Test iterator conversion  into list.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T15:10:14.316429Z",
     "start_time": "2020-10-19T15:10:13.199905Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: simple_benchmark in ./venv/lib/python3.8/site-packages (0.0.9)\r\n"
     ]
    }
   ],
   "source": [
    "!pip3 install simple_benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T19:32:56.026872Z",
     "start_time": "2020-10-19T19:32:48.739434Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from simple_benchmark import BenchmarkBuilder\n",
    "from heapq import nsmallest\n",
    "\n",
    "\n",
    "b = BenchmarkBuilder()\n",
    "\n",
    "\n",
    "@b.add_function()\n",
    "def convert_by_type_constructor(size):\n",
    "    list(iter(range(size)))\n",
    "\n",
    "@b.add_function()\n",
    "def convert_by_list_comprehension(size):\n",
    "    [e for e in iter(range(size))]\n",
    "\n",
    "@b.add_function()\n",
    "def convert_by_unpacking(size):\n",
    "    [*iter(range(size))]\n",
    "\n",
    "\n",
    "@b.add_arguments('Size')\n",
    "def argument_provider():\n",
    "    for exp in range(2, 22):\n",
    "        size = 2**exp\n",
    "        yield size, size\n",
    "\n",
    "r = b.run()\n",
    "r.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmark muliple pyhton version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T14:49:02.515483Z",
     "start_time": "2020-10-19T14:47:40.199804Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test for python 3.6\n",
      "test for python 3.7\n",
      "test for python 3.8\n",
      "test for python 3.9\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'conversion type': ['constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension',\n",
       "  'constructor',\n",
       "  'unpacking',\n",
       "  'comprehension'],\n",
       " 'timing in ms': [0.001166,\n",
       "  0.00097,\n",
       "  0.002654,\n",
       "  0.011248,\n",
       "  0.010484,\n",
       "  0.024443,\n",
       "  0.227935,\n",
       "  0.223395,\n",
       "  0.368549,\n",
       "  2.95632,\n",
       "  2.903608,\n",
       "  4.328522,\n",
       "  30.558531,\n",
       "  32.697345,\n",
       "  47.468047,\n",
       "  0.001073,\n",
       "  0.000973,\n",
       "  0.002952,\n",
       "  0.013333,\n",
       "  0.012878,\n",
       "  0.028878,\n",
       "  0.196517,\n",
       "  0.180095,\n",
       "  0.309312,\n",
       "  3.104935,\n",
       "  3.192227,\n",
       "  4.571884,\n",
       "  34.277222,\n",
       "  33.617671,\n",
       "  45.834312,\n",
       "  0.000818,\n",
       "  0.00072,\n",
       "  0.002701,\n",
       "  0.012152,\n",
       "  0.01162,\n",
       "  0.026483,\n",
       "  0.227024,\n",
       "  0.224213,\n",
       "  0.360594,\n",
       "  2.864774,\n",
       "  2.723088,\n",
       "  4.27246,\n",
       "  31.15616,\n",
       "  31.09001,\n",
       "  43.792778,\n",
       "  0.000843,\n",
       "  0.001004,\n",
       "  0.002511,\n",
       "  0.013028,\n",
       "  0.013169,\n",
       "  0.026506,\n",
       "  0.243107,\n",
       "  0.247908,\n",
       "  0.371132,\n",
       "  2.862348,\n",
       "  3.064079,\n",
       "  4.343387,\n",
       "  31.420523,\n",
       "  30.987473,\n",
       "  40.993682],\n",
       " 'size': [100,\n",
       "  100,\n",
       "  100,\n",
       "  1000,\n",
       "  1000,\n",
       "  1000,\n",
       "  10000,\n",
       "  10000,\n",
       "  10000,\n",
       "  100000,\n",
       "  100000,\n",
       "  100000,\n",
       "  1000000,\n",
       "  1000000,\n",
       "  1000000,\n",
       "  100,\n",
       "  100,\n",
       "  100,\n",
       "  1000,\n",
       "  1000,\n",
       "  1000,\n",
       "  10000,\n",
       "  10000,\n",
       "  10000,\n",
       "  100000,\n",
       "  100000,\n",
       "  100000,\n",
       "  1000000,\n",
       "  1000000,\n",
       "  1000000,\n",
       "  100,\n",
       "  100,\n",
       "  100,\n",
       "  1000,\n",
       "  1000,\n",
       "  1000,\n",
       "  10000,\n",
       "  10000,\n",
       "  10000,\n",
       "  100000,\n",
       "  100000,\n",
       "  100000,\n",
       "  1000000,\n",
       "  1000000,\n",
       "  1000000,\n",
       "  100,\n",
       "  100,\n",
       "  100,\n",
       "  1000,\n",
       "  1000,\n",
       "  1000,\n",
       "  10000,\n",
       "  10000,\n",
       "  10000,\n",
       "  100000,\n",
       "  100000,\n",
       "  100000,\n",
       "  1000000,\n",
       "  1000000,\n",
       "  1000000],\n",
       " 'python version': ['3.6',\n",
       "  '3.6',\n",
       "  '3.6',\n",
       "  '3.6',\n",
       "  '3.6',\n",
       "  '3.6',\n",
       "  '3.6',\n",
       "  '3.6',\n",
       "  '3.6',\n",
       "  '3.6',\n",
