LanguageAndRuntimeCompare2.0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descriptive 1-2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following section, we'll compare the language and the runtime of the top 250 movies on douban and imdb respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Languages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll have a look at what languages are most common among the top 250 movies on each website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "from os import sep\n",
    "\n",
    "douban_df = pd.read_csv(\"douban_movie_all.csv\")\n",
    "imdb_df = pd.read_csv(f\"imdb_movie_all.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "douban_df.columns = ['Name', 'Year', 'Rate', 'Directors', 'Writers', 'Stars', 'Genres', 'Country', 'Language', 'Runtime']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Directors</th>\n",
       "      <th>Writers</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>Runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>肖申克的救赎</td>\n",
       "      <td>1994</td>\n",
       "      <td>9.7</td>\n",
       "      <td>弗兰克·德拉邦特</td>\n",
       "      <td>弗兰克·德拉邦特 / 斯蒂芬·金</td>\n",
       "      <td>蒂姆·罗宾斯 / 摩根·弗里曼 / 鲍勃·冈顿 / 威廉姆·赛德勒 / 克兰西·布朗 / 吉...</td>\n",
       "      <td>剧情/犯罪</td>\n",
       "      <td>美国</td>\n",
       "      <td>英语</td>\n",
       "      <td>142分钟</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>霸王别姬</td>\n",
       "      <td>1993</td>\n",
       "      <td>9.6</td>\n",
       "      <td>陈凯歌</td>\n",
       "      <td>芦苇 / 李碧华</td>\n",
       "      <td>张国荣 / 张丰毅 / 巩俐 / 葛优 / 英达 / 蒋雯丽 / 吴大维 / 吕齐 / 雷汉...</td>\n",
       "      <td>剧情/爱情/同性</td>\n",
       "      <td>中国大陆 / 中国香港</td>\n",
       "      <td>汉语普通话</td>\n",
       "      <td>171分钟</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>阿甘正传</td>\n",
       "      <td>1994</td>\n",
       "      <td>9.5</td>\n",
       "      <td>罗伯特·泽米吉斯</td>\n",
       "      <td>艾瑞克·罗斯 / 温斯顿·格鲁姆</td>\n",
       "      <td>汤姆·汉克斯 / 罗宾·怀特 / 加里·西尼斯 / 麦凯尔泰·威廉逊 / 莎莉·菲尔德 / ...</td>\n",
       "      <td>剧情/爱情</td>\n",
       "      <td>美国</td>\n",
       "      <td>英语</td>\n",
       "      <td>142分钟</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Year  Rate Directors           Writers  \\\n",
       "0  肖申克的救赎  1994   9.7  弗兰克·德拉邦特  弗兰克·德拉邦特 / 斯蒂芬·金   \n",
       "1    霸王别姬  1993   9.6       陈凯歌          芦苇 / 李碧华   \n",
       "2    阿甘正传  1994   9.5  罗伯特·泽米吉斯  艾瑞克·罗斯 / 温斯顿·格鲁姆   \n",
       "\n",
       "                                               Stars    Genres       Country  \\\n",
       "0  蒂姆·罗宾斯 / 摩根·弗里曼 / 鲍勃·冈顿 / 威廉姆·赛德勒 / 克兰西·布朗 / 吉...     剧情/犯罪            美国   \n",
       "1  张国荣 / 张丰毅 / 巩俐 / 葛优 / 英达 / 蒋雯丽 / 吴大维 / 吕齐 / 雷汉...  剧情/爱情/同性   中国大陆 / 中国香港   \n",
       "2  汤姆·汉克斯 / 罗宾·怀特 / 加里·西尼斯 / 麦凯尔泰·威廉逊 / 莎莉·菲尔德 / ...     剧情/爱情            美国   \n",
       "\n",
       "  Language Runtime  \n",
       "0       英语   142分钟  \n",
       "1    汉语普通话   171分钟  \n",
       "2       英语   142分钟  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Level</th>\n",
       "      <th>Directors</th>\n",
       "      <th>Writers</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>Country</th>\n",
       "      <th>Language</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Box_Office_USA</th>\n",
       "      <th>Box_Office_World</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>9.3</td>\n",
       "      <td>IIB</td>\n",
       "      <td>Frank Darabont</td>\n",
       "      <td>Stephen King/Frank Darabont</td>\n",
       "      <td>Tim Robbins/Morgan Freeman/Bob Gunton/See full...</td>\n",
       "      <td>Drama</td>\n",
       "      <td>142 min</td>\n",
       "      <td>USA</td>\n",
       "      <td>English</td>\n",
       "      <td>$25,000,000</td>\n",
       "      <td>$28,699,976</td>\n",
       "      <td>$28,817,291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>9.2</td>\n",
       "      <td>IIB</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
       "      <td>Mario Puzo/Francis Ford Coppola/1 more credit</td>\n",
       "      <td>Marlon Brando/Al Pacino/James Caan/See full ca...</td>\n",
       "      <td>Crime/Drama</td>\n",
       "      <td>175 min</td>\n",
       "      <td>USA</td>\n",
       "      <td>English/Italian/Latin</td>\n",
       "      <td>$6,000,000</td>\n",
       "      <td>$134,966,411</td>\n",
       "      <td>$246,120,986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>1974</td>\n",
