basic_cleanup_animes_and_users.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "import ast\n",
    "from cleaning import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:70% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use this to keep the cells wider, very nice on wide screens, set the percentage as you like it\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:70% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "animes = pd.read_csv('anime_filtered.csv')\n",
    "users = pd.read_csv('users_filtered.csv', na_values=user_na_values, keep_default_na=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams.update(mpl.rcParamsDefault)  # for recovering from xkcd\n",
    "\n",
    "# taken from https://github.com/ctokheim/matplotlibrc\n",
    "#plt.style.use('seaborn')\n",
    "plt.style.use('seaborn-talk')\n",
    "#plt.style.use('ggplot')\n",
    "#plt.xkcd()\n",
    "\n",
    "conf = {\n",
    "    'font.size': 14.0,\n",
    "    'axes.grid': True,\n",
    "    'axes.axisbelow': True,\n",
    "    'axes.edgecolor': 'black',\n",
    "#    'axes.facecolor': '#E5E5EF',\n",
    "#    'axes.facecolor': '#E0E0EF',\n",
    "    'axes.labelcolor': 'black',\n",
    "    'axes.titlesize': 15.6,\n",
    "    'axes.labelsize': 'large',    \n",
    "    'figure.figsize': (12, 7),\n",
    "    'figure.titlesize': 'x-large',\n",
    "    'grid.linewidth': 1.3,\n",
    "    'xtick.labelsize': 'large',\n",
    "    'ytick.labelsize': 'large',\n",
    "    'xtick.color': 'black',\n",
    "    'ytick.color': 'black',\n",
    "    'legend.fontsize': 'large',\n",
    "}\n",
    "\n",
    "plt.rcParams.update(conf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py:886: MatplotlibDeprecationWarning: \n",
      "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n",
      "  \"found relative to the 'datapath' directory.\".format(key))\n"
     ]
    },
    {
     "data": {
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       "          'ytick.right': False})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   "source": [
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     "data": {
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       "['bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_watching</th>\n",
       "      <th>user_completed</th>\n",
       "      <th>user_onhold</th>\n",
       "      <th>user_dropped</th>\n",
       "      <th>user_plantowatch</th>\n",
       "      <th>user_days_spent_watching</th>\n",
       "      <th>access_rank</th>\n",
       "      <th>stats_mean_score</th>\n",
       "      <th>stats_rewatched</th>\n",
       "      <th>stats_episodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.161330e+05</td>\n",
       "      <td>116133.000000</td>\n",
       "      <td>116133.000000</td>\n",
       "      <td>116133.000000</td>\n",
       "      <td>116133.000000</td>\n",
       "      <td>116133.000000</td>\n",
       "      <td>1.161330e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116133.000000</td>\n",
       "      <td>116133.000000</td>\n",
       "      <td>1.161330e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.810832e+06</td>\n",
       "      <td>14.610283</td>\n",
       "      <td>195.498807</td>\n",
       "      <td>11.338018</td>\n",
       "      <td>11.818518</td>\n",
       "      <td>74.936332</td>\n",
       "      <td>9.329691e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.713389</td>\n",
       "      <td>13.991079</td>\n",
       "      <td>6.611762e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.996139e+06</td>\n",
       "      <td>32.459424</td>\n",
       "      <td>256.533148</td>\n",
       "      <td>31.622640</td>\n",
       "      <td>31.117077</td>\n",
       "      <td>177.889249</td>\n",
       "      <td>7.636053e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.538595</td>\n",
       "      <td>54.731186</td>\n",
       "      <td>7.155270e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.990980e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.246000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.310000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.349000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.615770e+05</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>4.990000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.880000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.017000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.464591e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>9.277000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.460000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.642000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.251737e+06</td>\n",
       "      <td>2934.000000</td>\n",
       "      <td>11536.000000</td>\n",
       "      <td>2562.000000</td>\n",
       "      <td>2457.000000</td>\n",
       "      <td>12051.000000</td>\n",
       "      <td>2.601351e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9404.000000</td>\n",
       "      <td>2.437949e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id  user_watching  user_completed    user_onhold  \\\n",
       "count  1.161330e+05  116133.000000   116133.000000  116133.000000   \n",
       "mean   1.810832e+06      14.610283      195.498807      11.338018   \n",
       "std    1.996139e+06      32.459424      256.533148      31.622640   \n",
       "min    1.000000e+00       0.000000        0.000000       0.000000   \n",
       "25%    1.990980e+05       3.000000       47.000000       0.000000   \n",
       "50%    5.615770e+05       7.000000      121.000000       4.000000   \n",
       "75%    3.464591e+06      16.000000      253.000000      12.000000   \n",
       "max    7.251737e+06    2934.000000    11536.000000    2562.000000   \n",
       "\n",
       "        user_dropped  user_plantowatch  user_days_spent_watching  access_rank  \\\n",
       "count  116133.000000     116133.000000              1.161330e+05          0.0   \n",
       "mean       11.818518         74.936332              9.329691e+01          NaN   \n",
       "std        31.117077        177.889249              7.636053e+03          NaN   \n",
       "min         0.000000          0.000000              0.000000e+00          NaN   \n",
       "25%         0.000000          5.000000              2.246000e+01          NaN   \n",
       "50%         3.000000         27.000000              4.990000e+01          NaN   \n",
       "75%        12.000000         80.000000              9.277000e+01          NaN   \n",
       "max      2457.000000      12051.000000              2.601351e+06          NaN   \n",
       "\n",
       "       stats_mean_score  stats_rewatched  stats_episodes  \n",
       "count     116133.000000    116133.000000    1.161330e+05  \n",
       "mean           7.713389        13.991079    6.611762e+03  \n",
       "std            1.538595        54.731186    7.155270e+05  \n",
       "min            0.000000         0.000000    0.000000e+00  \n",
       "25%            7.310000         0.000000    1.349000e+03  \n",
       "50%            7.880000         1.000000    3.017000e+03  \n",
       "75%            8.460000        10.000000    5.642000e+03  \n",
       "max           10.000000      9404.000000    2.437949e+08  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# dropping users with invalid and obviously incorrect dates\n",
    "users['birth_date'] = pd.to_datetime(users['birth_date'], errors='coerce')\n",
    "users['last_online'] = pd.to_datetime(users['last_online'], errors='coerce')\n",
    "users['join_date'] = pd.to_datetime(users['join_date'], errors='coerce')\n",
    "users = users.dropna(subset=['birth_date', 'last_online', 'join_date'])\n",
    "#users = users[users['last_online'].dt.year > 1950]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
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       "      <th>count</th>\n",
       "      <td>1.161310e+05</td>\n",
       "      <td>116131.000000</td>\n",
       "      <td>116131.00000</td>\n",
       "      <td>116131.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.810839e+06</td>\n",
       "      <td>14.610259</td>\n",
       "      <td>195.49259</td>\n",
       "      <td>11.338084</td>\n",
       "      <td>11.818429</td>\n",
       "      <td>74.935771</td>\n",
       "      <td>9.329698e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.713378</td>\n",
       "      <td>13.991286</td>\n",
       "      <td>6.611773e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.996147e+06</td>\n",
       "      <td>32.459676</td>\n",
       "      <td>256.52762</td>\n",
       "      <td>31.622901</td>\n",
       "      <td>31.117304</td>\n",
       "      <td>177.890713</td>\n",
       "      <td>7.636119e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.538603</td>\n",
       "      <td>54.731634</td>\n",
       "      <td>7.155332e+05</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.991000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>47.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.246000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.310000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.349000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.615770e+05</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>121.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>4.990000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.880000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.017000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.464671e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>253.00000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>9.277000e+01</td>\n",
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       "      <td>10.000000</td>\n",
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       "      <td>2934.000000</td>\n",
       "      <td>11536.00000</td>\n",
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       "      <td>2457.000000</td>\n",
       "      <td>12051.000000</td>\n",
       "      <td>2.601351e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9404.000000</td>\n",
       "      <td>2.437949e+08</td>\n",
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      ],
      "text/plain": [
       "            user_id  user_watching  user_completed    user_onhold  \\\n",
       "count  1.161310e+05  116131.000000    116131.00000  116131.000000   \n",
       "mean   1.810839e+06      14.610259       195.49259      11.338084   \n",
       "std    1.996147e+06      32.459676       256.52762      31.622901   \n",
       "min    1.000000e+00       0.000000         0.00000       0.000000   \n",
       "25%    1.991000e+05       3.000000        47.00000       0.000000   \n",
       "50%    5.615770e+05       7.000000       121.00000       4.000000   \n",
       "75%    3.464671e+06      16.000000       253.00000      12.000000   \n",
       "max    7.251737e+06    2934.000000     11536.00000    2562.000000   \n",
       "\n",
       "        user_dropped  user_plantowatch  user_days_spent_watching  access_rank  \\\n",
       "count  116131.000000     116131.000000              1.161310e+05          0.0   \n",
       "mean       11.818429         74.935771              9.329698e+01          NaN   \n",
       "std        31.117304        177.890713              7.636119e+03          NaN   \n",
       "min         0.000000          0.000000              0.000000e+00          NaN   \n",
       "25%         0.000000          5.000000              2.246000e+01          NaN   \n",
       "50%         3.000000         27.000000              4.990000e+01          NaN   \n",
       "75%        12.000000         80.000000              9.277000e+01          NaN   \n",
       "max      2457.000000      12051.000000              2.601351e+06          NaN   \n",
       "\n",
       "       stats_mean_score  stats_rewatched  stats_episodes  \n",
       "count     116131.000000    116131.000000    1.161310e+05  \n",
       "mean           7.713378        13.991286    6.611773e+03  \n",
       "std            1.538603        54.731634    7.155332e+05  \n",
       "min            0.000000         0.000000    0.000000e+00  \n",
       "25%            7.310000         0.000000    1.349000e+03  \n",
       "50%            7.880000         1.000000    3.017000e+03  \n",
       "75%            8.460000        10.000000    5.642000e+03  \n",
       "max           10.000000      9404.000000    2.437949e+08  "
      ]
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   "source": [
    "users.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_watching</th>\n",
       "      <th>user_completed</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>karthiga</td>\n",
       "      <td>2255153</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.31</td>\n",
       "      <td>Female</td>\n",
       "      <td>Chennai, India</td>\n",
       "      <td>1990-04-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-03-03</td>\n",
       "      <td>2014-02-04 01:32:00</td>\n",
       "      <td>7.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RedvelvetDaisuki</td>\n",
       "      <td>1897606</td>\n",
       "      <td>61</td>\n",
       "      <td>396</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>206</td>\n",
       "      <td>118.07</td>\n",
       "      <td>Female</td>\n",
       "      <td>Manila</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-12-13</td>\n",
       "      <td>1900-05-13 02:47:00</td>\n",
       "      <td>6.78</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7094.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Damonashu</td>\n",
       "      <td>37326</td>\n",
       "      <td>45</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>25</td>\n",
       "      <td>59</td>\n",
       "      <td>83.70</td>\n",
       "      <td>Male</td>\n",
       "      <td>Detroit,Michigan</td>\n",
       "      <td>1991-08-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-02-13</td>\n",
       "      <td>1900-03-24 12:48:00</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4936.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bskai</td>\n",
       "      <td>228342</td>\n",
       "      <td>25</td>\n",
       "      <td>414</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>167.16</td>\n",
       "      <td>Male</td>\n",
       "      <td>Nayarit, Mexico</td>\n",
       "      <td>1990-12-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009-08-31</td>\n",
       "      <td>2014-05-12 16:35:00</td>\n",
       "      <td>8.27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>terune_uzumaki</td>\n",
       "      <td>327311</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.20</td>\n",
       "      <td>Female</td>\n",
       "      <td>Malaysia, Kuantan</td>\n",
       "      <td>1998-08-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-05-10</td>\n",
       "      <td>2012-10-18 19:06:00</td>\n",
       "      <td>9.70</td>\n",
       "      <td>6.0</td>\n",
       "      <td>920.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           username  user_id  user_watching  user_completed  user_onhold  \\\n",
       "0          karthiga  2255153              3              49            1   \n",
       "1  RedvelvetDaisuki  1897606             61             396           39   \n",
       "2         Damonashu    37326             45             195           27   \n",
       "3             bskai   228342             25             414            2   \n",
       "4    terune_uzumaki   327311              5               5            0   \n",
       "\n",
       "   user_dropped  user_plantowatch  user_days_spent_watching  gender  \\\n",
       "0             0                 0                     55.31  Female   \n",
       "1             0               206                    118.07  Female   \n",
       "2            25                59                     83.70    Male   \n",
       "3             5                11                    167.16    Male   \n",
       "4             0                 0                     15.20  Female   \n",
       "\n",
       "            location birth_date  access_rank  join_date         last_online  \\\n",
       "0    Chennai, India  1990-04-29          NaN 2013-03-03 2014-02-04 01:32:00   \n",
       "1             Manila 1995-01-01          NaN 2012-12-13 1900-05-13 02:47:00   \n",
       "2   Detroit,Michigan 1991-08-01          NaN 2008-02-13 1900-03-24 12:48:00   \n",
       "3    Nayarit, Mexico 1990-12-14          NaN 2009-08-31 2014-05-12 16:35:00   \n",
       "4  Malaysia, Kuantan 1998-08-24          NaN 2010-05-10 2012-10-18 19:06:00   \n",
       "\n",
       "   stats_mean_score  stats_rewatched  stats_episodes  \n",
       "0              7.43              0.0          3391.0  \n",
       "1              6.78             80.0          7094.0  \n",
       "2              6.15              6.0          4936.0  \n",
       "3              8.27              1.0         10081.0  \n",
       "4              9.70              6.0           920.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203d90e7278>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['gender'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203d90e7278>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['gender'].value_counts().plot.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "username                            object\n",
       "user_id                              int64\n",
       "user_watching                        int64\n",
       "user_completed                       int64\n",
       "user_onhold                          int64\n",
       "user_dropped                         int64\n",
       "user_plantowatch                     int64\n",
       "user_days_spent_watching           float64\n",
       "gender                              object\n",
       "location                            object\n",
       "birth_date                  datetime64[ns]\n",
       "access_rank                        float64\n",
       "join_date                   datetime64[ns]\n",
       "last_online                 datetime64[ns]\n",
       "stats_mean_score                   float64\n",
       "stats_rewatched                    float64\n",
       "stats_episodes                     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203d90e7278>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['birth_date'].dt.year.hist(bins=50, figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203d90e7278>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['last_online'].dt.year.hist(bins=50, figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "is_executing": false
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    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1993-01-01    365\n",
       "1991-01-01    362\n",
       "1990-01-01    352\n",
       "1992-01-01    352\n",
       "1994-01-01    335\n",
       "1995-01-01    307\n",
       "1989-01-01    307\n",
       "1996-01-01    242\n",
       "1988-01-01    209\n",
       "1997-01-01    204\n",
       "1987-01-01    173\n",
       "1998-01-01    133\n",
       "1986-01-01    130\n",
       "1985-01-01    107\n",
       "1999-01-01     98\n",
       "1930-01-01     81\n",
       "1983-01-01     79\n",
       "1984-01-01     78\n",
       "1993-09-01     53\n",
       "1994-09-01     51\n",
       "1994-03-01     48\n",
       "1996-10-10     47\n",
       "1994-06-01     47\n",
       "1991-11-01     47\n",
       "1995-09-01     46\n",
       "1993-05-05     45\n",
       "1993-03-01     45\n",
       "1995-11-01     44\n",
       "1994-05-20     43\n",
       "1993-09-10     43\n",
       "             ... \n",
       "1977-12-05      1\n",
       "1980-03-18      1\n",
       "1977-06-26      1\n",
       "1972-10-09      1\n",
       "2013-11-15      1\n",
       "2013-10-18      1\n",
       "1980-11-15      1\n",
       "1980-04-13      1\n",
       "1973-06-07      1\n",
       "1930-01-18      1\n",
       "1971-03-22      1\n",
       "1978-01-26      1\n",
       "1981-12-24      1\n",
       "1979-09-12      1\n",
       "1963-09-18      1\n",
       "1961-06-06      1\n",
       "1982-01-18      1\n",
       "1982-06-12      1\n",
       "1983-09-29      1\n",
       "2013-06-05      1\n",
       "2017-12-29      1\n",
       "1974-01-08      1\n",
       "1976-04-21      1\n",
       "1932-05-02      1\n",
       "1930-01-19      1\n",
       "2003-08-19      1\n",
       "2003-09-15      1\n",
       "2010-07-22      1\n",
       "2019-09-10      1\n",
       "1944-11-19      1\n",
       "Name: birth_date, Length: 10310, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['birth_date'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
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   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27130</th>\n",
       "      <td>LulyPie</td>\n",
       "      <td>231964</td>\n",
       "      <td>88</td>\n",
       "      <td>219</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>181</td>\n",
       "      <td>138.56</td>\n",
       "      <td>Female</td>\n",
       "      <td>SinSendGoodMe</td>\n",
       "      <td>1944-11-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009-09-09</td>\n",
       "      <td>1900-01-13 01:01:00</td>\n",
       "      <td>7.54</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8319.0</td>\n",
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       "      username  user_id  user_watching  user_completed  user_onhold  \\\n",
       "27130  LulyPie   231964             88             219            1   \n",
       "\n",
       "       user_dropped  user_plantowatch  user_days_spent_watching  gender  \\\n",
       "27130             7               181                    138.56  Female   \n",
       "\n",
       "            location birth_date  access_rank  join_date         last_online  \\\n",
       "27130  SinSendGoodMe 1944-11-19          NaN 2009-09-09 1900-01-13 01:01:00   \n",
       "\n",
       "       stats_mean_score  stats_rewatched  stats_episodes  \n",
       "27130              7.54             23.0          8319.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
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       "1900-05-13 14:11:00    7\n",
       "1900-05-13 16:19:00    7\n",
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       "1900-05-11 11:43:00    6\n",
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       "2013-09-13 08:29:00    6\n",
       "2018-05-14 12:44:00    6\n",
       "1900-05-12 10:21:00    5\n",
       "1900-05-12 13:21:00    5\n",
       "1900-05-13 12:04:00    5\n",
       "1900-01-18 13:37:00    5\n",
       "2013-09-13 13:00:00    5\n",
       "1900-05-09 15:48:00    5\n",
       "1900-05-14 12:06:00    5\n",
       "1900-05-14 08:57:00    5\n",
       "1900-05-12 20:22:00    5\n",
       "1900-05-14 08:24:00    5\n",
       "1900-05-11 12:52:00    5\n",
       "1900-05-13 09:19:00    5\n",
       "1900-05-13 17:14:00    5\n",
       "1900-05-13 14:01:00    5\n",
       "1900-05-13 13:55:00    5\n",
       "2013-09-13 08:16:00    5\n",
       "1900-05-11 16:30:00    5\n",
       "1900-05-16 19:55:00    5\n",
       "1900-05-12 17:29:00    5\n",
       "1900-05-12 12:10:00    5\n",
       "1900-05-13 12:55:00    5\n",
       "2018-05-16 12:58:00    5\n",
       "1900-05-13 13:31:00    5\n",
       "                      ..\n",
       "2013-10-26 16:28:00    1\n",
       "1900-02-28 10:36:00    1\n",
       "1900-01-20 23:01:00    1\n",
       "1900-03-27 17:26:00    1\n",
       "2016-04-20 17:02:00    1\n",
       "2017-01-14 18:32:00    1\n",
       "2016-11-02 21:23:00    1\n",
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       "2013-10-02 06:45:00    1\n",
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       "2018-05-23 00:55:46    1\n",
       "2018-05-17 09:19:43    1\n",
       "2018-05-23 18:02:28    1\n",
       "1900-04-24 14:41:00    1\n",
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       "1900-01-22 18:48:00    1\n",
       "1900-04-03 20:54:00    1\n",
       "2017-12-20 19:43:00    1\n",
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       "2011-09-23 13:22:00    1\n",
       "1900-04-05 21:09:00    1\n",
       "1900-04-29 10:23:00    1\n",
       "1900-02-06 10:40:00    1\n",
       "1900-05-08 06:10:00    1\n",
       "2015-02-25 08:31:00    1\n",
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   "execution_count": 20,
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       "      <td>RedvelvetDaisuki</td>\n",
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       "      <td>61</td>\n",
       "      <td>396</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>Damonashu</td>\n",
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       "      <td>45</td>\n",
       "      <td>195</td>\n",
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       "      <td>25</td>\n",
       "      <td>59</td>\n",
       "      <td>83.70</td>\n",
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       "      <td>Detroit,Michigan</td>\n",
       "      <td>1991-08-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-02-13</td>\n",
       "      <td>1900-03-24 12:48:00</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4936.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>Bas_G</td>\n",
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       "      <td>35</td>\n",
       "      <td>114</td>\n",
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       "      <td>20</td>\n",
       "      <td>175</td>\n",
       "      <td>30.81</td>\n",
       "      <td>Male</td>\n",
       "      <td>Nijmegen, Nederland</td>\n",
       "      <td>1999-10-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-26</td>\n",
       "      <td>1900-05-10 13:53:00</td>\n",
       "      <td>7.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1847.0</td>\n",
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       "      <td>HimeAria</td>\n",
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       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>20.12</td>\n",
       "      <td>Female</td>\n",
       "      <td>Poland</td>\n",
       "      <td>1996-09-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-09-08</td>\n",
       "      <td>1900-04-27 10:52:00</td>\n",
       "      <td>8.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1314.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Slimak</td>\n",
       "      <td>61677</td>\n",
       "      <td>79</td>\n",
       "      <td>224</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>84</td>\n",
       "      <td>126.17</td>\n",
       "      <td>Male</td>\n",
       "      <td>Poland</td>\n",
       "      <td>1988-02-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-05-18</td>\n",
       "      <td>1900-05-01 05:04:00</td>\n",
       "      <td>7.77</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7472.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           username  user_id  user_watching  user_completed  user_onhold  \\\n",
       "1  RedvelvetDaisuki  1897606             61             396           39   \n",
       "2         Damonashu    37326             45             195           27   \n",
       "5             Bas_G  5015094             35             114            6   \n",
       "6          HimeAria  3129315              2              87            2   \n",
       "8            Slimak    61677             79             224            0   \n",
       "\n",
       "   user_dropped  user_plantowatch  user_days_spent_watching  gender  \\\n",
       "1             0               206                    118.07  Female   \n",
       "2            25                59                     83.70    Male   \n",
       "5            20               175                     30.81    Male   \n",
       "6             0                28                     20.12  Female   \n",
       "8             3                84                    126.17    Male   \n",
