showStatsAndClearData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "animes = pd.read_csv('../myanimelist-data-set-creator/AnimeDatasetGeneratorScript/AnimeList.csv')\n",
    "animelists = pd.read_csv('../myanimelist-data-set-creator/UserDatasetGeneratorScripts/UserAnimeList2.csv')\n",
    "users = pd.read_csv('../myanimelist-data-set-creator/UserDatasetGeneratorScripts/UserList2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[['gender', 'location', 'birth_date']] = users[['gender', 'location', 'birth_date']].fillna('')  # just weird fillna\n",
    "users.loc[4]['gender']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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                        ...                          \\\n",
       "0  Finished Airing                        ...                           \n",
       "1  Finished Airing                        ...                           \n",
       "2  Finished Airing                        ...                           \n",
       "3  Finished Airing                        ...                           \n",
       "4  Finished Airing                        ...                           \n",
       "\n",
       "                                          background    premiered  \\\n",
       "0  Inu x Boku SS was licensed by Sentai Filmworks...  Winter 2012   \n",
       "1                                                NaN  Spring 2007   \n",
       "2                                                NaN    Fall 2008   \n",
       "3  Princess Tutu aired in two parts. The first pa...  Summer 2002   \n",
       "4                                                NaN    Fall 2012   \n",
       "\n",
       "            broadcast                                            related  \\\n",
       "0  Fridays at Unknown  {'Adaptation': [{'mal_id': 17207, 'type': 'man...   \n",
       "1             Unknown  {'Adaptation': [{'mal_id': 759, 'type': 'manga...   \n",
       "2             Unknown  {'Adaptation': [{'mal_id': 101, 'type': 'manga...   \n",
       "3  Fridays at Unknown  {'Adaptation': [{'mal_id': 1581, 'type': 'mang...   \n",
       "4             Unknown  {'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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>anime_id</th>\n",
       "      <th>my_watched_episodes</th>\n",
       "      <th>my_start_date</th>\n",
       "      <th>my_finish_date</th>\n",
       "      <th>my_score</th>\n",
       "      <th>my_status</th>\n",
       "      <th>my_rewatching</th>\n",
       "      <th>my_rewatching_ep</th>\n",
       "      <th>my_last_updated</th>\n",
       "      <th>my_tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>karthiga</td>\n",
       "      <td>21</td>\n",
       "      <td>586</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1362307973</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>karthiga</td>\n",
       "      <td>59</td>\n",
       "      <td>26</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1362923691</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>karthiga</td>\n",
       "      <td>74</td>\n",
       "      <td>26</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1367081015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>karthiga</td>\n",
       "      <td>120</td>\n",
       "      <td>26</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1362308037</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>karthiga</td>\n",
       "      <td>178</td>\n",
       "      <td>26</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1364399953</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   username  anime_id  my_watched_episodes my_start_date my_finish_date  \\\n",
       "0  karthiga        21                  586    0000-00-00     0000-00-00   \n",
       "1  karthiga        59                   26    0000-00-00     0000-00-00   \n",
       "2  karthiga        74                   26    0000-00-00     0000-00-00   \n",
       "3  karthiga       120                   26    0000-00-00     0000-00-00   \n",
       "4  karthiga       178                   26    0000-00-00     0000-00-00   \n",
       "\n",
       "   my_score  my_status  my_rewatching  my_rewatching_ep  my_last_updated  \\\n",
       "0         9          1            NaN                 0       1362307973   \n",
       "1         7          2            NaN                 0       1362923691   \n",
       "2         7          2            NaN                 0       1367081015   \n",
       "3         7          2            NaN                 0       1362308037   \n",
       "4         7          2            0.0                 0       1364399953   \n",
       "\n",
       "  my_tags  \n",
       "0     NaN  \n",
       "1     NaN  \n",
       "2     NaN  \n",
       "3     NaN  \n",
       "4     NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animelists.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>55.31</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>shuzzable</td>\n",
       "      <td>2347781</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>35.48</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-03-25</td>\n",
       "      <td>2015-09-09 21:54:00</td>\n",
       "      <td>9.06</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2154.0</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         shuzzable  2347781             36              72           16   \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             2                25                     35.48           \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                                        NaN  2013-03-25  2015-09-09 21:54:00   \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              9.06              7.0          2154.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique animes: 14478\n",
      "unique users: 302675\n",
      "total list records: 80076112\n",
      "total ratings: 46358322\n",
      "users with gender: 217800\n",
      "users with data: 119632\n",
      "users without data: 183043\n"
     ]
    }
   ],
   "source": [
    "## showing stats\n",
    "print('unique animes:', len(animes))\n",
    "print('unique users:', len(users))\n",
    "print('total list records:', len(animelists))\n",
    "print('total ratings:', (animelists['my_score'] != 0).sum())\n",
    "print('users with gender:', (users['gender'] != '').sum())\n",
    "filled_mask = (users['gender'] != '') & (users['location'] != '') & (users['birth_date'] != '')\n",
    "print('users with data:', (filled_mask).sum())\n",
    "print('users without data:', (~filled_mask).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>anime_id</th>\n",
       "      <th>my_watched_episodes</th>\n",
       "      <th>my_start_date</th>\n",
       "      <th>my_finish_date</th>\n",
       "      <th>my_score</th>\n",
       "      <th>my_status</th>\n",
       "      <th>my_rewatching</th>\n",
       "      <th>my_rewatching_ep</th>\n",
       "      <th>my_last_updated</th>\n",
       "      <th>my_tags</th>\n",
       "      <th>gender</th>\n",
       "      <th>location</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>karthiga</td>\n",
       "      <td>21</td>\n",
       "      <td>586</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1362307973</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>Chennai, India</td>\n",
       "      <td>1990-04-29</td>\n",
       "      <td>Action, Adventure, Comedy, Super Power, Drama,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RedvelvetDaisuki</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1355480701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>Manila</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>Action, Adventure, Comedy, Super Power, Drama,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Damonashu</td>\n",
       "      <td>21</td>\n",
       "      <td>418</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1254296345</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>Detroit,Michigan</td>\n",
       "      <td>1991-08-01</td>\n",
       "      <td>Action, Adventure, Comedy, Super Power, Drama,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bskai</td>\n",
       "      <td>21</td>\n",
       "      <td>75</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1276637483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>Nayarit, Mexico</td>\n",
       "      <td>1990-12-14</td>\n",
       "      <td>Action, Adventure, Comedy, Super Power, Drama,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>shuzzable</td>\n",
       "      <td>21</td>\n",
       "      <td>579</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1391921419</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Action, Adventure, Comedy, Super Power, Drama,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           username  anime_id  my_watched_episodes my_start_date  \\\n",
       "0          karthiga        21                  586    0000-00-00   \n",
       "1  RedvelvetDaisuki        21                    0    0000-00-00   \n",
       "2         Damonashu        21                  418    0000-00-00   \n",
       "3             bskai        21                   75    0000-00-00   \n",
