31. Pandas使用explode实现一行变多行统计.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas使用explode实现一行变多行统计\n",
    "\n",
    "解决实际问题：一个字段包含多个值，怎样将这个值拆分成多行，然后实现统计\n",
    "\n",
    "比如：一个电影有多个分类、一个人有多个喜好，需要按分类、喜好做统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\n",
    "    \"./datas/movielens-1m/movies.dat\",\n",
    "    header=None,\n",
    "    names=\"MovieID::Title::Genres\".split(\"::\"),\n",
    "    sep=\"::\",\n",
    "    engine=\"python\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>MovieID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MovieID                               Title                        Genres\n",
       "0        1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1        2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2        3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3        4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4        5  Father of the Bride Part II (1995)                        Comedy"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***问题：怎样实现这样的统计，每个题材有多少部电影？***\n",
    "\n",
    "解决思路：\n",
    "* 将Genres按照分隔符|拆分\n",
    "* 按Genres拆分成多行\n",
    "* 统计每个Genres下的电影数目"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2、将Genres字段拆分成列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3883 entries, 0 to 3882\n",
      "Data columns (total 3 columns):\n",
      "MovieID    3883 non-null int64\n",
      "Title      3883 non-null object\n",
      "Genres     3883 non-null object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 91.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 当前的Genres字段是字符串类型\n",
    "type(df.iloc[0][\"Genres\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 新增一列\n",
    "df[\"Genre\"] = df[\"Genres\"].map(lambda x:x.split(\"|\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>MovieID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>[Animation, Children's, Comedy]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "      <td>[Adventure, Children's, Fantasy]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>[Comedy, Romance]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "      <td>[Comedy, Drama]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>[Comedy]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MovieID                               Title                        Genres  \\\n",
       "0        1                    Toy Story (1995)   Animation|Children's|Comedy   \n",
       "1        2                      Jumanji (1995)  Adventure|Children's|Fantasy   \n",
       "2        3             Grumpier Old Men (1995)                Comedy|Romance   \n",
       "3        4            Waiting to Exhale (1995)                  Comedy|Drama   \n",