       "\n",
       "              location birth_date  access_rank  join_date         last_online  \\\n",
       "1               Manila 1995-01-01          NaN 2012-12-13 1900-05-13 02:47:00   \n",
       "2     Detroit,Michigan 1991-08-01          NaN 2008-02-13 1900-03-24 12:48:00   \n",
       "5  Nijmegen, Nederland 1999-10-24          NaN 2015-11-26 1900-05-10 13:53:00   \n",
       "6               Poland 1996-09-26          NaN 2013-09-08 1900-04-27 10:52:00   \n",
       "8               Poland 1988-02-21          NaN 2008-05-18 1900-05-01 05:04:00   \n",
       "\n",
       "   stats_mean_score  stats_rewatched  stats_episodes  \n",
       "1              6.78             80.0          7094.0  \n",
       "2              6.15              6.0          4936.0  \n",
       "5              7.86              0.0          1847.0  \n",
       "6              8.23              0.0          1314.0  \n",
       "8              7.77              2.0          7472.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[users['last_online'].dt.year == 1900].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203d90e7278>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['join_date'].dt.year.hist(bins=14, figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\__init__.py:424: MatplotlibDeprecationWarning: \n",
      "Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warn_deprecated(\"2.2\", \"Passing one of 'on', 'true', 'off', 'false' as a \"\n"
     ]
    }
   ],
   "source": [
    "(2018 - users['birth_date'].dt.year).plot(kind='hist', bins=80, figsize=(16, 8), title='age')\n",
    "plt.gca().grid('on')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "#now really inferring the best estimation of last online\n",
    "users_last_list_update = get_users_last_list_update_cached()\n",
    "users_last_list_update['last_list_update'] = pd.to_datetime(users_last_list_update['last_list_update'])\n",
    "users_merged = pd.merge(users[['username', 'last_online']], users_last_list_update, left_on='username', right_index=True)\n",
    "users.loc[users['last_online'].dt.year == 1900, 'last_online'] = users_merged[users['last_online'].dt.year == 1900]['last_list_update']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last_list_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>karthiga</th>\n",
       "      <td>2013-05-05 14:38:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RedvelvetDaisuki</th>\n",
       "      <td>2018-05-04 14:59:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Damonashu</th>\n",
       "      <td>2017-07-10 06:52:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bskai</th>\n",
       "      <td>2014-05-12 06:42:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slimak</th>\n",
       "      <td>2018-05-01 12:05:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MistButterfly</th>\n",
       "      <td>2018-05-11 20:53:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kioniel</th>\n",
       "      <td>2017-09-20 00:55:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xinil</th>\n",
       "      <td>2018-03-27 00:36:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ihasabucket</th>\n",
       "      <td>2010-04-18 22:28:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xTheFallenx</th>\n",
       "      <td>2018-05-08 04:27:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-LawlietDN</th>\n",
       "      <td>2007-11-28 23:48:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lithuelle</th>\n",
       "      <td>2015-05-09 22:21:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scootarski</th>\n",
       "      <td>2013-09-14 11:36:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akihara</th>\n",
       "      <td>2018-05-11 18:46:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomoki-sama</th>\n",
       "      <td>2018-05-10 20:28:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sakurei</th>\n",
       "      <td>2018-04-14 08:11:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perfection_Freak</th>\n",
       "      <td>2018-05-11 05:18:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LyannaStark</th>\n",
       "      <td>2017-04-14 01:54:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magedgamed</th>\n",
       "      <td>2018-04-16 11:08:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ShinyShinigami</th>\n",
       "      <td>2018-05-11 21:14:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SenpajBiju</th>\n",
       "      <td>2018-05-11 20:24:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WhatsUpWitches</th>\n",
       "      <td>2018-02-26 02:15:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wizzaroo</th>\n",
       "      <td>2014-10-18 00:58:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seishuku</th>\n",
       "      <td>2018-05-10 21:33:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PrinceRen</th>\n",
       "      <td>2013-06-21 03:44:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ChicoDj_</th>\n",
       "      <td>2018-04-22 13:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brunomp</th>\n",
       "      <td>2018-05-09 15:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mukkashi</th>\n",
       "      <td>2018-05-11 12:02:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rukawa</th>\n",
       "      <td>2017-08-30 22:55:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Best-of-Anime</th>\n",
       "      <td>2012-12-01 13:52:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eirie</th>\n",
       "      <td>2014-01-04 13:04:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wargod</th>\n",
       "      <td>2010-03-29 04:24:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treakwon-Senji</th>\n",
       "      <td>2014-08-29 02:56:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JMc_SetoKai_LoVe</th>\n",
       "      <td>2009-12-23 05:45:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hinogurl_mikha</th>\n",
       "      <td>2008-04-05 11:36:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinfulrobot</th>\n",
       "      <td>2016-03-03 20:18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wishyan</th>\n",
       "      <td>2010-09-16 15:54:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StormyDayze</th>\n",
       "      <td>2017-03-11 16:52:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ecchi_master</th>\n",
       "      <td>2010-08-23 01:46:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blackdiamonds</th>\n",
       "      <td>2010-05-19 13:19:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sk8ergirl</th>\n",
       "      <td>1970-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>igrowgrass</th>\n",
       "      <td>2009-03-27 22:57:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deathnight6</th>\n",
       "      <td>2008-08-08 05:45:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kuro_Senin</th>\n",
       "      <td>2014-06-26 19:22:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slipinside</th>\n",
       "      <td>2009-10-15 22:42:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hotrod217</th>\n",
       "      <td>2010-01-04 22:19:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridgester</th>\n",
       "      <td>2009-12-20 16:49:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leo_584</th>\n",
       "      <td>2010-07-26 06:26:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joshua_Parris</th>\n",
       "      <td>2016-11-23 09:34:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Destoria-LeVaun</th>\n",
       "      <td>2009-12-05 02:03:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MariiTrindade_</th>\n",
       "      <td>2011-08-08 23:39:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mariapeterson</th>\n",
       "      <td>2012-08-02 08:45:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nemo584</th>\n",
       "      <td>2011-02-02 08:43:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>don-vvv</th>\n",
       "      <td>2010-01-11 08:08:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drteitelbaum</th>\n",
       "      <td>2013-09-30 09:47:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virtualreality</th>\n",
       "      <td>2009-11-06 21:14:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bestmarriages</th>\n",
       "      <td>2013-09-23 07:45:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zonnebuilding</th>\n",
       "      <td>2013-09-23 07:55:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>karyy</th>\n",
       "      <td>2013-08-05 12:45:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZAQekijouron02</th>\n",
       "      <td>2015-07-17 08:15:10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116108 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    last_list_update\n",
       "karthiga         2013-05-05 14:38:12\n",
       "RedvelvetDaisuki 2018-05-04 14:59:17\n",
       "Damonashu        2017-07-10 06:52:54\n",
       "bskai            2014-05-12 06:42:27\n",
       "Slimak           2018-05-01 12:05:21\n",
       "MistButterfly    2018-05-11 20:53:45\n",
       "kioniel          2017-09-20 00:55:26\n",
       "Xinil            2018-03-27 00:36:02\n",
       "ihasabucket      2010-04-18 22:28:10\n",
       "xTheFallenx      2018-05-08 04:27:20\n",
       "L-LawlietDN      2007-11-28 23:48:24\n",
       "Lithuelle        2015-05-09 22:21:25\n",
       "scootarski       2013-09-14 11:36:54\n",
       "Akihara          2018-05-11 18:46:58\n",
       "Tomoki-sama      2018-05-10 20:28:44\n",
       "Sakurei          2018-04-14 08:11:49\n",
       "Perfection_Freak 2018-05-11 05:18:44\n",
       "LyannaStark      2017-04-14 01:54:00\n",
       "magedgamed       2018-04-16 11:08:44\n",
       "ShinyShinigami   2018-05-11 21:14:36\n",
       "SenpajBiju       2018-05-11 20:24:16\n",
       "WhatsUpWitches   2018-02-26 02:15:57\n",
       "Wizzaroo         2014-10-18 00:58:26\n",
       "Seishuku         2018-05-10 21:33:35\n",
       "PrinceRen        2013-06-21 03:44:41\n",
       "ChicoDj_         2018-04-22 13:52:00\n",
       "brunomp          2018-05-09 15:20:00\n",
       "Mukkashi         2018-05-11 12:02:54\n",
       "Rukawa           2017-08-30 22:55:08\n",
       "Best-of-Anime    2012-12-01 13:52:57\n",
       "...                              ...\n",
       "Eirie            2014-01-04 13:04:07\n",
       "wargod           2010-03-29 04:24:12\n",
       "Treakwon-Senji   2014-08-29 02:56:03\n",
       "JMc_SetoKai_LoVe 2009-12-23 05:45:14\n",
       "hinogurl_mikha   2008-04-05 11:36:20\n",
       "sinfulrobot      2016-03-03 20:18:00\n",
       "wishyan          2010-09-16 15:54:40\n",
       "StormyDayze      2017-03-11 16:52:14\n",
       "ecchi_master     2010-08-23 01:46:02\n",
       "blackdiamonds    2010-05-19 13:19:06\n",
       "sk8ergirl        1970-01-01 00:00:00\n",
       "igrowgrass       2009-03-27 22:57:34\n",
       "Deathnight6      2008-08-08 05:45:18\n",
       "Kuro_Senin       2014-06-26 19:22:32\n",
       "slipinside       2009-10-15 22:42:45\n",
       "hotrod217        2010-01-04 22:19:20\n",
       "Ridgester        2009-12-20 16:49:52\n",
       "Leo_584          2010-07-26 06:26:13\n",
       "Joshua_Parris    2016-11-23 09:34:27\n",
       "Destoria-LeVaun  2009-12-05 02:03:13\n",
       "MariiTrindade_   2011-08-08 23:39:42\n",
       "mariapeterson    2012-08-02 08:45:33\n",
       "Nemo584          2011-02-02 08:43:31\n",
       "don-vvv          2010-01-11 08:08:38\n",
       "drteitelbaum     2013-09-30 09:47:23\n",
       "virtualreality   2009-11-06 21:14:59\n",
       "bestmarriages    2013-09-23 07:45:21\n",
       "Zonnebuilding    2013-09-23 07:55:09\n",
       "karyy            2013-08-05 12:45:06\n",
       "ZAQekijouron02   2015-07-17 08:15:10\n",
       "\n",
       "[116108 rows x 1 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_last_list_update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>last_online</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RedvelvetDaisuki</td>\n",
       "      <td>2018-05-04 14:59:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Damonashu</td>\n",
       "      <td>2017-07-10 06:52:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bas_G</td>\n",
       "      <td>2018-05-10 20:53:37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           username         last_online\n",
       "1  RedvelvetDaisuki 2018-05-04 14:59:17\n",
       "2         Damonashu 2017-07-10 06:52:54\n",
       "5             Bas_G 2018-05-10 20:53:37"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[users['username'].isin(['RedvelvetDaisuki', 'Damonashu', 'Bas_G'])][['username', 'last_online']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203d90e7278>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['last_online'].dt.year.hist(bins=11, figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "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>anime_id</th>\n",
       "      <th>title</th>\n",
       "      <th>title_english</th>\n",
       "      <th>title_japanese</th>\n",
       "      <th>title_synonyms</th>\n",
       "      <th>image_url</th>\n",
       "      <th>type</th>\n",
       "      <th>source</th>\n",
       "      <th>episodes</th>\n",
       "      <th>status</th>\n",
       "      <th>...</th>\n",
       "      <th>background</th>\n",
       "      <th>premiered</th>\n",
       "      <th>broadcast</th>\n",
       "      <th>related</th>\n",
       "      <th>producer</th>\n",
       "      <th>licensor</th>\n",
       "      <th>studio</th>\n",
       "      <th>genre</th>\n",
       "      <th>opening_theme</th>\n",
       "      <th>ending_theme</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11013</td>\n",
       "      <td>Inu x Boku SS</td>\n",
       "      <td>Inu X Boku Secret Service</td>\n",
       "      <td>妖狐×僕SS</td>\n",
       "      <td>Youko x Boku SS</td>\n",
       "      <td>https://myanimelist.cdn-dena.com/images/anime/...</td>\n",
       "      <td>TV</td>\n",
       "      <td>Manga</td>\n",
       "      <td>12</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>...</td>\n",
       "      <td>Inu x Boku SS was licensed by Sentai Filmworks...</td>\n",
       "      <td>Winter 2012</td>\n",
       "      <td>Fridays at Unknown</td>\n",
       "      <td>{'Adaptation': [{'mal_id': 17207, 'type': 'man...</td>\n",
       "      <td>Aniplex, Square Enix, Mainichi Broadcasting Sy...</td>\n",
       "      <td>Sentai Filmworks</td>\n",
       "      <td>David Production</td>\n",
       "      <td>Comedy, Supernatural, Romance, Shounen</td>\n",
       "      <td>['\"Nirvana\" by MUCC']</td>\n",
       "      <td>['#1: \"Nirvana\" by MUCC (eps 1, 11-12)', '#2: ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2104</td>\n",
       "      <td>Seto no Hanayome</td>\n",
       "      <td>My Bride is a Mermaid</td>\n",
       "      <td>瀬戸の花嫁</td>\n",
       "      <td>The Inland Sea Bride</td>\n",
       "      <td>https://myanimelist.cdn-dena.com/images/anime/...</td>\n",
       "      <td>TV</td>\n",
       "      <td>Manga</td>\n",
       "      <td>26</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spring 2007</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>{'Adaptation': [{'mal_id': 759, 'type': 'manga...</td>\n",
       "      <td>TV Tokyo, AIC, Square Enix, Sotsu</td>\n",
       "      <td>Funimation</td>\n",
       "      <td>Gonzo</td>\n",
       "      <td>Comedy, Parody, Romance, School, Shounen</td>\n",
       "      <td>['\"Romantic summer\" by SUN&amp;LUNAR']</td>\n",
       "      <td>['#1: \"Ashita e no Hikari (明日への光)\" by Asuka Hi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5262</td>\n",
       "      <td>Shugo Chara!! Doki</td>\n",
       "      <td>Shugo Chara!! Doki</td>\n",
       "      <td>しゅごキャラ！！どきっ</td>\n",
       "      <td>Shugo Chara Ninenme, Shugo Chara! Second Year</td>\n",
       "      <td>https://myanimelist.cdn-dena.com/images/anime/...</td>\n",
       "      <td>TV</td>\n",
       "      <td>Manga</td>\n",
       "      <td>51</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fall 2008</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>{'Adaptation': [{'mal_id': 101, 'type': 'manga...</td>\n",
       "      <td>TV Tokyo, Sotsu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Satelight</td>\n",
       "      <td>Comedy, Magic, School, Shoujo</td>\n",
       "      <td>['#1: \"Minna no Tamago (みんなのたまご)\" by Shugo Cha...</td>\n",
       "      <td>['#1: \"Rottara Rottara (ロッタラ ロッタラ)\" by Buono! ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>721</td>\n",
       "      <td>Princess Tutu</td>\n",
       "      <td>Princess Tutu</td>\n",
       "      <td>プリンセスチュチュ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://myanimelist.cdn-dena.com/images/anime/...</td>\n",
       "      <td>TV</td>\n",
       "      <td>Original</td>\n",
       "      <td>38</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>...</td>\n",
       "      <td>Princess Tutu aired in two parts. The first pa...</td>\n",
       "      <td>Summer 2002</td>\n",
       "      <td>Fridays at Unknown</td>\n",
       "      <td>{'Adaptation': [{'mal_id': 1581, 'type': 'mang...</td>\n",
       "      <td>Memory-Tech, GANSIS, Marvelous AQL</td>\n",
       "      <td>ADV Films</td>\n",
       "      <td>Hal Film Maker</td>\n",
       "      <td>Comedy, Drama, Magic, Romance, Fantasy</td>\n",
       "      <td>['\"Morning Grace\" by Ritsuko Okazaki']</td>\n",
       "      <td>['\"Watashi No Ai Wa Chiisaikeredo\" by Ritsuko ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12365</td>\n",
       "      <td>Bakuman. 3rd Season</td>\n",
       "      <td>Bakuman.</td>\n",
       "      <td>バクマン。</td>\n",
       "      <td>Bakuman Season 3</td>\n",
       "      <td>https://myanimelist.cdn-dena.com/images/anime/...</td>\n",
       "      <td>TV</td>\n",
       "      <td>Manga</td>\n",
       "      <td>25</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fall 2012</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>{'Adaptation': [{'mal_id': 9711, 'type': 'mang...</td>\n",
       "      <td>NHK, Shueisha</td>\n",
       "      <td>NaN</td>\n",
       "      <td>J.C.Staff</td>\n",
       "      <td>Comedy, Drama, Romance, Shounen</td>\n",
       "      <td>['#1: \"Moshimo no Hanashi (もしもの話)\" by nano.RIP...</td>\n",
       "      <td>['#1: \"Pride on Everyday\" by Sphere (eps 1-13)...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   anime_id                title              title_english title_japanese  \\\n",
       "0     11013        Inu x Boku SS  Inu X Boku Secret Service         妖狐×僕SS   \n",
       "1      2104     Seto no Hanayome      My Bride is a Mermaid          瀬戸の花嫁   \n",
       "2      5262   Shugo Chara!! Doki         Shugo Chara!! Doki    しゅごキャラ！！どきっ   \n",
       "3       721        Princess Tutu              Princess Tutu      プリンセスチュチュ   \n",
       "4     12365  Bakuman. 3rd Season                   Bakuman.          バクマン。   \n",
       "\n",
       "                                  title_synonyms  \\\n",
       "0                                Youko x Boku SS   \n",
       "1                           The Inland Sea Bride   \n",
       "2  Shugo Chara Ninenme, Shugo Chara! Second Year   \n",
       "3                                            NaN   \n",
       "4                               Bakuman Season 3   \n",
       "\n",
       "                                           image_url type    source  episodes  \\\n",
       "0  https://myanimelist.cdn-dena.com/images/anime/...   TV     Manga        12   \n",
       "1  https://myanimelist.cdn-dena.com/images/anime/...   TV     Manga        26   \n",
       "2  https://myanimelist.cdn-dena.com/images/anime/...   TV     Manga        51   \n",
       "3  https://myanimelist.cdn-dena.com/images/anime/...   TV  Original        38   \n",
       "4  https://myanimelist.cdn-dena.com/images/anime/...   TV     Manga        25   \n",
       "\n",
       "            status  ...                                         background  \\\n",
       "0  Finished Airing  ...  Inu x Boku SS was licensed by Sentai Filmworks...   \n",
       "1  Finished Airing  ...                                                NaN   \n",
       "2  Finished Airing  ...                                                NaN   \n",
       "3  Finished Airing  ...  Princess Tutu aired in two parts. The first pa...   \n",
       "4  Finished Airing  ...                                                NaN   \n",
       "\n",
       "     premiered           broadcast  \\\n",
       "0  Winter 2012  Fridays at Unknown   \n",
       "1  Spring 2007             Unknown   \n",
       "2    Fall 2008             Unknown   \n",
       "3  Summer 2002  Fridays at Unknown   \n",
       "4    Fall 2012             Unknown   \n",
       "\n",
       "                                             related  \\\n",
       "0  {'Adaptation': [{'mal_id': 17207, 'type': 'man...   \n",
       "1  {'Adaptation': [{'mal_id': 759, 'type': 'manga...   \n",
       "2  {'Adaptation': [{'mal_id': 101, 'type': 'manga...   \n",
       "3  {'Adaptation': [{'mal_id': 1581, 'type': 'mang...   \n",
       "4  {'Adaptation': [{'mal_id': 9711, 'type': 'mang...   \n",
       "\n",
       "                                            producer          licensor  \\\n",
       "0  Aniplex, Square Enix, Mainichi Broadcasting Sy...  Sentai Filmworks   \n",
       "1                  TV Tokyo, AIC, Square Enix, Sotsu        Funimation   \n",
       "2                                    TV Tokyo, Sotsu               NaN   \n",
       "3                 Memory-Tech, GANSIS, Marvelous AQL         ADV Films   \n",
       "4                                      NHK, Shueisha               NaN   \n",
       "\n",
       "             studio                                     genre  \\\n",
       "0  David Production    Comedy, Supernatural, Romance, Shounen   \n",
       "1             Gonzo  Comedy, Parody, Romance, School, Shounen   \n",
       "2         Satelight             Comedy, Magic, School, Shoujo   \n",
       "3    Hal Film Maker    Comedy, Drama, Magic, Romance, Fantasy   \n",
       "4         J.C.Staff           Comedy, Drama, Romance, Shounen   \n",
       "\n",
       "                                       opening_theme  \\\n",
       "0                              ['\"Nirvana\" by MUCC']   \n",
       "1                 ['\"Romantic summer\" by SUN&LUNAR']   \n",
       "2  ['#1: \"Minna no Tamago (みんなのたまご)\" by Shugo Cha...   \n",
       "3             ['\"Morning Grace\" by Ritsuko Okazaki']   \n",
       "4  ['#1: \"Moshimo no Hanashi (もしもの話)\" by nano.RIP...   \n",
       "\n",
       "                                        ending_theme  \n",
       "0  ['#1: \"Nirvana\" by MUCC (eps 1, 11-12)', '#2: ...  \n",
       "1  ['#1: \"Ashita e no Hikari (明日への光)\" by Asuka Hi...  \n",
       "2  ['#1: \"Rottara Rottara (ロッタラ ロッタラ)\" by Buono! ...  \n",
       "3  ['\"Watashi No Ai Wa Chiisaikeredo\" by Ritsuko ...  \n",
       "4  ['#1: \"Pride on Everyday\" by Sphere (eps 1-13)...  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       6855\n",
       "2       1187\n",
       "12      1034\n",
       "13       637\n",
       "3        558\n",
       "26       542\n",
       "0        503\n",
       "4        373\n",
       "6        303\n",
       "24       214\n",
       "52       199\n",
       "25       176\n",
       "10       152\n",
       "5        144\n",
       "51       111\n",
       "50        92\n",
       "7         86\n",
       "11        86\n",
       "39        85\n",
       "8         66\n",
       "9         48\n",
       "22        47\n",
       "20        46\n",
       "23        37\n",
       "16        34\n",
       "48        34\n",
       "14        33\n",
       "49        33\n",
       "47        29\n",
       "15        27\n",
       "        ... \n",
       "726        1\n",
       "1006       1\n",
       "220        1\n",
       "172        1\n",
       "132        1\n",
       "124        1\n",
       "122        1\n",
       "154        1\n",
       "162        1\n",
       "258        1\n",
       "330        1\n",
       "1274       1\n",
       "1306       1\n",
       "1818       1\n",
       "67         1\n",
       "83         1\n",
       "131        1\n",
       "147        1\n",
       "155        1\n",
       "203        1\n",
       "283        1\n",
       "291        1\n",
       "331        1\n",
       "475        1\n",
       "1787       1\n",
       "87         1\n",
       "431        1\n",
       "79         1\n",
       "92         1\n",
       "1471       1\n",
       "Name: episodes, Length: 196, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes['episodes'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Gothicbabe26\n",
      "0       LeoAnimeMan\n",
      "1       narutofan69\n",
      "2           Angel_D\n",
      "3    dheiin_society\n",
      "4           heizwei\n",
      "5           warrose\n",
      "6   Takehara_Minami\n",
      "7           ozieboy\n",
      "8        YoruNoMajo\n",
      "9              Xiet\n",
      "10       Kihara-Kun\n",
      "11          donakos\n",
      "12        Amaru-kun\n",
      "13     Archeologist\n",
      "14  konan_akatsuki_\n",
      "15           MoiZzZ\n",
      "16   TheDerpyMuffin\n",
      "17            ajkla\n",
      "18      zaturama007\n",
      "19         thomasrm\n",
      "20       The-Marker\n",
      "21           Taizai\n",
      "22       Koska-chan\n",
      "23    asswithnolife\n",
      "num of users for ban: 24\n"
     ]
    }
   ],
   "source": [
    "# banning users with 65535 episodes of single anime in their list -> not trustworthy\n",
    "users_for_ban = get_users_for_episode_count_ban_cached()\n",
    "print(users_for_ban)\n",
    "print('num of users for ban:', len(users_for_ban))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users = users[~users['username'].isin(users_for_ban)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['anime_id', 'title', 'title_english', 'title_japanese',\n",
       "       'title_synonyms', 'image_url', 'type', 'source', 'episodes', 'status',\n",
       "       'airing', 'aired_string', 'aired', 'duration', 'rating', 'score',\n",
       "       'scored_by', 'rank', 'popularity', 'members', 'favorites', 'background',\n",
       "       'premiered', 'broadcast', 'related', 'producer', 'licensor', 'studio',\n",
       "       'genre', 'opening_theme', 'ending_theme'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "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>anime_id</th>\n",
       "      <th>title</th>\n",
       "      <th>episodes</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11013</td>\n",
       "      <td>Inu x Boku SS</td>\n",
       "      <td>12</td>\n",
       "      <td>24 min. per ep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2104</td>\n",
       "      <td>Seto no Hanayome</td>\n",
       "      <td>26</td>\n",
       "      <td>24 min. per ep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5262</td>\n",
       "      <td>Shugo Chara!! Doki</td>\n",
       "      <td>51</td>\n",
       "      <td>24 min. per ep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>721</td>\n",
       "      <td>Princess Tutu</td>\n",
       "      <td>38</td>\n",
       "      <td>16 min. per ep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12365</td>\n",
       "      <td>Bakuman. 3rd Season</td>\n",
       "      <td>25</td>\n",
       "      <td>24 min. per ep.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   anime_id                title  episodes         duration\n",
       "0     11013        Inu x Boku SS        12  24 min. per ep.\n",
       "1      2104     Seto no Hanayome        26  24 min. per ep.\n",
       "2      5262   Shugo Chara!! Doki        51  24 min. per ep.\n",
       "3       721        Princess Tutu        38  16 min. per ep.\n",
       "4     12365  Bakuman. 3rd Season        25  24 min. per ep."