       "4         shuzzable        21                  579    0000-00-00   \n",
       "\n",
       "  my_finish_date  my_score  my_status  my_rewatching  my_rewatching_ep  \\\n",
       "0     0000-00-00         9          1            NaN                 0   \n",
       "1     0000-00-00         0          3            0.0                 0   \n",
       "2     0000-00-00        10          1            0.0                 0   \n",
       "3     0000-00-00         8          1            0.0                 0   \n",
       "4     0000-00-00        10          1            0.0                 0   \n",
       "\n",
       "   my_last_updated my_tags  gender          location  birth_date  \\\n",
       "0       1362307973     NaN  Female   Chennai, India   1990-04-29   \n",
       "1       1355480701     NaN  Female            Manila  1995-01-01   \n",
       "2       1254296345     NaN    Male  Detroit,Michigan  1991-08-01   \n",
       "3       1276637483     NaN    Male   Nayarit, Mexico  1990-12-14   \n",
       "4       1391921419     NaN                                         \n",
       "\n",
       "                                               genre  \n",
       "0  Action, Adventure, Comedy, Super Power, Drama,...  \n",
       "1  Action, Adventure, Comedy, Super Power, Drama,...  \n",
       "2  Action, Adventure, Comedy, Super Power, Drama,...  \n",
       "3  Action, Adventure, Comedy, Super Power, Drama,...  \n",
       "4  Action, Adventure, Comedy, Super Power, Drama,...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined = pd.merge(animelists, users[['username', 'gender', 'location', 'birth_date']], on='username', how='inner')\n",
    "joined = pd.merge(joined, animes[['anime_id', 'genre']], on='anime_id', how='inner')\n",
    "joined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting known and unknown usernames\n",
    "filled_list_mask = (joined['gender'] != '') & (joined['location'] != '') & (joined['birth_date'] != '')\n",
    "not_filled_list_mask = ~filled_list_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65fe3fe4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71179305c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# showing stats per filled and unfilled users, specifically ratings means, ratings variances and genres rated and genres total in lists\n",
    "# total rating mean per user hist\n",
    "\n",
    "#animelists.groupby('username')['my_score'].mean().plot(kind='hist', bins=50, title='all mean')\n",
    "animelists[filled_list_mask].groupby('username')['my_score'].mean().plot(kind='hist', bins=50, color='blue', alpha=0.5, title='means')\n",
    "animelists[not_filled_list_mask].groupby('username')['my_score'].mean().plot(kind='hist', bins=50, color='red', alpha=0.5)\n",
    "plt.show()\n",
    "#animelists.groupby('username')['my_score'].var().plot(kind='hist', bins=50, title='all var')\n",
    "animelists[filled_list_mask].groupby('username')['my_score'].var().plot(kind='hist', bins=50, color='blue', alpha=0.5, title='variances')\n",
    "animelists[not_filled_list_mask].groupby('username')['my_score'].var().plot(kind='hist', bins=50, color='red', alpha=0.5)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined['genre'].fillna('', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "filled_genres = joined[filled_list_mask]['genre'].apply(lambda x: x.replace(' ', '').split(','))\n",
    "not_filled_genres = joined[not_filled_list_mask]['genre'].apply(lambda x: x.replace(' ', '').split(','))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "filled_genres_hist = pd.Series([y for x in filled_genres for y in x]).value_counts()\n",
    "not_filled_genres_hist = pd.Series([y for x in not_filled_genres for y in x]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_filled_genres_hist = filled_genres_hist / filled_genres_hist.sum()\n",
    "norm_not_filled_genres_hist = not_filled_genres_hist / not_filled_genres_hist.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71177925c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 12))\n",
    "norm_filled_genres_hist.plot(kind='bar', color='blue', alpha=0.5, title='normalized genres in lists histogram')\n",
    "norm_not_filled_genres_hist.plot(kind='bar', color='red', alpha=0.5)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "filled_rated_genres = joined[filled_list_mask & (joined['my_score'] != 0)]['genre'].apply(lambda x: x.replace(' ', '').split(','))\n",
    "not_filled_rated_genres = joined[not_filled_list_mask & (joined['my_score'] != 0)]['genre'].apply(lambda x: x.replace(' ', '').split(','))\n",
    "filled_rated_genres_hist = pd.Series([y for x in filled_rated_genres for y in x]).value_counts()\n",
    "not_filled_rated_genres_hist = pd.Series([y for x in not_filled_rated_genres for y in x]).value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_filled_rated_genres_hist = filled_rated_genres_hist / filled_rated_genres_hist.sum()\n",
    "norm_not_filled_rated_genres_hist = not_filled_rated_genres_hist / not_filled_rated_genres_hist.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63fa856d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 12))\n",
    "norm_filled_rated_genres_hist.plot(kind='bar', color='blue', alpha=0.5, title='normalized genres in lists histogram')\n",
    "norm_not_filled_rated_genres_hist.plot(kind='bar', color='red', alpha=0.5)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "anime count filled: 14474\n",
      "anime count not filled: 14475\n",
      "anime all: 14478\n"
     ]
    }
   ],
   "source": [
    "# unique animes for filled users and non filled users\n",
    "print('anime count filled:', len(joined[filled_list_mask]['anime_id'].unique()))\n",
    "print('anime count not filled:', len(joined[not_filled_list_mask]['anime_id'].unique()))\n",
    "print('anime all:', len(joined['anime_id'].unique()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "## saving only people with demograhic data and their data into new csv files\n",
    "animes[animes['anime_id'].isin(joined[filled_list_mask]['anime_id'].unique())].to_csv('anime_filtered.csv', index=False, sep=',', encoding='utf-8')\n",
    "users[users['username'].isin(joined[filled_list_mask]['username'].unique())].to_csv('users_filtered.csv', index=False, sep=',', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined[filled_list_mask][['username', 'anime_id', 'my_watched_episodes', 'my_start_date',\n",
    "       'my_finish_date', 'my_score', 'my_status', 'my_rewatching',\n",
    "       'my_rewatching_ep', 'my_last_updated', 'my_tags']].to_csv('animelists_filtered.csv', index=False, sep=',', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# check loading filtered data\n",
    "anime_filtered = pd.read_csv('anime_filtered.csv')\n",
    "users_filtered = pd.read_csv('users_filtered.csv')\n",
    "animelists_filtered = pd.read_csv('animelists_filtered.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14478.000000</td>\n",
       "      <td>14478.000000</td>\n",
       "      <td>14478.000000</td>\n",
       "      <td>1.447800e+04</td>\n",
       "      <td>12904.000000</td>\n",
       "      <td>14478.000000</td>\n",
       "      <td>1.447800e+04</td>\n",
       "      <td>14478.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17377.229866</td>\n",
       "      <td>11.308399</td>\n",
       "      <td>6.142482</td>\n",
       "      <td>1.146003e+04</td>\n",
       "      <td>6439.065406</td>\n",
       "      <td>7220.259566</td>\n",
       "      <td>2.296640e+04</td>\n",
       "      <td>311.649606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13165.315011</td>\n",
       "      <td>43.443451</td>\n",
       "      <td>1.463981</td>\n",
       "      <td>4.310519e+04</td>\n",
       "      <td>3720.227608</td>\n",
       "      <td>4170.080564</td>\n",
       "      <td>7.498136e+04</td>\n",
       "      <td>2615.554211</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4389.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.550000</td>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>3216.250000</td>\n",
       "      <td>3611.500000</td>\n",
       "      <td>2.450000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15135.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.370000</td>\n",
       "      <td>5.010000e+02</td>\n",
       "      <td>6441.500000</td>\n",
       "      <td>7225.500000</td>\n",
       "      <td>1.679500e+03</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31146.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.060000</td>\n",
       "      <td>3.941500e+03</td>\n",
       "      <td>9664.000000</td>\n",
       "      <td>10827.750000</td>\n",
       "      <td>1.037900e+04</td>\n",
       "      <td>23.000000</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>14487.000000</td>\n",
       "      <td>1.456378e+06</td>\n",
       "      <td>106895.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           anime_id      episodes         score     scored_by          rank  \\\n",
       "count  14478.000000  14478.000000  14478.000000  1.447800e+04  12904.000000   \n",
       "mean   17377.229866     11.308399      6.142482  1.146003e+04   6439.065406   \n",
       "std    13165.315011     43.443451      1.463981  4.310519e+04   3720.227608   \n",
