generator, we create a function. For an iterator, we use in-built functions `iter()` and `next()`. -- For a generator, we use the keyword 'yield' to yield/return an object at a time. -- A generator may have as many 'yield' statements as you want. -- A generator will save the states of the local variables every time 'yield' will pause the loop. -- An iterator does not use local variables; it only needs an iterable to iterate on. -- Using a class, you can implement your own iterator, but not a generator. -- Generators are fast, compact, and simpler. -- Iterators are more memory-efficient. +# isinstance checks hierarchy +print(isinstance(g, GuideDog)) # True +print(isinstance(g, Dog)) # True +print(isinstance(g, Animal)) # True +``` + +**Use Case:** A payment processing system uses an abstract `PaymentProcessor` base class enforcing a `process(amount)` interface. Concrete subclasses `StripeProcessor`, `PayPalProcessor`, and `BankTransferProcessor` implement it differently — new providers are added without modifying existing code (Open/Closed Principle). -## Q. How can you copy an object in Python? +## Q. Differentiate between `append()` and `extend()` methods of a list. -To copy an object in Python, you can try copy.copy () or copy.deepcopy() for the general case. You cannot copy all objects but most of them. +| Method | Action | Argument | Time | Result on `[1,2]` | +|--------|--------|----------|------|-------------------| +| `append(x)` | Adds `x` as a **single element** | Any object | O(1) amortized | `[1, 2, [3, 4]]` if x=`[3,4]` | +| `extend(iterable)` | Adds **each element** of iterable | Any iterable | O(k) where k=len(iterable) | `[1, 2, 3, 4]` if x=`[3,4]` | -## Q. How you can convert a number to a string? +```py +# append — adds the argument as ONE new element +lst1 = [1, 2, 3] +lst1.append([4, 5]) # the list [4, 5] becomes a single element +print(lst1) # [1, 2, 3, [4, 5]] +print(len(lst1)) # 4 -In order to convert a number into a string, use the inbuilt function str(). If you want a octal or hexadecimal representation, use the inbuilt function oct() or hex(). +# extend — unpacks iterable and adds each element +lst2 = [1, 2, 3] +lst2.extend([4, 5]) # 4 and 5 added as separate elements +print(lst2) # [1, 2, 3, 4, 5] +print(len(lst2)) # 5 -## Q. What is module and package in Python? +# extend works with any iterable +lst3 = [1, 2] +lst3.extend("abc") # extends with individual characters +print(lst3) # [1, 2, 'a', 'b', 'c'] -In Python, module is the way to structure program. Each Python program file is a module, which imports other modules like objects and attributes. +lst3.extend(range(3)) +print(lst3) # [1, 2, 'a', 'b', 'c', 0, 1, 2] -The folder of Python program is a package of modules. A package can have modules or subfolders. +# += operator uses extend semantics, not append +lst4 = [1, 2] +lst4 += [3, 4] # equivalent to lst4.extend([3, 4]) +print(lst4) # [1, 2, 3, 4] + +# Performance: extend is more efficient than repeated append in a loop +import timeit +t1 = timeit.timeit("lst=[]; [lst.append(i) for i in range(1000)]", number=10000) +t2 = timeit.timeit("lst=[]; lst.extend(range(1000))", number=10000) +print(f"append loop: {t1:.3f}s extend: {t2:.3f}s") # extend ~3x faster +``` + +**Use Case:** A web scraper collects page results in batches; `extend()` merges each batch into the master results list efficiently, while `append()` would wrap each batch in a nested list, requiring flattening later. -## Q. Mention what are the rules for local and global variables in Python? +## Q. Explain `ctypes` and why you would use them? -Local variables: If a variable is assigned a new value anywhere within the function\'s body, it\'s assumed to be local. +`ctypes` is Python\'s built-in **foreign function interface (FFI)** library. It allows Python to call functions in compiled C shared libraries (`.so` on Linux, `.dll` on Windows, `.dylib` on macOS), pass C data types, and access C memory directly — without writing a C extension module. -Global variables: Those variables that are only referenced inside a function are implicitly global. +**When to use:** +- Call existing C/C++ libraries that have no Python wrapper +- Access OS-level APIs +- Achieve near-C performance for CPU-intensive routines +- Interface with hardware drivers or system calls -## Q. How can you share global variables across modules? +```py +import ctypes +import ctypes.util +import sys -To share global variables across modules within a single program, create a special module. Import the config module in all modules of your application. The module will be available as a global variable across modules. +# Load the C standard library +if sys.platform == "win32": + libc = ctypes.CDLL("msvcrt.dll") +else: + libc = ctypes.CDLL(ctypes.util.find_library("c")) - +# Call C's printf +libc.printf(b"Hello from C: %d\n", ctypes.c_int(42)) -## Q. Explain how can you make a Python Script executable on Unix? +# Define argument and return types for type safety +libc.strlen.argtypes = [ctypes.c_char_p] +libc.strlen.restype = ctypes.c_size_t +print(libc.strlen(b"hello")) # 5 -To make a Python Script executable on Unix, you need to do two things, +# Define a C struct +class Point(ctypes.Structure): + _fields_ = [("x", ctypes.c_double), ("y", ctypes.c_double)] - Script file\'s mode must be executable and - the first line must begin with # ( #!/usr/local/bin/python) +p = Point(3.0, 4.0) +print(f"Point({p.x}, {p.y})") -## Q. Explain how to delete a file in Python? +# Load a custom shared library (example) +# lib = ctypes.CDLL("./my_fast_lib.so") +# lib.fast_sum.argtypes = [ctypes.POINTER(ctypes.c_double), ctypes.c_int] +# lib.fast_sum.restype = ctypes.c_double +# arr = (ctypes.c_double * 5)(1.0, 2.0, 3.0, 4.0, 5.0) +# print(lib.fast_sum(arr, 5)) # 15.0 -By using a command os.remove (filename) or os.unlink(filename) +# Access raw memory (advanced) +buf = ctypes.create_string_buffer(10) +buf.value = b"Hello" +print(buf.raw) # b'Hello\x00\x00\x00\x00\x00' +``` + +> **Security Note:** `ctypes` bypasses Python\'s memory safety — incorrect pointer arithmetic or type mismatches can cause segfaults or memory corruption. Always specify `argtypes` and `restype`. + +**Use Case:** A real-time image processing service calls a highly optimized C++ computer vision library via `ctypes` to apply SIMD-accelerated filters on video frames, achieving 200 FPS — impossible with pure Python. -## Q. Explain how can you generate random numbers in Python? +## Q. How do you go about packaging Python code? -To generate random numbers in Python, you need to import command as +Python packaging means bundling code so others can install it via `pip`. The modern standard uses **`pyproject.toml`** (PEP 517/518/621) with a build backend such as `hatchling`, `setuptools`, or `flit`. + +``` +my_package/ +├── pyproject.toml # build metadata & dependencies +├── src/ +│ └── my_package/ +│ ├── __init__.py +│ └── core.py +├── tests/ +│ └── test_core.py +└── README.md +``` + +```toml +# pyproject.toml +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "my-package" +version = "1.0.0" +description = "Example package" +readme = "README.md" +requires-python = ">=3.10" +license = {text = "MIT"} +dependencies = [ + "requests>=2.28", + "pydantic>=2.0", +] + +[project.optional-dependencies] +dev = ["pytest", "mypy", "ruff"] + +[project.scripts] +my-cli = "my_package.cli:main" # console entry point +``` ```py -import random -random.random() +# src/my_package/__init__.py +from .core import greet +__version__ = "1.0.0" +__all__ = ["greet"] + +# src/my_package/core.py +def greet(name: str) -> str: + return f"Hello, {name}!" ``` -This returns a random floating point number in the range (0,1) +```bash +# Build distribution files +pip install build +python -m build # creates dist/my_package-1.0.0.tar.gz + .whl -- Explain how can you access a module written in Python from C? +# Publish to PyPI +pip install twine +twine upload dist/* -You can access a module written in Python from C by following method, +# Install locally (editable mode for development) +pip install -e ".