       "  '3.6',\n",
       "  '3.6',\n",
       "  '3.6',\n",
       "  '3.6',\n",
       "  '3.6',\n",
       "  '3.7',\n",
       "  '3.7',\n",
       "  '3.7',\n",
       "  '3.7',\n",
       "  '3.7',\n",
       "  '3.7',\n",
       "  '3.7',\n",
       "  '3.7',\n",
       "  '3.7',\n",
       "  '3.7',\n",
       "  '3.7',\n",
       "  '3.7',\n",
       "  '3.7',\n",
       "  '3.7',\n",
       "  '3.7',\n",
       "  '3.8',\n",
       "  '3.8',\n",
       "  '3.8',\n",
       "  '3.8',\n",
       "  '3.8',\n",
       "  '3.8',\n",
       "  '3.8',\n",
       "  '3.8',\n",
       "  '3.8',\n",
       "  '3.8',\n",
       "  '3.8',\n",
       "  '3.8',\n",
       "  '3.8',\n",
       "  '3.8',\n",
       "  '3.8',\n",
       "  '3.9',\n",
       "  '3.9',\n",
       "  '3.9',\n",
       "  '3.9',\n",
       "  '3.9',\n",
       "  '3.9',\n",
       "  '3.9',\n",
       "  '3.9',\n",
       "  '3.9',\n",
       "  '3.9',\n",
       "  '3.9',\n",
       "  '3.9',\n",
       "  '3.9',\n",
       "  '3.9',\n",
       "  '3.9']}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import PIPE, run\n",
    "\n",
    "import seaborn as sns\n",
    "import pandas\n",
    "\n",
    "simple_data = {'constructor': [], 'unpacking': [], 'comprehension': [],\n",
    "        'size': [], 'python version': []}\n",
    "\n",
    "data = {'conversion type': [], 'timing in ms': [], 'size': [], 'python version': []}\n",
    "\n",
    "size_test = 100, 1000, 10_000, 100_000, 1_000_000\n",
    "for version in ['3.6', '3.7', '3.8', '3.9']:\n",
    "    print('test for python', version)\n",
    "    for size in size_test:\n",
    "        command = [f'python{version}', 'perf_test_convert_iterator.py', f'--size={size}']\n",
    "        result = run(command, stdout=PIPE, stderr=PIPE, universal_newlines=True)\n",
    "        constructor, unpacking,  comprehension = result.stdout.split()\n",
    "        \n",
    "        data['conversion type'].extend(['constructor', 'unpacking', 'comprehension'])\n",
    "        data['timing in ms'].extend([float(constructor), float(unpacking), float(comprehension)])\n",
    "        data['python version'].extend([version] * 3) # same version for each conversion type\n",
    "        data['size'].extend([size] * 3) # same size for each conversion type\n",
    "        \n",
    "        simple_data['constructor'].append(float(constructor))\n",
    "        simple_data['unpacking'].append(float(unpacking))\n",
    "        simple_data['comprehension'].append(float(comprehension))\n",
    "        simple_data['python version'].append(version)\n",
    "        simple_data['size'].append(size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "40.245270 - 25.355489"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T14:58:12.171822Z",
     "start_time": "2020-10-19T14:58:12.147460Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>constructor</th>\n",
       "      <th>unpacking</th>\n",
       "      <th>comprehension</th>\n",
       "      <th>size</th>\n",
       "      <th>python version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001166</td>\n",
       "      <td>0.000970</td>\n",
       "      <td>0.002654</td>\n",
       "      <td>100</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.011248</td>\n",
       "      <td>0.010484</td>\n",
       "      <td>0.024443</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.227935</td>\n",
       "      <td>0.223395</td>\n",
       "      <td>0.368549</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.956320</td>\n",
       "      <td>2.903608</td>\n",
       "      <td>4.328522</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.558531</td>\n",
       "      <td>32.697345</td>\n",
       "      <td>47.468047</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.001073</td>\n",
       "      <td>0.000973</td>\n",
       "      <td>0.002952</td>\n",
       "      <td>100</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.013333</td>\n",
       "      <td>0.012878</td>\n",
       "      <td>0.028878</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.196517</td>\n",
       "      <td>0.180095</td>\n",
       "      <td>0.309312</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.104935</td>\n",
       "      <td>3.192227</td>\n",
       "      <td>4.571884</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>34.277222</td>\n",
       "      <td>33.617671</td>\n",
       "      <td>45.834312</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000818</td>\n",
       "      <td>0.000720</td>\n",
       "      <td>0.002701</td>\n",
       "      <td>100</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.012152</td>\n",
       "      <td>0.011620</td>\n",
       "      <td>0.026483</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.227024</td>\n",