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes[['anime_id', 'title', 'episodes', 'duration']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#animes['duration'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# calculating watch time in minutes and adding it to new column for anime\n",
    "def str_to_minutes(string):\n",
    "    m = re.search('((\\d+) hr\\.)?( ?(\\d+) min\\.)?( ?(\\d+) sec\\.)?( per ep\\.)?', string)\n",
    "    hours = 0 if m.group(2) is None else int(m.group(2))\n",
    "    minutes = 0 if m.group(4) is None else int(m.group(4))\n",
    "    seconds = 0 if m.group(6) is None else int(m.group(6))\n",
    "    return hours * 60 + minutes + seconds / 60\n",
    "\n",
    "animes['duration_min'] = animes['duration'].apply(str_to_minutes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>anime_id</th>\n",
       "      <th>title</th>\n",
       "      <th>episodes</th>\n",
       "      <th>duration</th>\n",
       "      <th>duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11013</td>\n",
       "      <td>Inu x Boku SS</td>\n",
       "      <td>12</td>\n",
       "      <td>24 min. per ep.</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2104</td>\n",
       "      <td>Seto no Hanayome</td>\n",
       "      <td>26</td>\n",
       "      <td>24 min. per ep.</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5262</td>\n",
       "      <td>Shugo Chara!! Doki</td>\n",
       "      <td>51</td>\n",
       "      <td>24 min. per ep.</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>721</td>\n",
       "      <td>Princess Tutu</td>\n",
       "      <td>38</td>\n",
       "      <td>16 min. per ep.</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12365</td>\n",
       "      <td>Bakuman. 3rd Season</td>\n",
       "      <td>25</td>\n",
       "      <td>24 min. per ep.</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   anime_id                title  episodes         duration  duration_min\n",
       "0     11013        Inu x Boku SS        12  24 min. per ep.          24.0\n",
       "1      2104     Seto no Hanayome        26  24 min. per ep.          24.0\n",
       "2      5262   Shugo Chara!! Doki        51  24 min. per ep.          24.0\n",
       "3       721        Princess Tutu        38  16 min. per ep.          16.0\n",
       "4     12365  Bakuman. 3rd Season        25  24 min. per ep.          24.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes[['anime_id', 'title', 'episodes', 'duration', 'duration_min']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_watching</th>\n",
       "      <th>user_completed</th>\n",
       "      <th>user_onhold</th>\n",
       "      <th>user_dropped</th>\n",
       "      <th>user_plantowatch</th>\n",
       "      <th>user_days_spent_watching</th>\n",
       "      <th>gender</th>\n",
       "      <th>location</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>access_rank</th>\n",
       "      <th>join_date</th>\n",
       "      <th>last_online</th>\n",
       "      <th>stats_mean_score</th>\n",
       "      <th>stats_rewatched</th>\n",
       "      <th>stats_episodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7528</th>\n",
       "      <td>heeroYuy</td>\n",
       "      <td>20638</td>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>38.51</td>\n",
       "      <td>Male</td>\n",
       "      <td>Philippines, digos city</td>\n",
       "      <td>1990-12-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007-11-07</td>\n",
       "      <td>2008-02-16 03:46:00</td>\n",
       "      <td>9.51</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2297.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      username  user_id  user_watching  user_completed  user_onhold  \\\n",
       "7528  heeroYuy    20638             18              69            3   \n",
       "\n",
       "      user_dropped  user_plantowatch  user_days_spent_watching gender  \\\n",
       "7528             4                32                     38.51   Male   \n",
       "\n",
       "                     location birth_date  access_rank  join_date  \\\n",
       "7528  Philippines, digos city 1990-12-29          NaN 2007-11-07   \n",
       "\n",
       "             last_online  stats_mean_score  stats_rewatched  stats_episodes  \n",
       "7528 2008-02-16 03:46:00              9.51              0.0          2297.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[users['username'] == 'heeroYuy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_watching</th>\n",
       "      <th>user_completed</th>\n",
       "      <th>user_onhold</th>\n",
       "      <th>user_dropped</th>\n",
       "      <th>user_plantowatch</th>\n",
       "      <th>user_days_spent_watching</th>\n",
       "      <th>gender</th>\n",
       "      <th>location</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>access_rank</th>\n",
       "      <th>join_date</th>\n",
       "      <th>last_online</th>\n",
       "      <th>stats_mean_score</th>\n",
       "      <th>stats_rewatched</th>\n",
       "      <th>stats_episodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20197</th>\n",
       "      <td>abystoma2</td>\n",
       "      <td>3336425</td>\n",
       "      <td>180</td>\n",
       "      <td>5121</td>\n",
       "      <td>296</td>\n",
       "      <td>0</td>\n",
       "      <td>969</td>\n",
       "      <td>629.24</td>\n",
       "      <td>Male</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>1997-06-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-11-14</td>\n",
       "      <td>2018-05-18 01:22:00</td>\n",
       "      <td>4.74</td>\n",
       "      <td>147.0</td>\n",
       "      <td>44151.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        username  user_id  user_watching  user_completed  user_onhold  \\\n",
       "20197  abystoma2  3336425            180            5121          296   \n",
       "\n",
       "       user_dropped  user_plantowatch  user_days_spent_watching gender  \\\n",
       "20197             0               969                    629.24   Male   \n",
       "\n",
       "             location birth_date  access_rank  join_date         last_online  \\\n",
       "20197  Czech Republic 1997-06-02          NaN 2013-11-14 2018-05-18 01:22:00   \n",
       "\n",
       "       stats_mean_score  stats_rewatched  stats_episodes  \n",
       "20197              4.74            147.0         44151.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[users['username'] == 'abystoma2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# one weird user with 2601351.02 days watches ruins the plot, ommiting him by xlim\n",
    "users['user_days_spent_watching'].hist(bins=5000, figsize=(16, 8))\n",
    "plt.gca().set_yscale('log')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2601351.02"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['user_days_spent_watching'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_watching</th>\n",
       "      <th>user_completed</th>\n",
       "      <th>user_onhold</th>\n",
       "      <th>user_dropped</th>\n",
       "      <th>user_plantowatch</th>\n",
       "      <th>user_days_spent_watching</th>\n",
       "      <th>gender</th>\n",
       "      <th>location</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>access_rank</th>\n",
       "      <th>join_date</th>\n",
       "      <th>last_online</th>\n",
       "      <th>stats_mean_score</th>\n",
       "      <th>stats_rewatched</th>\n",
       "      <th>stats_episodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19777</th>\n",
       "      <td>warrose</td>\n",
       "      <td>443232</td>\n",
       "      <td>35</td>\n",
       "      <td>2034</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2601351.02</td>\n",
       "      <td>Male</td>\n",
       "      <td>San Luis</td>\n",
       "      <td>1994-01-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-02-01</td>\n",
       "      <td>2014-12-28 21:54:00</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>243794881.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      username  user_id  user_watching  user_completed  user_onhold  \\\n",
       "19777  warrose   443232             35            2034            0   \n",
       "\n",
       "       user_dropped  user_plantowatch  user_days_spent_watching gender  \\\n",
       "19777             1                 7                2601351.02   Male   \n",
       "\n",
       "       location birth_date  access_rank  join_date         last_online  \\\n",
       "19777  San Luis 1994-01-21          NaN 2011-02-01 2014-12-28 21:54:00   \n",
       "\n",
       "       stats_mean_score  stats_rewatched  stats_episodes  \n",
       "19777              1.01              0.0     243794881.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[users['user_days_spent_watching'] == 2601351.02]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# now finally fixing days watch times\n",
    "user_days_spent_watching = get_user_days_spent_watching_cached()\n",
    "users_merged = pd.merge(users[['username']], user_days_spent_watching, left_on='username', right_index=True)\n",
    "users['user_days_spent_watching'] = users_merged['days_spent_watching']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users['user_days_spent_watching'].hist(bins=1000, figsize=(16, 8))\n",
    "plt.gca().set_yscale('log')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['username', 'user_id', 'user_watching', 'user_completed', 'user_onhold',\n",
       "       'user_dropped', 'user_plantowatch', 'user_days_spent_watching',\n",
       "       'gender', 'location', 'birth_date', 'access_rank', 'join_date',\n",
       "       'last_online', 'stats_mean_score', 'stats_rewatched', 'stats_episodes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# so we need also to check number of watched episodes and fix them\n",
    "users['stats_episodes'].hist(bins=5000, figsize=(16, 8))\n",
    "plt.gca().set_yscale('log')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# actually fixing number of episodes\n",
    "user_episodes_cached = get_users_stats_episodes_cached()\n",
    "users_merged = pd.merge(users[['username']], user_episodes_cached, left_on='username', right_index=True)\n",
    "users['stats_episodes'] = users_merged['stats_episodes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users['stats_episodes'].hist(bins=500, figsize=(16, 8))\n",
    "plt.gca().set_yscale('log')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>stats_episodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47808</th>\n",
       "      <td>ComfyLoli</td>\n",
       "      <td>120652.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101868</th>\n",
       "      <td>Tsutaee</td>\n",
       "      <td>118093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41507</th>\n",
       "      <td>TsukasaKei</td>\n",
       "      <td>100333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30447</th>\n",
       "      <td>spacecowboy</td>\n",
       "      <td>93622.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20966</th>\n",
       "      <td>Dedzapadlo</td>\n",
       "      <td>82260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74907</th>\n",
       "      <td>De_Baer</td>\n",
       "      <td>76422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62254</th>\n",
       "      <td>VincentHarkonnen</td>\n",
       "      <td>74422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74013</th>\n",
       "      <td>xbhrjd</td>\n",
       "      <td>74347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8728</th>\n",
       "      <td>dumnorix98</td>\n",
       "      <td>62347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17366</th>\n",
       "      <td>Loomir</td>\n",
       "      <td>61370.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                username  stats_episodes\n",
       "47808          ComfyLoli        120652.0\n",
       "101868           Tsutaee        118093.0\n",
       "41507         TsukasaKei        100333.0\n",
       "30447        spacecowboy         93622.0\n",
       "20966         Dedzapadlo         82260.0\n",
       "74907            De_Baer         76422.0\n",
       "62254   VincentHarkonnen         74422.0\n",
       "74013             xbhrjd         74347.0\n",
       "8728          dumnorix98         62347.0\n",
       "17366             Loomir         61370.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.loc[users['stats_episodes'].nlargest(10).index][['username', 'stats_episodes']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>username</th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_watching</th>\n",
       "      <th>user_completed</th>\n",
       "      <th>user_onhold</th>\n",
       "      <th>user_dropped</th>\n",
       "      <th>user_plantowatch</th>\n",
       "      <th>user_days_spent_watching</th>\n",
       "      <th>gender</th>\n",
       "      <th>location</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>access_rank</th>\n",
       "      <th>join_date</th>\n",
       "      <th>last_online</th>\n",
       "      <th>stats_mean_score</th>\n",
       "      <th>stats_rewatched</th>\n",
       "      <th>stats_episodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [username, user_id, user_watching, user_completed, user_onhold, user_dropped, user_plantowatch, user_days_spent_watching, gender, location, birth_date, access_rank, join_date, last_online, stats_mean_score, stats_rewatched, stats_episodes]\n",
       "Index: []"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[users['stats_episodes'] == 133457]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now I have some basic filtering done and data is fixed, inspecting birth date and age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users['age'] = 2018 - users['birth_date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:848: UserWarning: aspect is not supported for Axes with xscale=linear, yscale=log\n",
      "  self.apply_aspect()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203d90e7278>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['age'].value_counts().sort_index().plot.bar(figsize=(16, 10), title='users age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:848: UserWarning: aspect is not supported for Axes with xscale=linear, yscale=log\n",
      "  self.apply_aspect()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203d90e7278>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['age'].value_counts().sort_index().plot.bar(figsize=(16, 10), title='users age', logy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:848: UserWarning: aspect is not supported for Axes with xscale=linear, yscale=log\n",
      "  self.apply_aspect()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203d90e7278>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['birth_date'].dt.dayofyear.value_counts().sort_index().plot.bar(figsize=(16, 10), title='day of year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:2575: UserWarning: aspect is not supported for Axes with xscale=linear, yscale=log\n",
      "  self.apply_aspect()\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:4371: UserWarning: aspect is not supported for Axes with xscale=linear, yscale=log\n",
      "  self.apply_aspect()\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:4371: UserWarning: aspect is not supported for Axes with xscale=linear, yscale=log\n",
      "  self.apply_aspect()\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:848: UserWarning: aspect is not supported for Axes with xscale=linear, yscale=log\n",
      "  self.apply_aspect()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this shows how people born in 1.1. are biased and thus can not be trusted\n",
    "total_count = users['age'].count()\n",
    "first_jan_count = users[users['birth_date'].dt.dayofyear == 1]['age'].count()\n",
    "plt.figure(figsize=(16, 10))\n",
    "ax = plt.gca()\n",
    "\n",
    "users_in_first_jan = users[users['birth_date'].dt.dayofyear == 1]['age'].value_counts()\n",
    "users_in_first_jan = users_in_first_jan.reindex(users['age'].value_counts().index)\n",
    "users['age'].value_counts().sort_index().plot.bar(ax=ax, title='users age all vs 1.1.', color='blue', alpha=0.5)\n",
    "users_in_first_jan.sort_index().plot.bar(ax=ax, color='red', alpha=0.5, secondary_y=True)\n",
    "#ax.set_xlim(users['age'].min(), users['age'].max())\n",
    "#plt.xticks(users['age'].value_counts().index)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import zodiac_sign\n",
    "import matplotlib.dates as mdates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users['zodiac_sign'] = users['birth_date'].apply(zodiac_sign.get_zodiac_sign)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n",
       " <a list of 12 Text xticklabel objects>)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['zodiac_sign'].value_counts().sort_index().plot.bar(figsize=(16, 10), title='Zodiac sign')\n",
    "plt.xticks(rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# česky:\n",
    "en_cz_zodiac = {\n",
    "    'Aries': 'Beran',\n",
    "    'Taurus': 'Býk',\n",
    "    'Gemini': 'Blíženci',\n",
    "    'Cancer': 'Rak',\n",
    "    'Leo': 'Lev',\n",
    "    'Virgo': 'Panna',\n",
    "    'Libra': 'Váhy',\n",
    "    'Scorpio': 'Štír',\n",
    "    'Sagittarius': 'Střelec',\n",
    "    'Capricorn': 'Kozoroh',\n",
    "    'Aquarius': 'Vodnář',\n",
    "    'Pisces': 'Ryby',\n",
    "}\n",
    "\n",
    "time_ordered_zodiac = ['Kozoroh', 'Vodnář', 'Ryby', 'Beran', 'Býk', 'Blíženci', 'Rak', 'Lev', 'Panna', 'Váhy', 'Štír', 'Střelec']\n",
    "users['zodiac_sign'].apply(lambda x: en_cz_zodiac[x]).value_counts().reindex(time_ordered_zodiac).plot.bar(figsize=(16, 10), title='Znamení zvěrokruhu')\n",
    "plt.xticks(rotation=0)\n",
    "plt.savefig('graphs/znameni-zverokruhu-2.png', bbox_inches=\"tight\")\n",
    "# todo: seřadit zvěrokruh podle data, ne podle abecedy\n",
    "# todo 2: dodat ke všem grafům grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203e95012e8>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['zodiac_sign'].value_counts().sort_index().plot.pie(figsize=(16, 10), title='Zodiac sign') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((22, 12), (19, 1))"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capricorn_idx = [(index, name) for index, name in zodiac_sign.en_dict if name == 'Capricorn'][0][0]\n",
    "capri_r = zodiac_sign.sign_dates[capricorn_idx]\n",
    "capri_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "capri_range_condition = ((users['birth_date'].dt.day >= capri_r[0][0]) & (users['birth_date'].dt.month >= capri_r[0][1])) | ((users['birth_date'].dt.day <=capri_r[1][0]) & (users['birth_date'].dt.month <=capri_r[1][1]))\n",
    "plt.figure(figsize=(16, 10))\n",
    "#capri_days = users[capri_range_condition]['birth_date'].dt.dayofyear.value_counts()\n",
    "capri_days = users[capri_range_condition].groupby([(users['birth_date'].dt.month),(users['birth_date'].dt.day)]).agg('count') ['birth_date']\n",
    "capri_days_2 = capri_days.rename(index={12: 0}, level=0).sort_index()\n",
    "capri_days_2.plot.bar(ax=plt.gca(), title='day of year')\n",
    "#plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m-%d'))\n",
    "plt.gca().get_xaxis().set_major_formatter(\n",
    "    mpl.ticker.FuncFormatter(lambda x, p: '{}.{}.'.format(capri_days_2.index[x][0] if capri_days_2.index[x][0] == 1 else 12, capri_days_2.index[x][1])))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(16, 10))\n",
    "capri_days_2.plot.bar(ax=plt.gca(), title='Kozoroh-den v roce')\n",
    "plt.gca().get_xaxis().set_major_formatter(\n",
    "    mpl.ticker.FuncFormatter(lambda x, p: '{}.{}.'.format(capri_days_2.index[x][0] if capri_days_2.index[x][0] == 1 else 12, capri_days_2.index[x][1])))\n",
    "plt.xlabel('datum narození')\n",
    "plt.savefig('graphs/zverokruh-dny', bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n",
       " <a list of 12 Text xticklabel objects>)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nyní bez 1.1.\n",
    "users[users['birth_date'].dt.dayofyear != 1]['zodiac_sign'].apply(lambda x: en_cz_zodiac[x]).value_counts().sort_index().plot.bar(figsize=(16, 10), title='Znamení zvěrokruhu')\n",
    "plt.xticks(rotation=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## now I'll be filtering out all people with too litle watch time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "users with less than hour watching:  2105\n"
     ]
    }
   ],
   "source": [
    "print('users with less than hour watching: ', (users['user_days_spent_watching'] <= 1/2).sum())  # 4 hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203e0b47d68>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['birth_date'].dt.dayofyear.value_counts().sort_index().plot.bar(figsize=(16, 10), title='day of year', color='blue', alpha=0.5)\n",
    "users[users['user_days_spent_watching'] > 1/6]['birth_date'].dt.dayofyear.value_counts().sort_index().plot.bar(figsize=(16, 10), color='red', alpha=0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203e0b47d68>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[users['user_days_spent_watching'] <= 1/2]['birth_date'].dt.dayofyear.value_counts().sort_index().plot.bar(figsize=(16, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203ec1a2470>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_too_few_watch = users[users['user_days_spent_watching'] <= 1/2]['age'].value_counts()\n",
    "users_too_few_watch = users_too_few_watch.reindex(users['age'].value_counts().index)\n",
    "users['age'].value_counts().sort_index().plot.bar(figsize=(16, 10), color='blue', alpha=0.5)\n",
    "users_too_few_watch.sort_index().plot.bar(figsize=(16, 10), color='red', alpha=0.5, secondary_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users['user_days_spent_watching'].hist(bins=1000, figsize=(16, 8))\n",
    "plt.gca().set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users['stats_episodes'].hist(bins=500, figsize=(16, 8))\n",
    "plt.gca().set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# filtering\n",
    "users = users[users['user_days_spent_watching'] > 1/2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users['user_days_spent_watching'].hist(bins=1000, figsize=(16, 8))\n",
    "plt.gca().set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users['stats_episodes'].hist(bins=500, figsize=(16, 8))\n",
    "plt.gca().set_yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## filtering by age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203e0b47d68>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['age'].value_counts().sort_index().plot.bar(figsize=(16, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users = users[(users['age'] > 9) & (users['age'] < 50)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203e0b47d68>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['age'].value_counts().sort_index().plot.bar(figsize=(16, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users['user_days_spent_watching'].hist(bins=1000, figsize=(16, 8))\n",
    "plt.gca().set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users['stats_episodes'].hist(bins=500, figsize=(16, 8))\n",
    "plt.gca().set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203e0b47d68>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['birth_date'].dt.dayofyear.value_counts().sort_index().plot.bar(figsize=(16, 10), title='day of year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# filtering users born in 1.1., too many false people here\n",
    "users = users[users['birth_date'].dt.dayofyear != 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203e0b47d68>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['birth_date'].dt.dayofyear.value_counts().sort_index().plot.bar(figsize=(16, 10), title='day of year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203e0b47d68>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['age'].value_counts().sort_index().plot.bar(figsize=(16, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users['user_days_spent_watching'].hist(bins=1000, figsize=(16, 8))\n",
    "plt.gca().set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users['stats_episodes'].hist(bins=500, figsize=(16, 8))\n",
    "plt.gca().set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "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>username</th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_watching</th>\n",
       "      <th>user_completed</th>\n",
       "      <th>user_onhold</th>\n",
       "      <th>user_dropped</th>\n",
       "      <th>user_plantowatch</th>\n",
       "      <th>user_days_spent_watching</th>\n",
       "      <th>gender</th>\n",
       "      <th>location</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>access_rank</th>\n",
       "      <th>join_date</th>\n",
       "      <th>last_online</th>\n",
       "      <th>stats_mean_score</th>\n",
       "      <th>stats_rewatched</th>\n",
       "      <th>stats_episodes</th>\n",
       "      <th>age</th>\n",
       "      <th>zodiac_sign</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>karthiga</td>\n",
       "      <td>2255153</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.091667</td>\n",
       "      <td>Female</td>\n",
       "      <td>Chennai, India</td>\n",
       "      <td>1990-04-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-03-03</td>\n",
       "      <td>2014-02-04 01:32:00</td>\n",
       "      <td>7.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3391.0</td>\n",
       "      <td>28</td>\n",
       "      <td>Taurus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Damonashu</td>\n",
       "      <td>37326</td>\n",
       "      <td>45</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>25</td>\n",
       "      <td>59</td>\n",
       "      <td>82.574306</td>\n",
       "      <td>Male</td>\n",
       "      <td>Detroit,Michigan</td>\n",
       "      <td>1991-08-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-02-13</td>\n",
       "      <td>2017-07-10 06:52:54</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4903.0</td>\n",
       "      <td>27</td>\n",
       "      <td>Leo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bskai</td>\n",
       "      <td>228342</td>\n",
       "      <td>25</td>\n",
       "      <td>414</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>159.483333</td>\n",
       "      <td>Male</td>\n",
       "      <td>Nayarit, Mexico</td>\n",
       "      <td>1990-12-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009-08-31</td>\n",
       "      <td>2014-05-12 16:35:00</td>\n",
       "      <td>8.27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9701.0</td>\n",
       "      <td>28</td>\n",
       "      <td>Sagittarius</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>terune_uzumaki</td>\n",
       "      <td>327311</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.394444</td>\n",
       "      <td>Female</td>\n",
       "      <td>Malaysia, Kuantan</td>\n",
       "      <td>1998-08-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-05-10</td>\n",
       "      <td>2012-10-18 19:06:00</td>\n",
       "      <td>9.70</td>\n",
       "      <td>6.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>20</td>\n",
       "      <td>Virgo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bas_G</td>\n",
       "      <td>5015094</td>\n",
       "      <td>35</td>\n",
       "      <td>114</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>175</td>\n",
       "      <td>30.458333</td>\n",
       "      <td>Male</td>\n",
       "      <td>Nijmegen, Nederland</td>\n",
       "      <td>1999-10-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-26</td>\n",
       "      <td>2018-05-10 20:53:37</td>\n",
       "      <td>7.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>19</td>\n",
       "      <td>Scorpio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         username  user_id  user_watching  user_completed  user_onhold  \\\n",
       "0        karthiga  2255153              3              49            1   \n",
       "2       Damonashu    37326             45             195           27   \n",
       "3           bskai   228342             25             414            2   \n",
       "4  terune_uzumaki   327311              5               5            0   \n",
       "5           Bas_G  5015094             35             114            6   \n",
       "\n",
       "   user_dropped  user_plantowatch  user_days_spent_watching  gender  \\\n",
       "0             0                 0                 55.091667  Female   \n",
       "2            25                59                 82.574306    Male   \n",
       "3             5                11                159.483333    Male   \n",
       "4             0                 0                 11.394444  Female   \n",
       "5            20               175                 30.458333    Male   \n",
       "\n",
       "              location birth_date  access_rank  join_date         last_online  \\\n",
       "0      Chennai, India  1990-04-29          NaN 2013-03-03 2014-02-04 01:32:00   \n",
       "2     Detroit,Michigan 1991-08-01          NaN 2008-02-13 2017-07-10 06:52:54   \n",
       "3      Nayarit, Mexico 1990-12-14          NaN 2009-08-31 2014-05-12 16:35:00   \n",
       "4    Malaysia, Kuantan 1998-08-24          NaN 2010-05-10 2012-10-18 19:06:00   \n",
       "5  Nijmegen, Nederland 1999-10-24          NaN 2015-11-26 2018-05-10 20:53:37   \n",
       "\n",
       "   stats_mean_score  stats_rewatched  stats_episodes  age  zodiac_sign  \n",
       "0              7.43              0.0          3391.0   28       Taurus  \n",
       "2              6.15              6.0          4903.0   27          Leo  \n",
       "3              8.27              1.0          9701.0   28  Sagittarius  \n",
       "4              9.70              6.0           697.0   20        Virgo  \n",
       "5              7.86              0.0          1847.0   19      Scorpio  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users = users.drop(['zodiac_sign'], axis=1)\n",
    "users = users.drop(['age'], axis=1)  # everyone can easily calculate this column, so there is no need to keep it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x203f15b06d8>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# showing scatterplot before and after dropping few users with very large animelists\n",
    "sns.pairplot(users[['user_days_spent_watching',\n",
    "       'gender', 'birth_date', 'join_date',\n",
    "       'last_online', 'stats_episodes']], hue='gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "going to delete 6 users with 70k episodes or more, there is too few of them and they mess with graphs\n"
     ]
    }
   ],
   "source": [
    "print('going to delete {} users with 70k episodes or more, there is too few of them and they mess with graphs'.format((users['stats_episodes'] >= 70000).sum()))\n",
    "users_small = users[users['stats_episodes'] < 70000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x203f2cc3240>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# showing scatterplot after and after dropping few users with very large animelists\n",
    "sns.pairplot(users_small[['user_days_spent_watching',\n",
    "       'gender', 'birth_date', 'join_date',\n",
    "       'last_online', 'stats_episodes']], hue='gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "users_small[['username']].to_csv('cache_usernames_after_filtering.csv', sep=',', encoding='utf-8')\n",
    "users = users_small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>username</th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_watching</th>\n",
       "      <th>user_completed</th>\n",
       "      <th>user_onhold</th>\n",
       "      <th>user_dropped</th>\n",
       "      <th>user_plantowatch</th>\n",
       "      <th>user_days_spent_watching</th>\n",
       "      <th>gender</th>\n",
       "      <th>location</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>access_rank</th>\n",
       "      <th>join_date</th>\n",
       "      <th>last_online</th>\n",
       "      <th>stats_mean_score</th>\n",
       "      <th>stats_rewatched</th>\n",
       "      <th>stats_episodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>karthiga</td>\n",
       "      <td>2255153</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.091667</td>\n",
       "      <td>Female</td>\n",
       "      <td>Chennai, India</td>\n",
       "      <td>1990-04-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-03-03</td>\n",
       "      <td>2014-02-04 01:32:00</td>\n",
       "      <td>7.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Damonashu</td>\n",
       "      <td>37326</td>\n",
       "      <td>45</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>25</td>\n",
       "      <td>59</td>\n",
       "      <td>82.574306</td>\n",
       "      <td>Male</td>\n",
       "      <td>Detroit,Michigan</td>\n",
       "      <td>1991-08-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-02-13</td>\n",
       "      <td>2017-07-10 06:52:54</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4903.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bskai</td>\n",
       "      <td>228342</td>\n",
       "      <td>25</td>\n",
       "      <td>414</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>159.483333</td>\n",
       "      <td>Male</td>\n",
       "      <td>Nayarit, Mexico</td>\n",
       "      <td>1990-12-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009-08-31</td>\n",
       "      <td>2014-05-12 16:35:00</td>\n",
       "      <td>8.27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9701.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>terune_uzumaki</td>\n",
       "      <td>327311</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.394444</td>\n",
       "      <td>Female</td>\n",
       "      <td>Malaysia, Kuantan</td>\n",
       "      <td>1998-08-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-05-10</td>\n",
       "      <td>2012-10-18 19:06:00</td>\n",
       "      <td>9.70</td>\n",
       "      <td>6.0</td>\n",
       "      <td>697.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bas_G</td>\n",
       "      <td>5015094</td>\n",
       "      <td>35</td>\n",
       "      <td>114</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>175</td>\n",
       "      <td>30.458333</td>\n",
       "      <td>Male</td>\n",
       "      <td>Nijmegen, Nederland</td>\n",
       "      <td>1999-10-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-26</td>\n",
       "      <td>2018-05-10 20:53:37</td>\n",
       "      <td>7.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1847.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         username  user_id  user_watching  user_completed  user_onhold  \\\n",
       "0        karthiga  2255153              3              49            1   \n",
       "2       Damonashu    37326             45             195           27   \n",
       "3           bskai   228342             25             414            2   \n",
       "4  terune_uzumaki   327311              5               5            0   \n",
       "5           Bas_G  5015094             35             114            6   \n",
       "\n",
       "   user_dropped  user_plantowatch  user_days_spent_watching  gender  \\\n",
       "0             0                 0                 55.091667  Female   \n",
       "2            25                59                 82.574306    Male   \n",
       "3             5                11                159.483333    Male   \n",
       "4             0                 0                 11.394444  Female   \n",
       "5            20               175                 30.458333    Male   \n",
       "\n",
       "              location birth_date  access_rank  join_date         last_online  \\\n",
       "0      Chennai, India  1990-04-29          NaN 2013-03-03 2014-02-04 01:32:00   \n",
