01_mln-baselines/movielens/movielens-descriptive-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# MovieLens descriptive analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "outputs": [],
   "source": [
    "figure_path = '/home/weiss/git/thesis/doc/figures/'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style='whitegrid', font_scale=1.5)\n",
    "\n",
    "RANDOM = 2021"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = {\n",
    "    'ml-100k': '/home/weiss/rs_data/ml-100k/',\n",
    "}\n",
    "\n",
    "dt='ml-100k'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/weiss/rs_data/ml-100k/\n",
      "/\n",
      "    README\n",
      "    u2.test\n",
      "    u4.test\n",
      "    u2.base\n",
      "    allbut.pl\n",
      "    u3.base\n",
      "    u.data\n",
      "    u.occupation\n",
      "    ua.test\n",
      "    u5.base\n",
      "    u.genre\n",
      "    u3.test\n",
      "    ub.test\n",
      "    mku.sh\n",
      "    u.user\n",
      "    u1.test\n",
      "    ua.base\n",
      "    u1.base\n",
      "    u.item\n",
      "    u5.test\n",
      "    ub.base\n",
      "    u4.base\n",
      "    ml-100k.pkl\n",
      "    u.info\n",
      "processed/\n",
      "    test.csv\n",
      "    rX_val.csv\n",
      "    rX_test_ns.csv\n",
      "    rX_test.csv\n",
      "    train.csv\n",
      "    rX_train.csv\n",
      "    rX_val_ns.csv\n",
      "    test_ns.csv\n",
      "    rX_train_ns.csv\n",
      "    val.csv\n",
      "    val_ns.csv\n",
      "    train_ns.csv\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Check structure\n",
    "def list_files(startpath):\n",
    "    print(startpath)\n",
    "    for root, dirs, files in os.walk(startpath):\n",
    "        level = root.replace(startpath, '').count(os.sep)\n",
    "        indent = ' ' * 4 * (level)\n",
    "        print('{}{}/'.format(indent, os.path.basename(root)))\n",
    "        subindent = ' ' * 4 * (level + 1)\n",
    "        for f in files:\n",
    "            print('{}{}'.format(subindent, f))\n",
    "dirs = [x[0] for x in os.walk(datasets[dt])]\n",
    "ml = filter(lambda dirName: dirName if ('ml' in dirName) else '', list(dirs))\n",
    "dt_dir_name= list(ml)[0]\n",
    "print(list_files(dt_dir_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratings data (100000, 4)\n",
      "user data (943, 5)\n",
      "movie data (1682, 24)\n"
     ]
    }
   ],
   "source": [
    "# Select ratings and tags\n",
    "if dt=='ml-100k':\n",
    "  ratings_data = pd.read_csv(dt_dir_name +'/'+ 'u.data', delimiter='\\t', names=['userId', 'movieId', 'rating', 'timestamp'])\n",
    "  user_data = pd.read_csv(dt_dir_name +'/'+ 'u.user', delimiter='|', names=['user id', 'age' ,'gender' ,'occupation' , 'zip code'])\n",
    "  genres = ['unknown', 'Action' ,'Adventure' ,'Animation',\n",
    "              'Children' ,'Comedy' ,'Crime' ,'Documentary' ,'Drama' ,'Fantasy',\n",
    "              'Film-Noir' ,'Horror' ,'Musical' ,'Mystery' ,'Romance' ,'Sci-Fi',\n",
    "              'Thriller' ,'War' ,'Western']\n",
    "  movie_data = pd.read_csv(dt_dir_name +'/'+ 'u.item', delimiter='|', names=['movie id' ,'movie title' ,'release date' ,'video release date' ,\n",
    "              'IMDb URL'] + genres)\n",