       "      <td>0.224213</td>\n",
       "      <td>0.360594</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.864774</td>\n",
       "      <td>2.723088</td>\n",
       "      <td>4.272460</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>31.156160</td>\n",
       "      <td>31.090010</td>\n",
       "      <td>43.792778</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000843</td>\n",
       "      <td>0.001004</td>\n",
       "      <td>0.002511</td>\n",
       "      <td>100</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.013028</td>\n",
       "      <td>0.013169</td>\n",
       "      <td>0.026506</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.243107</td>\n",
       "      <td>0.247908</td>\n",
       "      <td>0.371132</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.862348</td>\n",
       "      <td>3.064079</td>\n",
       "      <td>4.343387</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>31.420523</td>\n",
       "      <td>30.987473</td>\n",
       "      <td>40.993682</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    constructor  unpacking  comprehension     size python version\n",
       "0      0.001166   0.000970       0.002654      100            3.6\n",
       "1      0.011248   0.010484       0.024443     1000            3.6\n",
       "2      0.227935   0.223395       0.368549    10000            3.6\n",
       "3      2.956320   2.903608       4.328522   100000            3.6\n",
       "4     30.558531  32.697345      47.468047  1000000            3.6\n",
       "5      0.001073   0.000973       0.002952      100            3.7\n",
       "6      0.013333   0.012878       0.028878     1000            3.7\n",
       "7      0.196517   0.180095       0.309312    10000            3.7\n",
       "8      3.104935   3.192227       4.571884   100000            3.7\n",
       "9     34.277222  33.617671      45.834312  1000000            3.7\n",
       "10     0.000818   0.000720       0.002701      100            3.8\n",
       "11     0.012152   0.011620       0.026483     1000            3.8\n",
       "12     0.227024   0.224213       0.360594    10000            3.8\n",
       "13     2.864774   2.723088       4.272460   100000            3.8\n",
       "14    31.156160  31.090010      43.792778  1000000            3.8\n",
       "15     0.000843   0.001004       0.002511      100            3.9\n",
       "16     0.013028   0.013169       0.026506     1000            3.9\n",
       "17     0.243107   0.247908       0.371132    10000            3.9\n",
       "18     2.862348   3.064079       4.343387   100000            3.9\n",
       "19    31.420523  30.987473      40.993682  1000000            3.9"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ = pandas.DataFrame(simple_data)\n",
    "df_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "up to ~40%  faster than comprehension if you are using the type constructor or unpacking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## python 3.6 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T09:44:29.696083Z",
     "start_time": "2020-10-19T09:44:29.685454Z"
    }
   },
   "source": [
    "### size < 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T14:58:18.681595Z",
     "start_time": "2020-10-19T14:58:18.366166Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-44-37f230551d64>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  ax = df_[df_['python version'] == '3.6'][df_['size'] < 10000].set_index('size').plot(kind='bar')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Python3.6: convert itertaror into  list')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_[df_['python version'] == '3.6'][df_['size'] < 10000].set_index('size').plot(kind='bar')\n",
    "\n",
    "ax.set_ylabel('Time in ms')\n",
    "ax.set_title('Python3.6: convert itertaror into  list')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T13:33:11.636689Z",
     "start_time": "2020-10-19T13:33:11.634154Z"
    }
   },
   "source": [
    "### size >= 10000 < 1mil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T14:59:02.332629Z",
     "start_time": "2020-10-19T14:59:02.173052Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-49-3d92ce496f6d>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  ax = df_[df_['python version'] == '3.6'][1000000 > df_['size']][df_['size'] >= 10000].set_index('size').plot(kind='bar')\n",
      "<ipython-input-49-3d92ce496f6d>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  ax = df_[df_['python version'] == '3.6'][1000000 > df_['size']][df_['size'] >= 10000].set_index('size').plot(kind='bar')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Python3.6: convert itertaror into  list')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_[df_['python version'] == '3.6'][1000000 > df_['size']][df_['size'] >= 10000].set_index('size').plot(kind='bar')\n",