       "2     Detroit,Michigan 1991-08-01          NaN 2008-02-13 2017-07-10 06:52:54   \n",
       "3      Nayarit, Mexico 1990-12-14          NaN 2009-08-31 2014-05-12 16:35:00   \n",
       "4    Malaysia, Kuantan 1998-08-24          NaN 2010-05-10 2012-10-18 19:06:00   \n",
       "5  Nijmegen, Nederland 1999-10-24          NaN 2015-11-26 2018-05-10 20:53:37   \n",
       "\n",
       "   stats_mean_score  stats_rewatched  stats_episodes  \n",
       "0              7.43              0.0          3391.0  \n",
       "2              6.15              6.0          4903.0  \n",
       "3              8.27              1.0          9701.0  \n",
       "4              9.70              6.0           697.0  \n",
       "5              7.86              0.0          1847.0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_watching</th>\n",
       "      <th>user_completed</th>\n",
       "      <th>user_onhold</th>\n",
       "      <th>user_dropped</th>\n",
       "      <th>user_plantowatch</th>\n",
       "      <th>user_days_spent_watching</th>\n",
       "      <th>access_rank</th>\n",
       "      <th>stats_mean_score</th>\n",
       "      <th>stats_rewatched</th>\n",
       "      <th>stats_episodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.087110e+05</td>\n",
       "      <td>108711.000000</td>\n",
       "      <td>108711.000000</td>\n",
       "      <td>108711.000000</td>\n",
       "      <td>108711.000000</td>\n",
       "      <td>108711.000000</td>\n",
       "      <td>108711.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108711.000000</td>\n",
       "      <td>108711.000000</td>\n",
       "      <td>108711.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.817687e+06</td>\n",
       "      <td>14.767503</td>\n",
       "      <td>196.458178</td>\n",
       "      <td>11.388167</td>\n",
       "      <td>11.733716</td>\n",
       "      <td>75.578589</td>\n",
       "      <td>61.913873</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.747612</td>\n",
       "      <td>14.197993</td>\n",
       "      <td>3779.871255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.998269e+06</td>\n",
       "      <td>32.746591</td>\n",
       "      <td>244.945751</td>\n",
       "      <td>30.830825</td>\n",
       "      <td>30.978991</td>\n",
       "      <td>178.653664</td>\n",
       "      <td>59.211762</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.451368</td>\n",
       "      <td>55.376799</td>\n",
       "      <td>3678.336729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500694</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.994345e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>21.066319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.330000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1269.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.675030e+05</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>46.190278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.890000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2797.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.477036e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>84.461806</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.460000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5135.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.251737e+06</td>\n",
       "      <td>2934.000000</td>\n",
       "      <td>5479.000000</td>\n",
       "      <td>2562.000000</td>\n",
       "      <td>2457.000000</td>\n",
       "      <td>12051.000000</td>\n",
       "      <td>952.654595</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9404.000000</td>\n",
       "      <td>62347.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id  user_watching  user_completed    user_onhold  \\\n",
       "count  1.087110e+05  108711.000000   108711.000000  108711.000000   \n",
       "mean   1.817687e+06      14.767503      196.458178      11.388167   \n",
       "std    1.998269e+06      32.746591      244.945751      30.830825   \n",
       "min    1.000000e+00       0.000000        0.000000       0.000000   \n",
       "25%    1.994345e+05       3.000000       50.000000       0.000000   \n",
       "50%    5.675030e+05       7.000000      123.000000       4.000000   \n",
       "75%    3.477036e+06      16.000000      254.000000      12.000000   \n",
       "max    7.251737e+06    2934.000000     5479.000000    2562.000000   \n",
       "\n",
       "        user_dropped  user_plantowatch  user_days_spent_watching  access_rank  \\\n",
       "count  108711.000000     108711.000000             108711.000000          0.0   \n",
       "mean       11.733716         75.578589                 61.913873          NaN   \n",
       "std        30.978991        178.653664                 59.211762          NaN   \n",
       "min         0.000000          0.000000                  0.500694          NaN   \n",
       "25%         0.000000          6.000000                 21.066319          NaN   \n",
       "50%         3.000000         27.000000                 46.190278          NaN   \n",
       "75%        12.000000         81.000000                 84.461806          NaN   \n",
       "max      2457.000000      12051.000000                952.654595          NaN   \n",
       "\n",
       "       stats_mean_score  stats_rewatched  stats_episodes  \n",
       "count     108711.000000    108711.000000   108711.000000  \n",
       "mean           7.747612        14.197993     3779.871255  \n",
       "std            1.451368        55.376799     3678.336729  \n",
       "min            0.000000         0.000000        7.000000  \n",
       "25%            7.330000         0.000000     1269.500000  \n",
       "50%            7.890000         1.000000     2797.000000  \n",
       "75%            8.460000        10.000000     5135.000000  \n",
       "max           10.000000      9404.000000    62347.000000  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "anime_ids_to_keep = get_anime_ids_after_username_clear_cached()\n",
    "animes = animes[animes['anime_id'].isin(anime_ids_to_keep['anime_id'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>anime_id</th>\n",
       "      <th>episodes</th>\n",
       "      <th>score</th>\n",
       "      <th>scored_by</th>\n",
       "      <th>rank</th>\n",
       "      <th>popularity</th>\n",
       "      <th>members</th>\n",
       "      <th>favorites</th>\n",
       "      <th>duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14402.000000</td>\n",
       "      <td>14402.000000</td>\n",
       "      <td>14402.000000</td>\n",
       "      <td>1.440200e+04</td>\n",
       "      <td>12830.000000</td>\n",
       "      <td>14402.000000</td>\n",
       "      <td>1.440200e+04</td>\n",
       "      <td>14402.000000</td>\n",
       "      <td>14402.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17277.255798</td>\n",
       "      <td>11.345230</td>\n",
       "      <td>6.153981</td>\n",
       "      <td>1.152048e+04</td>\n",
       "      <td>6413.727903</td>\n",
       "      <td>7185.746632</td>\n",
       "      <td>2.308745e+04</td>\n",
       "      <td>313.293779</td>\n",
       "      <td>25.574998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13127.312926</td>\n",
       "      <td>43.537058</td>\n",
       "      <td>1.451175</td>\n",
       "      <td>4.321072e+04</td>\n",
       "      <td>3711.461883</td>\n",
       "      <td>4148.889249</td>\n",
       "      <td>7.516038e+04</td>\n",
       "      <td>2622.348612</td>\n",
       "      <td>25.524690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4356.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.560000</td>\n",
       "      <td>4.800000e+01</td>\n",
       "      <td>3202.000000</td>\n",
       "      <td>3595.000000</td>\n",
       "      <td>2.530000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14903.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.380000</td>\n",
       "      <td>5.135000e+02</td>\n",
       "      <td>6407.000000</td>\n",
       "      <td>7189.500000</td>\n",
       "      <td>1.707500e+03</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31008.750000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.070000</td>\n",
       "      <td>3.995500e+03</td>\n",
       "      <td>9612.750000</td>\n",
       "      <td>10774.250000</td>\n",
       "      <td>1.050800e+04</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>37916.000000</td>\n",
       "      <td>1818.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.009477e+06</td>\n",
       "      <td>12919.000000</td>\n",
       "      <td>14484.000000</td>\n",
       "      <td>1.456378e+06</td>\n",
       "      <td>106895.000000</td>\n",
       "      <td>231.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           anime_id      episodes         score     scored_by          rank  \\\n",
       "count  14402.000000  14402.000000  14402.000000  1.440200e+04  12830.000000   \n",
       "mean   17277.255798     11.345230      6.153981  1.152048e+04   6413.727903   \n",
       "std    13127.312926     43.537058      1.451175  4.321072e+04   3711.461883   \n",
       "min        1.000000      0.000000      0.000000  0.000000e+00      0.000000   \n",
       "25%     4356.250000      1.000000      5.560000  4.800000e+01   3202.000000   \n",
       "50%    14903.000000      1.000000      6.380000  5.135000e+02   6407.000000   \n",
       "75%    31008.750000     12.000000      7.070000  3.995500e+03   9612.750000   \n",
       "max    37916.000000   1818.000000     10.000000  1.009477e+06  12919.000000   \n",
       "\n",
       "         popularity       members      favorites  duration_min  \n",
       "count  14402.000000  1.440200e+04   14402.000000  14402.000000  \n",
       "mean    7185.746632  2.308745e+04     313.293779     25.574998  \n",
       "std     4148.889249  7.516038e+04    2622.348612     25.524690  \n",
       "min        0.000000  0.000000e+00       0.000000      0.000000  \n",
       "25%     3595.000000  2.530000e+02       0.000000      5.000000  \n",
       "50%     7189.500000  1.707500e+03       2.000000     23.000000  \n",
       "75%    10774.250000  1.050800e+04      23.000000     30.000000  \n",
       "max    14484.000000  1.456378e+06  106895.000000    231.000000  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering anime with lots of missing info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203f2bc3400>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes['source'].value_counts().sort_index().plot.bar(figsize=(16, 10), title='anime sources')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "anime to delete:  (4207, 32)\n"
     ]
    }
   ],
   "source": [
    "# now dropping anime with unknown studio and unkown source\n",
    "delete_filter = (animes['source'] == 'Unknown')\n",
    "animes_to_delete = animes[delete_filter]\n",
    "animes_to_remain = animes[~(delete_filter)]\n",
    "anime_ids_to_delete = animes_to_delete['anime_id']\n",
    "print('anime to delete: ', animes_to_delete.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "anime to delete:  (7574, 32)\n"
     ]
    }
   ],
   "source": [
    "# now with unkown source\n",
    "delete_filter = (animes['source'] == 'Unknown') | (animes['studio'].isnull())\n",
    "animes_to_delete = animes[delete_filter]\n",
    "animes_to_remain = animes[~(delete_filter)]\n",
    "anime_ids_to_delete = animes_to_delete['anime_id']\n",
    "print('anime to delete: ', animes_to_delete.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "anime to delete:  (7727, 32)\n"
     ]
    }
   ],
   "source": [
    "# now not aired anime, because I don't care about them\n",
    "delete_filter = delete_filter | ((animes['status'] == 'Not yet aired'))\n",
    "animes_to_delete = animes[delete_filter]\n",
    "animes_to_remain = animes[~(delete_filter)]\n",
    "anime_ids_to_delete = animes_to_delete['anime_id']\n",
    "print('anime to delete: ', animes_to_delete.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "animes without aired json info (122, 33)\n"
     ]
    }
   ],
   "source": [
    "# I need year of airing start, so I will be filtering all anime without that\n",
    "animes['aired_from_year'] = pd.to_datetime(animes['aired'].apply(lambda x: ast.literal_eval(x.replace('\\'', '\"'))['from']), errors='coerce').dt.year\n",
    "print('animes without aired json info', animes[~(delete_filter) & animes['aired_from_year'].isnull()].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "pycharm": {
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       "      <th>anime_id</th>\n",
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       "      <th>199</th>\n",
       "      <td>5199</td>\n",
       "      <td>Seto no Hanayome Fan Disc</td>\n",
       "      <td>Special</td>\n",
       "      <td>Manga</td>\n",
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       "      <td>Comedy, Parody, School</td>\n",
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       "      <th>289</th>\n",
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       "      <td>Tang Lang Bu Chan</td>\n",
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       "      <th>435</th>\n",
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       "      <td>Kyoto Animation: Hana-hen</td>\n",
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       "      <td>Finished Airing</td>\n",
       "      <td>Slice of Life</td>\n",
       "      <td>Jun, 2010</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5950</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>7803.0</td>\n",
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       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>34037</td>\n",
       "      <td>Kitakaze no Kureta Table-kake</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Other</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Fantasy, Historical</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>13898</td>\n",
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       "      <th>657</th>\n",
       "      <td>34595</td>\n",
       "      <td>\"Parade\" de Satie</td>\n",
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       "      <td>13793</td>\n",
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       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>2616</td>\n",
       "      <td>Sora Iro no Tane</td>\n",
       "      <td>Special</td>\n",
       "      <td>Picture book</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Kids</td>\n",
       "      <td>Nov, 1992</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6406</td>\n",
       "      <td>PG - Children</td>\n",
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       "      <th>1044</th>\n",
       "      <td>28119</td>\n",
       "      <td>Kuma no Minakuro to Kouhei Jiisan</td>\n",
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       "      <th>1084</th>\n",
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       "      <td>Eyeshield 21: Jump Festa 2005 Special</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>PG-13 - Teens 13 or older</td>\n",
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       "      <td>12005</td>\n",
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       "      <th>1382</th>\n",
       "      <td>23581</td>\n",
       "      <td>Romeo no Aoi Sora Specials</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>9688</td>\n",
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       "      <th>1440</th>\n",
       "      <td>33484</td>\n",
       "      <td>Shiroi Zou</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>13510</td>\n",
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       "      <th>1465</th>\n",
       "      <td>4085</td>\n",
       "      <td>Yonimo Osoroshii Grimm Douwa</td>\n",
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       "      <th>1582</th>\n",
       "      <td>11735</td>\n",
       "      <td>Coluboccoro</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Sci-Fi, Fantasy</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>7577</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>7946.0</td>\n",
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       "      <th>1688</th>\n",
       "      <td>5947</td>\n",
       "      <td>Tengen Toppa Gurren Lagann Movie: Lagann-hen S...</td>\n",
       "      <td>OVA</td>\n",
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       "      <td>Comedy</td>\n",
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       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1954.0</td>\n",
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       "      <th>1721</th>\n",
       "      <td>19401</td>\n",
       "      <td>Kuusou no Sora Tobu Kikaitachi</td>\n",
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       "      <td>2002</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8328</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>6471.0</td>\n",
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       "      <th>1783</th>\n",
       "      <td>8626</td>\n",
       "      <td>Good Morning</td>\n",
       "      <td>Music</td>\n",
       "      <td>Music</td>\n",
       "      <td>Finished Airing</td>\n",
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       "      <td>Apr, 2008</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6839</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>7860.0</td>\n",
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       "      <th>1928</th>\n",
       "      <td>10765</td>\n",
       "      <td>Hikaru no Go: Tokubetsu-hen - Sabaki no Ichiky...</td>\n",
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       "      <td>Manga</td>\n",
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       "      <td>Game, Comedy, Supernatural, Shounen</td>\n",
       "      <td>2002</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4848</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>1523.0</td>\n",
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       "      <th>1935</th>\n",
       "      <td>2515</td>\n",
       "      <td>Detective Conan OVA 05: The Target is Kogoro! ...</td>\n",
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       "      <td>Manga</td>\n",
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       "      <td>Adventure, Mystery, Comedy, Police, Shounen</td>\n",
       "      <td>2005</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>32041</td>\n",
       "      <td>Mori no Densetsu: Dai Ni Gakushou</td>\n",
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       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9932</td>\n",
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       "      <td>6585.0</td>\n",
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       "      <th>2324</th>\n",
       "      <td>17815</td>\n",
       "      <td>Kyoto Animation: Kasa-hen</td>\n",
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       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Fantasy</td>\n",
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       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6079</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>6870.0</td>\n",
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       "      <th>2643</th>\n",
       "      <td>25973</td>\n",
       "      <td>I Love Picnic</td>\n",
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       "      <td>Original</td>\n",
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       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11370</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>8452.0</td>\n",
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       "      <th>3178</th>\n",
       "      <td>30663</td>\n",
       "      <td>Docchi mo Maid</td>\n",
       "      <td>ONA</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>9489</td>\n",
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       "      <th>3192</th>\n",
       "      <td>23643</td>\n",
       "      <td>Nezumi no Yomeiri</td>\n",
       "      <td>Movie</td>\n",
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       "      <td>Finished Airing</td>\n",
       "      <td>Kids, Fantasy</td>\n",
       "      <td>1961</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12052</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>11453.0</td>\n",
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       "    <tr>\n",
       "      <th>3307</th>\n",
       "      <td>28377</td>\n",
       "      <td>Choujikuu Robo Meguru</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy, Mecha</td>\n",
       "      <td>Nov, 2014</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8425</td>\n",
       "      <td>None</td>\n",
       "      <td>9885.0</td>\n",
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       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>26019</td>\n",
       "      <td>Rokujouma no Shinryakusha!?</td>\n",
       "      <td>Special</td>\n",
       "      <td>Light novel</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Romance, Supernatural, Comedy, School</td>\n",
       "      <td>Jun, 2010</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5070</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>4549.0</td>\n",
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       "    <tr>\n",
       "      <th>3451</th>\n",
       "      <td>22695</td>\n",
       "      <td>Dragon Ball Z: Summer Vacation Special</td>\n",
       "      <td>Special</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Action, Adventure, Comedy, Super Power, Martia...</td>\n",
       "      <td>Jul, 1992</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4409</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>3907.0</td>\n",
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       "    <tr>\n",
       "      <th>3718</th>\n",
       "      <td>36760</td>\n",
       "      <td>Detective Conan: The Magician of Starlight</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Space</td>\n",
       "      <td>2012</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7982</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>3769.0</td>\n",
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       "      <th>3915</th>\n",
       "      <td>32884</td>\n",
       "      <td>Shounen Ashibe 2: Ashibe no Kanashii Yume</td>\n",
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       "      <td>{'from': None, 'to': None}</td>\n",
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       "      <th>10975</th>\n",
       "      <td>10189</td>\n",
       "      <td>Hunter x Hunter Pilot</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Manga</td>\n",
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       "      <td>Action, Adventure, Shounen, Super Power</td>\n",
       "      <td>1998</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3245</td>\n",
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       "      <th>11095</th>\n",
       "      <td>36818</td>\n",
       "      <td>Chikyuu wa Guruguru Merry-Go-Round</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Kids, Space</td>\n",
       "      <td>2017</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14119</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>9867.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11232</th>\n",
       "      <td>17787</td>\n",
       "      <td>Ouritsu Uchuugun: Honneamise no Tsubasa - Pilo...</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Sci-Fi, Seinen</td>\n",
       "      <td>1985</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8304</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>7215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11294</th>\n",
       "      <td>26015</td>\n",
       "      <td>Backkom: Jung-gug Gijeogwi</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy, Kids</td>\n",
       "      <td>2013</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11289</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>8166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11296</th>\n",
       "      <td>32920</td>\n",
       "      <td>Yongshi</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Other</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Historical, Martial Arts</td>\n",
       "      <td>2007</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13044</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>12770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11434</th>\n",
       "      <td>24681</td>\n",
       "      <td>Kazoku Robinson Hyouryuuki: Fushigi na Shima n...</td>\n",
       "      <td>Special</td>\n",
       "      <td>Novel</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Slice of Life, Adventure, Historical, Drama</td>\n",
       "      <td>2001</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11982</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>10806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11493</th>\n",
       "      <td>25967</td>\n",
       "      <td>Backkom Sports</td>\n",
       "      <td>TV</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy, Kids, Sports</td>\n",
       "      <td>2009</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10597</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>7273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11662</th>\n",
       "      <td>8740</td>\n",
       "      <td>One Piece Film: Strong World Episode 0</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Action, Adventure, Comedy, Fantasy, Shounen, S...</td>\n",
       "      <td>Apr, 2010</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1647</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11755</th>\n",
       "      <td>21663</td>\n",
       "      <td>Last Orders</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Action, Samurai, Sci-Fi</td>\n",
       "      <td>Oct, 1997</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8876</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>7949.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11785</th>\n",
       "      <td>17811</td>\n",
       "      <td>Kyoto Animation: Hassou-hen</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>Apr, 2012</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6156</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>8125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11809</th>\n",
       "      <td>2512</td>\n",
       "      <td>Detective Conan OVA 02: 16 Suspects</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Adventure, Mystery, Comedy, Police, Shounen</td>\n",
       "      <td>2002</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3616</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>1923.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11813</th>\n",
       "      <td>34719</td>\n",
       "      <td>the FLY BanD!</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Music</td>\n",
       "      <td>1998</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13694</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>12415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11912</th>\n",
       "      <td>25599</td>\n",
       "      <td>Vampiyan Kids Promotion</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy, Fantasy, Kids</td>\n",
       "      <td>1999</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12739</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>12608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12190</th>\n",
       "      <td>17791</td>\n",
       "      <td>Kyoto Animation: Ikitaku Naru Omise-hen</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>Jul, 2012</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5661</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>6355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12649</th>\n",
       "      <td>8815</td>\n",
       "      <td>Domo TV</td>\n",
       "      <td>TV</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>2008</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8283</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>5911.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12900</th>\n",
       "      <td>10520</td>\n",
       "      <td>Nurse Witch Komugi-chan Magikarte Z Special</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy, Parody</td>\n",
       "      <td>2005</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7512</td>\n",
       "      <td>PG - Children</td>\n",
       "      <td>7932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13048</th>\n",
       "      <td>13375</td>\n",
       "      <td>Kyoto Animation: Sora-hen</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Slice of Life</td>\n",
       "      <td>Jul, 2010</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6118</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>8216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13067</th>\n",
       "      <td>3684</td>\n",
       "      <td>Dimension Loop</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>2001</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6732</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>8060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13287</th>\n",
       "      <td>25979</td>\n",
       "      <td>Sheep in the Island</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>2004</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12712</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>12024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13428</th>\n",
       "      <td>37019</td>\n",
       "      <td>Komaneko: Hajime no Ippo</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>2003</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13159</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>10952.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13651</th>\n",
       "      <td>34187</td>\n",
       "      <td>Shima no Kuregata no Hanashi</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Fantasy, Kids</td>\n",
       "      <td>2010</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14113</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>12048.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13728</th>\n",
       "      <td>25985</td>\n",
       "      <td>Contact</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Sci-Fi, Comedy, Kids</td>\n",
       "      <td>Not available</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11577</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>8747.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13773</th>\n",
       "      <td>2514</td>\n",
       "      <td>Detective Conan OVA 04: Conan and Kid and Crys...</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Adventure, Mystery, Police, Shounen</td>\n",
       "      <td>2004</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3535</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>1320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13869</th>\n",
       "      <td>36579</td>\n",
       "      <td>Fettu to Ccine to Gummi Seijin</td>\n",
       "      <td>ONA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Mar, 2015</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13979</td>\n",
       "      <td>PG - Children</td>\n",
       "      <td>10084.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13945</th>\n",
       "      <td>37133</td>\n",
       "      <td>Tenkousei wa Kounin Kaikeishi!</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Slice of Life, School</td>\n",
       "      <td>Mar, 2013</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13845</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>12395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13982</th>\n",
       "      <td>32885</td>\n",
       "      <td>Magnerobo Ga-Keen Recap</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Action, Sci-Fi, Mecha</td>\n",
       "      <td>Not available</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13366</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>11124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14112</th>\n",
       "      <td>30723</td>\n",
       "      <td>Midori no Makibao Compilation OVA</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Sports, Comedy</td>\n",
       "      <td>Not available</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13112</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>11240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14368</th>\n",
       "      <td>37493</td>\n",
       "      <td>Watashitachi no Mirai</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Kids</td>\n",
       "      <td>Apr, 2018</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14466</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>12704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14380</th>\n",
       "      <td>37494</td>\n",
       "      <td>Chikyuu to no Yakusoku</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Kids</td>\n",
       "      <td>Apr, 2018</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14468</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>9913.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14466</th>\n",
       "      <td>36315</td>\n",
       "      <td>Mushi Mushi Mura no Nakama-tachi: Minna Ii Tok...</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Kids</td>\n",
       "      <td>2017</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14191</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>11396.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>122 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       anime_id                                              title     type  \\\n",
       "199        5199                          Seto no Hanayome Fan Disc  Special   \n",
       "217       35952                                  Devilman Memorial      OVA   \n",
       "248        9509                                 Yami no Purple Eye    Music   \n",
       "289       31972                                  Tang Lang Bu Chan    Movie   \n",
       "435       13373                          Kyoto Animation: Hana-hen  Special   \n",
       "577       34037                      Kitakaze no Kureta Table-kake    Movie   \n",
       "657       34595                                  \"Parade\" de Satie    Movie   \n",
       "794        2616                                   Sora Iro no Tane  Special   \n",
       "1044      28119                  Kuma no Minakuro to Kouhei Jiisan      OVA   \n",
       "1084       6418              Eyeshield 21: Jump Festa 2005 Special  Special   \n",
       "1228      25969                                              Clock  Special   \n",
       "1382      23581                         Romeo no Aoi Sora Specials  Special   \n",
       "1440      33484                                         Shiroi Zou    Movie   \n",
       "1465       4085                       Yonimo Osoroshii Grimm Douwa      OVA   \n",
       "1582      11735                                        Coluboccoro      OVA   \n",
       "1688       5947  Tengen Toppa Gurren Lagann Movie: Lagann-hen S...      OVA   \n",
       "1721      19401                     Kuusou no Sora Tobu Kikaitachi    Movie   \n",
       "1783       8626                                       Good Morning    Music   \n",
       "1928      10765  Hikaru no Go: Tokubetsu-hen - Sabaki no Ichiky...      OVA   \n",
       "1935       2515  Detective Conan OVA 05: The Target is Kogoro! ...      OVA   \n",
       "2104      32041                  Mori no Densetsu: Dai Ni Gakushou    Movie   \n",
       "2324      17815                          Kyoto Animation: Kasa-hen  Special   \n",
       "2643      25973                                      I Love Picnic  Special   \n",
       "3178      30663                                     Docchi mo Maid      ONA   \n",
       "3192      23643                                  Nezumi no Yomeiri    Movie   \n",