[dev]" +``` -Module = =PyImport_ImportModule(""); +**Key files:** +- `pyproject.toml` — replaces `setup.py` / `setup.cfg` +- `src/` layout — prevents accidental imports of uninstalled code during tests +- `.whl` (wheel) — binary distribution, faster to install than sdist + +**Use Case:** A data science team packages shared feature engineering utilities as an internal PyPI package, enabling all microservices to `pip install internal-features==2.3.1` with pinned versions — eliminating copy-paste drift between 12 separate repos. -## Q. Mention the use of // operator in Python? +## Q. What does `__some_variable__` (dunder / magic attribute) mean? -It is a Floor Divisionoperator , which is used for dividing two operands with the result as quotient showing only digits before the decimal point. For instance, 10//5 = 2 and 10.0//5.0 = 2.0. +Names surrounded by double underscores (`__name__`) are called **dunder** (double-underscore) names or **magic attributes/methods**. Python reserves this naming convention for the interpreter\'s own protocol methods and special attributes. You should never invent your own `__custom__` names — that namespace belongs to Python. - +```py +# Dunder methods define how objects respond to built-in operations +class Vector: + def __init__(self, x: float, y: float) -> None: + self.x, self.y = x, y -## Q. Mention five benefits of using Python? + def __repr__(self) -> str: # repr(v) + return f"Vector({self.x}, {self.y})" -- Python comprises of a huge standard library for most Internet platforms like Email, HTML, etc. -- Python does not require explicit memory management as the interpreter itself allocates the memory to newvariables and free them automatically -- Provide easy readability due to use of square brackets -- Easy-to-learn for beginners -- Having the built-in data types saves programming time and effort from declaring variables + def __str__(self) -> str: # str(v) / print(v) + return f"({self.x}, {self.y})" - + def __add__(self, other: "Vector") -> "Vector": # v1 + v2 + return Vector(self.x + other.x, self.y + other.y) -## Q. Mention the use of the split function in Python? + def __len__(self) -> int: # len(v) + return 2 -The use of the split function in Python is that it breaks a string into shorter strings using the defined separator. It gives a list of all words present in the string. + def __eq__(self, other: object) -> bool: # v1 == v2 + if not isinstance(other, Vector): return NotImplemented + return self.x == other.x and self.y == other.y - + def __hash__(self) -> int: # hash(v), use in sets/dicts + return hash((self.x, self.y)) -## Q. Mention what is the difference between Django, Pyramid, and Flask? + def __bool__(self) -> bool: # bool(v), if v: + return self.x != 0 or self.y != 0 -Flask is a "microframework" primarily build for a small application with simpler requirements. In flask, you have to use external libraries. Flask is ready to use. +v1 = Vector(1, 2) +v2 = Vector(3, 4) +print(v1 + v2) # (4, 6) — calls __add__ +print(len(v1)) # 2 — calls __len__ +print(repr(v1)) # Vector(1, 2) — calls __repr__ -Pyramid are build for larger applications. It provides flexibility and lets the developer use the right tools for their project. The developer can choose the database, URL structure, templating style and more. Pyramid is heavy configurable. +# Common special attributes +print(__name__) # '__main__' when run directly, else module name +print(Vector.__doc__) # class docstring +print(Vector.__dict__) # namespace dict of the class +print(v1.__class__.__name__) # 'Vector' +``` -Like Pyramid, Django can also used for larger applications. It includes an ORM. +**Common dunders:** -- You are having multiple Memcache servers running Python, in which one of the memcacher server fails, and it has your data, will it ever try to get key data from that one failed server? +| Dunder | Triggered by | +|--------|-------------| +| `__init__` | `ClassName()` | +| `__str__` | `str()`, `print()` | +| `__repr__` | `repr()`, REPL display | +| `__len__` | `len()` | +| `__iter__` / `__next__` | `for` loops | +| `__enter__` / `__exit__` | `with` statement | +| `__getitem__` | `obj[key]` | +| `__call__` | `obj()` | -The data in the failed server won\'t get removed, but there is a provision for auto-failure, which you can configure for multiple nodes. Fail-over can be triggered during any kind of socket or Memcached server level errors and not during normal client errors like adding an existing key, etc. +**Use Case:** A DataFrame-like class overrides `__getitem__`, `__setitem__`, `__len__`, and `__iter__` to give users a familiar `df["column"]` and `for row in df:` interface, with custom memory-mapped storage underneath. -## Q. Explain how you can minimize the Memcached server outages in your Python Development? +## Q. What is the difference between `str()` and `repr()`? -- When one instance fails, several of them goes down, this will put larger load on the database server when lost data is reloaded as client make a request. To avoid this, if your code has been written to minimize cache stampedes then it will leave a minimal impact -- Another way is to bring up an instance of Memcached on a new machine using the lost machines IP address -- Code is another option to minimize server outages as it gives you the liberty to change the Memcached server list with minimal work -- Setting timeout value is another option that some Memcached clients implement for Memcached server outage. When your Memcached server goes down, the client will keep trying to send a request till the time-out limit is reached +| | `str()` | `repr()` | +|--|---------|---------| +| **Purpose** | Human-readable, display | Unambiguous, developer/debug | +| **Ideal reader** | End users | Developers | +| **Goal** | Pretty / concise | Reproducible (`eval(repr(x)) == x` when possible) | +| **Fallback** | Falls back to `__repr__` | No fallback | +| **Used by** | `print()`, f-strings `{x}` | REPL, `!r` format, `logging` | - +```py +from datetime import datetime -## Q. Explain what is Dogpile effect? How can you prevent this effect? +dt = datetime(2025, 6, 15, 12, 30) +print(str(dt)) # 2025-06-15 12:30:00 — readable +print(repr(dt)) # datetime.datetime(2025, 6, 15, 12, 30) — reproducible -Dogpile effect is referred to the event when cache expires, and websites are hit by the multiple requests made by the client at the same time. -This effect can be prevented by using semaphore lock. In this system when value expires, first process acquires the lock and starts generating new value. +# Custom class: implement both +class Money: + def __init__(self, amount: float, currency: str = "GBP") -> None: + self.amount = amount + self.currency = currency - + def __str__(self) -> str: + return f"{self.currency} {self.amount:,.2f}" # user-facing -## Q. Explain how Memcached should not be used in your Python project? + def __repr__(self) -> str: + return f"Money({self.amount!r}, {self.currency!r})" # debug -Memcached common misuse is to use it as a data store, and not as a cache. Never use Memcached as the only source of the information you need to run your application. Data should always be available through another source as well. Memcached is just a key or value store and cannot perform query over the data or iterate over the contents to extract information. +m = Money(1234.5) +print(str(m)) # GBP 1,234.50 +print(repr(m)) # Money(1234.5, 'GBP') -Memcached does not offer any form of security either in encryption or authentication +# f-string format specifiers +print(f"{m}") # GBP 1,234.50 — uses __str__ +print(f"{m!r}") # Money(1234.5, 'GBP') — uses __repr__ +print(f"{m!s}") # GBP 1,234.50 — explicitly __str__ + +# Why repr matters for strings (shows quotes, escapes) +s = "hello\nworld" +print(str(s)) # hello\nworld (newline rendered) +print(repr(s)) # 'hello\\nworld' (escape shown — unambiguous) +``` + +**Use Case:** A logging framework uses `repr()` to record all variable values in exception tracebacks — `Money(1234.5, 'GBP')` in the log is immediately copy-pasteable to a Python REPL for debugging, whereas `GBP 1,234.50` is ambiguous and unrepresentable. -## Q. What is List Comprehensions feature of Python used for? +## Q. What are Middlewares? -List comprehensions help to create and manage lists in a simpler and clearer way than using `map()`, `filter()` and `lambda`. Each list comprehension consists of an expression followed by a for clause, then zero or more for or if clauses. +**Middleware** is code that sits between the incoming request and the application handler (or between the response and the client), forming a processing pipeline. Each middleware wraps the next, processing the request on the way in and the response on the way out. - +In Python web frameworks: +- **Django:** `MIDDLEWARE` list in settings — each class implements `__call__` +- **FastAPI / Starlette:** `@app.middleware("http")` or `add_middleware()` +- **WSGI:** Callables that wrap a WSGI app -## Q. What are lambda expressions, list comprehensions and generator expressions? +```py +# WSGI-style middleware (framework-agnostic) +from typing import Callable +import time -**Lambda expressions** +# Type aliases for WSGI +Environ = dict +StartResponse = Callable +WSGIApp = Callable[[Environ, StartResponse], list[bytes]] -are a shorthand technique for creating single line, anonymous functions. Their simple, inline nature often – though not always – leads to more readable and concise code than the alternative of formal function declarations. On the other hand, their terse inline nature, by definition, very much limits what they are capable of doing and