    "\n",
    "ax.set_ylabel('Time in ms')\n",
    "ax.set_title('Python3.6: convert itertaror into  list')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### size = 1mil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T14:58:32.134672Z",
     "start_time": "2020-10-19T14:58:31.982964Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-47-e180722d0dc0>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  ax = df_[df_['python version'] == '3.6'][1000000 == df_['size']].set_index('size').plot(kind='bar')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Python3.6: convert itertaror into  list')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_[df_['python version'] == '3.6'][1000000 == df_['size']].set_index('size').plot(kind='bar')\n",
    "\n",
    "ax.set_ylabel('Time in ms')\n",
    "ax.set_title('Python3.6: convert itertaror into  list')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T14:28:49.200751Z",
     "start_time": "2020-10-19T14:28:49.198583Z"
    }
   },
   "source": [
    "## Compare across different versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T14:59:13.262791Z",
     "start_time": "2020-10-19T14:59:13.233779Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>conversion type</th>\n",
       "      <th>timing in ms</th>\n",
       "      <th>size</th>\n",
       "      <th>python version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>constructor</td>\n",
       "      <td>0.001166</td>\n",
       "      <td>100</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>0.000970</td>\n",
       "      <td>100</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>0.002654</td>\n",
       "      <td>100</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>constructor</td>\n",
       "      <td>0.011248</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>0.010484</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>0.024443</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>constructor</td>\n",
       "      <td>0.227935</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>0.223395</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>0.368549</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>constructor</td>\n",
       "      <td>2.956320</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>2.903608</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>4.328522</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>constructor</td>\n",
       "      <td>30.558531</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>32.697345</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>47.468047</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>constructor</td>\n",
       "      <td>0.001073</td>\n",
       "      <td>100</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>0.000973</td>\n",
       "      <td>100</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>0.002952</td>\n",
       "      <td>100</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>constructor</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>0.012878</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>0.028878</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>constructor</td>\n",
       "      <td>0.196517</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>0.180095</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>0.309312</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>constructor</td>\n",
       "      <td>3.104935</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>3.192227</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>4.571884</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>constructor</td>\n",
       "      <td>34.277222</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>33.617671</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>45.834312</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>constructor</td>\n",
       "      <td>0.000818</td>\n",
       "      <td>100</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>0.000720</td>\n",
       "      <td>100</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>0.002701</td>\n",
       "      <td>100</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>constructor</td>\n",
       "      <td>0.012152</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>0.011620</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>0.026483</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>constructor</td>\n",
       "      <td>0.227024</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>0.224213</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>0.360594</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>constructor</td>\n",