       "      <td>9.0</td>\n",
       "      <td>IIB</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
       "      <td>Francis Ford Coppola/Mario Puzo/1 more credit</td>\n",
       "      <td>Al Pacino/Robert De Niro/Robert Duvall/See ful...</td>\n",
       "      <td>Crime/Drama</td>\n",
       "      <td>202 min</td>\n",
       "      <td>USA</td>\n",
       "      <td>English/Italian/Spanish/Latin/Sicilian</td>\n",
       "      <td>$13,000,000</td>\n",
       "      <td>$47,834,595</td>\n",
       "      <td>$48,035,783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name  Year  Rate Level             Directors  \\\n",
       "0  The Shawshank Redemption  1994   9.3   IIB        Frank Darabont   \n",
       "1             The Godfather  1972   9.2   IIB  Francis Ford Coppola   \n",
       "2    The Godfather: Part II  1974   9.0   IIB  Francis Ford Coppola   \n",
       "\n",
       "                                         Writers  \\\n",
       "0                    Stephen King/Frank Darabont   \n",
       "1  Mario Puzo/Francis Ford Coppola/1 more credit   \n",
       "2  Francis Ford Coppola/Mario Puzo/1 more credit   \n",
       "\n",
       "                                               Stars       Genres  Runtime  \\\n",
       "0  Tim Robbins/Morgan Freeman/Bob Gunton/See full...        Drama  142 min   \n",
       "1  Marlon Brando/Al Pacino/James Caan/See full ca...  Crime/Drama  175 min   \n",
       "2  Al Pacino/Robert De Niro/Robert Duvall/See ful...  Crime/Drama  202 min   \n",
       "\n",
       "  Country                                Language       Budget Box_Office_USA  \\\n",
       "0     USA                                 English  $25,000,000    $28,699,976   \n",
       "1     USA                   English/Italian/Latin   $6,000,000   $134,966,411   \n",
       "2     USA  English/Italian/Spanish/Latin/Sicilian  $13,000,000    $47,834,595   \n",
       "\n",
       "  Box_Office_World  \n",
       "0      $28,817,291  \n",
       "1     $246,120,986  \n",
       "2      $48,035,783  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(douban_df.head(3))\n",
    "display(imdb_df.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "def calc_lang(demo, name=\"imdb\"):\n",
    "    \n",
    "    demo['l_Language'] = demo['Language'].fillna(\"\").map(lambda x: x.replace(' ', '').split(\"/\"))\n",
    "    # del demo['Language']\n",
    "    # display(demo.head(3))\n",
    "    \n",
    "    temp_df = demo['l_Language'].apply(pd.Series)\n",
    "    try: \n",
    "        temp_df = temp_df.rename(columns = lambda x : 'tag_' + str(x+1))\n",
    "        temp_df.fillna(pd.NA,inplace=True)\n",
    "    except TypeError:\n",
    "        pass\n",
    "    temp_df.sample(3)\n",
    "    \n",
    "    tags_df = temp_df.merge(demo[[\"Rate\"]], left_index=True, right_index=True,how=\"left\")\n",
    "    tags_df.sample(3)\n",
    "    \n",
    "    rowtags_df = tags_df.melt(id_vars = ['Rate'], value_name = \"tag\")\n",
    "    rowtags_df.sort_values(\"Rate\").head(3)\n",
    "    \n",
    "    rowtags_df.dropna(inplace=True)\n",
    "    del rowtags_df[\"variable\"]\n",
    "    # display(rowtags_df.head(3))\n",
    "    \n",
    "    count_df = pd.DataFrame(rowtags_df['tag'].value_counts())\n",
    "    count_df['id'] = range(1, len(count_df)+1)\n",
    "    count_df['language'] = count_df.index\n",
    "    count_df.set_index('id', inplace=True)\n",
    "    count_df.columns = [\"count\", \"language\"]\n",
    "#     display(count_df)\n",
    "    top_ten = count_df.head(10)\n",
    "    \n",
    "# This changes Chinese into English\n",
    "    if count_df['language'][1] != \"English\":\n",
    "        name = \"Douban\"\n",
    "        translation = {\"英语\":\"English\", \"日语\":\"Japanese\", \"汉语普通话\":\"Mandarin\", \"法语\":\"French\",\n",
    "                       \"德语\":\"German\", \"粤语\":\"Cantonese\", \"意大利语\":\"Italian\", \"西班牙语\":\"Spanish\",\n",
    "                       \"俄语\":\"Russian\", \"韩语\":\"Korean\"}\n",
    "        top_ten['language'] = top_ten['language'].map(translation)\n",
    "    \n",
    "    print(\"\\nThese are the most common languages of the movies on {}:\\n\".format(name))\n",
    "    display(top_ten.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "These are the most common languages of the movies on Douban:\n",