       "min        1.000000      0.000000      0.000000  0.000000e+00      0.000000   \n",
       "25%     4389.250000      1.000000      5.550000  4.600000e+01   3216.250000   \n",
       "50%    15135.000000      1.000000      6.370000  5.010000e+02   6441.500000   \n",
       "75%    31146.500000     12.000000      7.060000  3.941500e+03   9664.000000   \n",
       "max    37916.000000   1818.000000     10.000000  1.009477e+06  12919.000000   \n",
       "\n",
       "         popularity       members      favorites  \n",
       "count  14478.000000  1.447800e+04   14478.000000  \n",
       "mean    7220.259566  2.296640e+04     311.649606  \n",
       "std     4170.080564  7.498136e+04    2615.554211  \n",
       "min        0.000000  0.000000e+00       0.000000  \n",
       "25%     3611.500000  2.450000e+02       0.000000  \n",
       "50%     7225.500000  1.679500e+03       2.000000  \n",
       "75%    10827.750000  1.037900e+04      23.000000  \n",
       "max    14487.000000  1.456378e+06  106895.000000  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14474.000000</td>\n",
       "      <td>14474.000000</td>\n",
       "      <td>14474.000000</td>\n",
       "      <td>1.447400e+04</td>\n",
       "      <td>12901.000000</td>\n",
       "      <td>14474.000000</td>\n",
       "      <td>1.447400e+04</td>\n",
       "      <td>14474.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17371.948183</td>\n",
       "      <td>11.310971</td>\n",
       "      <td>6.144179</td>\n",
       "      <td>1.146319e+04</td>\n",
       "      <td>6439.625068</td>\n",
       "      <td>7220.277256</td>\n",
       "      <td>2.297275e+04</td>\n",
       "      <td>311.735595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13163.266015</td>\n",
       "      <td>43.449161</td>\n",
       "      <td>1.460617</td>\n",
       "      <td>4.311072e+04</td>\n",
       "      <td>3719.462602</td>\n",
       "      <td>4168.959000</td>\n",
       "      <td>7.499075e+04</td>\n",
       "      <td>2615.910510</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4387.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.550000</td>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>3218.000000</td>\n",
       "      <td>3613.250000</td>\n",
       "      <td>2.450000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15128.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.370000</td>\n",
       "      <td>5.010000e+02</td>\n",
       "      <td>6442.000000</td>\n",
       "      <td>7225.500000</td>\n",
       "      <td>1.682500e+03</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31142.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.060000</td>\n",
       "      <td>3.947250e+03</td>\n",
       "      <td>9664.000000</td>\n",
       "      <td>10826.750000</td>\n",
       "      <td>1.038050e+04</td>\n",
       "      <td>23.000000</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>14487.000000</td>\n",
       "      <td>1.456378e+06</td>\n",
       "      <td>106895.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           anime_id      episodes         score     scored_by          rank  \\\n",
       "count  14474.000000  14474.000000  14474.000000  1.447400e+04  12901.000000   \n",
       "mean   17371.948183     11.310971      6.144179  1.146319e+04   6439.625068   \n",
       "std    13163.266015     43.449161      1.460617  4.311072e+04   3719.462602   \n",
       "min        1.000000      0.000000      0.000000  0.000000e+00      0.000000   \n",
       "25%     4387.500000      1.000000      5.550000  4.600000e+01   3218.000000   \n",
       "50%    15128.000000      1.000000      6.370000  5.010000e+02   6442.000000   \n",
       "75%    31142.000000     12.000000      7.060000  3.947250e+03   9664.000000   \n",
       "max    37916.000000   1818.000000     10.000000  1.009477e+06  12919.000000   \n",
       "\n",
       "         popularity       members      favorites  \n",
       "count  14474.000000  1.447400e+04   14474.000000  \n",
       "mean    7220.277256  2.297275e+04     311.735595  \n",
       "std     4168.959000  7.499075e+04    2615.910510  \n",
       "min        0.000000  0.000000e+00       0.000000  \n",
       "25%     3613.250000  2.450000e+02       0.000000  \n",
       "50%     7225.500000  1.682500e+03       2.000000  \n",
       "75%    10826.750000  1.038050e+04      23.000000  \n",
       "max    14487.000000  1.456378e+06  106895.000000  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes[animes['anime_id'].isin(joined[filled_list_mask]['anime_id'].unique())].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14474.000000</td>\n",
       "      <td>14474.000000</td>\n",
       "      <td>14474.000000</td>\n",
       "      <td>1.447400e+04</td>\n",
       "      <td>12901.000000</td>\n",
       "      <td>14474.000000</td>\n",
       "      <td>1.447400e+04</td>\n",
       "      <td>14474.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17371.948183</td>\n",
       "      <td>11.310971</td>\n",
       "      <td>6.144179</td>\n",
       "      <td>1.146319e+04</td>\n",
       "      <td>6439.625068</td>\n",
       "      <td>7220.277256</td>\n",
       "      <td>2.297275e+04</td>\n",
       "      <td>311.735595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13163.266015</td>\n",
       "      <td>43.449161</td>\n",
       "      <td>1.460617</td>\n",
       "      <td>4.311072e+04</td>\n",
       "      <td>3719.462602</td>\n",
       "      <td>4168.959000</td>\n",
       "      <td>7.499075e+04</td>\n",
       "      <td>2615.910510</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4387.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.550000</td>\n",
       "      <td>4.600000e+01</td>\n",
       "      <td>3218.000000</td>\n",
       "      <td>3613.250000</td>\n",
       "      <td>2.450000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15128.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.370000</td>\n",
       "      <td>5.010000e+02</td>\n",
       "      <td>6442.000000</td>\n",
       "      <td>7225.500000</td>\n",
       "      <td>1.682500e+03</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31142.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.060000</td>\n",
       "      <td>3.947250e+03</td>\n",
       "      <td>9664.000000</td>\n",
       "      <td>10826.750000</td>\n",
       "      <td>1.038050e+04</td>\n",
       "      <td>23.000000</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>14487.000000</td>\n",
       "      <td>1.456378e+06</td>\n",
       "      <td>106895.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           anime_id      episodes         score     scored_by          rank  \\\n",
       "count  14474.000000  14474.000000  14474.000000  1.447400e+04  12901.000000   \n",
       "mean   17371.948183     11.310971      6.144179  1.146319e+04   6439.625068   \n",
       "std    13163.266015     43.449161      1.460617  4.311072e+04   3719.462602   \n",
       "min        1.000000      0.000000      0.000000  0.000000e+00      0.000000   \n",
       "25%     4387.500000      1.000000      5.550000  4.600000e+01   3218.000000   \n",
       "50%    15128.000000      1.000000      6.370000  5.010000e+02   6442.000000   \n",
       "75%    31142.000000     12.000000      7.060000  3.947250e+03   9664.000000   \n",
       "max    37916.000000   1818.000000     10.000000  1.009477e+06  12919.000000   \n",
       "\n",
       "         popularity       members      favorites  \n",
       "count  14474.000000  1.447400e+04   14474.000000  \n",
       "mean    7220.277256  2.297275e+04     311.735595  \n",
       "std     4168.959000  7.499075e+04    2615.910510  \n",
       "min        0.000000  0.000000e+00       0.000000  \n",
       "25%     3613.250000  2.450000e+02       0.000000  \n",
       "50%     7225.500000  1.682500e+03       2.000000  \n",
       "75%    10826.750000  1.038050e+04      23.000000  \n",
       "max    14487.000000  1.456378e+06  106895.000000  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anime_filtered.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>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>3.026750e+05</td>\n",
       "      <td>302675.000000</td>\n",
       "      <td>302675.000000</td>\n",
       "      <td>302675.000000</td>\n",
       "      <td>302675.000000</td>\n",
       "      <td>302675.000000</td>\n",
       "      <td>3.026750e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>302546.000000</td>\n",
       "      <td>302546.000000</td>\n",
       "      <td>3.025460e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.061401e+06</td>\n",
       "      <td>13.400007</td>\n",
       "      <td>166.638289</td>\n",
       "      <td>9.861796</td>\n",
       "      <td>10.550366</td>\n",
       "      <td>64.040430</td>\n",
       "      <td>6.907645e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.120476</td>\n",
       "      <td>10.375460</td>\n",
       "      <td>4.846288e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.118037e+06</td>\n",
       "      <td>34.747045</td>\n",
       "      <td>239.359931</td>\n",
       "      <td>34.270394</td>\n",
       "      <td>43.119854</td>\n",
       "      <td>157.485891</td>\n",
