Five Python scripts useful for data science tasks. Stop wrestling with repetitive tasks and focus on actual analysis.
from data_quality_checker import data_quality_report
import pandas as pd
df = pd.read_csv('your_data.csv')
data_quality_report(df) # Generates comprehensive quality reportfrom smart_file_merger import smart_file_merger
# Combines all CSV, Excel, and JSON files automatically
merged_df = smart_file_merger('/path/to/data/folder')from dataset_profiler import quick_profile
df = pd.read_csv('sales_data.csv')
quick_profile(df) # Shows stats, correlations, and insightsfrom data_version_manager import DataVersionManager
vm = DataVersionManager("my_project")
vm.save_version(df, "Initial clean dataset")
# Make changes...
vm.save_version(df_processed, "After removing outliers")
vm.list_versions() # See all saved versionsfrom multi_format_exporter import DataExporter
exporter = DataExporter(df, "final_analysis")
exporter.export_all() # Creates Excel, JSON, CSV, and SQLite files- Python 3.7+
- pandas
- numpy
- matplotlib
- seaborn
- openpyxl
pip install pandas numpy matplotlib seaborn openpyxlOr use the provided requirements file:
pip install -r requirements.txt