    "  genre_list =  pd.read_csv(dt_dir_name +'/'+ 'u.genre', delimiter='|', names=['user id', 'age' ,'gender' ,'occupation' , 'zip code'])\n",
    "  print('ratings data', ratings_data.shape)\n",
    "  print('user data', user_data.shape)\n",
    "  print('movie data', movie_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "scrolled": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   userId  movieId  rating  timestamp\n",
      "0     196      242       3  881250949\n",
      "1     186      302       3  891717742\n",
      "2      22      377       1  878887116\n",
      "3     244       51       2  880606923\n",
      "4     166      346       1  886397596\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count   Dtype\n",
      "---  ------     --------------   -----\n",
      " 0   userId     100000 non-null  int64\n",
      " 1   movieId    100000 non-null  int64\n",
      " 2   rating     100000 non-null  int64\n",
      " 3   timestamp  100000 non-null  int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 3.1 MB\n",
      "None\n",
      "Ratings null?\n",
      " userId       False\n",
      "movieId      False\n",
      "rating       False\n",
      "timestamp    False\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "# Check ratings data\n",
    "print(ratings_data.head())\n",
    "print(ratings_data.info())\n",
    "print('Ratings null?\\n', ratings_data.isnull().any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   user id  age gender  occupation zip code\n",
      "0        1   24      M  technician    85711\n",
      "1        2   53      F       other    94043\n",
      "2        3   23      M      writer    32067\n",
      "3        4   24      M  technician    43537\n",
      "4        5   33      F       other    15213\n",
      "Users null?\n",
      " user id       False\n",
      "age           False\n",
      "gender        False\n",
      "occupation    False\n",
      "zip code      False\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "# Check user data\n",
    "print(user_data.head())\n",
    "print('Users null?\\n', user_data.isnull().any())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   movie id        movie title release date  video release date  \\\n",
      "0         1   Toy Story (1995)  01-Jan-1995                 NaN   \n",
      "1         2   GoldenEye (1995)  01-Jan-1995                 NaN   \n",
      "2         3  Four Rooms (1995)  01-Jan-1995                 NaN   \n",
      "3         4  Get Shorty (1995)  01-Jan-1995                 NaN   \n",
      "4         5     Copycat (1995)  01-Jan-1995                 NaN   \n",
      "\n",
      "                                            IMDb URL  unknown  Action  \\\n",
      "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...        0       0   \n",
      "1  http://us.imdb.com/M/title-exact?GoldenEye%20(...        0       1   \n",
      "2  http://us.imdb.com/M/title-exact?Four%20Rooms%...        0       0   \n",
      "3  http://us.imdb.com/M/title-exact?Get%20Shorty%...        0       1   \n",
      "4  http://us.imdb.com/M/title-exact?Copycat%20(1995)        0       0   \n",
      "\n",
      "   Adventure  Animation  Children  ...  Fantasy  Film-Noir  Horror  Musical  \\\n",
      "0          0          1         1  ...        0          0       0        0   \n",