       "      <td>4.741020e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.574451</td>\n",
       "      <td>44.239666</td>\n",
       "      <td>4.473811e+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>2.322455e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.363000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.090000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.160000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.167583e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>3.958000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.384000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.883446e+06</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>7.994000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.440000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.849000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.273306e+06</td>\n",
       "      <td>6370.000000</td>\n",
       "      <td>11536.000000</td>\n",
       "      <td>9729.000000</td>\n",
       "      <td>10823.000000</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  3.026750e+05  302675.000000   302675.000000  302675.000000   \n",
       "mean   2.061401e+06      13.400007      166.638289       9.861796   \n",
       "std    2.118037e+06      34.747045      239.359931      34.270394   \n",
       "min    1.000000e+00       0.000000        0.000000       0.000000   \n",
       "25%    2.322455e+05       2.000000       28.000000       0.000000   \n",
       "50%    1.167583e+06       6.000000       96.000000       2.000000   \n",
       "75%    3.883446e+06      14.000000      217.000000      10.000000   \n",
       "max    7.273306e+06    6370.000000    11536.000000    9729.000000   \n",
       "\n",
       "        user_dropped  user_plantowatch  user_days_spent_watching  access_rank  \\\n",
       "count  302675.000000     302675.000000              3.026750e+05          0.0   \n",
       "mean       10.550366         64.040430              6.907645e+01          NaN   \n",
       "std        43.119854        157.485891              4.741020e+03          NaN   \n",
       "min         0.000000          0.000000              0.000000e+00          NaN   \n",
       "25%         0.000000          2.000000              1.363000e+01          NaN   \n",
       "50%         2.000000         20.000000              3.958000e+01          NaN   \n",
       "75%        10.000000         68.000000              7.994000e+01          NaN   \n",
       "max     10823.000000      12051.000000              2.601351e+06          NaN   \n",
       "\n",
       "       stats_mean_score  stats_rewatched  stats_episodes  \n",
       "count     302546.000000    302546.000000    3.025460e+05  \n",
       "mean           7.120476        10.375460    4.846288e+03  \n",
       "std            2.574451        44.239666    4.473811e+05  \n",
       "min            0.000000         0.000000    0.000000e+00  \n",
       "25%            7.090000         0.000000    8.160000e+02  \n",
       "50%            7.800000         0.000000    2.384000e+03  \n",
       "75%            8.440000         6.000000    4.849000e+03  \n",
       "max           10.000000      9404.000000    2.437949e+08  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>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": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[users['username'].isin(joined[filled_list_mask]['username'].unique())].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>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": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_filtered.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>my_watched_episodes</th>\n",
       "      <th>my_score</th>\n",
       "      <th>my_status</th>\n",
       "      <th>my_rewatching</th>\n",
       "      <th>my_rewatching_ep</th>\n",
       "      <th>my_last_updated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.007611e+07</td>\n",
       "      <td>8.007611e+07</td>\n",
       "      <td>8.007611e+07</td>\n",
       "      <td>8.007611e+07</td>\n",
       "      <td>6.333426e+07</td>\n",
       "      <td>8.007611e+07</td>\n",
       "      <td>8.007611e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.196189e+04</td>\n",
       "      <td>1.396328e+01</td>\n",
       "      <td>4.338410e+00</td>\n",
       "      <td>3.035068e+00</td>\n",
       "      <td>7.239367e-04</td>\n",
       "      <td>7.406522e+00</td>\n",
       "      <td>1.383827e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.143202e+04</td>\n",
       "      <td>3.391269e+02</td>\n",
       "      <td>3.933398e+00</td>\n",
       "      <td>1.746709e+00</td>\n",
       "      <td>2.689633e-02</td>\n",
       "      <td>1.110973e+04</td>\n",
       "      <td>1.401150e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.762000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.323886e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.476000e+03</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.404043e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.047900e+04</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.467512e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.791600e+04</td>\n",
       "      <td>6.553500e+04</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.677722e+07</td>\n",
       "      <td>1.526976e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           anime_id  my_watched_episodes      my_score     my_status  \\\n",
       "count  8.007611e+07         8.007611e+07  8.007611e+07  8.007611e+07   \n",
       "mean   1.196189e+04         1.396328e+01  4.338410e+00  3.035068e+00   \n",
       "std    1.143202e+04         3.391269e+02  3.933398e+00  1.746709e+00   \n",
       "min    1.000000e+00         0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    1.762000e+03         0.000000e+00  0.000000e+00  2.000000e+00   \n",
       "50%    8.476000e+03         3.000000e+00  6.000000e+00  2.000000e+00   \n",
       "75%    2.047900e+04         1.300000e+01  8.000000e+00  4.000000e+00   \n",
       "max    3.791600e+04         6.553500e+04  1.000000e+01  5.500000e+01   \n",
       "\n",
       "       my_rewatching  my_rewatching_ep  my_last_updated  \n",
       "count   6.333426e+07      8.007611e+07     8.007611e+07  \n",
       "mean    7.239367e-04      7.406522e+00     1.383827e+09  \n",
       "std     2.689633e-02      1.110973e+04     1.401150e+08  \n",
       "min     0.000000e+00      0.000000e+00     0.000000e+00  \n",
       "25%     0.000000e+00      0.000000e+00     1.323886e+09  \n",
       "50%     0.000000e+00      0.000000e+00     1.404043e+09  \n",
       "75%     0.000000e+00      0.000000e+00     1.467512e+09  \n",
       "max     1.000000e+00      1.677722e+07     1.526976e+09  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animelists.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>my_watched_episodes</th>\n",
       "      <th>my_score</th>\n",
       "      <th>my_status</th>\n",
       "      <th>my_rewatching</th>\n",
       "      <th>my_rewatching_ep</th>\n",
       "      <th>my_last_updated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.580201e+07</td>\n",
       "      <td>3.580201e+07</td>\n",
       "      <td>3.580201e+07</td>\n",
       "      <td>3.580201e+07</td>\n",
       "      <td>2.803259e+07</td>\n",
       "      <td>3.580201e+07</td>\n",
       "      <td>3.580201e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.169585e+04</td>\n",
       "      <td>1.571154e+01</td>\n",
       "      <td>4.538479e+00</td>\n",
       "      <td>3.038820e+00</td>\n",
       "      <td>7.662510e-04</td>\n",
       "      <td>1.604731e-01</td>\n",
       "      <td>1.378708e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.137968e+04</td>\n",
       "      <td>4.755021e+02</td>\n",
       "      <td>3.915358e+00</td>\n",
       "      <td>1.746312e+00</td>\n",
       "      <td>2.767063e-02</td>\n",
       "      <td>9.433500e+02</td>\n",
       "      <td>1.381206e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.690000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.313564e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.875000e+03</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.396696e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.967100e+04</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.463287e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.791600e+04</td>\n",
       "      <td>6.553500e+04</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.644513e+06</td>\n",
       "      <td>1.526975e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           anime_id  my_watched_episodes      my_score     my_status  \\\n",
       "count  3.580201e+07         3.580201e+07  3.580201e+07  3.580201e+07   \n",
       "mean   1.169585e+04         1.571154e+01  4.538479e+00  3.038820e+00   \n",
       "std    1.137968e+04         4.755021e+02  3.915358e+00  1.746312e+00   \n",
       "min    1.000000e+00         0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    1.690000e+03         0.000000e+00  0.000000e+00  2.000000e+00   \n",
       "50%    7.875000e+03         3.000000e+00  6.000000e+00  2.000000e+00   \n",