       "      <td>2.864774</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>2.723088</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>4.272460</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>constructor</td>\n",
       "      <td>31.156160</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>31.090010</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>43.792778</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>constructor</td>\n",
       "      <td>0.000843</td>\n",
       "      <td>100</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>0.001004</td>\n",
       "      <td>100</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>0.002511</td>\n",
       "      <td>100</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>constructor</td>\n",
       "      <td>0.013028</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>0.013169</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>0.026506</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>constructor</td>\n",
       "      <td>0.243107</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>0.247908</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>0.371132</td>\n",
       "      <td>10000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>constructor</td>\n",
       "      <td>2.862348</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>3.064079</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>4.343387</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>constructor</td>\n",
       "      <td>31.420523</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>unpacking</td>\n",
       "      <td>30.987473</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>comprehension</td>\n",
       "      <td>40.993682</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   conversion type  timing in ms     size python version\n",
       "0      constructor      0.001166      100            3.6\n",
       "1        unpacking      0.000970      100            3.6\n",
       "2    comprehension      0.002654      100            3.6\n",
       "3      constructor      0.011248     1000            3.6\n",
       "4        unpacking      0.010484     1000            3.6\n",
       "5    comprehension      0.024443     1000            3.6\n",
       "6      constructor      0.227935    10000            3.6\n",
       "7        unpacking      0.223395    10000            3.6\n",
       "8    comprehension      0.368549    10000            3.6\n",
       "9      constructor      2.956320   100000            3.6\n",
       "10       unpacking      2.903608   100000            3.6\n",
       "11   comprehension      4.328522   100000            3.6\n",
       "12     constructor     30.558531  1000000            3.6\n",
       "13       unpacking     32.697345  1000000            3.6\n",
       "14   comprehension     47.468047  1000000            3.6\n",
       "15     constructor      0.001073      100            3.7\n",
       "16       unpacking      0.000973      100            3.7\n",
       "17   comprehension      0.002952      100            3.7\n",
       "18     constructor      0.013333     1000            3.7\n",
       "19       unpacking      0.012878     1000            3.7\n",
       "20   comprehension      0.028878     1000            3.7\n",
       "21     constructor      0.196517    10000            3.7\n",
       "22       unpacking      0.180095    10000            3.7\n",
       "23   comprehension      0.309312    10000            3.7\n",
       "24     constructor      3.104935   100000            3.7\n",
       "25       unpacking      3.192227   100000            3.7\n",
       "26   comprehension      4.571884   100000            3.7\n",
       "27     constructor     34.277222  1000000            3.7\n",
       "28       unpacking     33.617671  1000000            3.7\n",
       "29   comprehension     45.834312  1000000            3.7\n",
       "30     constructor      0.000818      100            3.8\n",
       "31       unpacking      0.000720      100            3.8\n",
       "32   comprehension      0.002701      100            3.8\n",
       "33     constructor      0.012152     1000            3.8\n",
       "34       unpacking      0.011620     1000            3.8\n",
       "35   comprehension      0.026483     1000            3.8\n",
       "36     constructor      0.227024    10000            3.8\n",
       "37       unpacking      0.224213    10000            3.8\n",
       "38   comprehension      0.360594    10000            3.8\n",
       "39     constructor      2.864774   100000            3.8\n",
       "40       unpacking      2.723088   100000            3.8\n",
       "41   comprehension      4.272460   100000            3.8\n",
       "42     constructor     31.156160  1000000            3.8\n",