       "3307      28377                              Choujikuu Robo Meguru      OVA   \n",
       "3315      26019                        Rokujouma no Shinryakusha!?  Special   \n",
       "3451      22695             Dragon Ball Z: Summer Vacation Special  Special   \n",
       "3718      36760         Detective Conan: The Magician of Starlight    Movie   \n",
       "3915      32884          Shounen Ashibe 2: Ashibe no Kanashii Yume      OVA   \n",
       "...         ...                                                ...      ...   \n",
       "10975     10189                              Hunter x Hunter Pilot      OVA   \n",
       "11095     36818                 Chikyuu wa Guruguru Merry-Go-Round    Movie   \n",
       "11232     17787  Ouritsu Uchuugun: Honneamise no Tsubasa - Pilo...  Special   \n",
       "11294     26015                         Backkom: Jung-gug Gijeogwi  Special   \n",
       "11296     32920                                            Yongshi    Movie   \n",
       "11434     24681  Kazoku Robinson Hyouryuuki: Fushigi na Shima n...  Special   \n",
       "11493     25967                                     Backkom Sports       TV   \n",
       "11662      8740             One Piece Film: Strong World Episode 0      OVA   \n",
       "11755     21663                                        Last Orders  Special   \n",
       "11785     17811                        Kyoto Animation: Hassou-hen  Special   \n",
       "11809      2512                Detective Conan OVA 02: 16 Suspects      OVA   \n",
       "11813     34719                                      the FLY BanD!  Special   \n",
       "11912     25599                            Vampiyan Kids Promotion  Special   \n",
       "12190     17791            Kyoto Animation: Ikitaku Naru Omise-hen  Special   \n",
       "12649      8815                                            Domo TV       TV   \n",
       "12900     10520        Nurse Witch Komugi-chan Magikarte Z Special  Special   \n",
       "13048     13375                          Kyoto Animation: Sora-hen  Special   \n",
       "13067      3684                                     Dimension Loop      OVA   \n",
       "13287     25979                                Sheep in the Island  Special   \n",
       "13428     37019                           Komaneko: Hajime no Ippo  Special   \n",
       "13651     34187                       Shima no Kuregata no Hanashi      OVA   \n",
       "13728     25985                                            Contact  Special   \n",
       "13773      2514  Detective Conan OVA 04: Conan and Kid and Crys...      OVA   \n",
       "13869     36579                     Fettu to Ccine to Gummi Seijin      ONA   \n",
       "13945     37133                     Tenkousei wa Kounin Kaikeishi!      OVA   \n",
       "13982     32885                            Magnerobo Ga-Keen Recap      OVA   \n",
       "14112     30723                  Midori no Makibao Compilation OVA      OVA   \n",
       "14368     37493                              Watashitachi no Mirai      OVA   \n",
       "14380     37494                             Chikyuu to no Yakusoku      OVA   \n",
       "14466     36315  Mushi Mushi Mura no Nakama-tachi: Minna Ii Tok...      OVA   \n",
       "\n",
       "             source           status  \\\n",
       "199           Manga  Finished Airing   \n",
       "217           Manga  Finished Airing   \n",
       "248           Manga  Finished Airing   \n",
       "289           Other  Finished Airing   \n",
       "435        Original  Finished Airing   \n",
       "577           Other  Finished Airing   \n",
       "657        Original  Finished Airing   \n",
       "794    Picture book  Finished Airing   \n",
       "1044           Book  Finished Airing   \n",
       "1084          Manga  Finished Airing   \n",
       "1228       Original  Finished Airing   \n",
       "1382          Novel  Finished Airing   \n",
       "1440          Other  Finished Airing   \n",
       "1465          Other  Finished Airing   \n",
       "1582       Original  Finished Airing   \n",
       "1688       Original  Finished Airing   \n",
       "1721       Original  Finished Airing   \n",
       "1783          Music  Finished Airing   \n",
       "1928          Manga  Finished Airing   \n",
       "1935          Manga  Finished Airing   \n",
       "2104       Original  Finished Airing   \n",
       "2324       Original  Finished Airing   \n",
       "2643       Original  Finished Airing   \n",
       "3178       Original  Finished Airing   \n",
       "3192       Original  Finished Airing   \n",
       "3307       Original  Finished Airing   \n",
       "3315    Light novel  Finished Airing   \n",
       "3451          Manga  Finished Airing   \n",
       "3718          Manga  Finished Airing   \n",
       "3915   4-koma manga  Finished Airing   \n",
       "...             ...              ...   \n",
       "10975         Manga  Finished Airing   \n",
       "11095      Original  Finished Airing   \n",
       "11232      Original  Finished Airing   \n",
       "11294      Original  Finished Airing   \n",
       "11296         Other  Finished Airing   \n",
       "11434         Novel  Finished Airing   \n",
       "11493      Original  Finished Airing   \n",
       "11662         Manga  Finished Airing   \n",
       "11755      Original  Finished Airing   \n",
       "11785      Original  Finished Airing   \n",
       "11809         Manga  Finished Airing   \n",
       "11813      Original  Finished Airing   \n",
       "11912      Original  Finished Airing   \n",
       "12190      Original  Finished Airing   \n",
       "12649      Original  Finished Airing   \n",
       "12900      Original  Finished Airing   \n",
       "13048      Original  Finished Airing   \n",
       "13067      Original  Finished Airing   \n",
       "13287      Original  Finished Airing   \n",
       "13428      Original  Finished Airing   \n",
       "13651      Original  Finished Airing   \n",
       "13728      Original  Finished Airing   \n",
       "13773         Manga  Finished Airing   \n",
       "13869      Original  Finished Airing   \n",
       "13945      Original  Finished Airing   \n",
       "13982      Original  Finished Airing   \n",
       "14112         Manga  Finished Airing   \n",
       "14368      Original  Finished Airing   \n",
       "14380      Original  Finished Airing   \n",
       "14466      Original  Finished Airing   \n",
       "\n",
       "                                                   genre   aired_string  \\\n",
       "199                               Comedy, Parody, School      Sep, 2008   \n",
       "217                 Action, Horror, Demons, Supernatural           1999   \n",
       "248    Psychological, Supernatural, Music, Horror, My...           1987   \n",
       "289                                           Historical           1988   \n",
       "435                                        Slice of Life      Jun, 2010   \n",
       "577                                  Fantasy, Historical           1967   \n",
       "657                                      Music, Dementia      May, 2016   \n",
       "794                                                 Kids      Nov, 1992   \n",
       "1044                                         Drama, Kids      Jan, 1999   \n",
       "1084                     Action, Comedy, Shounen, Sports           2005   \n",
       "1228                                            Dementia           1999   \n",
       "1382         Adventure, Drama, Historical, Slice of Life           2001   \n",
       "1440                            Action, Historical, Kids           1963   \n",
       "1465                     Dementia, Horror, Psychological           2000   \n",
       "1582                                     Sci-Fi, Fantasy           2007   \n",
       "1688                                              Comedy      Jan, 2009   \n",
       "1721                                          Historical           2002   \n",
       "1783                                Slice of Life, Music      Apr, 2008   \n",
       "1928                 Game, Comedy, Supernatural, Shounen           2002   \n",
       "1935         Adventure, Mystery, Comedy, Police, Shounen           2005   \n",
       "2104                                             Fantasy           2014   \n",
       "2324                                             Fantasy      Jun, 2012   \n",
       "2643                                        Kids, Comedy           2003   \n",
       "3178             Action, Comedy, Magic, Ecchi, Shoujo Ai           2004   \n",
       "3192                                       Kids, Fantasy           1961   \n",
       "3307                                       Comedy, Mecha      Nov, 2014   \n",
       "3315               Romance, Supernatural, Comedy, School      Jun, 2010   \n",
       "3451   Action, Adventure, Comedy, Super Power, Martia...      Jul, 1992   \n",
       "3718                                               Space           2012   \n",
       "3915                               Comedy, Slice of Life  Not available   \n",
       "...                                                  ...            ...   \n",
       "10975            Action, Adventure, Shounen, Super Power           1998   \n",
       "11095                                        Kids, Space           2017   \n",
       "11232                                     Sci-Fi, Seinen           1985   \n",
       "11294                                       Comedy, Kids           2013   \n",
       "11296                           Historical, Martial Arts           2007   \n",
       "11434        Slice of Life, Adventure, Historical, Drama           2001   \n",
       "11493                               Comedy, Kids, Sports           2009   \n",
       "11662  Action, Adventure, Comedy, Fantasy, Shounen, S...      Apr, 2010   \n",
       "11755                            Action, Samurai, Sci-Fi      Oct, 1997   \n",
       "11785                                            Fantasy      Apr, 2012   \n",
       "11809        Adventure, Mystery, Comedy, Police, Shounen           2002   \n",
       "11813                                              Music           1998   \n",
       "11912                              Comedy, Fantasy, Kids           1999   \n",
       "12190                                             Sci-Fi      Jul, 2012   \n",
       "12649                                             Comedy           2008   \n",
       "12900                                     Comedy, Parody           2005   \n",
       "13048                                      Slice of Life      Jul, 2010   \n",
       "13067                                             Sci-Fi           2001   \n",
       "13287                                             Comedy           2004   \n",
       "13428                                             Comedy           2003   \n",
       "13651                                      Fantasy, Kids           2010   \n",
       "13728                               Sci-Fi, Comedy, Kids  Not available   \n",
       "13773                Adventure, Mystery, Police, Shounen           2004   \n",
       "13869                                             Comedy      Mar, 2015   \n",
       "13945                              Slice of Life, School      Mar, 2013   \n",
       "13982                              Action, Sci-Fi, Mecha  Not available   \n",
       "14112                                     Sports, Comedy  Not available   \n",
       "14368                                               Kids      Apr, 2018   \n",
       "14380                                               Kids      Apr, 2018   \n",
       "14466                                               Kids           2017   \n",
       "\n",
       "                            aired  aired_from_year  popularity  \\\n",
       "199    {'from': None, 'to': None}              NaN        4616   \n",
       "217    {'from': None, 'to': None}              NaN        9179   \n",
       "248    {'from': None, 'to': None}              NaN        8760   \n",
       "289    {'from': None, 'to': None}              NaN       11021   \n",
       "435    {'from': None, 'to': None}              NaN        5950   \n",
       "577    {'from': None, 'to': None}              NaN       13898   \n",
       "657    {'from': None, 'to': None}              NaN       13793   \n",
       "794    {'from': None, 'to': None}              NaN        6406   \n",
       "1044   {'from': None, 'to': None}              NaN       13110   \n",
       "1084   {'from': None, 'to': None}              NaN        4971   \n",
       "1228   {'from': None, 'to': None}              NaN       12005   \n",
       "1382   {'from': None, 'to': None}              NaN        9688   \n",
       "1440   {'from': None, 'to': None}              NaN       13510   \n",
       "1465   {'from': None, 'to': None}              NaN        8528   \n",
       "1582   {'from': None, 'to': None}              NaN        7577   \n",
       "1688   {'from': None, 'to': None}              NaN        2535   \n",
       "1721   {'from': None, 'to': None}              NaN        8328   \n",
       "1783   {'from': None, 'to': None}              NaN        6839   \n",
       "1928   {'from': None, 'to': None}              NaN        4848   \n",
       "1935   {'from': None, 'to': None}              NaN        3803   \n",
       "2104   {'from': None, 'to': None}              NaN        9932   \n",
       "2324   {'from': None, 'to': None}              NaN        6079   \n",
       "2643   {'from': None, 'to': None}              NaN       11370   \n",
       "3178   {'from': None, 'to': None}              NaN        9489   \n",
       "3192   {'from': None, 'to': None}              NaN       12052   \n",
       "3307   {'from': None, 'to': None}              NaN        8425   \n",
       "3315   {'from': None, 'to': None}              NaN        5070   \n",
       "3451   {'from': None, 'to': None}              NaN        4409   \n",
       "3718   {'from': None, 'to': None}              NaN        7982   \n",
       "3915   {'from': None, 'to': None}              NaN       13116   \n",
       "...                           ...              ...         ...   \n",
       "10975  {'from': None, 'to': None}              NaN        3245   \n",
       "11095  {'from': None, 'to': None}              NaN       14119   \n",
       "11232  {'from': None, 'to': None}              NaN        8304   \n",
       "11294  {'from': None, 'to': None}              NaN       11289   \n",
       "11296  {'from': None, 'to': None}              NaN       13044   \n",
       "11434  {'from': None, 'to': None}              NaN       11982   \n",
       "11493  {'from': None, 'to': None}              NaN       10597   \n",
       "11662  {'from': None, 'to': None}              NaN        1647   \n",
       "11755  {'from': None, 'to': None}              NaN        8876   \n",
       "11785  {'from': None, 'to': None}              NaN        6156   \n",
       "11809  {'from': None, 'to': None}              NaN        3616   \n",
       "11813  {'from': None, 'to': None}              NaN       13694   \n",
       "11912  {'from': None, 'to': None}              NaN       12739   \n",
       "12190  {'from': None, 'to': None}              NaN        5661   \n",
       "12649  {'from': None, 'to': None}              NaN        8283   \n",
       "12900  {'from': None, 'to': None}              NaN        7512   \n",
       "13048  {'from': None, 'to': None}              NaN        6118   \n",
       "13067  {'from': None, 'to': None}              NaN        6732   \n",
       "13287  {'from': None, 'to': None}              NaN       12712   \n",
       "13428  {'from': None, 'to': None}              NaN       13159   \n",
       "13651  {'from': None, 'to': None}              NaN       14113   \n",
       "13728  {'from': None, 'to': None}              NaN       11577   \n",
       "13773  {'from': None, 'to': None}              NaN        3535   \n",
       "13869  {'from': None, 'to': None}              NaN       13979   \n",
       "13945  {'from': None, 'to': None}              NaN       13845   \n",
       "13982  {'from': None, 'to': None}              NaN       13366   \n",
       "14112  {'from': None, 'to': None}              NaN       13112   \n",
       "14368  {'from': None, 'to': None}              NaN       14466   \n",
       "14380  {'from': None, 'to': None}              NaN       14468   \n",
       "14466  {'from': None, 'to': None}              NaN       14191   \n",
       "\n",
       "                               rating     rank  \n",
       "199         PG-13 - Teens 13 or older   2904.0  \n",
       "217    R - 17+ (violence & profanity)   7412.0  \n",
       "248         PG-13 - Teens 13 or older   9046.0  \n",
       "289                      G - All Ages   7909.0  \n",
       "435                      G - All Ages   7803.0  \n",
       "577                      G - All Ages  10900.0  \n",
       "657                      G - All Ages   9536.0  \n",
       "794                     PG - Children   7487.0  \n",
       "1044                     G - All Ages  11001.0  \n",
       "1084        PG-13 - Teens 13 or older   3988.0  \n",
       "1228                     G - All Ages   9900.0  \n",
       "1382                     G - All Ages   4601.0  \n",
       "1440                     G - All Ages  12106.0  \n",
       "1465   R - 17+ (violence & profanity)   6710.0  \n",
       "1582                     G - All Ages   7946.0  \n",
       "1688        PG-13 - Teens 13 or older   1954.0  \n",
       "1721                     G - All Ages   6471.0  \n",
       "1783                     G - All Ages   7860.0  \n",
       "1928                     G - All Ages   1523.0  \n",
       "1935        PG-13 - Teens 13 or older   2177.0  \n",
       "2104                     G - All Ages   6585.0  \n",
       "2324                     G - All Ages   6870.0  \n",
       "2643                     G - All Ages   8452.0  \n",
       "3178        PG-13 - Teens 13 or older   9344.0  \n",
       "3192                     G - All Ages  11453.0  \n",
       "3307                             None   9885.0  \n",
       "3315        PG-13 - Teens 13 or older   4549.0  \n",
       "3451        PG-13 - Teens 13 or older   3907.0  \n",
       "3718                     G - All Ages   3769.0  \n",
       "3915                     G - All Ages  12130.0  \n",
       "...                               ...      ...  \n",
       "10975       PG-13 - Teens 13 or older   2307.0  \n",
       "11095                    G - All Ages   9867.0  \n",
       "11232       PG-13 - Teens 13 or older   7215.0  \n",
       "11294                    G - All Ages   8166.0  \n",
       "11296                    G - All Ages  12770.0  \n",
       "11434                    G - All Ages  10806.0  \n",
       "11493                    G - All Ages   7273.0  \n",
       "11662       PG-13 - Teens 13 or older    415.0  \n",
       "11755       PG-13 - Teens 13 or older   7949.0  \n",
       "11785                    G - All Ages   8125.0  \n",
       "11809       PG-13 - Teens 13 or older   1923.0  \n",
       "11813                    G - All Ages  12415.0  \n",
       "11912                    G - All Ages  12608.0  \n",
       "12190                    G - All Ages   6355.0  \n",
       "12649                    G - All Ages   5911.0  \n",
       "12900                   PG - Children   7932.0  \n",
       "13048                    G - All Ages   8216.0  \n",
       "13067       PG-13 - Teens 13 or older   8060.0  \n",
       "13287                    G - All Ages  12024.0  \n",
       "13428                    G - All Ages  10952.0  \n",
       "13651                    G - All Ages  12048.0  \n",
       "13728                    G - All Ages   8747.0  \n",
       "13773       PG-13 - Teens 13 or older   1320.0  \n",
       "13869                   PG - Children  10084.0  \n",
       "13945       PG-13 - Teens 13 or older  12395.0  \n",
       "13982       PG-13 - Teens 13 or older  11124.0  \n",
       "14112                    G - All Ages  11240.0  \n",
       "14368                    G - All Ages  12704.0  \n",
       "14380                    G - All Ages   9913.0  \n",
       "14466                    G - All Ages  11396.0  \n",
       "\n",
       "[122 rows x 12 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes[~(delete_filter) & animes['aired_from_year'].isnull()][['anime_id', 'title', 'type', 'source', 'status', 'genre', 'aired_string', 'aired', 'aired_from_year', 'popularity', 'rating', 'rank']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# now, for anime with airing year in aired_string column, I use that year to fill aired_from_year\n",
    "years_from_string = animes['aired_string'].str.extract(r'(\\d{4})')\n",
    "animes.loc[animes['aired_from_year'].isnull(), 'aired_from_year'] = years_from_string[animes['aired_from_year'].isnull()]\n",
    "# after that, I have finally airing year of all anime which were not filtered out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "anime to delete:  (7734, 33)\n"
     ]
    }
   ],
   "source": [
    "delete_filter = delete_filter | ((animes['aired_string'] == 'Not available'))\n",
    "animes_to_delete = animes[delete_filter]\n",
    "animes_to_remain = animes[~(delete_filter)]\n",
    "anime_ids_to_delete = animes_to_delete['anime_id']\n",
    "print('anime to delete: ', animes_to_delete.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>anime_id</th>\n",
       "      <th>title</th>\n",
       "      <th>type</th>\n",
       "      <th>source</th>\n",
       "      <th>status</th>\n",
       "      <th>genre</th>\n",
       "      <th>aired_string</th>\n",
       "      <th>aired</th>\n",
       "      <th>aired_from_year</th>\n",
       "      <th>popularity</th>\n",
       "      <th>rating</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>5199</td>\n",
       "      <td>Seto no Hanayome Fan Disc</td>\n",
       "      <td>Special</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy, Parody, School</td>\n",
       "      <td>Sep, 2008</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4616</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>2904.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>35952</td>\n",
       "      <td>Devilman Memorial</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Action, Horror, Demons, Supernatural</td>\n",
       "      <td>1999</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9179</td>\n",
       "      <td>R - 17+ (violence &amp; profanity)</td>\n",
       "      <td>7412.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>9509</td>\n",
       "      <td>Yami no Purple Eye</td>\n",
       "      <td>Music</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Psychological, Supernatural, Music, Horror, My...</td>\n",
       "      <td>1987</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8760</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>9046.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>31972</td>\n",
       "      <td>Tang Lang Bu Chan</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Other</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Historical</td>\n",
       "      <td>1988</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11021</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>7909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>13373</td>\n",
       "      <td>Kyoto Animation: Hana-hen</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Slice of Life</td>\n",
       "      <td>Jun, 2010</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5950</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>7803.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>34037</td>\n",
       "      <td>Kitakaze no Kureta Table-kake</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Other</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Fantasy, Historical</td>\n",
       "      <td>1967</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13898</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>10900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>34595</td>\n",
       "      <td>\"Parade\" de Satie</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Music, Dementia</td>\n",
       "      <td>May, 2016</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13793</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>9536.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>2616</td>\n",
       "      <td>Sora Iro no Tane</td>\n",
       "      <td>Special</td>\n",
       "      <td>Picture book</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Kids</td>\n",
       "      <td>Nov, 1992</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6406</td>\n",
       "      <td>PG - Children</td>\n",
       "      <td>7487.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1044</th>\n",
       "      <td>28119</td>\n",
       "      <td>Kuma no Minakuro to Kouhei Jiisan</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Book</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Drama, Kids</td>\n",
       "      <td>Jan, 1999</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13110</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>11001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>6418</td>\n",
       "      <td>Eyeshield 21: Jump Festa 2005 Special</td>\n",
       "      <td>Special</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Action, Comedy, Shounen, Sports</td>\n",
       "      <td>2005</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4971</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>3988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>25969</td>\n",
       "      <td>Clock</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Dementia</td>\n",
       "      <td>1999</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12005</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>9900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>23581</td>\n",
       "      <td>Romeo no Aoi Sora Specials</td>\n",
       "      <td>Special</td>\n",
       "      <td>Novel</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Adventure, Drama, Historical, Slice of Life</td>\n",
       "      <td>2001</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9688</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>4601.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>33484</td>\n",
       "      <td>Shiroi Zou</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Other</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Action, Historical, Kids</td>\n",
       "      <td>1963</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13510</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>12106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>4085</td>\n",
       "      <td>Yonimo Osoroshii Grimm Douwa</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Other</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Dementia, Horror, Psychological</td>\n",
       "      <td>2000</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8528</td>\n",
       "      <td>R - 17+ (violence &amp; profanity)</td>\n",
       "      <td>6710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1582</th>\n",
       "      <td>11735</td>\n",
       "      <td>Coluboccoro</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Sci-Fi, Fantasy</td>\n",
       "      <td>2007</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7577</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>7946.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688</th>\n",
       "      <td>5947</td>\n",
       "      <td>Tengen Toppa Gurren Lagann Movie: Lagann-hen S...</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Jan, 2009</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2535</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>1954.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1721</th>\n",
       "      <td>19401</td>\n",
       "      <td>Kuusou no Sora Tobu Kikaitachi</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Historical</td>\n",
       "      <td>2002</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8328</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>6471.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1783</th>\n",
       "      <td>8626</td>\n",
       "      <td>Good Morning</td>\n",
       "      <td>Music</td>\n",
       "      <td>Music</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Slice of Life, Music</td>\n",
       "      <td>Apr, 2008</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6839</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>7860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>10765</td>\n",
       "      <td>Hikaru no Go: Tokubetsu-hen - Sabaki no Ichiky...</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Game, Comedy, Supernatural, Shounen</td>\n",
       "      <td>2002</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4848</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>1523.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>2515</td>\n",
       "      <td>Detective Conan OVA 05: The Target is Kogoro! ...</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Adventure, Mystery, Comedy, Police, Shounen</td>\n",
       "      <td>2005</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3803</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>2177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2104</th>\n",
       "      <td>32041</td>\n",
       "      <td>Mori no Densetsu: Dai Ni Gakushou</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>2014</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9932</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>6585.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2324</th>\n",
       "      <td>17815</td>\n",
       "      <td>Kyoto Animation: Kasa-hen</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>Jun, 2012</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6079</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>6870.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2643</th>\n",
       "      <td>25973</td>\n",
       "      <td>I Love Picnic</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Kids, Comedy</td>\n",
       "      <td>2003</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11370</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>8452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3178</th>\n",
       "      <td>30663</td>\n",
       "      <td>Docchi mo Maid</td>\n",
       "      <td>ONA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Action, Comedy, Magic, Ecchi, Shoujo Ai</td>\n",
       "      <td>2004</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9489</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>9344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3192</th>\n",
       "      <td>23643</td>\n",
       "      <td>Nezumi no Yomeiri</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Kids, Fantasy</td>\n",
       "      <td>1961</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12052</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>11453.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3307</th>\n",
       "      <td>28377</td>\n",
       "      <td>Choujikuu Robo Meguru</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy, Mecha</td>\n",
       "      <td>Nov, 2014</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8425</td>\n",
       "      <td>None</td>\n",
       "      <td>9885.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>26019</td>\n",
       "      <td>Rokujouma no Shinryakusha!?</td>\n",
       "      <td>Special</td>\n",
       "      <td>Light novel</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Romance, Supernatural, Comedy, School</td>\n",
       "      <td>Jun, 2010</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5070</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>4549.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3451</th>\n",
       "      <td>22695</td>\n",
       "      <td>Dragon Ball Z: Summer Vacation Special</td>\n",
       "      <td>Special</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Action, Adventure, Comedy, Super Power, Martia...</td>\n",
       "      <td>Jul, 1992</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4409</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>3907.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3718</th>\n",
       "      <td>36760</td>\n",
       "      <td>Detective Conan: The Magician of Starlight</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Space</td>\n",
       "      <td>2012</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7982</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>3769.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4051</th>\n",
       "      <td>3849</td>\n",
       "      <td>The SoulTaker: Tamashii-gari - Komugi Daisakusen</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>2001</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7589</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>7425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10638</th>\n",
       "      <td>8628</td>\n",
       "      <td>Chains &amp; Rings</td>\n",
       "      <td>Music</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Fantasy, Music</td>\n",
       "      <td>2006</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8684</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>8171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10796</th>\n",
       "      <td>6553</td>\n",
       "      <td>Junjou Romantica Special</td>\n",
       "      <td>Special</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy, Romance, Shounen Ai</td>\n",
       "      <td>2008</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2557</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>1220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10844</th>\n",
       "      <td>1369</td>\n",
       "      <td>Detective Conan OVA 01: Conan vs. Kid vs. Yaiba</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Adventure, Mystery, Comedy, Shounen</td>\n",
       "      <td>2000</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3361</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>2272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10975</th>\n",
       "      <td>10189</td>\n",
       "      <td>Hunter x Hunter Pilot</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Action, Adventure, Shounen, Super Power</td>\n",
       "      <td>1998</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3245</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>2307.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11095</th>\n",
       "      <td>36818</td>\n",
       "      <td>Chikyuu wa Guruguru Merry-Go-Round</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Kids, Space</td>\n",
       "      <td>2017</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14119</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>9867.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11232</th>\n",
       "      <td>17787</td>\n",
       "      <td>Ouritsu Uchuugun: Honneamise no Tsubasa - Pilo...</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Sci-Fi, Seinen</td>\n",
       "      <td>1985</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8304</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>7215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11294</th>\n",
       "      <td>26015</td>\n",
       "      <td>Backkom: Jung-gug Gijeogwi</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy, Kids</td>\n",
       "      <td>2013</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11289</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>8166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11296</th>\n",
       "      <td>32920</td>\n",