       "75%    1.967100e+04         1.300000e+01  8.000000e+00  4.000000e+00   \n",
       "max    3.791600e+04         6.553500e+04  1.000000e+01  5.500000e+01   \n",
       "\n",
       "       my_rewatching  my_rewatching_ep  my_last_updated  \n",
       "count   2.803259e+07      3.580201e+07     3.580201e+07  \n",
       "mean    7.662510e-04      1.604731e-01     1.378708e+09  \n",
       "std     2.767063e-02      9.433500e+02     1.381206e+08  \n",
       "min     0.000000e+00      0.000000e+00     0.000000e+00  \n",
       "25%     0.000000e+00      0.000000e+00     1.313564e+09  \n",
       "50%     0.000000e+00      0.000000e+00     1.396696e+09  \n",
       "75%     0.000000e+00      0.000000e+00     1.463287e+09  \n",
       "max     1.000000e+00      5.644513e+06     1.526975e+09  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined[filled_list_mask].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>my_watched_episodes</th>\n",
       "      <th>my_score</th>\n",
       "      <th>my_status</th>\n",
       "      <th>my_rewatching</th>\n",
       "      <th>my_rewatching_ep</th>\n",
       "      <th>my_last_updated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.580201e+07</td>\n",
       "      <td>3.580201e+07</td>\n",
       "      <td>3.580201e+07</td>\n",
       "      <td>3.580201e+07</td>\n",
       "      <td>2.803259e+07</td>\n",
       "      <td>3.580201e+07</td>\n",
       "      <td>3.580201e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.169585e+04</td>\n",
       "      <td>1.571154e+01</td>\n",
       "      <td>4.538479e+00</td>\n",
       "      <td>3.038820e+00</td>\n",
       "      <td>7.662510e-04</td>\n",
       "      <td>1.604731e-01</td>\n",
       "      <td>1.378708e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.137968e+04</td>\n",
       "      <td>4.755021e+02</td>\n",
       "      <td>3.915358e+00</td>\n",
       "      <td>1.746312e+00</td>\n",
       "      <td>2.767063e-02</td>\n",
       "      <td>9.433500e+02</td>\n",
       "      <td>1.381206e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.690000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.313564e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.875000e+03</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.396696e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.967100e+04</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.463287e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.791600e+04</td>\n",
       "      <td>6.553500e+04</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.644513e+06</td>\n",
       "      <td>1.526975e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           anime_id  my_watched_episodes      my_score     my_status  \\\n",
       "count  3.580201e+07         3.580201e+07  3.580201e+07  3.580201e+07   \n",
       "mean   1.169585e+04         1.571154e+01  4.538479e+00  3.038820e+00   \n",
       "std    1.137968e+04         4.755021e+02  3.915358e+00  1.746312e+00   \n",
       "min    1.000000e+00         0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    1.690000e+03         0.000000e+00  0.000000e+00  2.000000e+00   \n",
       "50%    7.875000e+03         3.000000e+00  6.000000e+00  2.000000e+00   \n",
       "75%    1.967100e+04         1.300000e+01  8.000000e+00  4.000000e+00   \n",
       "max    3.791600e+04         6.553500e+04  1.000000e+01  5.500000e+01   \n",
       "\n",
       "       my_rewatching  my_rewatching_ep  my_last_updated  \n",
       "count   2.803259e+07      3.580201e+07     3.580201e+07  \n",
       "mean    7.662510e-04      1.604731e-01     1.378708e+09  \n",
       "std     2.767063e-02      9.433500e+02     1.381206e+08  \n",
       "min     0.000000e+00      0.000000e+00     0.000000e+00  \n",
       "25%     0.000000e+00      0.000000e+00     1.313564e+09  \n",
       "50%     0.000000e+00      0.000000e+00     1.396696e+09  \n",
       "75%     0.000000e+00      0.000000e+00     1.463287e+09  \n",
       "max     1.000000e+00      5.644513e+06     1.526975e+09  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animelists_filtered.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>my_watched_episodes</th>\n",
       "      <th>my_score</th>\n",
       "      <th>my_status</th>\n",
       "      <th>my_rewatching</th>\n",
       "      <th>my_rewatching_ep</th>\n",
       "      <th>my_last_updated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.580201e+07</td>\n",
       "      <td>3.580201e+07</td>\n",
       "      <td>3.580201e+07</td>\n",
       "      <td>3.580201e+07</td>\n",
       "      <td>2.803259e+07</td>\n",
       "      <td>3.580201e+07</td>\n",
       "      <td>3.580201e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.169585e+04</td>\n",
       "      <td>1.571154e+01</td>\n",
       "      <td>4.538479e+00</td>\n",
       "      <td>3.038820e+00</td>\n",
       "      <td>7.662510e-04</td>\n",
       "      <td>1.604731e-01</td>\n",
       "      <td>1.378708e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.137968e+04</td>\n",
       "      <td>4.755021e+02</td>\n",
       "      <td>3.915358e+00</td>\n",
       "      <td>1.746312e+00</td>\n",
       "      <td>2.767063e-02</td>\n",
       "      <td>9.433500e+02</td>\n",
       "      <td>1.381206e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.690000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.313564e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.875000e+03</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.396696e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.967100e+04</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.463287e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.791600e+04</td>\n",
       "      <td>6.553500e+04</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.644513e+06</td>\n",
       "      <td>1.526975e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           anime_id  my_watched_episodes      my_score     my_status  \\\n",
       "count  3.580201e+07         3.580201e+07  3.580201e+07  3.580201e+07   \n",
       "mean   1.169585e+04         1.571154e+01  4.538479e+00  3.038820e+00   \n",
       "std    1.137968e+04         4.755021e+02  3.915358e+00  1.746312e+00   \n",
       "min    1.000000e+00         0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    1.690000e+03         0.000000e+00  0.000000e+00  2.000000e+00   \n",
       "50%    7.875000e+03         3.000000e+00  6.000000e+00  2.000000e+00   \n",
       "75%    1.967100e+04         1.300000e+01  8.000000e+00  4.000000e+00   \n",
       "max    3.791600e+04         6.553500e+04  1.000000e+01  5.500000e+01   \n",
       "\n",
       "       my_rewatching  my_rewatching_ep  my_last_updated  \n",
       "count   2.803259e+07      3.580201e+07     3.580201e+07  \n",
       "mean    7.662510e-04      1.604731e-01     1.378708e+09  \n",
       "std     2.767063e-02      9.433500e+02     1.381206e+08  \n",
       "min     0.000000e+00      0.000000e+00     0.000000e+00  \n",
       "25%     0.000000e+00      0.000000e+00     1.313564e+09  \n",
       "50%     0.000000e+00      0.000000e+00     1.396696e+09  \n",
       "75%     0.000000e+00      0.000000e+00     1.463287e+09  \n",
       "max     1.000000e+00      5.644513e+06     1.526975e+09  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined[filled_list_mask][['username', 'anime_id', 'my_watched_episodes', 'my_start_date',\n",
    "       'my_finish_date', 'my_score', 'my_status', 'my_rewatching',\n",
    "       'my_rewatching_ep', 'my_last_updated', 'my_tags']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>anime_id</th>\n",
       "      <th>my_watched_episodes</th>\n",
       "      <th>my_start_date</th>\n",
       "      <th>my_finish_date</th>\n",
       "      <th>my_score</th>\n",
       "      <th>my_status</th>\n",
       "      <th>my_rewatching</th>\n",
       "      <th>my_rewatching_ep</th>\n",
       "      <th>my_last_updated</th>\n",
       "      <th>my_tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>karthiga</td>\n",
       "      <td>21</td>\n",
       "      <td>586</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1362307973</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RedvelvetDaisuki</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1355480701</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Damonashu</td>\n",
       "      <td>21</td>\n",
       "      <td>418</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1254296345</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bskai</td>\n",
       "      <td>21</td>\n",
       "      <td>75</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1276637483</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Slimak</td>\n",
       "      <td>21</td>\n",
       "      <td>834</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>0000-00-00</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1525176321</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           username  anime_id  my_watched_episodes my_start_date  \\\n",
       "0          karthiga        21                  586    0000-00-00   \n",
       "1  RedvelvetDaisuki        21                    0    0000-00-00   \n",