their applicability. Being anonymous and inline, the only way to use the same lambda function in multiple locations in your code is to specify it redundantly. +class TimingMiddleware: + def __init__(self, app: WSGIApp) -> None: + self.app = app -**List comprehensions** + def __call__(self, environ: Environ, start_response: StartResponse): + start = time.perf_counter() + result = self.app(environ, start_response) + duration = time.perf_counter() - start + print(f"Request took {duration * 1000:.2f}ms") + return result -provide a concise syntax for creating lists. List comprehensions are commonly used to make lists where each element is the result of some operation(s) applied to each member of another sequence or iterable. They can also be used to create a subsequence of those elements whose members satisfy a certain condition. In Python, list comprehensions provide an alternative to using the built-in map() and filter() functions. +# FastAPI / Starlette middleware +from fastapi import FastAPI, Request +from fastapi.responses import Response -As the applied usage of lambda expressions and list comprehensions can overlap, opinions vary widely as to when and where to use one vs. the other. One point to bear in mind, though, is that a list comprehension executes somewhat faster than a comparable solution using map and lambda (some quick tests yielded a performance difference of roughly 10%). This is because calling a lambda function creates a new stack frame while the expression in the list comprehension is evaluated without doing so. +app = FastAPI() -**Generator expressions** +@app.middleware("http") +async def add_correlation_id(request: Request, call_next) -> Response: + import uuid + correlation_id = request.headers.get("X-Correlation-ID", str(uuid.uuid4())) + response = await call_next(request) # pass to next middleware / handler + response.headers["X-Correlation-ID"] = correlation_id + return response -are syntactically and functionally similar to list comprehensions but there are some fairly significant differences between the ways the two operate and, accordingly, when each should be used. In a nutshell, iterating over a generator expression or list comprehension will essentially do the same thing, but the list comprehension will create the entire list in memory first while the generator expression will create the items on the fly as needed. Generator expressions can therefore be used for very large (and even infinite) sequences and their lazy (i.e., on demand) generation of values results in improved performance and lower memory usage. It is worth noting, though, that the standard Python list methods can be used on the result of a list comprehension, but not directly on that of a generator expression. +@app.middleware("http") +async def log_requests(request: Request, call_next) -> Response: + print(f"→ {request.method} {request.url.path}") + response = await call_next(request) + print(f"← {response.status_code}") + return response -- Consider the two approaches below for initializing an array and the arrays that will result. How will the resulting arrays differ and why should you use one initialization approach vs. the other? +# Middleware chain: log_requests → add_correlation_id → route handler +``` + +**Common middleware responsibilities:** +- Authentication / authorization +- Request logging and tracing +- Rate limiting +- CORS headers +- Response compression +- Request body validation + +**Use Case:** A microservice uses three middleware layers: `AuthMiddleware` (validates JWT), `RateLimitMiddleware` (100 req/min per user), and `TracingMiddleware` (injects OpenTelemetry spans) — all added to the pipeline in `main.py` without touching any route handler. + + + +## Q. How to generate random numbers? ```py - # INITIALIZING AN ARRAY -- METHOD 1 -... - x = [[1,2,3,4]] * 3 - x -[[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]] +import random +import secrets +import numpy as np +# ── random module (PRNG — Mersenne Twister, NOT cryptographically secure) ── - # INITIALIZING AN ARRAY -- METHOD 2 -... - y = [[1,2,3,4] for _ in range(3)] - y -[[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]] +# Integer in [a, b] inclusive +print(random.randint(1, 10)) - # WHICH METHOD SHOULD YOU USE AND WHY? -``` +# Float in [0.0, 1.0) +print(random.random()) -**Ans:** +# Float in [a, b) +print(random.uniform(1.5, 9.5)) -While both methods appear at first blush to produce the same result, there is an extremely significant difference between the two. Method 2 produces, as you would expect, an array of 3 elements, each of which is itself an independent 4-element array. In method 1, however, the members of the array all point to the same object. This can lead to what is most likely unanticipated and undesired behavior as shown below. +# Random choice from a sequence +colours = ["red", "green", "blue"] +print(random.choice(colours)) -```py -# MODIFYING THE x ARRAY FROM THE PRIOR CODE SNIPPET: -x[0][3] = 99 -x -[[1, 2, 3, 99], [1, 2, 3, 99], [1, 2, 3, 99]] +# Multiple choices (with replacement) +print(random.choices(colours, weights=[1, 2, 3], k=5)) -# UH-OH, DON'T THINK YOU WANTED THAT TO HAPPEN! -... -# MODIFYING THE y ARRAY FROM THE PRIOR CODE SNIPPET: -y[0][3] = 99 -y -[[1, 2, 3, 99], [1, 2, 3, 4], [1, 2, 3, 4]] -# THAT\'s MORE LIKE WHAT YOU EXPECTED! -... +# Sample without replacement +deck = list(range(1, 53)) +hand = random.sample(deck, k=5) + +# Shuffle in-place +random.shuffle(deck) + +# Reproducible results — set seed +random.seed(42) +print([random.randint(1, 100) for _ in range(5)]) +# [1, 82, 15, 4, 95] — always the same with seed=42 + +# Gaussian distribution +print(random.gauss(mu=0, sigma=1)) + +# ── secrets module (CSPRNG — cryptographically secure) ── +print(secrets.randbelow(100)) # int in [0, 100) +print(secrets.randbits(16)) # random 16-bit integer +token = secrets.token_hex(32) # 64-char hex string (256-bit) +url_token = secrets.token_urlsafe(16)# URL-safe base64 token +print(token) + +# ── numpy random (vectorized, reproducible with Generator) ── +rng = np.random.default_rng(seed=42) # modern API (replaces np.random.*) +print(rng.integers(1, 100, size=5)) # array of 5 random ints +print(rng.standard_normal(size=(3, 3))) # 3×3 matrix of N(0,1) ``` +> **Security note:** Always use `secrets` for tokens, passwords, OTPs, and anything security-sensitive. `random` is predictable if the seed is known. + +**Use Case:** An A/B testing service uses `random.seed(user_id)` to deterministically assign users to experiment groups (same user always gets same variant), while `secrets.token_urlsafe(32)` generates the secure session tokens for those same users. + -## Q. What will be printed out by the second append() statement below? +## Q. What is `virtualenv`? -```py -def append(list=[]): - # append the length of a list to the list - list.append(len(list)) - return list +A **virtual environment** is an isolated Python environment with its own interpreter, `pip`, and installed packages — completely separate from the system Python. This prevents dependency conflicts between projects. -append(['a','b']) -['a', 'b', 2] -``` -append() # calling with no arg uses default list value of [][0] +Python 3.3+ includes `venv` (stdlib); `virtualenv` is a faster third-party alternative with more features. -append() # but what happens when we AGAIN call append with no arg? +```bash +# Create a virtual environment (stdlib venv) +python -m venv .venv -**Ans:** +# Activate +# Windows: +.venv\Scripts\activate +# macOS / Linux: +source .venv/bin/activate -When the default value for a function argument is an expression, the expression is evaluated only once, not every time the function is called. Thus, once the list argument has been initialized to an empty array, subsequent calls to append without any argument specified will continue to use the same array to which list was originally initialized. This will therefore yield the following, presumably unexpected, behavior: +# Your prompt changes: (.venv) $ -append() # first call with no arg uses default list value of [][0] -append() # but then look what happens...