       "4        5  Father of the Bride Part II (1995)                        Comedy   \n",
       "\n",
       "                              Genre  \n",
       "0   [Animation, Children's, Comedy]  \n",
       "1  [Adventure, Children's, Fantasy]  \n",
       "2                 [Comedy, Romance]  \n",
       "3                   [Comedy, Drama]  \n",
       "4                          [Comedy]  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Animation', \"Children's\", 'Comedy']\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "# Genre的类型是列表\n",
    "print(df[\"Genre\"][0])\n",
    "print(type(df[\"Genre\"][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3883 entries, 0 to 3882\n",
      "Data columns (total 4 columns):\n",
      "MovieID    3883 non-null int64\n",
      "Title      3883 non-null object\n",
      "Genres     3883 non-null object\n",
      "Genre      3883 non-null object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 121.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3、使用explode将一行拆分成多行"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "语法：pandas.DataFrame.explode(column)  \n",
    "将dataframe的一个list-like的元素按行复制，index索引随之复制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df.explode(\"Genre\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MovieID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "      <td>Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "      <td>Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MovieID                     Title                        Genres       Genre\n",
       "0        1          Toy Story (1995)   Animation|Children's|Comedy   Animation\n",
       "0        1          Toy Story (1995)   Animation|Children's|Comedy  Children's\n",
       "0        1          Toy Story (1995)   Animation|Children's|Comedy      Comedy\n",
       "1        2            Jumanji (1995)  Adventure|Children's|Fantasy   Adventure\n",
       "1        2            Jumanji (1995)  Adventure|Children's|Fantasy  Children's\n",
       "1        2            Jumanji (1995)  Adventure|Children's|Fantasy     Fantasy\n",
       "2        3   Grumpier Old Men (1995)                Comedy|Romance      Comedy\n",
       "2        3   Grumpier Old Men (1995)                Comedy|Romance     Romance\n",
       "3        4  Waiting to Exhale (1995)                  Comedy|Drama      Comedy\n",