      "1          1          0         0  ...        0          0       0        0   \n",
      "2          0          0         0  ...        0          0       0        0   \n",
      "3          0          0         0  ...        0          0       0        0   \n",
      "4          0          0         0  ...        0          0       0        0   \n",
      "\n",
      "   Mystery  Romance  Sci-Fi  Thriller  War  Western  \n",
      "0        0        0       0         0    0        0  \n",
      "1        0        0       0         1    0        0  \n",
      "2        0        0       0         1    0        0  \n",
      "3        0        0       0         0    0        0  \n",
      "4        0        0       0         1    0        0  \n",
      "\n",
      "[5 rows x 24 columns]\n",
      "movie null?\n",
      " movie id              False\n",
      "movie title           False\n",
      "release date           True\n",
      "video release date     True\n",
      "IMDb URL               True\n",
      "unknown               False\n",
      "Action                False\n",
      "Adventure             False\n",
      "Animation             False\n",
      "Children              False\n",
      "Comedy                False\n",
      "Crime                 False\n",
      "Documentary           False\n",
      "Drama                 False\n",
      "Fantasy               False\n",
      "Film-Noir             False\n",
      "Horror                False\n",
      "Musical               False\n",
      "Mystery               False\n",
      "Romance               False\n",
      "Sci-Fi                False\n",
      "Thriller              False\n",
      "War                   False\n",
      "Western               False\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "# Check movie data\n",
    "print(movie_data.head())\n",
    "print('movie null?\\n', movie_data.isnull().any())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "userId\n",
      "405    737\n",
      "655    685\n",
      "13     636\n",
      "450    540\n",
      "276    518\n",
      "      ... \n",
      "685     20\n",
      "475     20\n",
      "36      20\n",
      "732     20\n",
      "596     20\n",
      "Length: 943, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "rating_count_by_user = ratings_data.groupby('userId').size().sort_values(ascending=False)\n",
    "print(rating_count_by_user)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  gender  count   percent\n",
      "0      F    273  0.289502\n",
      "1      M    670  0.710498\n"
     ]
    }
   ],
   "source": [
    "gender_prop = user_data.groupby(['gender']).size().reset_index(name='count')\n",
    "gender_prop['percent'] = gender_prop['count'] / gender_prop['count'].sum()\n",
    "print(gender_prop)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 360x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size = rating_count_by_user.size\n",
    "\n",
    "df = pd.DataFrame(dict(sorted_user_id=range(1,size+1), rating_counts=rating_count_by_user.values))\n",
    "\n",
    "sns.set(style='whitegrid', font_scale=1.3)\n",
    "image = sns.relplot(data=df,\n",
    "                    x=\"sorted_user_id\",\n",
    "                    y=\"rating_counts\",\n",
    "                    kind=\"line\",\n",
    "                    )\n",