[0, 1] -append() # successive calls keep extending the same default list! -[0, 1, 2] -append() # and so on, and so on, and so on... -[0, 1, 2, 3] +# Install packages — isolated to this environment +pip install fastapi uvicorn pandas -- How might one modify the implementation of the `append` method in the previous question to avoid the undesirable behavior described there? +# Freeze dependencies +pip freeze > requirements.txt -The following alternative implementation of the append method would be one of a number of ways to avoid the undesirable behavior described in the answer to the previous question: +# Deactivate +deactivate -```py -def append(list=None): - if list is None: - list = [] - # append the length of a list to the list - list.append(len(list)) - return list -append() -[0] -append() -[0] +# Delete — just remove the folder +rm -rf .venv ``` -Q: How can you swap the values of two variables with a single line of Python code? -Consider this simple example: -``` - x = 'X' - y = 'Y' -``` -In many other languages, swapping the values of x and y requires that you to do the following: -``` -tmp = x -x = y -y = tmp -x, y -('Y', 'X') +```py +# Verify you're in a virtual environment +import sys +print(sys.prefix) # /path/to/.venv +print(sys.base_prefix) # /usr/local/python3.x (system Python) +print(sys.prefix == sys.base_prefix) # False — we're in a venv + +# Modern alternative: pyenv + virtualenv managed by pyenv-virtualenv +# Even more modern: uv (extremely fast resolver, drop-in pip replacement) +# uv venv && uv pip install fastapi ``` -But in Python, makes it possible to do the swap with a single line of code (thanks to implicit tuple packing and unpacking) as follows: +**Directory structure:** ``` -x,y = y,x -x,y -('Y', 'X') +.venv/ +├── bin/ # python, pip executables (Scripts/ on Windows) +├── lib/ +│ └── python3.x/ +│ └── site-packages/ # installed packages live here +└── pyvenv.cfg # records base Python version ``` +**Best practices:** +- Never commit `.venv/` to version control (add to `.gitignore`) +- Commit `requirements.txt` or `pyproject.toml` instead +- Use `requirements.txt` for applications; `pyproject.toml` for libraries + +**Use Case:** A CI/CD pipeline creates a fresh `.venv` on every build run, installs pinned `requirements.txt`, and runs tests — guaranteeing reproducible, conflict-free builds regardless of what else is installed on the build agent. + -## Q. What will be printed out by the last statement below? +## Q. What is a context processor? + +In **Django**, a context processor is a function that receives an `HttpRequest` and returns a `dict` of variables that are **automatically injected into every template context**. They are configured in `settings.py` under `TEMPLATES[...]['OPTIONS']['context_processors']`. ```py -flist = [] -for i in range(3): - flist.append(lambda: i) +# myapp/context_processors.py -[f() for f in flist] # what will this print out? -``` -In any closure in Python, variables are bound by name. Thus, the above line of code will print out the following: -[2, 2, 2] -Presumably not what the author of the above code intended? +from django.http import HttpRequest -A workaround is to either create a separate function or to pass the args by name; e.g. +def site_settings(request: HttpRequest) -> dict: + """Injects site-wide settings into every template.""" + return { + "SITE_NAME": "MyApp", + "SUPPORT_EMAIL": "support@myapp.io", + "MAINTENANCE_MODE": False, + } -```py -flist = [] -for i in range(3): - flist.append(lambda i = i : i) +def user_notifications(request: HttpRequest) -> dict: + """Injects unread notification count for authenticated users.""" + if not request.user.is_authenticated: + return {"unread_count": 0} + # Import here to avoid circular imports + from notifications.models import Notification + count = Notification.objects.filter( + user=request.user, read=False + ).count() + return {"unread_count": count} +``` -[f() for f in flist] -[0, 1, 2] +```python +# settings.py — register the context processors +TEMPLATES = [{ + "BACKEND": "django.template.backends.django.DjangoTemplates", + "OPTIONS": { + "context_processors": [ + "django.template.context_processors.request", + "django.contrib.auth.context_processors.auth", + "myapp.context_processors.site_settings", # custom + "myapp.context_processors.user_notifications", # custom + ], + }, +}] ``` +```html + +{{ SITE_NAME }} +{{ unread_count }} notifications +``` + +> **Outside Django:** The term "context processor" generally means middleware or a request hook that enriches a context/request object with shared data before handlers execute. + +**Use Case:** An enterprise Django portal uses a context processor to inject the current user\'s `active_tenant`, `feature_flags`, and `branding_config` into every template render — eliminating repetitive `context.update(...)` calls across 200+ views. + -## Q. Is Python interpreted or compiled? - -As noted in Why Are There So Many Pythons?, this is, frankly, a bit of a trick question in that it is malformed. Python itself is nothing more than an interface definition (as is true with any language specification) of which there are multiple implementations. Accordingly, the question of whether "Python" is interpreted or compiled does not apply to the Python language itself; rather, it applies to each specific implementation of the Python specification. +## Q. What are `exec()` and `eval()`? -Further complicating the answer to this question is the fact that, in the case of CPython (the most common Python implementation), the answer really is "sort of both". Specifically, with CPython, code is first compiled and then interpreted. More precisely, it is not precompiled to native machine code, but rather to bytecode. While machine code is certainly faster, bytecode is more portable and secure. The bytecode is then interpreted in the case of CPython (or both interpreted and compiled to optimized machine code at runtime in the case of PyPy). +Both execute dynamically-generated Python code. `eval()` evaluates a **single expression** and returns its value. `exec()` executes **statements** (full code blocks) and returns `None`. - +```py +# eval — single expression, returns value +result = eval("2 ** 10 + 5") +print(result) # 1029 + +# eval with custom namespace (safer — restrict accessible names) +safe_globals = {"__builtins__": {}} # no built-ins +safe_locals = {"x": 10, "y": 20} +print(eval("x + y", safe_globals, safe_locals)) # 30 +# eval("open('file')", safe_globals, safe_locals) # NameError — open not available + +# exec — execute statements, returns None +code = """ +def multiply(a, b): + return a * b + +result = multiply(6, 7) +""" +namespace: dict = {} +exec(code, namespace) +print(namespace["result"]) # 42 +print(namespace["multiply"](3, 4)) # 12 -## Q. What are some alternative implementations to CPython? When and why might you use them? +# compile() + exec — for repeated execution (compile once) +compiled = compile("x * x", "", "eval") +for x in range(5): + print(eval(compiled, {"x": x})) # 0, 1, 4, 9, 16 +``` -One of the more prominent alternative implementations is Jython, a Python implementation written in Java that utilizes the Java Virtual Machine (JVM). While CPython produces bytecode to run on the CPython VM, Jython produces Java bytecode to run on the JVM. +> ⚠️ **Security warning:** NEVER pass untrusted user input to `eval()` or `exec()`. Even with `__builtins__: {}` restrictions can be bypassed. Use `ast.literal_eval()` for safe evaluation of literal expressions from untrusted sources. -Another is IronPython, written in C# and targeting the .NET stack. IronPython runs on Microsoft\'s Common Language Runtime (CLR). +```py +import ast -As also pointed out in Why Are There So Many Pythons?, it is entirely possible to survive without ever touching a non-CPython implementation of Python, but there are advantages to be had from switching, most of which are dependent on your technology stack. +# SAFE — only evaluates Python literals (str, int, float, list, dict, tuple, bool, None) +user_input = "[1, 2, 3, {'key': 'value'}]" +safe_data = ast.literal_eval(user_input) +print(safe_data) # [1, 2, 3, {'key': 'value'}] -Another noteworthy alternative implementation is PyPy whose key features include: +# ast.literal_eval raises ValueError for non-literals +# ast.literal_eval("__import__('os').system('rm -rf /')") # ValueError +``` -- Speed. Thanks to its Just-in-Time (JIT) compiler, Python programs often run faster on PyPy. -- Memory usage. Large, memory-hungry Python programs might end up taking less space with PyPy than they do in CPython. -- Compatibility. PyPy is highly compatible with existing python code. It supports cffi and can run popular Python libraries like Twisted and Django. -- Sandboxing. PyPy provides the ability to run untrusted code in a fully secure way. -- Stackless mode. PyPy comes by default with support for stackless mode, providing micro-threads for massive concurrency. +**Use Case:** A rule engine uses `exec()` to dynamically compile and cache user-defined business rule scripts at startup (compile-once), then `eval()` with a restricted namespace to evaluate them against transaction records — achieving configurable logic without code deployment. -## Q. What is unittest in Python? What\'s your approach to unit testing in Python? - -A unit testing framework in Python is known as unittest. It supports sharing of setups, automation testing, shutdown code for tests, aggregation of tests into collections etc. +## Q. How to pass command-line arguments? -The most fundamental answer to this question centers around Python\'s unittest testing framework. Basically, if a candidate doesn\'t mention unittest when answering this question, that should be a huge red flag. +Python provides three standard ways: `sys.argv` (raw), `argparse` (stdlib, recommended), and `click`/`typer` (third-party, decorator-based). -`unittest` supports test automation, sharing of setup and shutdown code for tests, aggregation of tests into collections, and independence of the tests from the reporting framework. The unittest module provides classes that make it easy to support these qualities for a set of tests. +```py +#!