       "2         Damonashu        21                  418    0000-00-00   \n",
       "3             bskai        21                   75    0000-00-00   \n",
       "8            Slimak        21                  834    0000-00-00   \n",
       "\n",
       "  my_finish_date  my_score  my_status  my_rewatching  my_rewatching_ep  \\\n",
       "0     0000-00-00         9          1            NaN                 0   \n",
       "1     0000-00-00         0          3            0.0                 0   \n",
       "2     0000-00-00        10          1            0.0                 0   \n",
       "3     0000-00-00         8          1            0.0                 0   \n",
       "8     0000-00-00        10          1            0.0                 0   \n",
       "\n",
       "   my_last_updated my_tags  \n",
       "0       1362307973     NaN  \n",
       "1       1355480701     NaN  \n",
       "2       1254296345     NaN  \n",
       "3       1276637483     NaN  \n",
       "8       1525176321     NaN  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined[filled_list_mask][['username', 'anime_id', 'my_watched_episodes', 'my_start_date',\n",
    "       'my_finish_date', 'my_score', 'my_status', 'my_rewatching',\n",
    "       'my_rewatching_ep', 'my_last_updated', 'my_tags']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80076112"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(filled_list_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35802006"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(joined[filled_list_mask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80076112"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(animelists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['username', 'anime_id', 'my_watched_episodes', 'my_start_date',\n",
       "       'my_finish_date', 'my_score', 'my_status', 'my_rewatching',\n",
       "       'my_rewatching_ep', 'my_last_updated', 'my_tags', 'gender', 'location',\n",
       "       'birth_date', 'genre'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined[filled_list_mask].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "anime_id            int64\n",
       "title              object\n",
       "title_english      object\n",
       "title_japanese     object\n",
       "title_synonyms     object\n",
       "image_url          object\n",
       "type               object\n",
       "source             object\n",
       "episodes            int64\n",
       "status             object\n",
       "airing               bool\n",
       "aired_string       object\n",
       "aired              object\n",
       "duration           object\n",
       "rating             object\n",
       "score             float64\n",
       "scored_by           int64\n",
       "rank              float64\n",
       "popularity          int64\n",
       "members             int64\n",
       "favorites           int64\n",
       "background         object\n",
       "premiered          object\n",
       "broadcast          object\n",
       "related            object\n",
       "producer           object\n",
       "licensor           object\n",
       "studio             object\n",
       "genre              object\n",
       "opening_theme      object\n",
       "ending_theme       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "anime_id            int64\n",
       "title              object\n",
       "title_english      object\n",
       "title_japanese     object\n",
       "title_synonyms     object\n",
       "image_url          object\n",
       "type               object\n",
       "source             object\n",
       "episodes            int64\n",
       "status             object\n",
       "airing               bool\n",
       "aired_string       object\n",
       "aired              object\n",
       "duration           object\n",
       "rating             object\n",
       "score             float64\n",
       "scored_by           int64\n",
       "rank              float64\n",
       "popularity          int64\n",
       "members             int64\n",
       "favorites           int64\n",
       "background         object\n",
       "premiered          object\n",
       "broadcast          object\n",
       "related            object\n",
       "producer           object\n",
       "licensor           object\n",
       "studio             object\n",
       "genre              object\n",
       "opening_theme      object\n",
       "ending_theme       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anime_filtered.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    12904\n",
       "Name: rank, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animes['rank'].apply(lambda x: x % 1).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Cannot convert non-finite values (NA or inf) to integer",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-118-4c1a6394062b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0manimes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'rank'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0manimes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'rank'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home.others/eb/easybuild/software/Python/3.6.4-foss-2018a/lib/python3.6/site-packages/pandas-0.20.3-py3.6-linux-x86_64.egg/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_deprecate_kwarg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home.others/eb/easybuild/software/Python/3.6.4-foss-2018a/lib/python3.6/site-packages/pandas-0.20.3-py3.6-linux-x86_64.egg/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, **kwargs)\u001b[0m\n\u001b[1;32m   3408\u001b[0m         \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3409\u001b[0m         new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors,\n\u001b[0;32m-> 3410\u001b[0;31m                                      **kwargs)\n\u001b[0m\u001b[1;32m   3411\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home.others/eb/easybuild/software/Python/3.6.4-foss-2018a/lib/python3.6/site-packages/pandas-0.20.3-py3.6-linux-x86_64.egg/pandas/core/internals.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[1;32m   3222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3223\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3224\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'astype'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3226\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home.others/eb/easybuild/software/Python/3.6.4-foss-2018a/lib/python3.6/site-packages/pandas-0.20.3-py3.6-linux-x86_64.egg/pandas/core/internals.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[1;32m   3089\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3090\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mgr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3091\u001b[0;31m             \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3092\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3093\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home.others/eb/easybuild/software/Python/3.6.4-foss-2018a/lib/python3.6/site-packages/pandas-0.20.3-py3.6-linux-x86_64.egg/pandas/core/internals.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, values, **kwargs)\u001b[0m\n\u001b[1;32m    469\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'raise'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    470\u001b[0m         return self._astype(dtype, copy=copy, errors=errors, values=values,\n\u001b[0;32m--> 471\u001b[0;31m                             **kwargs)\n\u001b[0m\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m     def _astype(self, dtype, copy=False, errors='raise', values=None,\n",
      "\u001b[0;32m/home.others/eb/easybuild/software/Python/3.6.4-foss-2018a/lib/python3.6/site-packages/pandas-0.20.3-py3.6-linux-x86_64.egg/pandas/core/internals.py\u001b[0m in \u001b[0;36m_astype\u001b[0;34m(self, dtype, copy, errors, values, klass, mgr, raise_on_error, **kwargs)\u001b[0m\n\u001b[1;32m    519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    520\u001b[0m                 \u001b[0;31m# _astype_nansafe works fine with 1-d only\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home.others/eb/easybuild/software/Python/3.6.4-foss-2018a/lib/python3.6/site-packages/pandas-0.20.3-py3.6-linux-x86_64.egg/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy)\u001b[0m\n\u001b[1;32m    618\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 620\u001b[0;31m             raise ValueError('Cannot convert non-finite values (NA or inf) to '\n\u001b[0m\u001b[1;32m    621\u001b[0m                              'integer')\n\u001b[1;32m    622\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Cannot convert non-finite values (NA or inf) to integer"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "(animes['rank'] - animes['rank'].astype(np.int64)).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# checking genres histogram for the other kaggle dataset\n",
    "anime_kaggle = pd.read_csv('../kaggle-dataset/anime.csv')\n",
    "rating_kaggle = pd.read_csv('../kaggle-dataset/rating.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>name</th>\n",
       "      <th>genre</th>\n",
       "      <th>type</th>\n",
       "      <th>episodes</th>\n",