    "\n",
    "image.set(xlabel=\"users sorted desc. by # ratings\",\n",
    "          ylabel=\"# ratings\"\n",
    "          )\n",
    "\n",
    "image.savefig(figure_path + '/ml-100k-long-tail-distribution.png', dpi=300, bbox_inches='tight')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/weiss/.conda/envs/GNN/lib/python3.7/site-packages/seaborn/categorical.py:3704: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "/home/weiss/.conda/envs/GNN/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 360x360 with 1 Axes>",
      "image/png": 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NhUajsegx+fn5ZicItVotcnNzUV9fL67LzMxEv3794OHhIdaUlpaipKRErMnKyoLJZMLEiRPFmpaWFpw6dUqsKS4uxrVr18QaIiJr6NFwjo2Nxf79+7F582Y4OTmhoqICFRUVqK2tFWu2bduG7777DiUlJSgoKMC7776LzMxMvP7662JNcHAwBg4cCJ1Oh8LCQmRkZGDLli0ICQmBUqkEAPj7+8Pd3R06nQ55eXnIzs5GXFwcZs2aJV62N2bMGAQGBiImJgbZ2dk4d+4cdDodNBqN2UlCIqKe1qNzzp999hkAYOnSpWbr586dC71eD+DeTSEbN25ERUUFlEolxo4di927d2Pq1KlivUqlQmpqKmJjY7FgwQKoVCrMnTsXa9asEWvkcjl27dqF2NhYLF68GAqFAkFBQYiKijJ77vfffx/x8fF44403YDKZEBAQgOjoaNjY8OZJIrIemSAIgrWb6M2ampqQl5fHqzWeEEs++YO1W7BYatgHFtf+JTSsGzvpWrMNn1i7BUng8JCISIIYzkREEsRwJiKSIIYzEZEEMZyJiCSI4UxEJEEMZyIiCWI4ExFJEMOZiEiCGM5ERBLEcCYikiCGMxGRBDGciYgkiOFMRCRBDGciIgliOBMRSRDDmYhIghjOREQSZFE45+bmIiMjQ1y+ffs23nrrLcybNw9JSUm4e/dutzVIRNQXWRTOiYmJOHfunLis1+tx7NgxjBgxAgaDAR9++GG3NUhE1BdZFM4XL16Ep6cnAKC5uRnffvst3nrrLSQnJ+PNN9/EV1991a1NEhH1NRaFc0NDAwYMGAAAOHv2LJqamhAUFAQAcHd3x/Xr17uvQyKiPsiicHZxcUFOTg4A4L//+78xduxYDB48GABw69YtKJXKbmuQiKgvsrWkaOHChUhKSsK3336LgoICbNiwQdyWk5MDV1fXbmuQiKgvsiicly1bBkdHR5w7dw6vvfYa5s6dK26rq6vD/Pnzu61BIqIHaW0xwVYht3YbFulsrxaFMwDMmTMHc+bMabc+NjbW4icjIupKtgo54td/Ye02LBL13oJO1VsUzteuXXvgNhsbG6hUKqhUqk49MRERPZhF4Txz5kzIZLKH1vzqV7/CkiVL8Oqrr3ZJY0REfZlF4fzee+9h27ZtePrppxEUFIRhw4ahsrISx44dQ3l5OZYsWQKj0Yh3330XMpkMISEh3d03EdETzaJwzsvLg5+fHxISEszWL1u2DH/84x9x+fJlbN26FVFRUfjss88YzkREj8mi65z/8pe/IDg4uMNt//zP/4wjR44AAF544QWUlJR0XXdERH2UReHc2tqKK1eudLituLgYra2tAAA7OzvY29s/cD8pKSlYuHAhvL294efnh7CwMPzwww/t6s6cOYMFCxZAo9EgMDAQKSkp7WqKiooQGhoKLy8v+Pv7IyEhQeyjTXl5OSIjI6HVauHn54f169ejrq7OrKa2thbr1q2Dr68vtFotVq1ahYqKike+JkRE3cmicP7Nb36DzZs3Iy0tDVVVVRAEAVVVVdi/fz+SkpLEW7kLCgowcuTIB+7n1KlTePnll/HZZ5/hz3/+M5566iksWbIEpaWlYs3Vq1exbNkyeHh44ODBg3jzzTeRnJyMzz77TKypq6tDWFgYVCoV0tLS8N577+HAgQPYvHmzWGMymbBixQpUVVXBYDBg27ZtOHnyJKKiosx6Wrt2LYxGI3bu3Ik9e/bg+vXriIiIgCAIlr2CRETdwKI557fffhv19fWIjo7GO++8I64XBAFBQUHiHYPPPPMM1qxZ88D93D8Cjo+Px7Fjx/D999+L89T79u2Dg4MDYmJiIJPJ4OrqigsXLuCjjz4SrwQ5fPgwampqkJiYCJVKBXd3d6xevRp6vR4rV66EUqlEZmYmCgoKcPToUfEXRnR0NJYvX47S0lKo1WpcunQJx48fR2pqKnx8fADce8e92bNn4+TJk5g8ebKlryMRUZeyKJxVKhW2bduGixcv4ty5c7hx4waeeuopaDQajBkzRqz7zW9+06knb2hoQGtrK4YMGSKuy8nJwdSpU80u3fP390dKSgrKysrg7OyMnJwcTJgwweza6oCAADQ2NiI/Px8+Pj7IycmBWq02G8lPmTIFcrkcRqMRarUaRqMRCoUCfn5+Ys2YMWPg4uICo9HIcCYiq7H4DkHgXnD9PIwfV2JiIhwdHREYGCiuq6yshLe3t1mdk5MTAODGjRtwdnZGZWUlHB0dzWraltvmizuqUSgUcHBwMKsZPHgw5HJ5u311dt45Ly+vU/UkPff/vesNzpw588iaJ/W4gN53bB0d14OOweJwFgQBeXl5uH79Opqamtptf9DVHA+ybds2HDlyBAaDAf379zfbdv8NL23zv4+6EcaSGkEQzGo6qr+/xhKenp4PPRlK1B16WzhZisdlYTgXFRUhMjISJSUlHZ4ok8lknQrnpKQk7Nu3D7t378Zzzz1ntq2jUWtVVRWAf4ygHR0dUV5eblZTWVkpbmv7npWVZVbT2tqKmpoas5qbN2/CZDKZjZ6rq6vbjbqJiHqSReG8ceNG3L17Fx988AGeffZZ2NnZ/eIn1Ov1OHjwIFJTU+Hh4dFuu1arxTfffGM2es3MzMTw4cPh7Ows1uj1etTX14sfApCZmYl+/fqJ+9RqtdixYwdKSkrwzDPPAACysrJgMpkwceJEsaalpQWnTp3ClClTANy7NPDatWtiDRGRNVh0Kd2PP/4InU6HF154AaNGjcKIESPafVkiNjYW+/fvx+bNm+Hk5ISKigpUVFSgtrZWrFm0aBFu3bqFjRs34uLFi/j6669hMBiwdOlSsSY4OBgDBw6ETqdDYWEhMjIysGXLFoSEhIhv/O/v7w93d3fodDrk5eUhOzsbcXFxmDVrFtRqNYB7c+iBgYGIiYlBdnY2zp07B51OB41GY3aSkIiop1k0cnZycur0HGxH2q5V/nnQAsDcuXOh1+sBACNGjEBKSgo2bdqEl156CcOGDUNkZCQWL14s1qtUKqSmpiI2NhYLFiyASqXC3LlzzS7jk8vl2LVrF2JjY7F48WIoFAoEBQW1u875/fffR3x8PN544w2YTCYEBAQgOjoaNjYW/d4iIuoWFoXz7373O+zevRv+/v7o16/fL36ywsJCi+p8fHzw5ZdfPrTG1dUVBoPhoTXOzs7YsWPHQ2sGDRok/mIgIpIKi8I5JycH169fx/PPPw9vb284ODiYbZfJZHzTfSKiLmRROJ84cQIAYG9v3+H1vF0x5UFERP9gUTj/9a9/7e4+iIjoZ3jWi4hIgh44cjYajXB3d4dSqYTRaHzkjnhdMBFR13lgOIeEhCAtLQ1eXl4ICQl54Lxy280i58+f77YmiYj6mgeGs8FgEN/kaM+ePTzpR0TUgx4Yzj+/Q27SpEk90gwREd1j0QnB559/HgUFBR1uu3DhAp5//vkubYqIqK+zKJyvXr2K5ubmDrc1Njbi+vXrXdoUEVFf91iX0plMJpw8eRKDBg3qqn6IiAgPmXPevn07du7cCeDeHYBtn/H3cyaTCQDw8ssvd1N7RER90wPD2dfXF4IgQBAEbN++HXPmzBHfT7mNnZ0dRo8ejZkzZ3Z7o0REfclDr9Zou2JDJpNh4cKFePrpp3usMSKivsyi99b4/e9/3919EBHRz1j8Aa9///vf8fnnn6O4uLjDD3h91HsrExGR5SwK59zcXLz22msYPXo0Lly4AE9PT9TV1aG4uBjDhw8X7yQkIqKuYdGldJs3b8aLL76IAwcOQBAEbNy4EUeOHEFaWhoEQcBrr73W3X0SEfUpFoXzhQsXMHv2bPH9NdouofPy8kJERASSkpK6r0Mioj7IonC+e/cu7O3tYWNjgyFDhqC8vFzcNmLECFy6dKnbGiQi6ossCudRo0bh6tWrAAAPDw8YDAZUV1ejpqYGBoMBLi4u3dokEVFfY9EJwd/+9rf4+9//DgBYuXIlli5dCn9/fwCAjY0NEhMTu69DIqI+yKJwDg0NFX+eMGECDh8+jMzMTDQ0NGDKlCl49tlnu61BIqK+6JHh3NTUhJSUFMycORMeHh4AgOHDh2PhwoXd3hwRUV/1yDlne3t7pKSkoLa2tif6ISIiWHhC0NPTExcuXOjuXoiI6P9ZFM5vv/02Pv30Uxw8eBA3b97s7p6IiPo8i04Izp8/H4IgICoqCsC9KzTu/8DXvLy8ru+OiKiPsiic33jjDX76NhFRD7IonFeuXNndfRAR0c881mcIEhFR9+jxcD59+jTCw8MxY8YMuLm5ITk52Wz7gQMH4Obm1u7rypUrZnVFRUUIDQ2Fl5cX/P39kZCQgNbWVrOa8vJyREZGQqvVws/PD+vXr0ddXZ1ZTW1tLdatWwdfX19otVqsWrUKFRUV3XPwREQWsvjN9rvKnTt34OrqiuDgYOj1+g5r5HI5MjIyzNYNHTpU/Lmurg5hYWHQaDRIS0tDWVkZdDodAIjfTSYTVqxYgf79+8NgMKChoQFRUVGIiorC1q1bxX2tXbsWxcXF2LlzJ+zs7BAXF4eIiAikpaVxnp2IrKbHw3n69OmYPn06ADz0rUadnJweuO3w4cOoqalBYmIiVCoV3N3dsXr1auj1eqxcuRJKpRKZmZkoKCjA0aNHMXLkSABAdHQ0li9fjtLSUqjValy6dAnHjx9HamoqfHx8AAB6vR6zZ8/GyZMnMXny5C48ciIiy0lyztlkMmHmzJkICAhAWFgYTp8+bbY9JycHEyZMgEqlEtcFBASgsbER+fn5Yo1arRaDGQCmTJkCuVwOo9EIADAajVAoFOIH2QLAmDFj4OLiItYQEVlDj4+cH2XUqFFISEiAu7s77ty5gy+++AKhoaHYu3evOLqtrKyEo6Oj2ePaltvmizuqUSgUcHBwMKsZPHgw5HJ5u311dt6Z13n3ft7e3tZuodPOnDnzyJon9biA3ndsHR3Xg47BonC+e/cugoKC8OGHH3b7O9BptVpotVpxeeLEifjpp5/w8ccfi+H8MI+aJxYEwaymo/r7ayzh6ekJe3v7Tj2G6HH1tnCyFI/rIdMa58+fF69+EAQBV69eRXNzM4B70w7PPfccfvzxx8ds1TLjx4/H5cuXxWVHR0dUVlaa1bQtt42WO6ppbW1FTU2NWc3NmzfFj91qU11d3W7UTUTUkx4YzgsWLMDEiRMxb948REdHQyaTmf1XXxCEHmkQAPLz881OEGq1WuTm5qK+vl5cl5mZiX79+olva6rValFaWoqSkhKxJisrCyaTCRMnThRrWlpacOrUKbGmuLgY165dE2uIiKzhgdMaRqMR+fn5yM/PR15eHgRBQHh4OAYPHozx48dDJpPhypUrGDduXKeesL6+XgzMlpYWVFRU4Pz581AqlRg5ciS2bdsGLy8v/NM//ZM455yZmYnt27eL+wgODsb27duh0+mwcuVKlJWVYcuWLQgJCYFSqQQA+Pv7w93dHTqdDuvXr0djYyPi4uIwa9YsqNVqAPdO/gUGBiImJgbvvvsu7O3tERcXB41GY3aSkIiop8kEC4bAra2t8PT0xMcff4zW1lYYjUbs2rULcrkcgwYNgr+/P/70pz9Z9IQnT540+2SVNn5+fti7dy82bdqEY8eOoaKiAkqlEmPHjkV4eDimTp1qVl9UVITY2Fjk5ORApVJhzpw5WLNmDWxt//H7pqysDLGxsThx4gQUCgWCgoIQFRVldpVHTU0N4uPjkZ6eDpPJhICAAERHRz/0Ur6fa2pqQl5eHuecnxBLPvmDtVuwWGrYBxbX/iU0rBs76VqzDZ90qj5+/Rfd1EnXinpvQafqHzhyXrx4McaNG4dx48bBzc0NADBkyBB4eHggICAAu3btwp49e1BfX4/MzEyLn3DSpEkoLCx84PZ169Zh3bp1j9yPq6srDAbDQ2ucnZ2xY8eOh9YMGjTogTfDEBFZywPDedq0acjPz8ff/vY3lJSUQCaTIT4+HlOnThWnNZRKJXx8fMSbSoiIqGs8MJxXrFgh/nz79m1MmjQJarUap0+fxkcffQTg3h13M2bMwLRp06DRaLq/WyKiPsKi65zb5mjbpjqam5vh5eWF8ePHw2g0IiUlBbm5ud3ZJxFRn2JROMtkMgwfPhx2dnYAIN5RN2/ePIwbNw5NTU3d1yERUR9kUTjb2Njgr3/9q7gsk8ng6+uLAQMGAACvUiAi6mK/6L01bGxssHfv3q7uhYiI/p8k35WOiKivYzgTEUkQw5mISIIYzkREEsRwJiKSIIYzEZEEMZyJiCSI4UxEJEEMZyIiCWI4ExFJEMOZiEiCGM5ERBLEcCYikiCGMxGRBDGciYgkiOFMRCRBDGciIgliOFOn3W1tsXYLFutNvRL93C/6mCrq22xsFTiTuMzabVjE+48fWbsFol+EI2ciIgliOBMRSRDDmYhIghjOREQSxHAmIpIghjMRkQT1eDifPn0a4eHhmDFjBtzc3JCcnNyu5syZM1iwYAE0Gg0CAwORkpLSrqaoqAihoaHw8vKCv78/EhIS0NraalZTXl6OyMhIaLVa+Pn5Yf369airqzOrqa2txbp16+Dr6wutVotVq1ahoqKiaw+aiKiTejyc79y5A1dXV6xduxZPP/10u+1Xr17FsmXL4OHhgYMHD+LNN99EcnIyPvvsM7Gmrq4OYWFhUKlUSEtLw3vvvYcDBw5g8+bNYo3JZMKKFStQVVUFg8GAbdu24eTJk4iKijJ7vrVr18JoNGLnzp3Ys2cPrl+/joiICAiC0H0vAhHRI/T4TSjTp0/H9OnTAQBJSUnttu/btw8ODg6IiYmBTCaDq6srLly4gI8++givvvoqAODw4cOoqalBYmIiVCoV3N3dsXr1auj1eqxcuRJKpRKZmZkoKCjA0aNHMXLkSABAdHQ0li9fjtLSUqjValy6dAnHjx9HamoqfHx8AAB6vR6zZ8/GyZMnMXny5B56VYiIzEluzjknJwdTp06FTCYT1/n7++PatWsoKysTayZMmACVSiXWBAQEoLGxEfn5+WKNWq0WgxkApkyZArlcDqPRCAAwGo1QKBTw8/MTa8aMGQMXFxexhojIGiR3+3ZlZSW8vb3N1jk5OQEAbty4AWdnZ1RWVsLR0dGspm25bb64oxqFQgEHBwezmsGDB0Mul7fbV2fnnfPy8jpV35vd/+cjdWfOnLGorrcdF2DZsT2pxwX0vmPr6LgedAySC2cAZqNmAOL87/3rLXns/QRBMKvpqP7+Gkt4enrC3t6+U4+hntHb/gF3xpN6bDwuCU5rdDRqraqqAvCPEbSjoyMqKyvNatqW20bLHdW0traipqbGrObmzZswmUxmddXV1e1G3UREPUly4azVapGVlWV2tURmZiaGDx8OZ2dnsSY3Nxf19fVmNf369YOHh4dYU1paipKSErEmKysLJpMJEydOFGtaWlpw6tQpsaa4uBjXrl0Ta4iIrKHHw7m+vh7nz5/H+fPn0dLSgoqKCpw/fx5XrlwBACxatAi3bt3Cxo0bcfHiRXz99dcwGAxYunSpuI/g4GAMHDgQOp0OhYWFyMjIwJYtWxASEgKlUgng3klEd3d36HQ65OXlITs7G3FxcZg1axbUajWAeyf/AgMDERMTg+zsbJw7dw46nQ4ajcbsJCERUU/r8TnnvLw8hIaGisv79+/H/v374efnh71792LEiBFISUnBpk2b8NJLL2HYsGGIjIzE4sWLxceoVCqkpqYiNjYWCxYsgEqlwty5c7FmzRqxRi6XY9euXYiNjcXixYuhUCgQFBTU7jrn999/H/Hx8XjjjTdgMpkQEBCA6Oho2NhI7j8VRNSH9Hg4T5o0CYWFhQ+t8fHxwZdffvnQGldXVxgMhofWODs7Y8eOHQ+tGTRoEPR6/UNriIh6GoeHREQSxHAmIpIghjMRkQQxnImIJIjhTEQkQQxnIiIJYjgTEUkQw5mISIIYzkREEsRwJiKSIIYzEZEEMZyJiCSI4UxEJEEMZyIiCWI4ExFJEMOZiEiCGM5ERBLEcCYikiCGMxGRBDGciYgkiOFMRCRBDGciIgliOBMRSRDDmYhIghjOREQSxHAmIpIghnM3am4xWbsFi/WmXon6AltrN/Aks1PIEfLHz6zdhkX+nPiqtVsgop/hyJmISIIYzkREEsRwJiKSIMmFc3JyMtzc3Np9tba2ijVnzpzBggULoNFoEBgYiJSUlHb7KSoqQmhoKLy8vODv74+EhASzfQBAeXk5IiMjodVq4efnh/Xr16Ourq7bj5GI6FEkeUJwxIgR2L9/v9k6W9t7rV69ehXLli1DcHAw9Ho98vPzsWHDBiiVSrz66r2TWnV1dQgLC4NGo0FaWhrKysqg0+kAQPxuMpmwYsUK9O/fHwaDAQ0NDYiKikJUVBS2bt3ag0dLRNSeJMNZLpfDycmpw2379u2Dg4MDYmJiIJPJ4OrqigsXLuCjjz4Sw/nw4cOoqalBYmIiVCoV3N3dsXr1auj1eqxcuRJKpRKZmZkoKCjA0aNHMXLkSABAdHQ0li9fjtLSUqjV6h47XiKi+0luWgMAysrKMG3aNAQGBiI8PBwFBQXitpycHEydOhUymUxc5+/vj2vXrqGsrEysmTBhAlQqlVgTEBCAxsZG5OfnizVqtVoMZgCYMmUK5HI5jEZjdx8iEdFDSW7k7OXlhcTERIwePRq3bt3CJ598gkWLFuHQoUMYOXIkKisr4e3tbfaYtlH2jRs34OzsjMrKSjg6OprVtC1XVFQAQIc1CoUCDg4OYk1n5OXltVt3f59Sd+bMGYvqeFzSYcmxPanHBfS+Y+vouB50DJIL5+nTp5ste3t748UXX8TevXuxYcMGADAbNQOAIAgdru/Io2oEQbBoP/fz9PSEvb19px8nJb3tL7qlntTjAp7cY+NxSXRa4+dsbW0xbtw4XL58GcC9EfD9I9uqqioA/xhBOzo6orKy0qymbblttNxRTWtrK2pqatqNqImIeprkw/nu3bsoLCwUg1er1SIrK0scLQNAZmYmhg8fDmdnZ7EmNzcX9fX1ZjX9+vWDh4eHWFNaWoqSkhKxJisrCyaTCRMnTuyJQyMieiDJhbNer8fJkydRWlqKc+fO4d/+7d9w5coVhISEAAAWLVqEW7duYePGjbh48SK+/vprGAwGLF26VNxHcHAwBg4cCJ1Oh8LCQmRkZGDLli0ICQmBUqkEcO8koru7O3Q6HfLy8pCdnY24uDjMmjWLV2oQkdVJbs75xo0bWLt2LaqrqzF48GB4eHhg37590Gg0AO5dA52SkoJNmzbhpZdewrBhwxAZGYnFixeL+1CpVEhNTUVsbCwWLFgAlUqFuXPnYs2aNWKNXC7Hrl27EBsbi8WLF0OhUCAoKAhRUVE9fsxERPeTXDj/+7//+yNrfHx88OWXXz60xtXVFQaD4aE1zs7O2LFjR6f6IyLqCZKb1iAiIoYzEZEkMZyJiCSI4UxEJEEMZyIiCWI4ExFJEMOZiEiCGM5ERBLEcCYikiCGMxGRBDGciYgkiOFMRCRBDGciIgliOBMRSRDDmYhIghjOREQSxHAmIpIghjMRkQQxnImIJIjhTEQkQQxnIiIJYjgTEUkQw5mISIIYzkREEsRwJiKSIIYzEZEEMZyJiCSI4UxEJEEMZyIiCWI4ExFJEMOZiEiCGM5ERBLEcAaQnp6O3/72t/D09MQLL7yAAwcOWLslIurj+nw4//DDD1i1ahWCgoLwX//1X1i8eDHWr1+P48ePW7s1IurDbK3dgLWlpqZiwoQJ+MMf/gAAGDNmDHJycvDxxx9jxowZj3y8IAgAgObm5g63D1Iquq7ZbtTU1NS5B/Qb2D2NdLHOHtdAxYBu6qTrdebYbAb2jj8voPN/Zv2UvSPGHnZcdnZ2kMlkZutkQlu69FGBgYFYuHAhIiMjxXVffPEFYmJikJubC7lc/tDH19bW4sKFC93dJhE9wTw9PWFvb2+2rnf8yulGlZWVcHR0NFvn5OSE5uZm3L59G0OHDn3o4wcMGICxY8dCoVC0+81HRGQJOzu7duv6fDgDaBeqnfnPhI2NDQb2ov8yElHv0OdPCDo6OqKiosJsXVVVFRQKBRwcHKzUFRH1dX0+nLVaLU6cOGG2LjMzExMmTHjkfDMRUXfp8+G8ZMkS5OTkYNu2bbh06RI+/fRTfPvtt1i6dKm1WyOiPqzPX60B3LsJJSkpCVeuXIGLiwvCw8Mxb948a7dFRH0Yw5mISIL6/LQGEZEUMZyJiCSI4UxEJEEMZyIiCWI49wKnT59GeHg4ZsyYATc3NyQnJ1u7pS6RkpKChQsXwtvbG35+fggLC8MPP/xg7bYe2+HDhzFv3jz4+vpi/PjxmD17Nnbv3t2pO097g0OHDsHNzQ1LliyxdiuPLTk5GW5ubu2+WltbrdYTb9/uBe7cuQNXV1cEBwdDr9dbu50uc+rUKbz88svQaDSwtbVFSkoKlixZgq+++gpqtdra7f1iQ4cORUREBEaNGgU7OztkZ2dj48aNkMvleP31163dXpe4dOkS/vSnP8HHx8farXSZESNGYP/+/WbrbG2tF5EM515g+vTpmD59OgAgKSnJyt10nZSUFLPl+Ph4HDt2DN9//z1CQkKs1NXj8/f3N1tWq9VIT0/HqVOnnohwbm5uxr/+679i7dq1yMrKQllZmbVb6hJyuRxOTk7WbkPEaQ2SjIaGBrS2tmLIkCHWbqXLCIKAs2fPwmg0YtKkSdZup0ts2rQJY8eOxUsvvWTtVrpUWVkZpk2bhsDAQISHh6OgoMCq/XDkTJKRmJgIR0dHBAYGWruVx1ZbW4tp06ahpaUFJpMJkZGRCA0NtXZbj+3o0aP4/vvvcejQIWu30qW8vLyQmJiI0aNH49atW/jkk0+waNEiHDp0CCNHjrRKTwxnkoRt27bhyJEjMBgM6N+/v7XbeWwDBgzAoUOH0NDQgNzcXGzevBkuLi6YP3++tVv7xa5fv4533nkHH374IVQqlbXb6VJt04ZtvL298eKLL2Lv3r3YsGGDVXpiOJPVJSUlYd++fdi9ezeee+45a7fTJWxsbMQRl7u7O27fvo2kpKReHc4//vgjqqursWjRInHd3bt3AQAeHh7Yt28fxo8fb632upStrS3GjRuHy5cvW68Hqz0zEQC9Xo+DBw8iNTUVHh4e1m6n29y9exeNjY3WbuOxTJ48GYcPHzZbt2XLFlRVVSEuLg7PPPOMlTrrenfv3kVhYSG8vLys1gPDuReor69HSUkJAKClpQUVFRU4f/48lEql1ebDukJsbCwOHjyI5ORkODk5iR960K9fv1796TLJycnw9vaGWq1GS0sLzpw5g5SUlF7/TocqlQpjx441Wzdo0CDcuXOn3freRq/XY8aMGRg+fLg453zlyhWrXrrKd6XrBU6ePNnhySQ/Pz/s3bvXCh11DTc3tw7Xz507t1dfz52QkID09HSUl5fD3t4earUa8+fPxyuvvPLEfYDDW2+9hbKyMqSmplq7lcfy5ptvIjs7G9XV1Rg8eDA8PDywcuVKaDQaq/XEcCYikiBe50xEJEEMZyIiCWI4ExFJEMOZiEiCGM5ERBLEcCYikiCGM1EXSE9Ph8FgaLf+wIEDcHNze2LeVpN6DsOZqAukp6d3eENQYGAg9u/fj6FDh1qhK+rNePs20QMIgoDm5mbY29v/4n0MHTqUwUy/CEfORP/vrbfeQlBQELKysjBv3jxoNBp88803SEpKwksvvQStVoupU6dixYoVuHjxotnjDh48iJKSEvGz51577TUAHU9ruLm5Yfv27fiP//gPTJ8+Hd7e3lixYkW7qY/y8nKEh4dj/Pjx8Pf3x5YtW/Dhhx8+8LZ3erJw5Ez0Mzdv3sSGDRvwxhtv4Fe/+hWeeuopZGdnY+nSpXj66adRW1uL/fv341/+5V9w5MgRODk5ISIiAtXV1SgsLMQHH3wAAI98v+MvvvgC7u7uiImJwa1bt6DX6/HHP/5RnLcWBAHh4eEoKyvD22+/DUdHR3z66acoKirq9teApIHhTPQztbW1SE5OxpQpU8R18fHx4s8mkwnTpk3Dr3/9a3zzzTdYsmQJnnnmGQwdOhR2dnaYMGGCRc8zYMAAbN++HTY29/7zWl1djYSEBFRVVWHYsGH47rvv8OOPPyIlJQXTpk0DcO+zCYOCgrruYEnSOK1B9DNKpdIsmAEgIyMDISEh8PX1hYeHBzQaDW7duoVLly794ucJCAgQgxkAnn32WQDAtWvXAADnzp2Dvb09fv3rX4s1CoUCM2bM+MXPSb0LR85EPzNs2DCz5bNnzyIiIgKBgYHQ6/VwcnKCTCbD73//ezQ3N//i5xk0aJDZsp2dHQCgqakJAHDjxg0MGTIEMpnsof3Rk4vhTPQz94fh//zP/8DOzg4ffPABbG3v/XMRBAG3bt3q1j6eeuop3Lx5E4IgmPVUVVXVrc9L0sFpDaKHaGhogK2trVlAfvvtt+0+csrOzk4c9XYFjUaDpqYmfP/99+K6lpYWHD9+vMueg6SN4Uz0EAEBAaipqcGGDRuQlZWFPXv2QK/XY/DgwWZ1o0ePxo0bN3DgwAGcPXv2seajAWDatGkYN24c3nrrLXz55Zf429/+hvDw8HYjaXpyMZyJHmLatGlYt24d/vd//xe/+93vcOTIEWzduhVDhgwxq1uwYAFeeOEFvPfee1i4cCHeeeedx3pemUyGHTt2wMvLC7GxsVi3bh08PDzw4osv9urPVyTL8WOqiHoJQRCwcOFCKJXKDt/Hg54sPCFIJFGff/45mpubMXr0aNTX1+PAgQM4d+4cduzYYe3WqAcwnIkkyt7eHnv27MFPP/0Ek8kEV1dXJCUl4fnnn7d2a9QDOK1BRCRBPCFIRCRBDGciIgliOBMRSRDDmYhIghjOREQS9H/WLFmFY0f/WQAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rating distribution\n",
