{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T03:42:51.460432Z",
     "start_time": "2020-10-16T03:42:51.452300Z"
    }
   },
   "outputs": [],
   "source": [
    "import timeit\n",
    "import numpy as np\n",
    "from timeit import Timer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T03:53:39.093194Z",
     "start_time": "2020-10-16T03:53:38.917494Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "list range  0.15586492500005988 milliseconds\n"
     ]
    }
   ],
   "source": [
    "import ray\n",
    "import time\n",
    "\n",
    "# Start Ray.\n",
    "#ray.init()\n",
    "\n",
    "@ray.remote\n",
    "def f(x):\n",
    "    time.sleep(1)\n",
    "    return x\n",
    "\n",
    "# Start 4 tasks in parallel.\n",
    "def test_1():\n",
    "    result_ids = []\n",
    "    for i in range(4):\n",
    "        result_ids.append(f.remote(i))\n",
    "\n",
    "# Wait for the tasks to complete and retrieve the results.\n",
    "# With at least 4 cores, this will take 1 second.\n",
    "results = ray.get(result_ids)  # [0, 1, 2, 3]\n",
    "\n",
    "expSize = 100\n",
    "\n",
    "t = Timer(\"test_1()\", \"from __main__ import test_1\")\n",
    "st = t.timeit(number=expSize)\n",
    "print(\"list range \",st, \"milliseconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T03:53:39.943508Z",
     "start_time": "2020-10-16T03:53:39.934516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T03:44:02.736183Z",
     "start_time": "2020-10-16T03:43:10.702454Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "concat  51.529789014000016 milliseconds\n",
      "append  0.25409345499974734 milliseconds\n",
      "comprehension  0.13832575100013855 milliseconds\n",
      "list range  0.07142862399996375 milliseconds\n",
      "\n",
      " Worse vs. best ratio 721.4165152338112 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#concat\n",
    "def test1():\n",
    "    l = []\n",
    "    for i in range(10000):\n",
    "        l = l + [i]\n",
    "\n",
    "#append\n",
    "def test2():\n",
    "    l = []\n",
    "    #Add a new element with append\n",
    "    for i in range(10000):\n",
    "        l.append(i)\n",
    "\n",
    "#comprehension\n",
    "def test3():\n",
    "    #Use bracket that contains elements\n",
    "    l = [i for i in range(10000)]\n",
    "    \n",
    "#list range\n",
    "def test4():\n",
    "    #Use list method\n",
    "    l = list(range(10000))\n",
    "    \n",
    "expSize = 100\n",
    "    \n",
    "t1 = Timer(\"test1()\", \"from __main__ import test1\")\n",
    "st1 = t1.timeit(number=expSize)\n",
    "print(\"concat \",st1, \"milliseconds\")\n",
    "t2 = Timer(\"test2()\", \"from __main__ import test2\")\n",
    "print(\"append \",t2.timeit(number=expSize), \"milliseconds\")\n",
    "t3 = Timer(\"test3()\", \"from __main__ import test3\")\n",
    "print(\"comprehension \",t3.timeit(number=expSize), \"milliseconds\")\n",
    "t4 = Timer(\"test4()\", \"from __main__ import test4\")\n",
    "st4 = t4.timeit(number=expSize)\n",
    "print(\"list range \",st4, \"milliseconds\")\n",
    "\n",
    "print ('\\n Worse vs. best ratio', st1/st4, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pop-front vs Pop-end\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-31T16:25:24.847388Z",
     "start_time": "2020-07-31T16:25:23.597476Z"
    }
   },
   "outputs": [],
   "source": [
    "#Pop the first element of a list\n",
    "pop_zero = Timer(\"x.pop(0)\",\n",
    "                'from __main__ import x')\n",
    "\n",
    "#Pop the last element of a list\n",
    "pop_end = Timer('x.pop()',\n",
    "                'from __main__ import x')\n",
    "\n",
    "#Number of experiments\n",
    "expSize = 100\n",
    "result = []\n",
    "\n",
    "for i in range(10000,1000001,20000):\n",
    "    \n",
    "    #Define the array\n",
    "    x = list(range(i))\n",
    "    \n",
    "    #Time the average results of 1000 experiments for each method\n",
    "    pt = pop_end.timeit(number=expSize)\n",
    "    pz = pop_zero.timeit(number=expSize)\n",
    "    \n",
    "    result.append((i,pz,pt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-31T16:25:30.192551Z",
     "start_time": "2020-07-31T16:25:29.872386Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "matrixDat = np.array( result )\n",
    "plt.plot(matrixDat[:,0], matrixDat[:,1], 'o', color='red',label='pop_zero');\n",
    "plt.plot(matrixDat[:,0], matrixDat[:,2], '+', color='blue',label='pop_end');\n",
    "leg = plt.legend(numpoints=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List vs Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-31T16:28:12.911466Z",
     "start_time": "2020-07-31T16:25:45.184849Z"
    }
   },
   "outputs": [],
   "source": [
    "import random\n",
    "result = []\n",
    "\n",
    "expSize = 1000\n",
    "\n",
    "for i in range(10000,1000001,20000):\n",
    "  \n",
    "    t = timeit.Timer(\"random.randrange(%d) in x\"%i,\n",
    "        \"from __main__ import random,x\")\n",
    "    \n",
    "    x = list(range(i))\n",
    "    lst_time = t.timeit(number=expSize)\n",
    "    \n",
    "    x = {j:None for j in range(i)}\n",
    "    d_time = t.timeit(number=expSize)\n",
    "    \n",
    "    result.append((i, lst_time, d_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-31T16:30:56.171450Z",
     "start_time": "2020-07-31T16:30:56.067957Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrixDat = np.array( result )\n",
    "plt.plot(matrixDat[:,0], matrixDat[:,1], 'o', color='red',label='list');\n",
    "plt.plot(matrixDat[:,0], matrixDat[:,2], '+', color='blue',label='set');\n",
    "leg = plt.legend(numpoints=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "scraping",
   "language": "python",
   "name": "scraping"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}