       "      <th>rating</th>\n",
       "      <th>members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32281</td>\n",
       "      <td>Kimi no Na wa.</td>\n",
       "      <td>Drama, Romance, School, Supernatural</td>\n",
       "      <td>Movie</td>\n",
       "      <td>1</td>\n",
       "      <td>9.37</td>\n",
       "      <td>200630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5114</td>\n",
       "      <td>Fullmetal Alchemist: Brotherhood</td>\n",
       "      <td>Action, Adventure, Drama, Fantasy, Magic, Mili...</td>\n",
       "      <td>TV</td>\n",
       "      <td>64</td>\n",
       "      <td>9.26</td>\n",
       "      <td>793665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28977</td>\n",
       "      <td>Gintama°</td>\n",
       "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
       "      <td>TV</td>\n",
       "      <td>51</td>\n",
       "      <td>9.25</td>\n",
       "      <td>114262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9253</td>\n",
       "      <td>Steins;Gate</td>\n",
       "      <td>Sci-Fi, Thriller</td>\n",
       "      <td>TV</td>\n",
       "      <td>24</td>\n",
       "      <td>9.17</td>\n",
       "      <td>673572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9969</td>\n",
       "      <td>Gintama&amp;#039;</td>\n",
       "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
       "      <td>TV</td>\n",
       "      <td>51</td>\n",
       "      <td>9.16</td>\n",
       "      <td>151266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   anime_id                              name  \\\n",
       "0     32281                    Kimi no Na wa.   \n",
       "1      5114  Fullmetal Alchemist: Brotherhood   \n",
       "2     28977                          Gintama°   \n",
       "3      9253                       Steins;Gate   \n",
       "4      9969                     Gintama&#039;   \n",
       "\n",
       "                                               genre   type episodes  rating  \\\n",
       "0               Drama, Romance, School, Supernatural  Movie        1    9.37   \n",
       "1  Action, Adventure, Drama, Fantasy, Magic, Mili...     TV       64    9.26   \n",
       "2  Action, Comedy, Historical, Parody, Samurai, S...     TV       51    9.25   \n",
       "3                                   Sci-Fi, Thriller     TV       24    9.17   \n",
       "4  Action, Comedy, Historical, Parody, Samurai, S...     TV       51    9.16   \n",
       "\n",
       "   members  \n",
       "0   200630  \n",
       "1   793665  \n",
       "2   114262  \n",
       "3   673572  \n",
       "4   151266  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anime_kaggle.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 8     1646019\n",
       "-1     1476496\n",
       " 7     1375287\n",
       " 9     1254096\n",
       " 10     955715\n",
       " 6      637775\n",
       " 5      282806\n",
       " 4      104291\n",
       " 3       41453\n",
       " 2       23150\n",
       " 1       16649\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_kaggle['rating'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>anime_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  anime_id  rating\n",
       "0        1        20       0\n",
       "1        1        24       0\n",
       "2        1        79       0\n",
       "3        1       226       0\n",
       "4        1       241       0"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_kaggle['rating'] = rating_kaggle['rating'].replace({-1: 0})  # just to be in same format with my and MAL data\n",
    "\n",
    "rating_kaggle.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>anime_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>Action, Comedy, Martial Arts, Shounen, Super P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>Action, Comedy, Martial Arts, Shounen, Super P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>Action, Comedy, Martial Arts, Shounen, Super P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>Action, Comedy, Martial Arts, Shounen, Super P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>Action, Comedy, Martial Arts, Shounen, Super P...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  anime_id  rating  \\\n",
       "0        1        20       0   \n",
       "1        3        20       8   \n",
       "2        5        20       6   \n",
       "3        6        20       0   \n",
       "4       10        20       0   \n",
       "\n",
       "                                               genre  \n",
       "0  Action, Comedy, Martial Arts, Shounen, Super P...  \n",
       "1  Action, Comedy, Martial Arts, Shounen, Super P...  \n",
       "2  Action, Comedy, Martial Arts, Shounen, Super P...  \n",
       "3  Action, Comedy, Martial Arts, Shounen, Super P...  \n",
       "4  Action, Comedy, Martial Arts, Shounen, Super P...  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_kaggle = pd.merge(rating_kaggle, anime_kaggle[['anime_id', 'genre']], on='anime_id', how='inner')\n",
    "joined_kaggle['genre'].fillna('', inplace=True)\n",
    "joined_kaggle.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_genre = joined_kaggle['genre'].apply(lambda x: x.replace(' ', '').split(','))\n",
    "kaggle_genre_hist = pd.Series([y for x in kaggle_genre for y in x]).value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_kaggle_genre_hist = kaggle_genre_hist / kaggle_genre_hist.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61050bd3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 12))\n",
    "plt.yticks(np.arange(0, 0.12, step=0.005))\n",
    "norm_kaggle_genre_hist.plot(kind='bar', color='blue', alpha=0.5, title='comparing kaggle and my rated data - genres')\n",
    "norm_filled_genres_hist.plot(kind='bar', color='red', alpha=0.5)\n",
    "plt.gca().set_axisbelow(True)\n",
    "plt.gca().yaxis.grid(color='gray', linestyle='dashed')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_rats = rating_kaggle[rating_kaggle['rating'] != 0].groupby('user_id')['rating']\n",
    "my_rats = animelists[filled_list_mask & (animelists['my_score'] != 0)].groupby('username')['my_score']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f610418a860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63ba457ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kaggle_rats.mean().plot(kind='hist', bins=50, color='blue', alpha=0.5, title='comparing kaggle and my rated data - rating means')\n",
    "my_rats.mean().plot(kind='hist', bins=50, color='red', alpha=0.5, secondary_y=True)\n",
    "plt.show()\n",
    "\n",
    "kaggle_rats.var().plot(kind='hist', bins=50, color='blue', alpha=0.5, title='comparing kaggle and my rated data - rating variances')\n",
    "my_rats.var().plot(kind='hist', bins=50, color='red', alpha=0.5, secondary_y=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_rats = rating_kaggle.groupby('user_id')['rating']\n",
    "my_rats = animelists[filled_list_mask].groupby('username')['my_score']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63b9a01b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63b97f38d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kaggle_rats.mean().plot(kind='hist', bins=50, color='blue', alpha=0.5, title='comparing kaggle and my rated data - rating means')\n",
    "my_rats.mean().plot(kind='hist', bins=50, color='red', alpha=0.5, secondary_y=True)\n",
    "plt.show()\n",
    "\n",
    "kaggle_rats.var().plot(kind='hist', bins=50, color='blue', alpha=0.5, title='comparing kaggle and my rated data - rating variances')\n",
    "my_rats.var().plot(kind='hist', bins=50, color='red', alpha=0.5, secondary_y=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>anime_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "      <td>8074</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1</td>\n",
       "      <td>11617</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1</td>\n",
       "      <td>11757</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1</td>\n",
       "      <td>15451</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>2</td>\n",
       "      <td>11771</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>3</td>\n",
       "      <td>154</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>3</td>\n",
       "      <td>170</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>3</td>\n",
       "      <td>199</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>3</td>\n",
       "      <td>225</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>3</td>\n",
       "      <td>341</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>3</td>\n",
       "      <td>430</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>3</td>\n",
       "      <td>527</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>3</td>\n",
       "      <td>552</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>3</td>\n",
       "      <td>813</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>3</td>\n",
       "      <td>1119</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>3</td>\n",
       "      <td>1121</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>3</td>\n",
       "      <td>1122</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>3</td>\n",