/usr/bin/env python3 +# cli_example.py + +import argparse +import sys + +def main() -> None: + parser = argparse.ArgumentParser( + description="Process a data file", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + + # Positional argument — required + parser.add_argument("input_file", help="Path to input CSV") + + # Optional flag with value + parser.add_argument( + "--output", "-o", + default="output.csv", + help="Path to output file", + ) + + # Integer option with validation + parser.add_argument( + "--batch-size", + type=int, + default=1000, + metavar="N", + help="Records per batch", + ) + + # Boolean flag + parser.add_argument( + "--verbose", "-v", + action="store_true", + help="Enable verbose logging", + ) + + # Enum-like choices + parser.add_argument( + "--format", + choices=["csv", "json", "parquet"], + default="csv", + ) + + args = parser.parse_args() + + if args.verbose: + print(f"Processing {args.input_file}") + print(f"Output: {args.output}") + print(f"Batch size: {args.batch_size}") + print(f"Format: {args.format}") -Assuming that the candidate does mention unittest (if they don\'t, you may just want to end the interview right then and there!), you should also ask them to describe the key elements of the unittest framework; namely, test fixtures, test cases, test suites and test runners. +if __name__ == "__main__": + main() +``` -A more recent addition to the unittest framework is mock. mock allows you to replace parts of your system under test with mock objects and make assertions about how they are to be used. mock is now part of the Python standard library, available as unittest.mock in Python 3.3 onwards. +```bash +# Usage +python cli_example.py data.csv --output results.csv --batch-size 500 --verbose --format json +python cli_example.py --help # auto-generated help text -The value and power of mock are well explained in An Introduction to Mocking in Python. As noted therein, system calls are prime candidates for mocking: whether writing a script to eject a CD drive, a web server which removes antiquated cache files from /tmp, or a socket server which binds to a TCP port, these calls all feature undesired side-effects in the context of unit tests. Similarly, keeping your unit-tests efficient and performant means keeping as much "slow code" as possible out of the automated test runs, namely filesystem and network access. +# sys.argv — raw access (not recommended for production CLIs) +# python script.py arg1 arg2 +# sys.argv = ['script.py', 'arg1', 'arg2'] +``` -[Note: This question is for Python developers who are also experienced in Java.] +```py +# Typer — modern, type-hint-driven CLI (third-party) +import typer - +app = typer.Typer() -## Q. How would you perform unit-testing on your Python code? +@app.command() +def process( + input_file: str, + output: str = "output.csv", + batch_size: int = 1000, + verbose: bool = False, +) -> None: + if verbose: + typer.echo(f"Processing {input_file}") -Ans. For this purpose, we have the module unittest in Python. It has the following members: -> -- FunctionTestCase -- SkipTest -- TestCase -- TestLoader -- TestResult -- TestSuite -- TextTestResult -- TextTestRunner -- defaultTestLoader -- expectedFailure -- findTestCases -- getTestCaseNames -- installHandler -- main -- makeSuite -- registerResult -- removeHandler -- removeResult -- skip -- skipIf -- skipUnless +if __name__ == "__main__": + app() +``` -Below are some Advanced Python Programming Interview Questions For Experienced. I recommend freshers to also refer these interview questions for advanced knowledge. +**Use Case:** A data engineering CLI tool uses `argparse` with `--env`, `--dry-run`, and `--config` flags, enabling both interactive use and automated invocation in Airflow BashOperator tasks with deterministic, documentable options. -## Q. How do I test a Python program or component? - -Python comes with two testing frameworks: -The documentation test module finds examples in the documentation strings for a module and runs them, comparing the output with the expected output given in the documentation string. +## Q. What are `ord()` and `chr()`? -The unittest moduleis a fancier testing framework modeled on Java and Smalltalk testing frameworks. - -For testing, it helps to write the program so that it may be easily tested by using good modular design. Your program should have almost all functionality encapsulated in either functions or class methods. And this sometimes has the surprising and delightful effect of making the program run faster because local variable accesses are faster than global accesses. +`ord(char)` returns the **Unicode code point** (integer) of a single character. `chr(code_point)` is the inverse — it returns the **character** for a given code point. Together they enable character-level arithmetic. -Furthermore the program should avoid depending on mutating global variables, since this makes testing much more difficult to do. -The "global main logic" of your program may be as simple as: +```py +# ord — character to code point +print(ord("A")) # 65 +print(ord("a")) # 97 +print(ord("0")) # 48 +print(ord("€")) # 8364 +print(ord("🐍")) # 128013 -if __name__=="__main__": - main_logic() +# chr — code point to character +print(chr(65)) # 'A' +print(chr(97)) # 'a' +print(chr(8364)) # '€' -at the bottom of the main module of your program. -Once your program is organized as a tractable collection of functions and class behaviors, you should write test functions that exercise the behaviors. +# Character arithmetic +print(chr(ord("A") + 1)) # 'B' +print(chr(ord("a") + 25)) # 'z' -A test suite can be associated with each module which automates a sequence of tests. +# Caesar cipher using ord/chr +def caesar_cipher(text: str, shift: int) -> str: + result = [] + for ch in text: + if ch.isalpha(): + base = ord("A") if ch.isupper() else ord("a") + shifted = (ord(ch) - base + shift) % 26 + base + result.append(chr(shifted)) + else: + result.append(ch) + return "".join(result) -You can make coding much more pleasant by writing your test functions in parallel with the "production code", since this makes it easy to find bugs and even design flaws earlier. +print(caesar_cipher("Hello, World!", 13)) # Uryyb, Jbeyq! (ROT13) +print(caesar_cipher("Uryyb, Jbeyq!", 13)) # Hello, World! -"Support modules" that are not intended to be the main module of a program may include a self-test of the module. +# Alphabet generation +alphabet = [chr(i) for i in range(ord("a"), ord("z") + 1)] +print(alphabet[:5]) # ['a', 'b', 'c', 'd', 'e'] -if __name__ == "__main__": - self_test() +# Check character ranges efficiently +def is_ascii_printable(ch: str) -> bool: + return 32 <= ord(ch) <= 126 +``` -Even programs that interact with complex external interfaces may be tested when the external interfaces are unavailable by using "fake" interfaces implemented in Python. +**Use Case:** A data masking service encodes sensitive identifiers by shifting each character\'s code point with a key-based offset (`ord(ch) + key % 26`) — a simple, reversible transformation that obscures values in test datasets without external crypto dependencies. -### FLASK +## Q. Difference between `input()` and `raw_input()`? -## Q. What is Flask & its benefits? +This distinction only applies to **Python 2**. In Python 3, `raw_input()` was **renamed to `input()`**. -Python Flask, as we've previously discussed, is a web microframework for Python. It is based on the '`Werkzeug`, `Jinja 2` and good intentions' BSD license. Two of its dependencies are Werkzeug and Jinja2. This means that it has around no dependencies on external libraries. Due to this, we can call it a light framework. A session uses a signed cookie to allow the user to look at and modify session contents. It will remember information from one request to another. However, to modify a session, the user must have the secret key `Flask.secret_key`. +| | Python 2 `raw_input()` | Python 2 `input()` | Python 3 `input()` | +|--|----------------------|---------------------|-------------------| +| Returns | `str` (raw text) | Evaluated result of `eval(text)` | `str` (raw text) | +| Safe | Yes | **No** — executes arbitrary code | Yes | +| Equivalent in Py3 | `input()` | `eval(input())` | N/A | -Flask is a web