    "\n",
    "sns.set(style='whitegrid', font_scale=1.3)\n",
    "g = sns.factorplot(\"rating\", data=ratings_data, kind='count')\n",
    "g.set_ylabels(\"# ratings\")\n",
    "g.savefig(figure_path + '/ml-100k-rating-distribution.png', dpi=300, bbox_inches='tight')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean 3.52986\n",
      "median 4.0\n"
     ]
    }
   ],
   "source": [
    "print('mean', ratings_data['rating'].mean())\n",
    "print('median', ratings_data['rating'].median())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set(style='whitegrid', font_scale=1.3)\n",
    "image = sns.histplot(data = user_data,\n",
    "                     x = 'age')\n",
    "\n",
    "image.set_ylabel('# users')\n",
    "image.set_xlabel('')\n",
    "\n",
    "image.figure.savefig(figure_path + 'ml-100k-user-age-hist.png', dpi=300, bbox_inches='tight')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = user_data.groupby(['occupation']).size().reset_index(name='count')\n",
    "\n",
    "sns.set(style='whitegrid', font_scale=1.3, rc={'ytick.labelsize':12})\n",
    "image = sns.barplot(data = df,\n",
    "            y = 'occupation',\n",
    "            x= 'count'\n",
    "            )\n",
    "image.set_xlabel('# users')\n",
    "\n",
    "image.figure.savefig(figure_path + 'ml-100k-user-occupation.png', dpi=300, bbox_inches='tight')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          genre  count\n",
      "0       unknown      2\n",
      "1        Action    251\n",
      "2     Adventure    135\n",
      "3     Animation     42\n",
      "4      Children    122\n",
      "5        Comedy    505\n",
      "6         Crime    109\n",
      "7   Documentary     50\n",
      "8         Drama    725\n",
      "9       Fantasy     22\n",
      "10    Film-Noir     24\n",
      "11       Horror     92\n",
      "12      Musical     56\n",
      "13      Mystery     61\n",
      "14      Romance    247\n",
      "15       Sci-Fi    101\n",
      "16     Thriller    251\n",
      "17          War     71\n",
      "18      Western     27\n"
     ]
    }
   ],
   "source": [
    "data = dict(genre=[], count=[])\n",
    "for genre in genres:\n",
    "    count = movie_data[genre].value_counts()[1]\n",
    "    data['genre'].append(genre)\n",
    "    data['count'].append(count)\n",
    "genre_counts = pd.DataFrame(data)\n",
    "print(genre_counts)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, image = plt.subplots()\n",
    "\n",
    "sns.set(style='whitegrid', font_scale=1.3, rc={'ytick.labelsize':12})\n",
    "image = sns.barplot(data = genre_counts,\n",
    "            y = 'genre',\n",
    "            x= 'count'\n",
    "            )\n",
    "image.set_xlabel('# movies')\n",
    "image.set_ylabel('genre')\n",
    "\n",
    "image.figure.savefig(figure_path + 'ml-100k-genre-counts.png', dpi=300, bbox_inches='tight')\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "pycharm-f57757a1",
   "language": "python",
   "display_name": "PyCharm (rs-via-gnn)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}