       "      <td>1132</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>3</td>\n",
       "      <td>1292</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>3</td>\n",
       "      <td>1313</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>3</td>\n",
       "      <td>1526</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>3</td>\n",
       "      <td>1535</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>3</td>\n",
       "      <td>1564</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3</td>\n",
       "      <td>1689</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3</td>\n",
       "      <td>1764</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>3</td>\n",
       "      <td>1943</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>3</td>\n",
       "      <td>2201</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>3</td>\n",
       "      <td>2404</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>3</td>\n",
       "      <td>2847</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813707</th>\n",
       "      <td>73515</td>\n",
       "      <td>10490</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813708</th>\n",
       "      <td>73515</td>\n",
       "      <td>10588</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813709</th>\n",
       "      <td>73515</td>\n",
       "      <td>11021</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813710</th>\n",
       "      <td>73515</td>\n",
       "      <td>11111</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813711</th>\n",
       "      <td>73515</td>\n",
       "      <td>11241</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813712</th>\n",
       "      <td>73515</td>\n",
       "      <td>11411</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813713</th>\n",
       "      <td>73515</td>\n",
       "      <td>11701</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813714</th>\n",
       "      <td>73515</td>\n",
       "      <td>11741</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813715</th>\n",
       "      <td>73515</td>\n",
       "      <td>11751</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813716</th>\n",
       "      <td>73515</td>\n",
       "      <td>11757</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813717</th>\n",
       "      <td>73515</td>\n",
       "      <td>11759</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813718</th>\n",
       "      <td>73515</td>\n",
       "      <td>11837</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813719</th>\n",
       "      <td>73515</td>\n",
       "      <td>12031</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813720</th>\n",
       "      <td>73515</td>\n",
       "      <td>12113</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813721</th>\n",
       "      <td>73515</td>\n",
       "      <td>12115</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813722</th>\n",
       "      <td>73515</td>\n",
       "      <td>12293</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813723</th>\n",
       "      <td>73515</td>\n",
       "      <td>12413</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813724</th>\n",
       "      <td>73515</td>\n",
       "      <td>12445</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813725</th>\n",
       "      <td>73515</td>\n",
       "      <td>12461</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813726</th>\n",
       "      <td>73515</td>\n",
       "      <td>12967</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813727</th>\n",
       "      <td>73515</td>\n",
       "      <td>13161</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813728</th>\n",
       "      <td>73515</td>\n",
       "      <td>13331</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813729</th>\n",
       "      <td>73515</td>\n",
       "      <td>13599</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813730</th>\n",
       "      <td>73515</td>\n",
       "      <td>13659</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813731</th>\n",
       "      <td>73515</td>\n",
       "      <td>14345</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813732</th>\n",
       "      <td>73515</td>\n",
       "      <td>16512</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813733</th>\n",
       "      <td>73515</td>\n",
       "      <td>17187</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813734</th>\n",
       "      <td>73515</td>\n",
       "      <td>22145</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813735</th>\n",
       "      <td>73516</td>\n",
       "      <td>790</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7813736</th>\n",
       "      <td>73516</td>\n",
       "      <td>8074</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6337241 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  anime_id  rating\n",
       "47             1      8074      10\n",
       "81             1     11617      10\n",
       "83             1     11757      10\n",
       "101            1     15451      10\n",
       "153            2     11771      10\n",
       "156            3        20       8\n",
       "157            3       154       6\n",
       "158            3       170       9\n",
       "159            3       199      10\n",
       "160            3       225       9\n",
       "161            3       341       6\n",
       "162            3       430       7\n",
       "163            3       527       7\n",
       "164            3       552       7\n",
       "165            3       813      10\n",
       "166            3      1119       7\n",
       "167            3      1121       7\n",
       "168            3      1122       7\n",
       "169            3      1132       8\n",
       "170            3      1292       6\n",
       "171            3      1313       8\n",
       "172            3      1526       7\n",
       "173            3      1535      10\n",
       "174            3      1564       7\n",
       "175            3      1689       8\n",
       "176            3      1764       6\n",
       "177            3      1943       8\n",
       "178            3      2201       7\n",
       "179            3      2404       7\n",
       "180            3      2847       7\n",
       "...          ...       ...     ...\n",
       "7813707    73515     10490       6\n",
       "7813708    73515     10588       9\n",
       "7813709    73515     11021       8\n",
       "7813710    73515     11111       8\n",
       "7813711    73515     11241       8\n",
       "7813712    73515     11411       5\n",
       "7813713    73515     11701       7\n",
       "7813714    73515     11741       9\n",
       "7813715    73515     11751       6\n",
       "7813716    73515     11757       9\n",
       "7813717    73515     11759       8\n",
       "7813718    73515     11837       9\n",
       "7813719    73515     12031       8\n",
       "7813720    73515     12113      10\n",
       "7813721    73515     12115      10\n",
       "7813722    73515     12293       8\n",
       "7813723    73515     12413       9\n",
       "7813724    73515     12445       8\n",
       "7813725    73515     12461       7\n",
       "7813726    73515     12967       7\n",
       "7813727    73515     13161       8\n",
       "7813728    73515     13331       9\n",
       "7813729    73515     13599       7\n",
       "7813730    73515     13659       8\n",
       "7813731    73515     14345       7\n",
       "7813732    73515     16512       7\n",
       "7813733    73515     17187       9\n",
       "7813734    73515     22145      10\n",
       "7813735    73516       790       9\n",
       "7813736    73516      8074       9\n",
       "\n",
       "[6337241 rows x 3 columns]"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_kaggle[rating_kaggle['rating'] != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61045fa860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anime_kaggle['genre'].fillna('', inplace=True)\n",
    "animes['genre'].fillna('', inplace=True)\n",
    "\n",
    "kaggle_genre_anime = anime_kaggle['genre'].apply(lambda x: x.replace(' ', '').split(','))\n",
    "kaggle_genre_anime_hist = pd.Series([y for x in kaggle_genre_anime for y in x]).value_counts()\n",
    "norm_kaggle_genre_anime_hist = kaggle_genre_anime_hist / kaggle_genre_anime_hist.sum()\n",
    "\n",
    "my_genre_anime = animes['genre'].apply(lambda x: x.replace(' ', '').split(','))\n",
    "my_genre_anime_hist = pd.Series([y for x in my_genre_anime for y in x]).value_counts()\n",
    "norm_my_genre_anime_hist = my_genre_anime_hist / my_genre_anime_hist.sum()\n",
    "\n",
    "plt.figure(figsize=(16, 12))\n",
    "norm_kaggle_genre_anime_hist.plot(kind='bar', color='blue', alpha=0.5, title='comparing kaggle and my rated data - genres only in anime')\n",
    "norm_filled_genres_hist.plot(kind='bar', color='red', alpha=0.5)\n",
    "plt.gca().set_axisbelow(True)\n",
    "plt.gca().yaxis.grid(color='gray', linestyle='dashed')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3836887750887916"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "116133 / 302675"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}