micro framework for Python based on "Werkzeug, Jinja 2 and good intentions" BSD licensed. Werkzeug and jingja are two of its dependencies. +```py +# Python 3 — input() always returns a string +name = input("Enter your name: ") +print(type(name)) # + +# Must convert manually for numeric input +age_str = input("Enter your age: ") +age = int(age_str) # explicit conversion — safe +print(age + 1) + +# Safe pattern with error handling +def read_positive_int(prompt: str) -> int: + while True: + raw = input(prompt).strip() + try: + value = int(raw) + if value > 0: + return value + print("Must be positive.") + except ValueError: + print(f"Not a valid integer: {raw!r}") -Flask is part of the micro-framework. Which means it will have little to no dependencies on external libraries. It makes the framework light while there is little dependency to update and less security bugs. +# Python 2 equivalent reference (DO NOT use input() in Python 2) +# raw_input("Enter: ") → str (safe) +# input("Enter: ") → eval(raw_input("Enter: ")) (dangerous — executes code!) - +# In Python 3, eval(input()) is the intentional equivalent of Python 2 input() +# But NEVER use this on untrusted data — use ast.literal_eval() instead +import ast -## Q. Mention what is Flask-WTF and what are their features? +safe_value = ast.literal_eval(input("Enter a list like [1,2,3]: ") or "[1]") +print(safe_value, type(safe_value)) +``` -Flask-WTF offers simple integration with WTForms. Features include for Flask WTF are - > - Integration with wtforms - Secure form with csrf token - Global csrf protection - Internationalization integration - Recaptcha supporting - File upload that works with Flask Uploads +**Use Case:** Migrating a Python 2 CLI tool to Python 3 requires replacing all `raw_input()` calls with `input()` and auditing all `input()` calls (which evaluated code in Py2) to add explicit type conversion — preventing a security regression. -## Q. Explain what is the common way for the Flask script to work? - -The common way for the flask script to work is - - Either it should be the import path for your application - Or the path to a Python file +## Q. Why do we write `if __name__ == "__main__"` in a Python script? -## Q. Explain how you can access sessions in Flask? +When Python runs a file, it sets the module-level `__name__` variable: +- To `"__main__"` if the file is **run directly** (`python script.py`) +- To the **module name** if the file is **imported** (`import script`) -A session basically allows you to remember information from one request to another. In a flask, it uses a signed cookie so the user can look at the session contents and modify. The user can modify the session if only it has the secret key Flask.secret_key. +The guard `if __name__ == "__main__":` ensures that certain code (usually the program entry point) **only executes when the file is run directly**, not when it is imported as a module. - +```py +# utils.py +import math -## Q. Is Flask an MVC model and if yes give an example showing MVC pattern for your application? +def circle_area(radius: float) -> float: + return math.pi * radius ** 2 -Basically, Flask is a minimalistic framework which behaves same as MVC framework. So MVC is a perfect fit for Flask, and the pattern for MVC we will consider for the following example +def circle_perimeter(radius: float) -> float: + return 2 * math.pi * radius -from flask import Flask -app = Flask(_name_) -@app.route("/") -def hello(): - return "Hello World" -app.run(debug = True) - -In this code your, +print(f"__name__ in utils.py = {__name__!r}") -Configuration part will be +if __name__ == "__main__": + # This block only runs when executed as: python utils.py + # It does NOT run when: import utils + print("Running as script — quick test:") + print(f"Area of r=5: {circle_area(5):.2f}") + print(f"Perimeter of r=5: {circle_perimeter(5):.2f}") +``` - from flask import Flask - app = Flask(_name_) +```py +# main.py — imports the module +import utils # __name__ in utils.py = 'utils' (guard block skipped) -View part will be +print(utils.circle_area(3)) # 28.27... +``` - @app.route("/") - def hello(): - return "Hello World" +```bash +$ python utils.py +# __name__ in utils.py = '__main__' +# Running as script — quick test: +# Area of r=5: 78.54 +``` -While your model or main part will be +**Why it matters:** +- Allows a module to be both importable (library) and directly executable (script) +- Prevents side effects (print, `sys.exit()`, network calls) from triggering on import +- Required for `multiprocessing` on Windows (workers re-import the main module) - app.run(debug = True) +**Use Case:** A data pipeline module defines reusable ETL functions at the top level and uses the `__main__` guard for a self-test run that seeds a local database — CI runs `python etl.py` for smoke tests, while Airflow imports the same module\'s functions into DAG tasks without triggering the seed. -## Q. Explain database connection in Python Flask? +## Q. What is PEP 8? -Best database for flask is MySQL. Flask supports database powered application (RDBS). Such system requires creating a schema, which requires piping the shema.sql file into a sqlite3 command. So you need to install `sqlite3` command in order to create or initiate the database in Flask. Flask allows to request database in three ways +**PEP 8** (Python Enhancement Proposal 8) is the official **style guide for Python code**. It defines conventions for formatting, naming, and structuring Python code to maximise readability and consistency across the ecosystem. - - `before_request()`: They are called before a request and pass no arguments. - - `after_request()`: They are called after a request and pass the response that will be sent to the client. - - `teardown_request()`: They are called in situation when exception is raised, and response are not guaranteed. They are called after the response been constructed. They are not allowed to modify the request, and their values are ignored. +**Key rules:** - +```py +# ── Indentation ────────────────────────────────────────────────── +# Use 4 spaces per indent (never tabs) +def good_function(): + if True: + pass + +# ── Line length ────────────────────────────────────────────────── +# Max 79 characters for code, 72 for docstrings +# Wrap long lines with parentheses (not backslash) +result = ( + some_long_variable_name + + another_long_variable_name + + yet_another_one +) + +# ── Imports ────────────────────────────────────────────────────── +# Standard library → third-party → local, each group separated by blank line +import os +import sys -## How will you sort result of student whose marks are unknown to you based on their roll numbers? +import requests # third-party -Using bubble sort. +from mymodule import helper # local -## Q. How will you check memory leak on Linux? +# ── Naming conventions ─────────────────────────────────────────── +my_variable = 1 # snake_case for variables and functions +MY_CONSTANT = 42 # UPPER_SNAKE_CASE for constants +ClassName = None # PascalCase for classes +_private_var = "private" # leading underscore = private +__name_mangled = "mangled" # double underscore = name mangling +__dunder__ = None # double underscore both sides = reserved -There are several tools and techniques that can be used to check for memory leaks on Linux: +def calculate_area(width: float, height: float) -> float: + """Return the area of a rectangle.""" + return width * height -- Valgrind: Valgrind is a powerful memory debugging tool that can be used to detect memory leaks, among other issues. It works by running the program in a simulated environment that tracks all memory allocations and deallocations, and generates a detailed report of any errors or leaks that it finds. +class BankAccount: + def __init__(self, owner: str) -> None: + self.owner = owner + self._balance: float = 0.0 # protected -- LeakSanitizer (LSan): LSan is a lightweight tool that is built into the Clang and GCC compilers. It can be used to detect memory leaks at runtime by instrumenting the code with memory tracking code. When a leak is detected, LSan generates a report that includes the stack trace of the leaking allocation. +# ── Whitespace ─────────────────────────────────────────────────── +x = 1 # spaces around = in assignment +y = x + 1 # spaces around operators +lst = [1, 2, 3] # no space before colon in slices +d = {"a": 1} # space after colon in dict -- AddressSanitizer (ASan): ASan is another tool built into the Clang and GCC compilers. It can be used to detect a wide range of memory errors, including memory leaks, buffer overflows, and use-after-free errors. Like LSan, ASan instruments the code with memory tracking code and generates a report when an error is detected. +f(arg1, arg2) # no space inside function call parens +lst[1:3] # no spaces in slice -- /proc/meminfo: The /proc/meminfo