       "      <td>Yongshi</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Other</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Historical, Martial Arts</td>\n",
       "      <td>2007</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13044</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>12770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11434</th>\n",
       "      <td>24681</td>\n",
       "      <td>Kazoku Robinson Hyouryuuki: Fushigi na Shima n...</td>\n",
       "      <td>Special</td>\n",
       "      <td>Novel</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Slice of Life, Adventure, Historical, Drama</td>\n",
       "      <td>2001</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11982</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>10806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11493</th>\n",
       "      <td>25967</td>\n",
       "      <td>Backkom Sports</td>\n",
       "      <td>TV</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy, Kids, Sports</td>\n",
       "      <td>2009</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10597</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>7273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11662</th>\n",
       "      <td>8740</td>\n",
       "      <td>One Piece Film: Strong World Episode 0</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Action, Adventure, Comedy, Fantasy, Shounen, S...</td>\n",
       "      <td>Apr, 2010</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1647</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11755</th>\n",
       "      <td>21663</td>\n",
       "      <td>Last Orders</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Action, Samurai, Sci-Fi</td>\n",
       "      <td>Oct, 1997</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8876</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>7949.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11785</th>\n",
       "      <td>17811</td>\n",
       "      <td>Kyoto Animation: Hassou-hen</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>Apr, 2012</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6156</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>8125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11809</th>\n",
       "      <td>2512</td>\n",
       "      <td>Detective Conan OVA 02: 16 Suspects</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Adventure, Mystery, Comedy, Police, Shounen</td>\n",
       "      <td>2002</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3616</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>1923.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11813</th>\n",
       "      <td>34719</td>\n",
       "      <td>the FLY BanD!</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Music</td>\n",
       "      <td>1998</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13694</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>12415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11912</th>\n",
       "      <td>25599</td>\n",
       "      <td>Vampiyan Kids Promotion</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy, Fantasy, Kids</td>\n",
       "      <td>1999</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12739</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>12608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12190</th>\n",
       "      <td>17791</td>\n",
       "      <td>Kyoto Animation: Ikitaku Naru Omise-hen</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>Jul, 2012</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5661</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>6355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12649</th>\n",
       "      <td>8815</td>\n",
       "      <td>Domo TV</td>\n",
       "      <td>TV</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>2008</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8283</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>5911.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12900</th>\n",
       "      <td>10520</td>\n",
       "      <td>Nurse Witch Komugi-chan Magikarte Z Special</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy, Parody</td>\n",
       "      <td>2005</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7512</td>\n",
       "      <td>PG - Children</td>\n",
       "      <td>7932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13048</th>\n",
       "      <td>13375</td>\n",
       "      <td>Kyoto Animation: Sora-hen</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Slice of Life</td>\n",
       "      <td>Jul, 2010</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6118</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>8216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13067</th>\n",
       "      <td>3684</td>\n",
       "      <td>Dimension Loop</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>2001</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6732</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>8060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13287</th>\n",
       "      <td>25979</td>\n",
       "      <td>Sheep in the Island</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>2004</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12712</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>12024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13428</th>\n",
       "      <td>37019</td>\n",
       "      <td>Komaneko: Hajime no Ippo</td>\n",
       "      <td>Special</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>2003</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13159</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>10952.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13651</th>\n",
       "      <td>34187</td>\n",
       "      <td>Shima no Kuregata no Hanashi</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Fantasy, Kids</td>\n",
       "      <td>2010</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14113</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>12048.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13773</th>\n",
       "      <td>2514</td>\n",
       "      <td>Detective Conan OVA 04: Conan and Kid and Crys...</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Adventure, Mystery, Police, Shounen</td>\n",
       "      <td>2004</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3535</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>1320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13869</th>\n",
       "      <td>36579</td>\n",
       "      <td>Fettu to Ccine to Gummi Seijin</td>\n",
       "      <td>ONA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Mar, 2015</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13979</td>\n",
       "      <td>PG - Children</td>\n",
       "      <td>10084.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13945</th>\n",
       "      <td>37133</td>\n",
       "      <td>Tenkousei wa Kounin Kaikeishi!</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Slice of Life, School</td>\n",
       "      <td>Mar, 2013</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13845</td>\n",
       "      <td>PG-13 - Teens 13 or older</td>\n",
       "      <td>12395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14368</th>\n",
       "      <td>37493</td>\n",
       "      <td>Watashitachi no Mirai</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Kids</td>\n",
       "      <td>Apr, 2018</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14466</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>12704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14380</th>\n",
       "      <td>37494</td>\n",
       "      <td>Chikyuu to no Yakusoku</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Kids</td>\n",
       "      <td>Apr, 2018</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14468</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>9913.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14466</th>\n",
       "      <td>36315</td>\n",
       "      <td>Mushi Mushi Mura no Nakama-tachi: Minna Ii Tok...</td>\n",
       "      <td>OVA</td>\n",
       "      <td>Original</td>\n",
       "      <td>Finished Airing</td>\n",
       "      <td>Kids</td>\n",
       "      <td>2017</td>\n",
       "      <td>{'from': None, 'to': None}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14191</td>\n",
       "      <td>G - All Ages</td>\n",
       "      <td>11396.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       anime_id                                              title     type  \\\n",
       "199        5199                          Seto no Hanayome Fan Disc  Special   \n",
       "217       35952                                  Devilman Memorial      OVA   \n",
       "248        9509                                 Yami no Purple Eye    Music   \n",
       "289       31972                                  Tang Lang Bu Chan    Movie   \n",
       "435       13373                          Kyoto Animation: Hana-hen  Special   \n",
       "577       34037                      Kitakaze no Kureta Table-kake    Movie   \n",
       "657       34595                                  \"Parade\" de Satie    Movie   \n",
       "794        2616                                   Sora Iro no Tane  Special   \n",
       "1044      28119                  Kuma no Minakuro to Kouhei Jiisan      OVA   \n",
       "1084       6418              Eyeshield 21: Jump Festa 2005 Special  Special   \n",
       "1228      25969                                              Clock  Special   \n",
       "1382      23581                         Romeo no Aoi Sora Specials  Special   \n",
       "1440      33484                                         Shiroi Zou    Movie   \n",
       "1465       4085                       Yonimo Osoroshii Grimm Douwa      OVA   \n",
       "1582      11735                                        Coluboccoro      OVA   \n",
       "1688       5947  Tengen Toppa Gurren Lagann Movie: Lagann-hen S...      OVA   \n",
       "1721      19401                     Kuusou no Sora Tobu Kikaitachi    Movie   \n",
       "1783       8626                                       Good Morning    Music   \n",
       "1928      10765  Hikaru no Go: Tokubetsu-hen - Sabaki no Ichiky...      OVA   \n",
       "1935       2515  Detective Conan OVA 05: The Target is Kogoro! ...      OVA   \n",
       "2104      32041                  Mori no Densetsu: Dai Ni Gakushou    Movie   \n",
       "2324      17815                          Kyoto Animation: Kasa-hen  Special   \n",
       "2643      25973                                      I Love Picnic  Special   \n",
       "3178      30663                                     Docchi mo Maid      ONA   \n",
       "3192      23643                                  Nezumi no Yomeiri    Movie   \n",
       "3307      28377                              Choujikuu Robo Meguru      OVA   \n",
       "3315      26019                        Rokujouma no Shinryakusha!?  Special   \n",
       "3451      22695             Dragon Ball Z: Summer Vacation Special  Special   \n",
       "3718      36760         Detective Conan: The Magician of Starlight    Movie   \n",
       "4051       3849   The SoulTaker: Tamashii-gari - Komugi Daisakusen  Special   \n",
       "...         ...                                                ...      ...   \n",
       "10638      8628                                     Chains & Rings    Music   \n",
       "10796      6553                           Junjou Romantica Special  Special   \n",
       "10844      1369    Detective Conan OVA 01: Conan vs. Kid vs. Yaiba      OVA   \n",
       "10975     10189                              Hunter x Hunter Pilot      OVA   \n",
       "11095     36818                 Chikyuu wa Guruguru Merry-Go-Round    Movie   \n",
       "11232     17787  Ouritsu Uchuugun: Honneamise no Tsubasa - Pilo...  Special   \n",
       "11294     26015                         Backkom: Jung-gug Gijeogwi  Special   \n",
       "11296     32920                                            Yongshi    Movie   \n",
       "11434     24681  Kazoku Robinson Hyouryuuki: Fushigi na Shima n...  Special   \n",
       "11493     25967                                     Backkom Sports       TV   \n",
       "11662      8740             One Piece Film: Strong World Episode 0      OVA   \n",
       "11755     21663                                        Last Orders  Special   \n",
       "11785     17811                        Kyoto Animation: Hassou-hen  Special   \n",
       "11809      2512                Detective Conan OVA 02: 16 Suspects      OVA   \n",
       "11813     34719                                      the FLY BanD!  Special   \n",
       "11912     25599                            Vampiyan Kids Promotion  Special   \n",
       "12190     17791            Kyoto Animation: Ikitaku Naru Omise-hen  Special   \n",
       "12649      8815                                            Domo TV       TV   \n",
       "12900     10520        Nurse Witch Komugi-chan Magikarte Z Special  Special   \n",
       "13048     13375                          Kyoto Animation: Sora-hen  Special   \n",
       "13067      3684                                     Dimension Loop      OVA   \n",
       "13287     25979                                Sheep in the Island  Special   \n",
       "13428     37019                           Komaneko: Hajime no Ippo  Special   \n",
       "13651     34187                       Shima no Kuregata no Hanashi      OVA   \n",
       "13773      2514  Detective Conan OVA 04: Conan and Kid and Crys...      OVA   \n",
       "13869     36579                     Fettu to Ccine to Gummi Seijin      ONA   \n",
       "13945     37133                     Tenkousei wa Kounin Kaikeishi!      OVA   \n",
       "14368     37493                              Watashitachi no Mirai      OVA   \n",
       "14380     37494                             Chikyuu to no Yakusoku      OVA   \n",
       "14466     36315  Mushi Mushi Mura no Nakama-tachi: Minna Ii Tok...      OVA   \n",
       "\n",
       "             source           status  \\\n",
       "199           Manga  Finished Airing   \n",
       "217           Manga  Finished Airing   \n",
       "248           Manga  Finished Airing   \n",
       "289           Other  Finished Airing   \n",
       "435        Original  Finished Airing   \n",
       "577           Other  Finished Airing   \n",
       "657        Original  Finished Airing   \n",
       "794    Picture book  Finished Airing   \n",
       "1044           Book  Finished Airing   \n",
       "1084          Manga  Finished Airing   \n",
       "1228       Original  Finished Airing   \n",
       "1382          Novel  Finished Airing   \n",
       "1440          Other  Finished Airing   \n",
       "1465          Other  Finished Airing   \n",
       "1582       Original  Finished Airing   \n",
       "1688       Original  Finished Airing   \n",
       "1721       Original  Finished Airing   \n",
       "1783          Music  Finished Airing   \n",
       "1928          Manga  Finished Airing   \n",
       "1935          Manga  Finished Airing   \n",
       "2104       Original  Finished Airing   \n",
       "2324       Original  Finished Airing   \n",
       "2643       Original  Finished Airing   \n",
       "3178       Original  Finished Airing   \n",
       "3192       Original  Finished Airing   \n",
       "3307       Original  Finished Airing   \n",
       "3315    Light novel  Finished Airing   \n",
       "3451          Manga  Finished Airing   \n",
       "3718          Manga  Finished Airing   \n",
       "4051       Original  Finished Airing   \n",
       "...             ...              ...   \n",
       "10638      Original  Finished Airing   \n",
       "10796         Manga  Finished Airing   \n",
       "10844         Manga  Finished Airing   \n",
       "10975         Manga  Finished Airing   \n",
       "11095      Original  Finished Airing   \n",
       "11232      Original  Finished Airing   \n",
       "11294      Original  Finished Airing   \n",
       "11296         Other  Finished Airing   \n",
       "11434         Novel  Finished Airing   \n",
       "11493      Original  Finished Airing   \n",
       "11662         Manga  Finished Airing   \n",
       "11755      Original  Finished Airing   \n",
       "11785      Original  Finished Airing   \n",
       "11809         Manga  Finished Airing   \n",
       "11813      Original  Finished Airing   \n",
       "11912      Original  Finished Airing   \n",
       "12190      Original  Finished Airing   \n",
       "12649      Original  Finished Airing   \n",
       "12900      Original  Finished Airing   \n",
       "13048      Original  Finished Airing   \n",
       "13067      Original  Finished Airing   \n",
       "13287      Original  Finished Airing   \n",
       "13428      Original  Finished Airing   \n",
       "13651      Original  Finished Airing   \n",
       "13773         Manga  Finished Airing   \n",
       "13869      Original  Finished Airing   \n",
       "13945      Original  Finished Airing   \n",
       "14368      Original  Finished Airing   \n",
       "14380      Original  Finished Airing   \n",
       "14466      Original  Finished Airing   \n",
       "\n",
       "                                                   genre aired_string  \\\n",
       "199                               Comedy, Parody, School    Sep, 2008   \n",
       "217                 Action, Horror, Demons, Supernatural         1999   \n",
       "248    Psychological, Supernatural, Music, Horror, My...         1987   \n",
       "289                                           Historical         1988   \n",
       "435                                        Slice of Life    Jun, 2010   \n",
       "577                                  Fantasy, Historical         1967   \n",
       "657                                      Music, Dementia    May, 2016   \n",
       "794                                                 Kids    Nov, 1992   \n",
       "1044                                         Drama, Kids    Jan, 1999   \n",
       "1084                     Action, Comedy, Shounen, Sports         2005   \n",
       "1228                                            Dementia         1999   \n",
       "1382         Adventure, Drama, Historical, Slice of Life         2001   \n",
       "1440                            Action, Historical, Kids         1963   \n",
       "1465                     Dementia, Horror, Psychological         2000   \n",
       "1582                                     Sci-Fi, Fantasy         2007   \n",
       "1688                                              Comedy    Jan, 2009   \n",
       "1721                                          Historical         2002   \n",
       "1783                                Slice of Life, Music    Apr, 2008   \n",
       "1928                 Game, Comedy, Supernatural, Shounen         2002   \n",
       "1935         Adventure, Mystery, Comedy, Police, Shounen         2005   \n",
       "2104                                             Fantasy         2014   \n",
       "2324                                             Fantasy    Jun, 2012   \n",
       "2643                                        Kids, Comedy         2003   \n",
       "3178             Action, Comedy, Magic, Ecchi, Shoujo Ai         2004   \n",
       "3192                                       Kids, Fantasy         1961   \n",
       "3307                                       Comedy, Mecha    Nov, 2014   \n",
       "3315               Romance, Supernatural, Comedy, School    Jun, 2010   \n",
       "3451   Action, Adventure, Comedy, Super Power, Martia...    Jul, 1992   \n",
       "3718                                               Space         2012   \n",
       "4051                                              Comedy         2001   \n",
       "...                                                  ...          ...   \n",
       "10638                                     Fantasy, Music         2006   \n",
       "10796                        Comedy, Romance, Shounen Ai         2008   \n",
       "10844                Adventure, Mystery, Comedy, Shounen         2000   \n",
       "10975            Action, Adventure, Shounen, Super Power         1998   \n",
       "11095                                        Kids, Space         2017   \n",
       "11232                                     Sci-Fi, Seinen         1985   \n",
       "11294                                       Comedy, Kids         2013   \n",
       "11296                           Historical, Martial Arts         2007   \n",
       "11434        Slice of Life, Adventure, Historical, Drama         2001   \n",
       "11493                               Comedy, Kids, Sports         2009   \n",
       "11662  Action, Adventure, Comedy, Fantasy, Shounen, S...    Apr, 2010   \n",
       "11755                            Action, Samurai, Sci-Fi    Oct, 1997   \n",
       "11785                                            Fantasy    Apr, 2012   \n",
       "11809        Adventure, Mystery, Comedy, Police, Shounen         2002   \n",
       "11813                                              Music         1998   \n",
       "11912                              Comedy, Fantasy, Kids         1999   \n",
       "12190                                             Sci-Fi    Jul, 2012   \n",
       "12649                                             Comedy         2008   \n",
       "12900                                     Comedy, Parody         2005   \n",
       "13048                                      Slice of Life    Jul, 2010   \n",
       "13067                                             Sci-Fi         2001   \n",
       "13287                                             Comedy         2004   \n",
       "13428                                             Comedy         2003   \n",
       "13651                                      Fantasy, Kids         2010   \n",
       "13773                Adventure, Mystery, Police, Shounen         2004   \n",
       "13869                                             Comedy    Mar, 2015   \n",
       "13945                              Slice of Life, School    Mar, 2013   \n",
       "14368                                               Kids    Apr, 2018   \n",
       "14380                                               Kids    Apr, 2018   \n",
       "14466                                               Kids         2017   \n",
       "\n",
       "                            aired  aired_from_year  popularity  \\\n",
       "199    {'from': None, 'to': None}              NaN        4616   \n",
       "217    {'from': None, 'to': None}              NaN        9179   \n",
       "248    {'from': None, 'to': None}              NaN        8760   \n",
       "289    {'from': None, 'to': None}              NaN       11021   \n",
       "435    {'from': None, 'to': None}              NaN        5950   \n",
       "577    {'from': None, 'to': None}              NaN       13898   \n",
       "657    {'from': None, 'to': None}              NaN       13793   \n",
       "794    {'from': None, 'to': None}              NaN        6406   \n",
       "1044   {'from': None, 'to': None}              NaN       13110   \n",
       "1084   {'from': None, 'to': None}              NaN        4971   \n",
       "1228   {'from': None, 'to': None}              NaN       12005   \n",
       "1382   {'from': None, 'to': None}              NaN        9688   \n",
       "1440   {'from': None, 'to': None}              NaN       13510   \n",
       "1465   {'from': None, 'to': None}              NaN        8528   \n",
       "1582   {'from': None, 'to': None}              NaN        7577   \n",
       "1688   {'from': None, 'to': None}              NaN        2535   \n",
       "1721   {'from': None, 'to': None}              NaN        8328   \n",
       "1783   {'from': None, 'to': None}              NaN        6839   \n",
       "1928   {'from': None, 'to': None}              NaN        4848   \n",
       "1935   {'from': None, 'to': None}              NaN        3803   \n",
       "2104   {'from': None, 'to': None}              NaN        9932   \n",
       "2324   {'from': None, 'to': None}              NaN        6079   \n",
       "2643   {'from': None, 'to': None}              NaN       11370   \n",
       "3178   {'from': None, 'to': None}              NaN        9489   \n",
       "3192   {'from': None, 'to': None}              NaN       12052   \n",
       "3307   {'from': None, 'to': None}              NaN        8425   \n",
       "3315   {'from': None, 'to': None}              NaN        5070   \n",
       "3451   {'from': None, 'to': None}              NaN        4409   \n",
       "3718   {'from': None, 'to': None}              NaN        7982   \n",
       "4051   {'from': None, 'to': None}              NaN        7589   \n",
       "...                           ...              ...         ...   \n",
       "10638  {'from': None, 'to': None}              NaN        8684   \n",
       "10796  {'from': None, 'to': None}              NaN        2557   \n",
       "10844  {'from': None, 'to': None}              NaN        3361   \n",
       "10975  {'from': None, 'to': None}              NaN        3245   \n",
       "11095  {'from': None, 'to': None}              NaN       14119   \n",
       "11232  {'from': None, 'to': None}              NaN        8304   \n",
       "11294  {'from': None, 'to': None}              NaN       11289   \n",
       "11296  {'from': None, 'to': None}              NaN       13044   \n",
       "11434  {'from': None, 'to': None}              NaN       11982   \n",
       "11493  {'from': None, 'to': None}              NaN       10597   \n",
       "11662  {'from': None, 'to': None}              NaN        1647   \n",
       "11755  {'from': None, 'to': None}              NaN        8876   \n",
       "11785  {'from': None, 'to': None}              NaN        6156   \n",
       "11809  {'from': None, 'to': None}              NaN        3616   \n",
       "11813  {'from': None, 'to': None}              NaN       13694   \n",
       "11912  {'from': None, 'to': None}              NaN       12739   \n",
       "12190  {'from': None, 'to': None}              NaN        5661   \n",
       "12649  {'from': None, 'to': None}              NaN        8283   \n",
       "12900  {'from': None, 'to': None}              NaN        7512   \n",
       "13048  {'from': None, 'to': None}              NaN        6118   \n",
       "13067  {'from': None, 'to': None}              NaN        6732   \n",
       "13287  {'from': None, 'to': None}              NaN       12712   \n",
       "13428  {'from': None, 'to': None}              NaN       13159   \n",
       "13651  {'from': None, 'to': None}              NaN       14113   \n",
       "13773  {'from': None, 'to': None}              NaN        3535   \n",
       "13869  {'from': None, 'to': None}              NaN       13979   \n",
       "13945  {'from': None, 'to': None}              NaN       13845   \n",
       "14368  {'from': None, 'to': None}              NaN       14466   \n",
       "14380  {'from': None, 'to': None}              NaN       14468   \n",
       "14466  {'from': None, 'to': None}              NaN       14191   \n",
       "\n",
       "                               rating     rank  \n",
       "199         PG-13 - Teens 13 or older   2904.0  \n",
       "217    R - 17+ (violence & profanity)   7412.0  \n",
       "248         PG-13 - Teens 13 or older   9046.0  \n",
       "289                      G - All Ages   7909.0  \n",
       "435                      G - All Ages   7803.0  \n",
       "577                      G - All Ages  10900.0  \n",
       "657                      G - All Ages   9536.0  \n",
       "794                     PG - Children   7487.0  \n",
       "1044                     G - All Ages  11001.0  \n",
       "1084        PG-13 - Teens 13 or older   3988.0  \n",
       "1228                     G - All Ages   9900.0  \n",
       "1382                     G - All Ages   4601.0  \n",
       "1440                     G - All Ages  12106.0  \n",
       "1465   R - 17+ (violence & profanity)   6710.0  \n",
       "1582                     G - All Ages   7946.0  \n",
       "1688        PG-13 - Teens 13 or older   1954.0  \n",
       "1721                     G - All Ages   6471.0  \n",
       "1783                     G - All Ages   7860.0  \n",
       "1928                     G - All Ages   1523.0  \n",
       "1935        PG-13 - Teens 13 or older   2177.0  \n",
       "2104                     G - All Ages   6585.0  \n",
       "2324                     G - All Ages   6870.0  \n",
       "2643                     G - All Ages   8452.0  \n",
       "3178        PG-13 - Teens 13 or older   9344.0  \n",
       "3192                     G - All Ages  11453.0  \n",
       "3307                             None   9885.0  \n",
       "3315        PG-13 - Teens 13 or older   4549.0  \n",
       "3451        PG-13 - Teens 13 or older   3907.0  \n",
       "3718                     G - All Ages   3769.0  \n",
       "4051        PG-13 - Teens 13 or older   7425.0  \n",
       "...                               ...      ...  \n",
       "10638                    G - All Ages   8171.0  \n",
       "10796       PG-13 - Teens 13 or older   1220.0  \n",
       "10844       PG-13 - Teens 13 or older   2272.0  \n",
       "10975       PG-13 - Teens 13 or older   2307.0  \n",
       "11095                    G - All Ages   9867.0  \n",
       "11232       PG-13 - Teens 13 or older   7215.0  \n",
       "11294                    G - All Ages   8166.0  \n",
       "11296                    G - All Ages  12770.0  \n",
       "11434                    G - All Ages  10806.0  \n",
       "11493                    G - All Ages   7273.0  \n",
       "11662       PG-13 - Teens 13 or older    415.0  \n",
       "11755       PG-13 - Teens 13 or older   7949.0  \n",
       "11785                    G - All Ages   8125.0  \n",
       "11809       PG-13 - Teens 13 or older   1923.0  \n",
       "11813                    G - All Ages  12415.0  \n",
       "11912                    G - All Ages  12608.0  \n",
       "12190                    G - All Ages   6355.0  \n",
       "12649                    G - All Ages   5911.0  \n",
       "12900                   PG - Children   7932.0  \n",
       "13048                    G - All Ages   8216.0  \n",
       "13067       PG-13 - Teens 13 or older   8060.0  \n",
       "13287                    G - All Ages  12024.0  \n",
       "13428                    G - All Ages  10952.0  \n",
       "13651                    G - All Ages  12048.0  \n",
       "13773       PG-13 - Teens 13 or older   1320.0  \n",
       "13869                   PG - Children  10084.0  \n",
       "13945       PG-13 - Teens 13 or older  12395.0  \n",
       "14368                    G - All Ages  12704.0  \n",
       "14380                    G - All Ages   9913.0  \n",
       "14466                    G - All Ages  11396.0  \n",
       "\n",
       "[115 rows x 12 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes[~(delete_filter) & animes['aired_from_year'].isnull()][['anime_id', 'title', 'type', 'source', 'status', 'genre', 'aired_string', 'aired', 'aired_from_year', 'popularity', 'rating', 'rank']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['anime_id', 'title', 'title_english', 'title_japanese',\n",
       "       'title_synonyms', 'image_url', 'type', 'source', 'episodes', 'status',\n",
       "       'airing', 'aired_string', 'aired', 'duration', 'rating', 'score',\n",
       "       'scored_by', 'rank', 'popularity', 'members', 'favorites', 'background',\n",
       "       'premiered', 'broadcast', 'related', 'producer', 'licensor', 'studio',\n",
       "       'genre', 'opening_theme', 'ending_theme', 'duration_min',\n",
       "       'aired_from_year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "anime_id                                                       37447\n",
       "title                                                Karakuri Circus\n",
       "title_english                                  Le Cirque de Karakuri\n",
       "title_japanese                                              からくりサーカス\n",
       "title_synonyms                                                   NaN\n",
       "image_url          https://myanimelist.cdn-dena.com/images/anime/...\n",
       "type                                                              TV\n",
       "source                                                         Manga\n",
       "episodes                                                          36\n",
       "status                                                 Not yet aired\n",
       "airing                                                         False\n",
       "aired_string                                           Not available\n",
       "aired                                     {'from': None, 'to': None}\n",
       "duration                                                     Unknown\n",
       "rating                                                          None\n",
       "score                                                              0\n",
       "scored_by                                                          0\n",
       "rank                                                             NaN\n",
       "popularity                                                      8300\n",
       "members                                                          986\n",
       "favorites                                                          0\n",
       "background                                                       NaN\n",
       "premiered                                                        NaN\n",
       "broadcast                                                    Unknown\n",
       "related            {'Adaptation': [{'mal_id': 868, 'type': 'manga...\n",
       "producer                                     Shogakukan, Twin Engine\n",
       "licensor                                                         NaN\n",
       "studio                                                   Studio VOLN\n",
       "genre                                    Adventure, Mystery, Shounen\n",
       "opening_theme                                                     []\n",
       "ending_theme                                                      []\n",
       "duration_min                                                       0\n",
       "aired_from_year                                                  NaN\n",
       "Name: 76, dtype: object"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes.loc[76]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Not available                   208\n",
       "2005                             45\n",
       "2004                             43\n",
       "Jul, 2018 to ?                   41\n",
       "2003                             38\n",
       "2000                             38\n",
       "2008                             38\n",
       "2006                             38\n",
       "2011                             32\n",
       "2009                             31\n",
       "2002                             31\n",
       "2018 to ?                        31\n",
       "2001                             28\n",
       "2012                             27\n",
       "2010                             27\n",
       "2013                             27\n",
       "1999                             27\n",
       "2007                             26\n",
       "1998                             24\n",
       "Oct, 2018 to ?                   23\n",
       "2014                             19\n",
       "1997                             17\n",
       "1985                             17\n",
       "1989                             16\n",
       "1992                             16\n",
       "2019 to ?                        16\n",
       "1990                             15\n",