      "\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>164</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46</td>\n",
       "      <td>Japanese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>Mandarin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39</td>\n",
       "      <td>French</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27</td>\n",
       "      <td>German</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>25</td>\n",
       "      <td>Cantonese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19</td>\n",
       "      <td>Italian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>Russian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Korean</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    count   language\n",
       "id                  \n",
       "1     164    English\n",
       "2      46   Japanese\n",
       "3      42   Mandarin\n",
       "4      39     French\n",
       "5      27     German\n",
       "6      25  Cantonese\n",
       "7      19    Italian\n",
       "8      15    Spanish\n",
       "9      12    Russian\n",
       "10     11     Korean"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "These are the most common languages of the movies on imdb:\n",
      "\n"
     ]
    },
    {
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       "      <th></th>\n",
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       "      <th>language</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43</td>\n",
       "      <td>French</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>German</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>26</td>\n",
       "      <td>Japanese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26</td>\n",
       "      <td>Italian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>Russian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13</td>\n",
       "      <td>Latin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>Arabic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>Hindi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    count  language\n",
       "id                 \n",
       "1     200   English\n",
       "2      43    French\n",
       "3      36    German\n",
       "4      30   Spanish\n",
       "5      26  Japanese\n",
       "6      26   Italian\n",
       "7      15   Russian\n",
       "8      13     Latin\n",
       "9       8    Arabic\n",
       "10      8     Hindi"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "calc_lang(douban_df)\n",
    "calc_lang(imdb_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As is shown above, English, Japanese, French, German, Italian, Spanish and Russian are in both dataframes. This indicates that both users on Douban and imdb like movies that use those languages. Meanwhile,  mandarin, cantonese and Korean only appear in the first dataframe of Douban while Latin, Hindi and Arabic only appears in the latter of imdb. This is because users of Douban are mostly Chinese and watched more movies in mandarin and cantonese, and vice versa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Runtime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we are going to see whether runtime affects users' ratings towards the films."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "pattern = re.compile(r'\\d+')\n",
    "\n",
    "def parse_time(time):\n",
    "    if time is not np.NaN:\n",
    "        time = re.findall(pattern, time)\n",
    "        return time[0]\n",
    "    else:\n",
    "        return np.NaN\n",
    "\n",
    "def calc_time(demo):\n",
    "    demo_df = demo[['Rate', 'Runtime']]\n",
    "    demo_df.dropna(inplace=True)\n",
    "    demo_df['Time(min)'] = demo_df['Runtime'].map(parse_time)\n",
    "\n",
    "    demo_df['Rate_p'] = demo_df['Rate'].map(lambda x: float(x) // 0.5 / 2)\n",
    "    \n",
    "    # display(demo_df.head(3))\n",
    "    \n",
    "    l = [9.5, 9, 8.5, 8]\n",
    "    ave = []\n",
    "    \n",
    "    for i in l:\n",
    "        rate_m = demo_df['Rate_p'] == i\n",
    "        demo_df_rated = demo_df[rate_m]\n",
    "        cnt = 0\n",
    "        tt = 0\n",
    "        for t in demo_df_rated['Time(min)']:\n",
    "            tt += int(t)\n",
    "            cnt += 1\n",
    "        if cnt != 0:\n",
    "            a = tt / cnt\n",
    "            ave.append([i, a])\n",
    "        else:\n",
    "            ave.append([i, np.NaN])\n",
    "        \n",