       "43       unpacking     31.090010  1000000            3.8\n",
       "44   comprehension     43.792778  1000000            3.8\n",
       "45     constructor      0.000843      100            3.9\n",
       "46       unpacking      0.001004      100            3.9\n",
       "47   comprehension      0.002511      100            3.9\n",
       "48     constructor      0.013028     1000            3.9\n",
       "49       unpacking      0.013169     1000            3.9\n",
       "50   comprehension      0.026506     1000            3.9\n",
       "51     constructor      0.243107    10000            3.9\n",
       "52       unpacking      0.247908    10000            3.9\n",
       "53   comprehension      0.371132    10000            3.9\n",
       "54     constructor      2.862348   100000            3.9\n",
       "55       unpacking      3.064079   100000            3.9\n",
       "56   comprehension      4.343387   100000            3.9\n",
       "57     constructor     31.420523  1000000            3.9\n",
       "58       unpacking     30.987473  1000000            3.9\n",
       "59   comprehension     40.993682  1000000            3.9"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas \n",
    "\n",
    "df = pandas.DataFrame(data)\n",
    "sns.set()\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare  conversion with type constructor "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### size <= 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T14:59:16.947721Z",
     "start_time": "2020-10-19T14:59:16.622085Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-51-20d2d478f425>:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  data=df[df['size'] <= 1000][df['conversion type'] == 'constructor'], kind=\"bar\",\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 530.8x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(\n",
    "    data=df[df['size'] <= 1000][df['conversion type'] == 'constructor'], kind=\"bar\",\n",
    "    x=\"size\", y=\"timing in ms\", hue=\"python version\",\n",
    "    ci=\"sd\", palette=\"dark\", alpha=.6, height=6\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T15:00:21.068789Z",
     "start_time": "2020-10-19T15:00:21.063598Z"
    }
   },
   "source": [
    "### size >= 10000 < 1 mil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T15:02:42.777009Z",
     "start_time": "2020-10-19T15:02:42.488019Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-56-50bf86d0959a>:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  data=df[df['size'] >= 10000][df['size'] < 1000000][df['conversion type'] == 'constructor'], kind=\"bar\",\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 530.8x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(\n",
    "    data=df[df['size'] >= 10000][df['size'] < 1000000][df['conversion type'] == 'constructor'], kind=\"bar\",\n",
    "    x=\"size\", y=\"timing in ms\", hue=\"python version\",\n",
    "    ci=\"sd\", palette=\"dark\", alpha=.6, height=6\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### size == 1 mil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T15:05:08.711443Z",
     "start_time": "2020-10-19T15:05:08.436536Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-57-67cb006e4ac4>:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  data=df[df['size'] == 1000000][df['conversion type'] == 'constructor'], kind=\"bar\",\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 530.8x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(\n",
    "    data=df[df['size'] == 1000000][df['conversion type'] == 'constructor'], kind=\"bar\",\n",
    "    x=\"size\", y=\"timing in ms\", hue=\"python version\",\n",
    "    ci=\"sd\", palette=\"dark\", alpha=.6, height=6\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T18:10:34.858650Z",
     "start_time": "2020-10-19T18:10:34.848444Z"
    }
   },
   "outputs": [],
   "source": [
    "class FirstTen:\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.current_value = 0\n",
    "        \n",
    "    def __iter__(self):\n",
    "        return self\n",
    "\n",
    "    def __next__(self):\n",
    "        if self.current_value == 10:\n",
    "            raise StopIteration\n",
    "            \n",
    "        result = self.current_value\n",
    "        self.current_value += 1\n",
    "        \n",
    "        return result\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-19T18:10:35.334527Z",
     "start_time": "2020-10-19T18:10:35.325180Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[*FirstTen()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "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": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "335.994px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}