file contains information about the system's memory usage, including the total amount of memory, the amount of free memory, and the amount of memory used by each process. By monitoring the values in this file over time, it may be possible to detect a memory leak by looking for a steady increase in the amount of memory used by a particular process. +# ── Blank lines ────────────────────────────────────────────────── +# 2 blank lines between top-level definitions +# 1 blank line between methods inside a class -- ps and top: The ps and top commands can be used to monitor the system's memory usage and identify processes that are consuming large amounts of memory. By monitoring the memory usage of a particular process over time, it may be possible to detect a memory leak by looking for a steady increase in the amount of memory used by that process. +# ── Tools that enforce PEP 8 ───────────────────────────────────── +# ruff — extremely fast linter + formatter (recommended) +# black — opinionated formatter +# flake8 — linter +# isort — import sorter +# mypy — type checker (PEP 484) +``` -In general, detecting and diagnosing memory leaks can be a complex and time-consuming process. It often requires a combination of tools and techniques, as well as a deep understanding of the code and the system's memory usage patterns. +**Use Case:** A team of 20 engineers enforces PEP 8 via `ruff` in pre-commit hooks and CI — every PR is automatically formatted before review, eliminating style discussions and reducing code review time by focusing on logic rather than whitespace. -## Q. How can you return multiple values from a function? +## Q. What parameters should you check when a server is down? -In Python, you can return multiple values from a function by packing them into a tuple or a list. Here's an example using a tuple: +When diagnosing a server outage, check these layers in order from infrastructure to application: ```py -def get_user_info(user_id): - # ... do some work to retrieve user info ... - name = "John Doe" - email = "johndoe@example.com" - age = 35 - return name, email, age - -# call the function and unpack the returned values -user_name, user_email, user_age = get_user_info(123) +import subprocess +import socket +import os +import pathlib +import shutil + +def run(cmd: str) -> str: + """Run a shell command and return output.""" + result = subprocess.run(cmd.split(), capture_output=True, text=True, timeout=10) + return result.stdout.strip() + +# 1. Connectivity — can we reach the host? +def check_ping(host: str) -> bool: + result = subprocess.run( + ["ping", "-c", "2", host], + capture_output=True, text=True, timeout=10 + ) + return result.returncode == 0 + +# 2. Port availability — is the service port open? +def check_port(host: str, port: int, timeout: float = 3.0) -> bool: + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.settimeout(timeout) + return s.connect_ex((host, port)) == 0 + +# 3. System resources +def check_resources() -> dict: + return { + "load_avg": os.getloadavg(), # (1m, 5m, 15m) + "disk_free": shutil.disk_usage("/"), # .free bytes + "cpu_count": os.cpu_count(), + } -# print the values -print(user_name) # Output: John Doe -print(user_email) # Output: johndoe@example.com -print(user_age) # Output: 35 +# 4. Process status +def check_process(name: str) -> bool: + result = subprocess.run( + ["pgrep", "-x", name], capture_output=True + ) + return result.returncode == 0 + +# 5. Log analysis — last N error lines +def tail_logs(log_path: str, n: int = 50) -> list[str]: + p = pathlib.Path(log_path) + if not p.exists(): + return [f"Log file not found: {log_path}"] + lines = p.read_text(encoding="utf-8", errors="replace").splitlines() + return [l for l in lines[-n:] if "ERROR" in l or "FATAL" in l] + +# Comprehensive health check report +def server_health_report(host: str, port: int) -> dict: + return { + "ping": check_ping(host), + "port_open": check_port(host, port), + "resources": check_resources(), + "app_running": check_process("python"), + "recent_errors": tail_logs("/var/log/app/error.log", n=20), + } ``` -In this example, the `get_user_info` function returns three values (`name`, `email`, and `age`) as a tuple. When the function is called, the returned tuple is unpacked into individual variables (`user_name`, `user_email`, and `user_age`) using tuple unpacking syntax. The variables can then be used as needed. +**Checklist:** -You can also return multiple values as a list or any other iterable. For example: +| Layer | Check | Tool | +|-------|-------|------| +| Network | ping, traceroute | `ping`, `traceroute` | +| DNS | name resolution | `nslookup`, `dig` | +| Port | TCP connect | `telnet`, `nc`, `ss -tlnp` | +| CPU | load average | `uptime`, `top`, `htop` | +| Memory | free RAM, OOM killer | `free -h`, `dmesg | grep -i oom` | +| Disk | space, I/O wait | `df -h`, `iostat` | +| Process | service running | `ps aux`, `systemctl status` | +| Logs | errors, tracebacks | `journalctl`, `tail -f app.log` | +| DB / deps | connection pool | app health endpoint | -```py -def get_user_info(user_id): - # ... do some work to retrieve user info ... - name = "John Doe" - email = "johndoe@example.com" - age = 35 - return [name, email, age] +**Use Case:** A Python-based monitoring agent runs `server_health_report()` every 60 seconds and publishes metrics to Prometheus — alerting on-call engineers via PagerDuty when `port_open` is `False` or load average exceeds 8. -# call the function and unpack the returned values -user_info = get_user_info(123) -user_name, user_email, user_age = user_info + -# print the values -print(user_name) # Output: John Doe -print(user_email) # Output: johndoe@example.com -print(user_age) # Output: 35 -``` +**Python for AI & Data Science** -In this example, the get_user_info function returns a list instead of a tuple, and the returned values are unpacked into variables using list unpacking syntax. +## # 22. Numerical Computing +
-## Q. What is the fastest way to swap the values bound to two variables? +## Q. How to get indices of N maximum values in a NumPy array? -In Python, the fastest way to swap the values bound to two variables is to use tuple unpacking, like this: +We can get the indices of N maximum values in a NumPy array using the below code: ```py -a, b = b, a +import numpy as np +arr = np.array([1, 3, 2, 4, 5]) +print(arr.argsort()[-3:][::-1]) ``` -In this code, the values of a and b are swapped by creating a tuple of the values in reverse order ((`b`, `a`)) and unpacking them into the variables a and b. The assignment is performed simultaneously, which makes this method faster than using a temporary variable, which requires an additional assignment. + -For example: +## Q. What is the difference between NumPy and SciPy? -```py -a = 5 -b = 10 +In an ideal world, NumPy would contain nothing but the array data type and the most basic operations: indexing, sorting, reshaping, basic element wise functions, et cetera. All numerical code would reside in SciPy. However, one of NumPy\'s important goals is compatibility, so NumPy tries to retain all features supported by either of its predecessors. Thus NumPy contains some linear algebra functions, even though these more properly belong in SciPy. In any case, SciPy contains more fully-featured versions of the linear algebra modules, as well as many other numerical algorithms. If you are doing scientific computing with python, you should probably install both NumPy and SciPy. Most new features belong in SciPy rather than NumPy. -# swap the values using tuple unpacking -a, b = b, a +## Q. Name few Python modules for Statistical, Numerical and scientific computations ? -print(a) # Output: 10 -print(b) # Output: 5 -``` +`numPy` – this module provides an array/matrix type, and it is useful for doing computations on arrays. +`scipy` – this module provides methods for doing numeric integrals, solving differential equations, etc +`pylab` – is a module for generating and saving plots -In this example, the values of `a` and `b` are swapped using tuple unpacking, and the final output shows that the values have indeed been swapped. +## Q. What is NumPy? Is it better than a list? +Python Programming Interview Questions - Numpy vs List -## Q. What is the importance of reference counting? +Python Programming Interview Questions – Numpy vs List -In Python, reference counting is a technique used for automatic memory management. It involves keeping track of the number of references to an object and automatically deallocating the object when there are no more references to it. +Ans. NumPy, a Python package, has made its place in the world of scientific computing. It can deal with large data sizes, and also has a powerful N-dimensional array object along with a set of advanced functions. -The importance of reference counting in Python can be summarized as