    "    ave_df = pd.DataFrame(ave)\n",
    "    ave_df.columns = [\"RateGroup\", \"average_time\"]\n",
    "    display(ave_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def disp_joint(demo, name):\n",
    "    demo_df = demo[['Rate', 'Runtime']]\n",
    "    demo_df.dropna(inplace=True)\n",
    "    demo_df['Time(min)'] = demo_df['Runtime'].map(parse_time)\n",
    "\n",
    "    demo_df['Rate_p'] = demo_df['Rate'].map(lambda x: float(x) // 0.5 / 2)\n",
    "    \n",
    "    dist1 = [int(i) for i in demo_df[\"Time(min)\"].values]\n",
    "    dist2 = [i for i in demo_df[\"Rate\"].values]\n",
    "    print('\\t\\t\\t'+name)\n",
    "    ax = sns.jointplot(x=dist2, y=dist1, kind='hex')\n",
    "    ax.set_axis_labels('Rate', 'Runtime', fontsize=10)\n",
    "    print()\n",
    "    \n",
    "def disp_box(demo, name):\n",
    "    demo_df = demo[['Rate', 'Runtime']]\n",
    "    demo_df.dropna(inplace=True)\n",
    "    demo_df['Time(min)'] = demo_df['Runtime'].map(parse_time)\n",
    "\n",
    "    demo_df['Rate_p'] = demo_df['Rate'].map(lambda x: float(x) // 0.5 / 2)\n",
    "    \n",
    "    dist1 = [int(i) for i in demo_df[\"Time(min)\"].values]\n",
    "    dist3 = [i for i in demo_df[\"Rate_p\"].values]\n",
    "    ax = sns.boxplot(x=dist3,y=dist1)\n",
    "    ax.set(xlabel='Rate(Group)', ylabel='Runtime', title=name+\"\\n\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RateGroup</th>\n",
       "      <th>average_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.5</td>\n",
       "      <td>139.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0</td>\n",
       "      <td>128.776471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.5</td>\n",
       "      <td>118.525974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>123.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RateGroup  average_time\n",
       "0        9.5    139.000000\n",
       "1        9.0    128.776471\n",
       "2        8.5    118.525974\n",
       "3        8.0    123.500000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\tDouban\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calc_time(douban_df)\n",
    "\n",
    "disp_joint(douban_df, \"Douban\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_box(douban_df, \"Douban\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RateGroup</th>\n",
       "      <th>average_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0</td>\n",
       "      <td>153.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.5</td>\n",
       "      <td>138.425532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>127.335025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RateGroup  average_time\n",
       "0        9.5           NaN\n",
       "1        9.0    153.400000\n",
       "2        8.5    138.425532\n",
       "3        8.0    127.335025"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\timdb\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calc_time(imdb_df)\n",
    "disp_joint(imdb_df, \"imdb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_box(imdb_df, \"imdb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the dataframes above, the value in the RateGroup column means x or above. For example, 9.5 represents ratings from 9.5 and above, while 9.0 represents ratings from 9.0(inclusive) up to 9.5(exclusive).\n",
    "\n",
    "We may first get a overall view about the distribution of ratings and runtime of the top 250 movies on each website from the joint-plots. Generally speaking, the scores are lower on imdb than that on Douban, while the range of both runtime and ratings are wider on imdb.\n",
    "\n",
    "In the box-plots and the dataframes above, we are able to see that the longer the runtime is, the higher average score it may get on both websites. There are no films on the imdb website that have rates over 9.5. However, we are still able to see that the films that have the highest ratings also have the longest runtime in either dateframe. This might indicates that users on both websites prefer longer films to shorter ones. However, it may also be true that the longer the film is, the better they are in quality.\n",
    "\n",
    "At the meantime, we can find out from the box-plots that the distribution of scores are more dispersive when the runtime gets longer on Douban, but on imdb, the range of distribution remains generally unchanged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}