       "1982                             15\n",
       "1931                             15\n",
       "1979                             14\n",
       "                               ... \n",
       "Nov, 2006                         1\n",
       "Feb 15, 1918                      1\n",
       "May 8, 1965 to Apr 28, 1966       1\n",
       "Sep 25, 2007 to Nov 25, 2007      1\n",
       "Apr 14, 1979 to Sep 29, 1979      1\n",
       "Feb 14, 2009 to May 8, 2009       1\n",
       "Apr 2, 2016 to Jun 25, 2016       1\n",
       "Oct 1, 1968                       1\n",
       "Aug 25, 2013                      1\n",
       "Jan 27, 2006 to Sep 26, 2006      1\n",
       "Jan 15, 1989 to Dec 24, 1989      1\n",
       "Dec 28, 1981                      1\n",
       "Sep 11, 2013 to Dec 26, 2013      1\n",
       "Apr 3, 2004 to Sep 25, 2004       1\n",
       "Jul 25, 2004                      1\n",
       "Feb 14, 1997 to Jan 23, 1998      1\n",
       "Apr, 1991 to Feb, 1994            1\n",
       "Apr 22, 2011 to Jul 10, 2011      1\n",
       "Jan 10, 2011 to ?                 1\n",
       "Jun 26, 1964 to Oct 6, 1984       1\n",
       "Dec 30, 1996                      1\n",
       "Oct 17, 2004                      1\n",
       "May 24, 2017 to Jun 27, 2017      1\n",
       "Mar 25, 1995                      1\n",
       "Jan 9, 1983 to Dec 25, 1983       1\n",
       "Oct 31, 2008                      1\n",
       "Apr 3, 1984 to Mar 19, 1985       1\n",
       "Oct 18, 1999                      1\n",
       "Mar 31, 2010                      1\n",
       "Dec 3, 2015                       1\n",
       "Name: aired_string, Length: 10003, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes['aired_string'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203f3a1f860>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes_to_remain['rating'].value_counts().sort_index().plot.bar(figsize=(16, 10), title='anime ranks', color='blue', alpha=0.5)\n",
    "animes_to_delete['rating'].value_counts().sort_index().plot.bar(figsize=(16, 10), title='anime ranks', color='red', alpha=0.5, secondary_y=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:6130: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:6130: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203f3a1f860>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes_to_remain['genre'].fillna('', inplace=True)\n",
    "animes_genres_to_remain = animes_to_remain['genre'].apply(lambda x: x.replace(' ', ''))\n",
    "genres_to_remain = animes_to_remain['genre'].apply(lambda x: x.replace(' ', '').split(','))\n",
    "genres_to_remain = pd.Series([y for x in genres_to_remain for y in x if y != '']).value_counts()\n",
    "genres_occurence_to_remain = pd.Series({genre: (animes_genres_to_remain.str.contains(genre)).sum() for genre in genres_to_remain.index})\n",
    "genres_occurence_to_remain.plot(kind='bar', figsize=(16, 10), color='blue', alpha=0.5)\n",
    "\n",
    "animes_to_delete['genre'].fillna('', inplace=True)\n",
    "animes_genres_to_delete = animes_to_delete['genre'].apply(lambda x: x.replace(' ', ''))\n",
    "genres_to_delete = animes_to_delete['genre'].apply(lambda x: x.replace(' ', '').split(','))\n",
    "genres_to_delete = pd.Series([y for x in genres_to_delete for y in x if y != '']).value_counts()\n",
    "genres_occurence_to_delete = pd.Series({genre: (animes_genres_to_delete.str.contains(genre)).sum() for genre in genres_to_delete.index})\n",
    "genres_occurence_to_delete.plot(kind='bar', figsize=(16, 10), color='red', alpha=0.5, secondary_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "animes = animes_to_remain\n",
    "animes[['anime_id']].to_csv('cache_anime_ids_after_cleaning.csv', sep=',', index=False, encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "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>anime_id</th>\n",
       "      <th>episodes</th>\n",
       "      <th>score</th>\n",
       "      <th>scored_by</th>\n",
       "      <th>rank</th>\n",
       "      <th>popularity</th>\n",
       "      <th>members</th>\n",
       "      <th>favorites</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>aired_from_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6668.000000</td>\n",
       "      <td>6668.000000</td>\n",
       "      <td>6668.000000</td>\n",
       "      <td>6.668000e+03</td>\n",
       "      <td>6312.000000</td>\n",
       "      <td>6668.000000</td>\n",
       "      <td>6.668000e+03</td>\n",
       "      <td>6668.000000</td>\n",
       "      <td>6668.000000</td>\n",
       "      <td>6553.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15064.977804</td>\n",
       "      <td>14.276395</td>\n",
       "      <td>6.848998</td>\n",
       "      <td>2.403501e+04</td>\n",
       "      <td>4327.645120</td>\n",
       "      <td>4479.515897</td>\n",
       "      <td>4.749037e+04</td>\n",
       "      <td>670.365627</td>\n",
       "      <td>28.442167</td>\n",
       "      <td>2007.028231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13161.364001</td>\n",
       "      <td>40.906929</td>\n",
       "      <td>0.927448</td>\n",
       "      <td>6.112103e+04</td>\n",
       "      <td>3170.699074</td>\n",
       "      <td>3453.338080</td>\n",
       "      <td>1.051211e+05</td>\n",
       "      <td>3823.072834</td>\n",
       "      <td>25.365980</td>\n",
       "      <td>10.283658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1942.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2592.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.350000</td>\n",
       "      <td>6.812500e+02</td>\n",
       "      <td>1710.750000</td>\n",
       "      <td>1691.750000</td>\n",
       "      <td>2.222750e+03</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10401.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.930000</td>\n",
       "      <td>3.966000e+03</td>\n",
       "      <td>3754.500000</td>\n",
       "      <td>3629.500000</td>\n",
       "      <td>1.033650e+04</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>30301.250000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.460000</td>\n",
       "      <td>1.976075e+04</td>\n",
       "      <td>6338.500000</td>\n",
       "      <td>6630.250000</td>\n",
       "      <td>4.336000e+04</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>37896.000000</td>\n",
       "      <td>1818.000000</td>\n",
       "      <td>9.520000</td>\n",
       "      <td>1.009477e+06</td>\n",
       "      <td>12856.000000</td>\n",
       "      <td>14468.000000</td>\n",
       "      <td>1.456378e+06</td>\n",
       "      <td>106895.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           anime_id     episodes        score     scored_by          rank  \\\n",
       "count   6668.000000  6668.000000  6668.000000  6.668000e+03   6312.000000   \n",
       "mean   15064.977804    14.276395     6.848998  2.403501e+04   4327.645120   \n",
       "std    13161.364001    40.906929     0.927448  6.112103e+04   3170.699074   \n",
       "min        1.000000     0.000000     0.000000  0.000000e+00      1.000000   \n",
       "25%     2592.000000     1.000000     6.350000  6.812500e+02   1710.750000   \n",
       "50%    10401.500000     6.000000     6.930000  3.966000e+03   3754.500000   \n",
       "75%    30301.250000    13.000000     7.460000  1.976075e+04   6338.500000   \n",
       "max    37896.000000  1818.000000     9.520000  1.009477e+06  12856.000000   \n",
       "\n",
       "         popularity       members      favorites  duration_min  \\\n",
       "count   6668.000000  6.668000e+03    6668.000000   6668.000000   \n",
       "mean    4479.515897  4.749037e+04     670.365627     28.442167   \n",
       "std     3453.338080  1.051211e+05    3823.072834     25.365980   \n",
       "min        1.000000  1.800000e+01       0.000000      0.000000   \n",
       "25%     1691.750000  2.222750e+03       3.000000     17.000000   \n",
       "50%     3629.500000  1.033650e+04      21.000000     24.000000   \n",
       "75%     6630.250000  4.336000e+04     142.000000     27.000000   \n",
       "max    14468.000000  1.456378e+06  106895.000000    163.000000   \n",
       "\n",
       "       aired_from_year  \n",
       "count      6553.000000  \n",
       "mean       2007.028231  \n",
       "std          10.283658  \n",
       "min        1942.000000  \n",
       "25%        2003.000000  \n",
       "50%        2010.000000  \n",
       "75%        2015.000000  \n",
       "max        2018.000000  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aired_string</th>\n",
       "      <th>aired_from_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Sep, 2008</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>1999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>1987</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>1988</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>Jun, 2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>1967</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>May, 2016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>Nov, 1992</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1044</th>\n",
       "      <td>Jan, 1999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>1999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>2001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>1963</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1582</th>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688</th>\n",
       "      <td>Jan, 2009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1721</th>\n",
       "      <td>2002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1783</th>\n",
       "      <td>Apr, 2008</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>2002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2104</th>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2324</th>\n",
       "      <td>Jun, 2012</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2643</th>\n",
       "      <td>2003</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3178</th>\n",
       "      <td>2004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3192</th>\n",
       "      <td>1961</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3307</th>\n",
       "      <td>Nov, 2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>Jun, 2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3451</th>\n",
       "      <td>Jul, 1992</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3718</th>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4051</th>\n",
       "      <td>2001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10638</th>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10796</th>\n",
       "      <td>2008</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10844</th>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10975</th>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11095</th>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11232</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11294</th>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11296</th>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11434</th>\n",
       "      <td>2001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11493</th>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11662</th>\n",
       "      <td>Apr, 2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11755</th>\n",
       "      <td>Oct, 1997</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11785</th>\n",
       "      <td>Apr, 2012</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11809</th>\n",
       "      <td>2002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11813</th>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11912</th>\n",
       "      <td>1999</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12190</th>\n",
       "      <td>Jul, 2012</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12649</th>\n",
       "      <td>2008</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12900</th>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13048</th>\n",
       "      <td>Jul, 2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13067</th>\n",
       "      <td>2001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13287</th>\n",
       "      <td>2004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13428</th>\n",
       "      <td>2003</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13651</th>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13773</th>\n",
       "      <td>2004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13869</th>\n",
       "      <td>Mar, 2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13945</th>\n",
       "      <td>Mar, 2013</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14368</th>\n",
       "      <td>Apr, 2018</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14380</th>\n",
       "      <td>Apr, 2018</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14466</th>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      aired_string  aired_from_year\n",
       "199      Sep, 2008              NaN\n",
       "217           1999              NaN\n",
       "248           1987              NaN\n",
       "289           1988              NaN\n",
       "435      Jun, 2010              NaN\n",
       "577           1967              NaN\n",
       "657      May, 2016              NaN\n",
       "794      Nov, 1992              NaN\n",
       "1044     Jan, 1999              NaN\n",
       "1084          2005              NaN\n",
       "1228          1999              NaN\n",
       "1382          2001              NaN\n",
       "1440          1963              NaN\n",
       "1465          2000              NaN\n",
       "1582          2007              NaN\n",
       "1688     Jan, 2009              NaN\n",
       "1721          2002              NaN\n",
       "1783     Apr, 2008              NaN\n",
       "1928          2002              NaN\n",
       "1935          2005              NaN\n",
       "2104          2014              NaN\n",
       "2324     Jun, 2012              NaN\n",
       "2643          2003              NaN\n",
       "3178          2004              NaN\n",
       "3192          1961              NaN\n",
       "3307     Nov, 2014              NaN\n",
       "3315     Jun, 2010              NaN\n",
       "3451     Jul, 1992              NaN\n",
       "3718          2012              NaN\n",
       "4051          2001              NaN\n",
       "...            ...              ...\n",
       "10638         2006              NaN\n",
       "10796         2008              NaN\n",
       "10844         2000              NaN\n",
       "10975         1998              NaN\n",
       "11095         2017              NaN\n",
       "11232         1985              NaN\n",
       "11294         2013              NaN\n",
       "11296         2007              NaN\n",
       "11434         2001              NaN\n",
       "11493         2009              NaN\n",
       "11662    Apr, 2010              NaN\n",
       "11755    Oct, 1997              NaN\n",
       "11785    Apr, 2012              NaN\n",
       "11809         2002              NaN\n",
       "11813         1998              NaN\n",
       "11912         1999              NaN\n",
       "12190    Jul, 2012              NaN\n",
       "12649         2008              NaN\n",
       "12900         2005              NaN\n",
       "13048    Jul, 2010              NaN\n",
       "13067         2001              NaN\n",
       "13287         2004              NaN\n",
       "13428         2003              NaN\n",
       "13651         2010              NaN\n",
       "13773         2004              NaN\n",
       "13869    Mar, 2015              NaN\n",
       "13945    Mar, 2013              NaN\n",
       "14368    Apr, 2018              NaN\n",
       "14380    Apr, 2018              NaN\n",
       "14466         2017              NaN\n",
       "\n",
       "[115 rows x 2 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes[animes['aired_from_year'].isnull()][['aired_string', 'aired_from_year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "animes.to_csv('anime_cleaned_2.csv', index=False, sep=',', encoding='utf-8')\n",
    "users.to_csv('users_cleaned_2.csv', index=False, sep=',', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# checking differences between cleaned 1 and cleaned 2\n",
    "# todo: add loading clean-locations.csv and check locations\n",
    "# todo: zjistit proč se vysraly grafy a opravit to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "animes_1 = pd.read_csv('anime_cleaned.csv')\n",
    "users_1 = pd.read_csv('users_cleaned.csv', na_values=user_na_values, keep_default_na=False)\n",
    "\n",
    "animes_2 = pd.read_csv('anime_cleaned_2.csv')\n",
    "users_2 = pd.read_csv('users_cleaned_2.csv', na_values=user_na_values, keep_default_na=False)\n",
    "\n",
    "animes_1 = animes_1.set_index('anime_id')\n",
    "users_1 = users_1.set_index('username')\n",
    "animes_2 = animes_2.set_index('anime_id')\n",
    "users_2 = users_2.set_index('username')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6668, 32), (6668, 32))"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes_1.shape, animes_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>title_english</th>\n",
       "      <th>title_japanese</th>\n",
       "      <th>title_synonyms</th>\n",
       "      <th>image_url</th>\n",
       "      <th>type</th>\n",
       "      <th>source</th>\n",
       "      <th>episodes</th>\n",
       "      <th>status</th>\n",
       "      <th>airing</th>\n",
       "      <th>...</th>\n",
       "      <th>broadcast</th>\n",
       "      <th>related</th>\n",
       "      <th>producer</th>\n",
       "      <th>licensor</th>\n",
       "      <th>studio</th>\n",
       "      <th>genre</th>\n",
       "      <th>opening_theme</th>\n",
       "      <th>ending_theme</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>aired_from_year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anime_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [title, title_english, title_japanese, title_synonyms, image_url, type, source, episodes, status, airing, aired_string, aired, duration, rating, score, scored_by, rank, popularity, members, favorites, background, premiered, broadcast, related, producer, licensor, studio, genre, opening_theme, ending_theme, duration_min, aired_from_year]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 32 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes_1.loc[~animes_1.index.isin(animes_2.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>image_url</th>\n",
       "      <th>type</th>\n",
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       "      <th>episodes</th>\n",
       "      <th>status</th>\n",
       "      <th>airing</th>\n",
       "      <th>...</th>\n",
       "      <th>broadcast</th>\n",
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       "      <th>producer</th>\n",
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       "      <th>genre</th>\n",
       "      <th>opening_theme</th>\n",
       "      <th>ending_theme</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>aired_from_year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anime_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "</table>\n",
       "<p>0 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [title, title_english, title_japanese, title_synonyms, image_url, type, source, episodes, status, airing, aired_string, aired, duration, rating, score, scored_by, rank, popularity, members, favorites, background, premiered, broadcast, related, producer, licensor, studio, genre, opening_theme, ending_theme, duration_min, aired_from_year]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 32 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes_2.loc[~animes_2.index.isin(animes_1.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((108711, 16), (108711, 16))"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_1.shape, users_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "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",
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       "\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>user_id</th>\n",
       "      <th>user_watching</th>\n",
       "      <th>user_completed</th>\n",
       "      <th>user_onhold</th>\n",
       "      <th>user_dropped</th>\n",
       "      <th>user_plantowatch</th>\n",
       "      <th>user_days_spent_watching</th>\n",
       "      <th>gender</th>\n",
       "      <th>location</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>access_rank</th>\n",
       "      <th>join_date</th>\n",
       "      <th>last_online</th>\n",
       "      <th>stats_mean_score</th>\n",
       "      <th>stats_rewatched</th>\n",
       "      <th>stats_episodes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>username</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [user_id, user_watching, user_completed, user_onhold, user_dropped, user_plantowatch, user_days_spent_watching, gender, location, birth_date, access_rank, join_date, last_online, stats_mean_score, stats_rewatched, stats_episodes]\n",
       "Index: []"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_1.loc[~users_1.index.isin(users_2.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "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",
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       "\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>user_id</th>\n",
       "      <th>user_watching</th>\n",
       "      <th>user_completed</th>\n",
       "      <th>user_onhold</th>\n",
       "      <th>user_dropped</th>\n",
       "      <th>user_plantowatch</th>\n",
       "      <th>user_days_spent_watching</th>\n",
       "      <th>gender</th>\n",
       "      <th>location</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>access_rank</th>\n",
       "      <th>join_date</th>\n",
       "      <th>last_online</th>\n",
       "      <th>stats_mean_score</th>\n",
       "      <th>stats_rewatched</th>\n",
       "      <th>stats_episodes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>username</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [user_id, user_watching, user_completed, user_onhold, user_dropped, user_plantowatch, user_days_spent_watching, gender, location, birth_date, access_rank, join_date, last_online, stats_mean_score, stats_rewatched, stats_episodes]\n",
       "Index: []"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_2.loc[~users_2.index.isin(users_1.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# todo: sjednotit cleaned datasety, když je to ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_locs = pd.read_csv('clean-locations.csv', na_values=user_na_values, keep_default_na=False, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "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>user_id</th>\n",
       "      <th>address</th>\n",
       "      <th>country</th>\n",
       "      <th>country_code</th>\n",
       "      <th>state</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>116109</td>\n",
       "      <td>Moscow, Russia</td>\n",
       "      <td>РФ</td>\n",
       "      <td>ru</td>\n",
       "      <td>Москва</td>\n",
       "      <td>Москва</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88661</td>\n",
       "      <td>Israel</td>\n",
       "      <td>ישראל</td>\n",
       "      <td>il</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6398953</td>\n",
       "      <td>Atlanta, Georgia</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>Atlanta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>264578</td>\n",
       "      <td>Montreal, Canada</td>\n",
       "      <td>Canada</td>\n",
       "      <td>ca</td>\n",
       "      <td>Québec</td>\n",
       "      <td>Montréal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134029</td>\n",
       "      <td>Gig Harbor, Washington, USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Gig Harbor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60732</td>\n",
       "      <td>Karlsruhe, Germany</td>\n",
       "      <td>Deutschland</td>\n",
       "      <td>de</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3349735</td>\n",
       "      <td>Brasil Rio de Janeiro</td>\n",
       "      <td>Brasil</td>\n",
       "      <td>br</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3256071</td>\n",
       "      <td>Szczytno, Poland</td>\n",
       "      <td>RP</td>\n",
       "      <td>pl</td>\n",
       "      <td>warmińsko-mazurskie</td>\n",
       "      <td>Szczytno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>25964</td>\n",
       "      <td>Philadelphia, PA</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Philadelphia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>236623</td>\n",
       "      <td>Sombor, Serbia</td>\n",
       "      <td>Србија</td>\n",
       "      <td>rs</td>\n",
       "      <td>Војводина</td>\n",
       "      <td>Сомбор</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id                      address      country country_code  \\\n",
       "0   116109               Moscow, Russia           РФ           ru   \n",
       "1    88661                       Israel        ישראל           il   \n",
       "2  6398953             Atlanta, Georgia          USA           us   \n",
       "3   264578             Montreal, Canada       Canada           ca   \n",
       "4   134029  Gig Harbor, Washington, USA          USA           us   \n",
       "5    60732           Karlsruhe, Germany  Deutschland           de   \n",
       "6  3349735        Brasil Rio de Janeiro       Brasil           br   \n",
       "7  3256071             Szczytno, Poland           RP           pl   \n",
       "8    25964             Philadelphia, PA          USA           us   \n",
       "9   236623               Sombor, Serbia       Србија           rs   \n",
       "\n",
       "                 state            city  \n",
       "0               Москва          Москва  \n",
       "1                  NaN             NaN  \n",
       "2              Georgia         Atlanta  \n",
       "3               Québec        Montréal  \n",
       "4           Washington      Gig Harbor  \n",
       "5    Baden-Württemberg             NaN  \n",
       "6                   RJ  Rio de Janeiro  \n",
       "7  warmińsko-mazurskie        Szczytno  \n",
       "8         Pennsylvania    Philadelphia  \n",
       "9            Војводина          Сомбор  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_locs.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>user_completed</th>\n",
       "      <th>user_onhold</th>\n",
       "      <th>user_dropped</th>\n",
       "      <th>user_plantowatch</th>\n",
       "      <th>user_days_spent_watching</th>\n",
       "      <th>gender</th>\n",
       "      <th>location</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>access_rank</th>\n",
       "      <th>join_date</th>\n",
       "      <th>last_online</th>\n",
       "      <th>stats_mean_score</th>\n",
       "      <th>stats_rewatched</th>\n",
       "      <th>stats_episodes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>username</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>karthiga</th>\n",
       "      <td>2255153</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.091667</td>\n",
       "      <td>Female</td>\n",
       "      <td>Chennai, India</td>\n",
       "      <td>1990-04-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-03-03</td>\n",
       "      <td>2014-02-04 01:32:00</td>\n",
       "      <td>7.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Damonashu</th>\n",
       "      <td>37326</td>\n",
       "      <td>45</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>25</td>\n",
       "      <td>59</td>\n",
       "      <td>82.574306</td>\n",
       "      <td>Male</td>\n",
       "      <td>Detroit,Michigan</td>\n",
       "      <td>1991-08-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-02-13</td>\n",
       "      <td>2017-07-10 06:52:54</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4903.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bskai</th>\n",
       "      <td>228342</td>\n",
       "      <td>25</td>\n",
       "      <td>414</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>159.483333</td>\n",
       "      <td>Male</td>\n",
       "      <td>Nayarit, Mexico</td>\n",
       "      <td>1990-12-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2009-08-31</td>\n",
       "      <td>2014-05-12 16:35:00</td>\n",
       "      <td>8.27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9701.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>terune_uzumaki</th>\n",
       "      <td>327311</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.394444</td>\n",
       "      <td>Female</td>\n",
       "      <td>Malaysia, Kuantan</td>\n",
       "      <td>1998-08-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-05-10</td>\n",
       "      <td>2012-10-18 19:06:00</td>\n",
       "      <td>9.70</td>\n",
       "      <td>6.0</td>\n",
       "      <td>697.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bas_G</th>\n",
       "      <td>5015094</td>\n",
       "      <td>35</td>\n",
       "      <td>114</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>175</td>\n",
       "      <td>30.458333</td>\n",
       "      <td>Male</td>\n",
       "      <td>Nijmegen, Nederland</td>\n",
       "      <td>1999-10-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-26</td>\n",
       "      <td>2018-05-10 20:53:37</td>\n",
       "      <td>7.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1847.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HimeAria</th>\n",
       "      <td>3129315</td>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>19.963194</td>\n",
       "      <td>Female</td>\n",
       "      <td>Poland</td>\n",
       "      <td>1996-09-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-09-08</td>\n",
       "      <td>2018-01-14 13:05:42</td>\n",
       "      <td>8.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skallington</th>\n",
       "      <td>326733</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.234722</td>\n",
       "      <td>Male</td>\n",
       "      <td>Norberg Sweden</td>\n",
       "      <td>1991-03-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-05-09</td>\n",
       "      <td>2010-05-12 06:29:00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slimak</th>\n",
       "      <td>61677</td>\n",
       "      <td>79</td>\n",
       "      <td>224</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>84</td>\n",
       "      <td>124.731944</td>\n",
       "      <td>Male</td>\n",
       "      <td>Poland</td>\n",
       "      <td>1988-02-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-05-18</td>\n",
       "      <td>2018-05-01 12:05:21</td>\n",
       "      <td>7.77</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7447.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elysiun</th>\n",
       "      <td>5927342</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.057639</td>\n",
       "      <td>Male</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>1984-11-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-25</td>\n",
       "      <td>2017-03-11 21:37:00</td>\n",
       "      <td>7.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jimothy000</th>\n",
       "      <td>47167</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.040972</td>\n",
       "      <td>Male</td>\n",
       "      <td>England,Manchester</td>\n",
       "      <td>1995-04-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-04-01</td>\n",
       "      <td>2008-04-27 16:00:00</td>\n",
       "      <td>9.33</td>\n",
       "      <td>5.0</td>\n",
       "      <td>126.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                user_id  user_watching  user_completed  user_onhold  \\\n",
       "username                                                              \n",
       "karthiga        2255153              3              49            1   \n",
       "Damonashu         37326             45             195           27   \n",
       "bskai            228342             25             414            2   \n",
       "terune_uzumaki   327311              5               5            0   \n",
       "Bas_G           5015094             35             114            6   \n",
       "HimeAria        3129315              2              87            2   \n",
       "Skallington      326733              0               3            0   \n",
       "Slimak            61677             79             224            0   \n",
       "Elysiun         5927342              7               4            0   \n",
       "jimothy000        47167              6               0            0   \n",
       "\n",
       "                user_dropped  user_plantowatch  user_days_spent_watching  \\\n",
       "username                                                                   \n",
       "karthiga                   0                 0                 55.091667   \n",
       "Damonashu                 25                59                 82.574306   \n",
       "bskai                      5                11                159.483333   \n",
       "terune_uzumaki             0                 0                 11.394444   \n",
       "Bas_G                     20               175                 30.458333   \n",
       "HimeAria                   0                28                 19.963194   \n",
       "Skallington                0                 0                  1.234722   \n",
       "Slimak                     3                84                124.731944   \n",
       "Elysiun                    0                 4                  2.057639   \n",
       "jimothy000                 0                 2                  2.040972   \n",
       "\n",
       "                gender             location birth_date  access_rank  \\\n",
       "username                                                              \n",
       "karthiga        Female      Chennai, India  1990-04-29          NaN   \n",
       "Damonashu         Male     Detroit,Michigan 1991-08-01          NaN   \n",
       "bskai             Male      Nayarit, Mexico 1990-12-14          NaN   \n",
       "terune_uzumaki  Female    Malaysia, Kuantan 1998-08-24          NaN   \n",
       "Bas_G             Male  Nijmegen, Nederland 1999-10-24          NaN   \n",
       "HimeAria        Female               Poland 1996-09-26          NaN   \n",
       "Skallington       Male       Norberg Sweden 1991-03-15          NaN   \n",
       "Slimak            Male               Poland 1988-02-21          NaN   \n",
       "Elysiun           Male             Nebraska 1984-11-16          NaN   \n",
       "jimothy000        Male   England,Manchester 1995-04-25          NaN   \n",
       "\n",
       "                join_date         last_online  stats_mean_score  \\\n",
       "username                                                          \n",
       "karthiga       2013-03-03 2014-02-04 01:32:00              7.43   \n",
       "Damonashu      2008-02-13 2017-07-10 06:52:54              6.15   \n",
       "bskai          2009-08-31 2014-05-12 16:35:00              8.27   \n",
       "terune_uzumaki 2010-05-10 2012-10-18 19:06:00              9.70   \n",
       "Bas_G          2015-11-26 2018-05-10 20:53:37              7.86   \n",
       "HimeAria       2013-09-08 2018-01-14 13:05:42              8.23   \n",
       "Skallington    2010-05-09 2010-05-12 06:29:00             10.00   \n",
       "Slimak         2008-05-18 2018-05-01 12:05:21              7.77   \n",
       "Elysiun        2016-12-25 2017-03-11 21:37:00              7.40   \n",
       "jimothy000     2008-04-01 2008-04-27 16:00:00              9.33   \n",
       "\n",
       "                stats_rewatched  stats_episodes  \n",