follows: +Yes, a NumPy array is better than a Python list. This is in the following ways: -- Automatic memory management: Reference counting allows Python to automatically manage memory without requiring explicit memory allocation or deallocation by the programmer. This simplifies programming and reduces the risk of memory leaks and other memory-related bugs. + It is more compact. + It is more convenient. + It Is more efficiently. + It is easier to read and write items with NumPy. -- Efficient memory management: By deallocating objects as soon as they are no longer needed, reference counting helps to minimize the amount of memory used by a program. This is especially important for long-running programs or programs that work with large data sets. +Read our latest tutorial on Python NumPy -- Improved performance: Because Python uses reference counting to manage memory, it can often achieve better performance than other languages that use garbage collection or other memory management techniques. This is because reference counting can be faster and more predictable than other methods, especially for small or short-lived objects. + -- Object lifetime management: Reference counting ensures that objects are deallocated as soon as they are no longer needed, which helps to avoid problems such as dangling pointers or use-after-free errors. This helps to ensure the correctness and reliability of Python programs. +## Q. How would you create an empty NumPy array? -Overall, reference counting is a key feature of Python's automatic memory management system, and it plays an important role in ensuring that Python programs are both efficient and reliable. - - -#### Q. Do functions return something even if there is not a `return statement? -#### Q. How do you reverse a list? -#### Q. How would you merge two sorted lists? -#### Q. How would you count the lines in a file? -#### Q. Is python compile language? -#### Q. Standard python libraries used? -#### Q. What is size of integer in python? -#### Q. What are serialization formats(in python)? -#### Q. How python manages memory? -#### Q. Is tuple mutable or immutable? -#### Q. Tell me some datastructures in Python? -#### Q. Why python instead of scala on spark when scala has better performance? -#### Q. How to access file on linux server using python? -#### Q. What is List Comprehension ? Show with example -#### Q. What version of Azure DataFactory you are using>? -#### Q. How to execute a java code? -#### Q. How to find PID of process and How to find how much resources a process is taking on linux? -#### Q. What are tools for ingestion? -#### Q. What is Python? -#### Q. Describe some features of Python. -#### Q. How does Python execute code? -#### Q. What are some built-in types in Python? -#### Q. What are bindings, i.e., what does it mean for a value to be bound to a variable? -#### Q. How do you create a list? -#### Q. How do you create a dictionary? -#### Q. What is a list comprehension? Why would you use one? -#### Q. What is a generator? What can it be used for? -#### Q. What is inheritance? -#### Q. What happens if you have an error in an __init__ statement? -#### Q. What happens in python if you try to divide by zero? -#### Q. How instance variables are different from class variables? -#### Q. Explain difference between Map vs Reduce Vs Filter ? -#### Q. What are Generators ? -#### Q. What are Iterators ? -#### Q. Can generator be used to create Iterators ? Give example -#### Q. Can iterators be used to create generator ? -#### Q. What are iterators and generators? -#### Q. What is Method Resolution Order ? -#### Q. Differentiate between append() and extend() methods ? -#### Q. How can you implement functional programming and why would you? -#### Q. Explain ctypes and why you would use them? -#### Q. What is multiple inheritence and when should you use it? -#### Q. What is a meta class? -#### Q. What are properties and what is the point? -#### Q. What is the difference between 2.7+ and 3? -#### Q. What is a unicode string? -#### Q. What does the yield statement do? -#### Q. What is a generator? -#### Q. Why would I use one? -#### Q. What is polymorphism, when would I use it? -#### Q. How do you go about packaging python code? -#### Q. Is Python compiled?, If yes how so, if not how so. -#### Q. What does __some-variable__ mean? -#### Q. 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What is static method, class method and instance method? -#### Q. Explain map, filter,reduce and lambda. -#### Q. Difference between new styled classed and old styled classes. -#### Q. What is diff between Python and Java? -#### Q. What is context processor? -#### Q. What is exec() and eval()? -#### Q. How to pass command line argument. -#### Q. What is yield ? -#### Q. What is ord() and chr()? -#### Q. What are Metaclasses? -#### Q. What is descriptor ? -#### Q. namespace vs scope ? -#### Q. What is MRO ? -#### Q. When to use list comprehensions and when to avoid list comprehensions ? -#### Q. What are Map, filter and reduce functions ? -#### Q. What are the different types of exceptions generated in python? -#### Q. How to write your own custom exception handling ? -#### Q. Difference between input and raw_input ? -#### Q. Why do we write `__name__` == `"__main__"` in a python script ? -#### Q. Why does the exception handling have a finally block ? -#### Q. Does python provide thread safe multi-threading ? -#### Q. What do you mean by non blocking IO ? -#### Q. What happens if an error occurs that is not handled in the except block? -#### Q. How are modules used in a Python program? -#### Q. How do you create a Python function? -#### Q. How is a Python class created? -#### Q. How is a Python class instantiated? -#### Q. How does a function return values? -#### Q. What happens when a function does not have a return statement? -#### Q. How do you create a dictionary which can preserve the order of pairs? -#### Q. Can you use mutable Data Structure as key in Dictionaries? -#### Q. What is difference between tuple and list? Where will you use tuple and where will you use list? -#### Q. Explain all the file processing modes supported by Python? -#### Q. What is PEP 8? -#### Q. What are parameters to consider for checking when server is down? -#### Q. How will you measure memory on Linux server? - -
- ↥ back to top +Ans. To create an empty array with NumPy, we have two options: + +a. Option 1 + + import numpy + numpy.array([]) + +array([], dtype=float64) + +b. Option 2 + + numpy.empty(shape=(0,0)) + +array([], shape=(0, 0), dtype=float64) + + From 4224b87e057dc8c8fe731f21ec0a84b3dea23eef Mon Sep 17 00:00:00 2001 From: Pradeep Kumar Date: Sun, 17 May 2026 20:46:56 +0530 Subject: [PATCH 5/5] Update README.md --- README.md | 50 -------------------------------------------------- 1 file changed, 50 deletions(-) diff --git a/README.md b/README.md index 1ba887f..e4ed906 100644 --- a/README.md +++ b/README.md @@ -2341,56 +2341,6 @@ print(list(cache._cache.keys())) # ['c', 'a', 'd'] **No.** Dictionary keys must be **hashable**, and all mutable built-in types (`list`, `dict`, `set`) are **not hashable**. Attempting to use them raises `TypeError`. -```py -# Mutable types — NOT hashable, cannot be dict keys -try: - d = {[1, 2, 3]: "value"} # list key -except TypeError as e: - print(f"TypeError: {e}") # unhashable type: 'list' - -try: - d = {{1: "a"}: "value"} # dict key -except TypeError as e: - print(f"TypeError: {e}") # unhashable type: 'dict' - -# Immutable types — hashable, valid dict keys -valid_keys = { - "string": 1, - 42: 2, - 3.14: 3, - True: 4, - (1, 2, 3): 5, # tuple of immutables — hashable - frozenset({1, 2}): 6, # frozenset — hashable -} -print(valid_keys[(1, 2, 3)]) # 5 - -# A tuple is only hashable if ALL its elements are hashable -try: - d = {(1, [2, 3]): "value"} # tuple containing a list -except TypeError as e: - print(f"TypeError: {e}") # unhashable type: 'list' - -# Making a custom object hashable -class Point: - def __init__(self, x: float, y: float) -> None: - self.x, self.y = x, y - - def __eq__(self, other: object) -> bool: - if not isinstance(other, Point): return NotImplemented - return self.x == other.x and self.y == other.y - - def __hash__(self) -> int: - return hash((self.x, self.y)) # hash based on immutable tuple - -grid: dict[Point, str] = {} -grid[Point(0, 0)] = "origin" -grid[Point(1, 2)] = "marker" -print(grid[Point(0, 0)]) # origin - -# Rule: if __eq__ is defined, __hash__ MUST also be defined -# Python sets __hash__ = None if __eq__ is defined without __hash__ -``` - **Rule:** Hashable = immutable structure + consistent `__hash__`. `hash(x) == hash(y)` whenever `x == y`. **Use Case:** A graph algorithm caches computed shortest paths using `frozenset({source, destination})` as a dict key — symmetric pairs `(A→B)` and `(B→A)` map to the same cache entry, halving cache misses.