       "username                                         \n",
       "karthiga                    0.0          3391.0  \n",
       "Damonashu                   6.0          4903.0  \n",
       "bskai                       1.0          9701.0  \n",
       "terune_uzumaki              6.0           697.0  \n",
       "Bas_G                       0.0          1847.0  \n",
       "HimeAria                    0.0          1314.0  \n",
       "Skallington                 7.0            73.0  \n",
       "Slimak                      2.0          7447.0  \n",
       "Elysiun                     0.0           121.0  \n",
       "jimothy000                  5.0           126.0  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "users_locs = users_locs.set_index('user_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "users = users.set_index('user_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((116733, 5), (108711, 15))"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_locs.shape, users.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": true
   },
   "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>address</th>\n",
       "      <th>country</th>\n",
       "      <th>country_code</th>\n",
       "      <th>state</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60732</th>\n",
       "      <td>Karlsruhe, Germany</td>\n",
       "      <td>Deutschland</td>\n",
       "      <td>de</td>\n",
       "      <td>Baden-Württemberg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54131</th>\n",
       "      <td>Canada, Montreal</td>\n",
       "      <td>Canada</td>\n",
       "      <td>ca</td>\n",
       "      <td>Québec</td>\n",
       "      <td>Montréal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3492957</th>\n",
       "      <td>Lublin</td>\n",
       "      <td>RP</td>\n",
       "      <td>pl</td>\n",
       "      <td>lubelskie</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271546</th>\n",
       "      <td>Asia, Japan</td>\n",
       "      <td>日本</td>\n",
       "      <td>jp</td>\n",
       "      <td>埼玉県</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12533</th>\n",
       "      <td>Limay, Bataan</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>ph</td>\n",
       "      <td>Bataan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452819</th>\n",
       "      <td>↓</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246382</th>\n",
       "      <td>Poland</td>\n",
       "      <td>RP</td>\n",
       "      <td>pl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304517</th>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>California</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4901</th>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2665751</th>\n",
       "      <td>2b980b01c8e3872c5fbd57759af98fbd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33372</th>\n",
       "      <td>Cambs, North Sea Region, EUSSR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91281</th>\n",
       "      <td>MN</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210403</th>\n",
       "      <td>Canada</td>\n",
       "      <td>Canada</td>\n",
       "      <td>ca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5690136</th>\n",
       "      <td>Soul Society</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109774</th>\n",
       "      <td>Under the bed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770595</th>\n",
       "      <td>Somewhere, California</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>California</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48774</th>\n",
       "      <td>Inside Elizabeth</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128929</th>\n",
       "      <td>Texas</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>Texas</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145720</th>\n",
       "      <td>Marília, São Paulo</td>\n",
       "      <td>Brasil</td>\n",
       "      <td>br</td>\n",
       "      <td>SP</td>\n",
       "      <td>Marília</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103333</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Deutschland</td>\n",
       "      <td>de</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1407517</th>\n",
       "      <td>Canada,Quebec</td>\n",
       "      <td>Canada</td>\n",
       "      <td>ca</td>\n",
       "      <td>Québec</td>\n",
       "      <td>Montréal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3754479</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>au</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443007</th>\n",
       "      <td>You probably wouldn't know ~</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4606579</th>\n",
       "      <td>Linz, Austria</td>\n",
       "      <td>Österreich</td>\n",
       "      <td>at</td>\n",
       "      <td>Oberösterreich</td>\n",
       "      <td>Linz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4582414</th>\n",
       "      <td>Russia</td>\n",
       "      <td>РФ</td>\n",
       "      <td>ru</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183390</th>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6034757</th>\n",
       "      <td>India</td>\n",
       "      <td>India</td>\n",
       "      <td>in</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3417037</th>\n",
       "      <td>dhaka,bangladesh</td>\n",
       "      <td>বাংলাদেশ</td>\n",
       "      <td>bd</td>\n",
       "      <td>ঢাকা বিভাগ</td>\n",
       "      <td>ঢাকা</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3662727</th>\n",
       "      <td>Afghan</td>\n",
       "      <td>Brasil</td>\n",
       "      <td>br</td>\n",
       "      <td>RJ</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903610</th>\n",
       "      <td>Casablanca, Morocco</td>\n",
       "      <td>Maroc ⵍⵎⵖⵔⵉⴱ المغرب</td>\n",
       "      <td>ma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Casablanca ⵜⴰⴷⴷⴰⵔⵜ ⵜⵓⵎⵍⵉⵍⵜ الدار البيضاء</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342230</th>\n",
       "      <td>Nisko, POLAND</td>\n",
       "      <td>RP</td>\n",
       "      <td>pl</td>\n",
       "      <td>podkarpackie</td>\n",
       "      <td>Nisko</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385307</th>\n",
       "      <td>Boondocks, Michigan</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5846127</th>\n",
       "      <td>London</td>\n",
       "      <td>UK</td>\n",
       "      <td>gb</td>\n",
       "      <td>England</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298963</th>\n",
       "      <td>Central Java, Indonesia</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>id</td>\n",
       "      <td>Jawa Tengah</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366572</th>\n",
       "      <td>NRW, Germany</td>\n",
       "      <td>Deutschland</td>\n",
       "      <td>de</td>\n",
       "      <td>Nordrhein-Westfalen</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3968289</th>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307335</th>\n",
       "      <td>chicago!</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5090784</th>\n",
       "      <td>France</td>\n",
       "      <td>France</td>\n",
       "      <td>fr</td>\n",
       "      <td>France métropolitaine</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82440</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Norge</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215863</th>\n",
       "      <td>Turkey,Istanbul</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804891</th>\n",
       "      <td>Los Angeles, California</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>California</td>\n",
       "      <td>LA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5884694</th>\n",
       "      <td>Nashville, TN</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Nashville-Davidson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283308</th>\n",
       "      <td>Duri, Indonesia</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>id</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JKT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480072</th>\n",
       "      <td>532</td>\n",
       "      <td>日本</td>\n",
       "      <td>jp</td>\n",
       "      <td>東京都</td>\n",
       "      <td>千代田区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304210</th>\n",
       "      <td>Japan</td>\n",
       "      <td>日本</td>\n",
       "      <td>jp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4473202</th>\n",
       "      <td>Redondo Beach, California</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>California</td>\n",
       "      <td>Redondo Beach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6661722</th>\n",
       "      <td>Valencia</td>\n",
       "      <td>España</td>\n",
       "      <td>es</td>\n",
       "      <td>Comunitat Valenciana</td>\n",
       "      <td>València</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22832</th>\n",
       "      <td>Nun-Ya</td>\n",
       "      <td>РФ</td>\n",
       "      <td>ru</td>\n",
       "      <td>Красноярский край</td>\n",
       "      <td>Зыковский сельсовет</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47589</th>\n",
       "      <td>United Kingdom, Wales</td>\n",
       "      <td>UK</td>\n",
       "      <td>gb</td>\n",
       "      <td>Wales</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2424103</th>\n",
       "      <td>Jakarta , Indonesia</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>id</td>\n",
       "      <td>JKT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53535</th>\n",
       "      <td>Turin</td>\n",
       "      <td>Italia</td>\n",
       "      <td>it</td>\n",
       "      <td>PIE</td>\n",
       "      <td>Torino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5961992</th>\n",
       "      <td>AssLand</td>\n",
       "      <td>Eesti</td>\n",
       "      <td>ee</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2171033</th>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406417</th>\n",
       "      <td>London, England</td>\n",
       "      <td>UK</td>\n",
       "      <td>gb</td>\n",
       "      <td>England</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21970</th>\n",
       "      <td>Marseille, Universe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54286</th>\n",
       "      <td>the Netherlands</td>\n",
       "      <td>Nederland</td>\n",
       "      <td>nl</td>\n",
       "      <td>Nederland</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57779</th>\n",
       "      <td>what do you want</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29393</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537661</th>\n",
       "      <td>Canada</td>\n",
       "      <td>Canada</td>\n",
       "      <td>ca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897606</th>\n",
       "      <td>Manila</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>ph</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manila</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7472 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  address              country country_code  \\\n",
       "user_id                                                                       \n",
       "60732                  Karlsruhe, Germany          Deutschland           de   \n",
       "54131                    Canada, Montreal               Canada           ca   \n",
       "3492957                            Lublin                   RP           pl   \n",
       "271546                        Asia, Japan                   日本           jp   \n",
       "12533                       Limay, Bataan          Philippines           ph   \n",
       "452819                                  ↓                  NaN          NaN   \n",
       "246382                             Poland                   RP           pl   \n",
       "304517                         California                  USA           us   \n",
       "4901                                  USA                  USA           us   \n",
       "2665751  2b980b01c8e3872c5fbd57759af98fbd                  NaN          NaN   \n",
       "33372      Cambs, North Sea Region, EUSSR                  NaN          NaN   \n",
       "91281                                  MN                  USA           us   \n",
       "210403                             Canada               Canada           ca   \n",
       "5690136                      Soul Society                  NaN          NaN   \n",
       "109774                      Under the bed                  NaN          NaN   \n",
       "770595              Somewhere, California                  USA           us   \n",
       "48774                    Inside Elizabeth                  NaN          NaN   \n",
       "128929                              Texas                  USA           us   \n",
       "145720                 Marília, São Paulo               Brasil           br   \n",
       "103333                            Germany          Deutschland           de   \n",
       "1407517                     Canada,Quebec               Canada           ca   \n",
       "3754479                         Australia            Australia           au   \n",
       "443007       You probably wouldn't know ~                  NaN          NaN   \n",
       "4606579                     Linz, Austria           Österreich           at   \n",
       "4582414                            Russia                   РФ           ru   \n",
       "183390                                USA                  USA           us   \n",
       "6034757                             India                India           in   \n",
       "3417037                  dhaka,bangladesh             বাংলাদেশ           bd   \n",
       "3662727                            Afghan               Brasil           br   \n",
       "1903610               Casablanca, Morocco  Maroc ⵍⵎⵖⵔⵉⴱ المغرب           ma   \n",
       "...                                   ...                  ...          ...   \n",
       "342230                      Nisko, POLAND                   RP           pl   \n",
       "385307                Boondocks, Michigan                  USA           us   \n",
       "5846127                            London                   UK           gb   \n",
       "298963            Central Java, Indonesia            Indonesia           id   \n",
       "366572                       NRW, Germany          Deutschland           de   \n",
       "3968289                               USA                  USA           us   \n",
       "307335                           chicago!                  USA           us   \n",
       "5090784                            France               France           fr   \n",
       "82440                              Norway                Norge           no   \n",
       "215863                    Turkey,Istanbul                  USA           us   \n",
       "804891            Los Angeles, California                  USA           us   \n",
       "5884694                     Nashville, TN                  USA           us   \n",
       "283308                    Duri, Indonesia            Indonesia           id   \n",
       "480072                                532                   日本           jp   \n",
       "304210                              Japan                   日本           jp   \n",
       "4473202         Redondo Beach, California                  USA           us   \n",
       "6661722                          Valencia               España           es   \n",
       "22832                              Nun-Ya                   РФ           ru   \n",
       "47589               United Kingdom, Wales                   UK           gb   \n",
       "2424103               Jakarta , Indonesia            Indonesia           id   \n",
       "53535                               Turin               Italia           it   \n",
       "5961992                           AssLand                Eesti           ee   \n",
       "2171033                               USA                  USA           us   \n",
       "406417                    London, England                   UK           gb   \n",
       "21970                 Marseille, Universe                  NaN          NaN   \n",
       "54286                     the Netherlands            Nederland           nl   \n",
       "57779                    what do you want                  USA           us   \n",
       "29393                              Kansas                  USA           us   \n",
       "1537661                            Canada               Canada           ca   \n",
       "1897606                            Manila          Philippines           ph   \n",
       "\n",
       "                         state                                      city  \n",
       "user_id                                                                   \n",
       "60732        Baden-Württemberg                                       NaN  \n",
       "54131                   Québec                                  Montréal  \n",
       "3492957              lubelskie                                       NaN  \n",
       "271546                     埼玉県                                       NaN  \n",
       "12533                   Bataan                                       NaN  \n",
       "452819                     NaN                                       NaN  \n",
       "246382                     NaN                                       NaN  \n",
       "304517              California                                       NaN  \n",
       "4901                       NaN                                       NaN  \n",
       "2665751                    NaN                                       NaN  \n",
       "33372                      NaN                                       NaN  \n",
       "91281                Minnesota                                       NaN  \n",
       "210403                     NaN                                       NaN  \n",
       "5690136                    NaN                                       NaN  \n",
       "109774                     NaN                                       NaN  \n",
       "770595              California                                       NaN  \n",
       "48774                      NaN                                       NaN  \n",
       "128929                   Texas                                       NaN  \n",
       "145720                      SP                                   Marília  \n",
       "103333                     NaN                                       NaN  \n",
       "1407517                 Québec                                  Montréal  \n",
       "3754479                    NaN                                       NaN  \n",
       "443007                     NaN                                       NaN  \n",
       "4606579         Oberösterreich                                      Linz  \n",
       "4582414                    NaN                                       NaN  \n",
       "183390                     NaN                                       NaN  \n",
       "6034757                    NaN                                       NaN  \n",
       "3417037             ঢাকা বিভাগ                                      ঢাকা  \n",
       "3662727                     RJ                            Rio de Janeiro  \n",
       "1903610                    NaN  Casablanca ⵜⴰⴷⴷⴰⵔⵜ ⵜⵓⵎⵍⵉⵍⵜ الدار البيضاء  \n",
       "...                        ...                                       ...  \n",
       "342230            podkarpackie                                     Nisko  \n",
       "385307                Michigan                                       NaN  \n",
       "5846127                England                                    London  \n",
       "298963             Jawa Tengah                                       NaN  \n",
       "366572     Nordrhein-Westfalen                                       NaN  \n",
       "3968289                    NaN                                       NaN  \n",
       "307335                Illinois                                   Chicago  \n",
       "5090784  France métropolitaine                                       NaN  \n",
       "82440                      NaN                                       NaN  \n",
       "215863                    Ohio                                       NaN  \n",
       "804891              California                                        LA  \n",
       "5884694              Tennessee                        Nashville-Davidson  \n",
       "283308                     NaN                                       JKT  \n",
       "480072                     東京都                                      千代田区  \n",
       "304210                     NaN                                       NaN  \n",
       "4473202             California                             Redondo Beach  \n",
       "6661722   Comunitat Valenciana                                  València  \n",
       "22832        Красноярский край                       Зыковский сельсовет  \n",
       "47589                    Wales                                       NaN  \n",
       "2424103                    JKT                                       NaN  \n",
       "53535                      PIE                                    Torino  \n",
       "5961992                    NaN                                       NaN  \n",
       "2171033                    NaN                                       NaN  \n",
       "406417                 England                                    London  \n",
       "21970                      NaN                                       NaN  \n",
       "54286                Nederland                                       NaN  \n",
       "57779                   Nevada                                       NaN  \n",
       "29393                   Kansas                                       NaN  \n",
       "1537661                    NaN                                       NaN  \n",
       "1897606                    NaN                                    Manila  \n",
       "\n",
       "[7472 rows x 5 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking that all users in my filtered data have some location\n",
    "users_locs.loc[~users_locs.index.isin(users.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_watching</th>\n",
       "      <th>user_completed</th>\n",
       "      <th>user_onhold</th>\n",
       "      <th>user_dropped</th>\n",
       "      <th>user_plantowatch</th>\n",
       "      <th>user_days_spent_watching</th>\n",
       "      <th>gender</th>\n",
       "      <th>location</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>access_rank</th>\n",
       "      <th>join_date</th>\n",
       "      <th>last_online</th>\n",
       "      <th>stats_mean_score</th>\n",
       "      <th>stats_rewatched</th>\n",
       "      <th>stats_episodes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [user_watching, user_completed, user_onhold, user_dropped, user_plantowatch, user_days_spent_watching, gender, location, birth_date, access_rank, join_date, last_online, stats_mean_score, stats_rewatched, stats_episodes]\n",
       "Index: []"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.loc[~users.index.isin(users_locs.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_watching</th>\n",
       "      <th>user_completed</th>\n",
       "      <th>user_onhold</th>\n",
       "      <th>user_dropped</th>\n",
       "      <th>user_plantowatch</th>\n",
       "      <th>user_days_spent_watching</th>\n",
       "      <th>gender</th>\n",
       "      <th>location</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>access_rank</th>\n",
       "      <th>join_date</th>\n",
       "      <th>last_online</th>\n",
       "      <th>stats_mean_score</th>\n",
       "      <th>stats_rewatched</th>\n",
       "      <th>stats_episodes</th>\n",
       "      <th>address</th>\n",
       "      <th>country</th>\n",
       "      <th>country_code</th>\n",
       "      <th>state</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>230</td>\n",
       "      <td>8</td>\n",
       "      <td>89</td>\n",
       "      <td>60</td>\n",
       "      <td>109.438194</td>\n",
       "      <td>Male</td>\n",
       "      <td>California</td>\n",
       "      <td>1985-03-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-11-05</td>\n",
       "      <td>2018-05-14 18:02:55</td>\n",
       "      <td>7.37</td>\n",
       "      <td>58.0</td>\n",
       "      <td>6536.0</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>California</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>230</td>\n",
       "      <td>8</td>\n",
       "      <td>89</td>\n",
       "      <td>60</td>\n",
       "      <td>109.438194</td>\n",
       "      <td>Male</td>\n",
       "      <td>California</td>\n",
       "      <td>1985-03-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-11-05</td>\n",
       "      <td>2018-05-14 18:02:55</td>\n",
       "      <td>7.37</td>\n",
       "      <td>58.0</td>\n",
       "      <td>6536.0</td>\n",
       "      <td>California</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>California</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>133</td>\n",
       "      <td>54</td>\n",
       "      <td>44</td>\n",
       "      <td>36</td>\n",
       "      <td>59.084028</td>\n",
       "      <td>Male</td>\n",
       "      <td>Oslo, Norway</td>\n",
       "      <td>1988-11-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-11-11</td>\n",
       "      <td>2016-07-28 10:39:03</td>\n",
       "      <td>7.31</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3521.0</td>\n",
       "      <td>Oslo, Norway</td>\n",
       "      <td>Norge</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oslo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>571</td>\n",
       "      <td>307</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>189.675000</td>\n",
       "      <td>Female</td>\n",
       "      <td>Melbourne, Australia</td>\n",
       "      <td>1989-01-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-11-13</td>\n",
       "      <td>2018-05-14 13:04:48</td>\n",
       "      <td>6.65</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11552.0</td>\n",
       "      <td>Melbourne, Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>au</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10</td>\n",
       "      <td>88</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>64.279861</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bergen, Norway</td>\n",
       "      <td>1988-01-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-01-05</td>\n",
       "      <td>2018-02-28 17:27:29</td>\n",
       "      <td>8.05</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3853.0</td>\n",
       "      <td>Bergen, Norway</td>\n",
       "      <td>Norge</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>34</td>\n",
       "      <td>722</td>\n",
       "      <td>11</td>\n",
       "      <td>43</td>\n",
       "      <td>322</td>\n",
       "      <td>178.508333</td>\n",
       "      <td>Male</td>\n",
       "      <td>Land of Rain and Fjords</td>\n",
       "      <td>1988-08-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-02-05</td>\n",
       "      <td>2018-05-20 04:57:26</td>\n",
       "      <td>5.78</td>\n",
       "      <td>36.0</td>\n",
       "      <td>10767.0</td>\n",
       "      <td>Land of Rain and Fjords</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>8</td>\n",
       "      <td>71</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>22.520139</td>\n",
       "      <td>Female</td>\n",
       "      <td>San Diego, California</td>\n",
       "      <td>1987-06-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-02-12</td>\n",
       "      <td>2016-05-24 19:13:00</td>\n",
       "      <td>8.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1346.0</td>\n",
       "      <td>San Diego, California</td>\n",
       "      <td>USA</td>\n",
       "      <td>us</td>\n",
       "      <td>California</td>\n",
       "      <td>San Diego</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>16</td>\n",
       "      <td>488</td>\n",
       "      <td>35</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>128.577778</td>\n",
       "      <td>Female</td>\n",
       "      <td>South-east Asia</td>\n",
       "      <td>1987-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-03-12</td>\n",
       "      <td>2013-05-31 10:49:00</td>\n",
       "      <td>6.28</td>\n",
       "      <td>283.0</td>\n",
       "      <td>7535.0</td>\n",
       "      <td>South-east Asia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>34</td>\n",
       "      <td>148</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>54.977778</td>\n",
       "      <td>Male</td>\n",
       "      <td>Calgary, AB</td>\n",
       "      <td>1990-02-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-04-01</td>\n",
       "      <td>2011-03-13 04:07:00</td>\n",
       "      <td>7.53</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3265.0</td>\n",
       "      <td>Calgary, AB</td>\n",
       "      <td>Canada</td>\n",
       "      <td>ca</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>6</td>\n",
       "      <td>86</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>48.333333</td>\n",
       "      <td>Male</td>\n",
       "      <td>Rotterdam</td>\n",
       "      <td>1983-10-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-04-27</td>\n",
       "      <td>2017-04-27 01:22:00</td>\n",
       "      <td>7.96</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2914.0</td>\n",
       "      <td>Rotterdam</td>\n",
       "      <td>Nederland</td>\n",
       "      <td>nl</td>\n",
       "      <td>Zuid-Holland</td>\n",
       "      <td>Rotterdam</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_watching  user_completed  user_onhold  user_dropped  \\\n",
       "user_id                                                             \n",
       "1                    4             230            8            89   \n",
       "1                    4             230            8            89   \n",
       "3                   48             133           54            44   \n",
       "4                   13             571          307             0   \n",
       "20                  10              88           11             2   \n",
       "36                  34             722           11            43   \n",
       "37                   8              71            9             6   \n",
       "48                  16             488           35            18   \n",
       "66                  34             148           13             6   \n",
       "81                   6              86            4             4   \n",
       "\n",
       "         user_plantowatch  user_days_spent_watching  gender  \\\n",
       "user_id                                                       \n",
       "1                      60                109.438194    Male   \n",
       "1                      60                109.438194    Male   \n",
       "3                      36                 59.084028    Male   \n",
       "4                      45                189.675000  Female   \n",
       "20                     19                 64.279861    Male   \n",
       "36                    322                178.508333    Male   \n",
       "37                     14                 22.520139  Female   \n",
       "48                      2                128.577778  Female   \n",
       "66                     10                 54.977778    Male   \n",
       "81                      1                 48.333333    Male   \n",
       "\n",
       "                        location birth_date  access_rank  join_date  \\\n",
       "user_id                                                               \n",
       "1                     California 1985-03-04          NaN 2004-11-05   \n",
       "1                     California 1985-03-04          NaN 2004-11-05   \n",
       "3                   Oslo, Norway 1988-11-11          NaN 2004-11-11   \n",
       "4           Melbourne, Australia 1989-01-10          NaN 2004-11-13   \n",
       "20                Bergen, Norway 1988-01-25          NaN 2005-01-05   \n",
       "36       Land of Rain and Fjords 1988-08-04          NaN 2005-02-05   \n",
       "37         San Diego, California 1987-06-18          NaN 2005-02-12   \n",
       "48               South-east Asia 1987-12-31          NaN 2005-03-12   \n",
       "66                   Calgary, AB 1990-02-09          NaN 2005-04-01   \n",
       "81                     Rotterdam 1983-10-17          NaN 2005-04-27   \n",
       "\n",
       "                last_online  stats_mean_score  stats_rewatched  \\\n",
       "user_id                                                          \n",
       "1       2018-05-14 18:02:55              7.37             58.0   \n",
       "1       2018-05-14 18:02:55              7.37             58.0   \n",
       "3       2016-07-28 10:39:03              7.31             15.0   \n",
       "4       2018-05-14 13:04:48              6.65             10.0   \n",
       "20      2018-02-28 17:27:29              8.05              7.0   \n",
       "36      2018-05-20 04:57:26              5.78             36.0   \n",
       "37      2016-05-24 19:13:00              8.07              0.0   \n",
       "48      2013-05-31 10:49:00              6.28            283.0   \n",
       "66      2011-03-13 04:07:00              7.53             50.0   \n",
       "81      2017-04-27 01:22:00              7.96              2.0   \n",
       "\n",
       "         stats_episodes                  address    country country_code  \\\n",
       "user_id                                                                    \n",
       "1                6536.0               California        USA           us   \n",
       "1                6536.0               California        USA           us   \n",
       "3                3521.0             Oslo, Norway      Norge           no   \n",
       "4               11552.0     Melbourne, Australia  Australia           au   \n",
       "20               3853.0           Bergen, Norway      Norge           no   \n",
       "36              10767.0  Land of Rain and Fjords        NaN          NaN   \n",
       "37               1346.0    San Diego, California        USA           us   \n",
       "48               7535.0          South-east Asia        NaN          NaN   \n",
       "66               3265.0              Calgary, AB     Canada           ca   \n",
       "81               2914.0                Rotterdam  Nederland           nl   \n",
       "\n",
       "                state       city  \n",
       "user_id                           \n",
       "1          California        NaN  \n",
       "1          California        NaN  \n",
       "3                 NaN       Oslo  \n",
       "4            Victoria        NaN  \n",
       "20                NaN        NaN  \n",
       "36                NaN        NaN  \n",
       "37         California  San Diego  \n",
       "48                NaN        NaN  \n",
       "66            Alberta        NaN  \n",
       "81       Zuid-Holland  Rotterdam  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_with_locs = pd.merge(users, users_locs, left_index=True, right_index=True, how='left')\n",
    "users_with_locs.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9529"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_with_locs['country'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99732, 20)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_with_locs[users_with_locs['country'].notnull()].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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