       "3        4  Waiting to Exhale (1995)                  Comedy|Drama       Drama"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4、实现拆分后的题材的统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23d73917cc8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Avv2EpFuBScDewM75sFNJe+d+Jqf/ICICuELSWpI2yPmYjXmeMGZjQS1t+pKmAq8ErgTWbwXy/Hu9fNgk4N62py3Maf3zmilpnqR5ixYtqqN4ZmaWVQ76klYHfgwcFhGPD3Voh7QYkBAxKyKmR8T0iRMnVi2emZm1qRT0Ja1ICvinRcS5OfnPkjbIj28APJjTFwJT2p4+Gbi/yuubmVkxVUbvCDgZuDUivtz20BxgRr49A/hZW/oBeRTP9sBjbs83M+utKjNydwA+ANwo6bqc9i/ACcBZkg4C7gH2zY+dD+wJLACeAg6s8NpmZlZCldE7l9O5nR5glw7HB3Bw2dczM7PqPCPXzKxBHPTNzBrEq2yajUJe+dOWFdf0zcwaxDV9s+WcrxqsnYO+mXXFawstHxz0zazn6rz68JVMMW7TNzNrEAd9M7MGcdA3M2sQB30zswZxR66ZGc3pXHZN38ysQRz0zcwaxM07Zmaj1LKYEOeavplZgzjom5k1SM+DvqTdJd0uaYGkI3v9+mZmTdbToC9pHPBNYA9gC2B/SVv0sgxmZk3W65r+tsCCiLgzIp4FzgD27nEZzMwaS2m/8h69mPQuYPeI+FC+/wFgu4j4WNsxM4GZ+e7mwO1dZD0BeKiGItaVz2jNy2XqfV4uU+/zcplg44iY2OmBXg/ZVIe0PmediJgFzCqUqTQvIqZXKVid+YzWvFym3uflMvU+L5dpaL1u3lkITGm7Pxm4v8dlMDNrrF4H/auBaZI2kbQSsB8wp8dlMDNrrJ4270TEYkkfAy4ExgGzI+LmGrIu1BzUg3xGa14uU+/zcpl6n5fLNISeduSamdnI8oxcM7MGcdA3M2sQB/1RRNIKkt490uUws+WXg/4oEhHPAx8b9sASJK22LPK1wVV5z5VMGf7IrvN7i6RR930fjeXKy8WMGrky+Nra8hurHbmSpgFfIK3hs0orPSJeXDCfjwGnRcQjFcszEfgwMJW2UVER8cGC+fwb8DfgTODJtnweLlmu1wLfA1aPiI0kbQV8JCL+uWA+44ALI+JNZcrRlo+A9wEvjojjJG0EvCgirqqSb1WSdgCui4gnJb0f2Ab4akT8X4m86nrP50fEq4q+/iB5/Qh4DfBj4JSIuLVEHj+n32TKdhHxtpEoV85nHLA+fb9795TM6y7gnFyeW8rkkfPZATgW2DiXS6lYxWJUzuv3EfGasmXpk9cYDvqXA8cAJwJvBQ4k/T3HFMzn30nzBa4BZpMCW+E3RdLvgN8A84HnWukR8eOC+dzVIbnUByXndyXwLmBORLwyp90UEa8okdcc4AMR8ViZsuQ8TgKeB94YES+TtDZwUUS8umA+l0fEjpKeoG8gan2x1iiY3w3AVsCWwA+Bk4F3RMTri+ST86rlPZf0TeD7EXF10TIMkt8awP6k70oApwCnR8QTXT5/yPciIn49QuU6hBQL/kz6bOXixJYly/NCUkw4kNQaMhs4IyIeL5jPbcAnGBgT/lKiTJ8DbgDOLROf+oiIMfkDzM+/b2xL+03JvATsRloAbgHwH8CmBfO4bqTfk0HKdWX+fW1b2vUl8zoLuIcUEL/W+imYxzV1lafm96lVrs8CB7WnjdR7DtwCLAb+mL/wNwI3VPw7JwCHAXcDvwTuAA4ZBe9/6XLl7+y6y6hcOwH3ka66TwVeUvRzUFM5niCd0J4FHs/3Hy+T11jeLvHp3BZ4R26iuQ9Yr0xGERGS/gT8ifQlWxs4R9LciDiiy2zOk7RnRJxfpgwtkl4AHA5sFBEzczPW5hFxXsks783NDZFnQR8KlLqEBn6Rf6r4e74UT2fb1Cz2/NBPGUjSOyLi3Hx77ajYPAc8Ieko4P3ATrmMK5bMq673fI+Srz+ApLeRaq6bkq5kto2IB/Pn7Vbg6wXyqqVptcZy3QuUvvrsUKZxwF65XFOBLwGnAa8Dzgc26zKrSyT9F3Au8EwrMSKuKVqmiHhh0ecMldmY/AFeDaxOWr/nFNIbu32JfA4lXX5dCOwLrJjTVwD+WOJM/HS+XepMTGrLPwK4Kd9flQpXEaQa1GmkS98HgR9RoVYErAS8Iv+sWOL57yMtvbEQOJ60iuq+JfK5ptPtCn/Xi0gn29fl+xsBB4yS93y9XJ6NSJWBMnmcCuw0yGO7FMzrcmAX0tXHxqR268+NVLlIV56XA0fl/+HhwOEV3u87c56v7fBY11e2wCUdfn5VsCwvzb+36fRT5u8bs236dZF0HHBydOiwk/SyKNmxVKE88yJiuqRrY2l78PURsVUvyzFI2XYmfUnvJjWJTQFmRMRlBfN5KSloCLi4zHvc7/1Zcnt5kmvBXwI2JJ08NgZujYiXF8ynlk74tvzmR8SrJN0YEf+Q034TEa8biXJJ6tiPFxGfK5nf6hHx14plWgF4V0ScVTGfWZGu+C/p8HBExBuL5jlmm3ckTQeOZmnPOADRZeeNpHXyza/0u9/K5+GiwSh/SXfKdy+Nck0yz0palaXNH5vSdmlYlKRTgY9HxKP5/trAl6LgqKLsS8CuEXF7zmsz4HSgqxEm+YtwQ6QOzdtKvH67VSW9knRFtkq+vWTp7ujyEjp3nAewKCK2q1imVp6bAIcwcCRX0dEtnwe2B/43Il4p6Q2kDs9CIuI5SU9JWjMqdMK3qaVpta5ytYJ77oCNqgEb+Gwe4PE34AJSB/9hEfGjAmV6Pr83lYJ+RMzMv99QJZ92Yzboky6fP03q3CrcJkxq0glSoNgIeCTfXovUWblJkcwknUBqcjotJ31c0o4RUXQf4GNIH7Qpkk4DdgD+sWAe7bZsBXyAiHgkB8gyVmwF/JzXHyR13e6dvwjXS9ooSg6na/MA8OV8+09ttyH9X7uqAUVEof9zl35Kah74OeU+my1/j4i/5HHaK0TEJZK+WDKvp4EbJc2l71DgQ0vkdRjwAlLT6OeBNwAzRqpckl5B6g9YJ99/iNQ0V3Yxx10j4ghJbyc1Q+5LaprpOuhncyV9ihqGX+fv2T/RVqkEvhMRfy+a11gO+osiovSyzK0vu6Rvk4bWnZ/v7wGUudzcE9g60gSrVg37WqBQ0I+IuZKuIdXwRKqlV9lxZ4X2js58RVP2/z5P0smkLxik9vn5BfPYALhZ0lX0/SIUqgXXWfNpqXGs99MR8bUaivSopNVJQ4FPk/QgaaBBGXV0wgMQS4eQ/pXU2VlFHeWaRWrDvwSWNEN+Fyg7oalVkdmTNHT0YanT/k/Dal1NH9yWFkCZ4dcn5XJ9K9//QE77UNGMxmybvqRdSJe6F9O3Z/zcgvkMmACjEjvT5HHeO7fO4jm4XlqguWmboR7vtrmiQ74HkDq4zslJ+wLHR8QPB3/WoHmtTPoA70g6IV0GfCsium5+Gmysd5Qc490v71mty+ESz61trLek9wLTgIsoMWpD0mHAb0mjV54iNWG9D1iTNJGw8DjvOuVa+b79mgzPiIjdSua3KqmDuputUTs9f0CfV5V+sHzVvg+peWdb0tX/eXU1/5UsU21/41iu6R8IvJR09lvyJSWN4iniIUn/Srp0C9KQvTJfqi8A1+YOF5Euw44q8PwvDfFY180VA54Y8QNJ80mX4CJNOCo8yzDXgk+OiPfTtymlaHkqB/chVNmO7uOkobF1BNR/INXE3kjfz2a3/8PJwFdJn+8bgN+RTgI/L9M0APUOswQmdGgyLDVcWtJbgf8mjQrbRNLWwHEFr/zuVJrJ3qrIvB/oNMmxKxFxZG5Gezz3OzwJ7F00n5qHXz8nadOI+GPO+8W0TfgqYiwH/a1aIwcq2p9Uw/sJ6Yt5GeU6y06XdCmpXV/AZyLiTwWeX3tzRZvbSH0W4wHKtKnnD/9ESStFxLNlC6K+M2hXIp20n4yCM2gH8WCF59Y51vvtpGUmSr1PEfEpgDzGfzqpmeKDwHclPRoRW5TI9hSWzmB/A3kGe5nyAc+3f4YkbcwQyzMM41hSbfpSgIi4LneEF/FB4HOkCl/rCrRqs9Mk4M2SVmlL+0HBPE4hNX+2mpkWAmcDZYL+p0nj/u8k/Y0bU/JvHMtB/wpJW5SptbbLNaePlx2mJemlEXFbW/PMwvx7Q0kbFrikf8cw5Sx6BdPKt73Z4jnyMgWk5QaKuhv4rdJyDO3t8V3X/KPfJBNJ+5C+9JVFxO4Vnn4ncKmkX9C3SabMVc31pCaBKichSHM01iA166xJ2k/6xrJ5RcTFkhRpePKxkn5D+mwUdTRwuaTWVdtOQKlmNWBxRDzWr8280Akk91eV6ZDuKA8B3Zl0VXQ+aZLc5RQP+ptGxHsk7Z/L+TeV7BzI/7tpwOak7/BtRZpV243loL8jMCMPuXuGpWuuFApmalscCyizONbhpA98p+aZIpf0bx3isTLNVi11Nlvcn39WAGqZIRgRP5VUdIQTkr4SEYdpkEXASgyPvCf/rJR/qlgfuE3S1fQ9gXRVJkmzgJeTJvhdSWre+XJUm3Vc5wz2C3IlpzXY4BMVBhvclPtAxuWgdijp7x3WMvgMtLyLNEzz2og4UNL6pBhRVOXh15J2GuSh7SQRBefIwNgO+lVqde1OJK27MwcgIq4f4o0eoK3jcI+IeLr9sX6XhsPlU/VydDC1NFvkNv3VI+LTFfNpv6JZgdR8UaZpoNV++99VytPSNtZ7tYh4crjjh1Gm9txuI2Bl0voz95GuHh8d8hnD6zTM8oAiGXS4qr2/Vd7c3FNmsMEhpCuHZ4D/Ic2M/3yXz631M9Dmb3l48WKlxeAepNyIm2MZOPy66Pe80/ctSCelyaS9xgsZs0E/X6KSO5C6Dq6D5HVvv6uuMh0kvyNNjR4urSNJ74+IH0k6fJAylu08raXZIrfpd/W3DKP9imYxqcmocCdZRLSGis4jf0lhyclp5aL5SXoNaWx92Su+9rJV6qyOiN1zM8DLSe3BnwReIelh4PdRcCXZbGoearlkmKWkfUlXEt2q66q23V4RcTQp8NNWrrOHe2LbZ2DriPhq+2OSPg6U/T/Mk7QWadjnfNJ7Vnjp74i4KA+iKD38OiL6tABI2pH0Xj1Ayb03xmzQ1yBT1ElflCIqLY4l6UWkTp/WDNHW2WMNUs2qW60NN+pbWCmps9niutyefzZ92/SLND19LyJ+256gtO542fbvi0nzKlr9MauShkoWHaP9FSpc8QG1LvccaSz1TZIeJV2pPQa8hdT/USboH8XAQNopbagy1T47tI5ykSaGfbVf2j92SOtK24n+25IuANaIiBuK5iPp4ojYhbZ5CG1pRfPaBfg30ufqPyJibtE8WsZs0KemKerAR0kfjkmky+iL6DuZYji7kT5gk0knoVbQfxz4l24ziYjv5Frq4xFxYoHXHy7fUuuPDGId0nDW9hpd0f6GrzPw6qdTWrdWae+Aj4i/5qFyhVW94ouIHfPvSiduSYeSTlo7AH8nDdf8PWld90IduUqTDfcEJklqnzC2BiUneuWa+AUR8UQe7rwN8PmIuLaX5codpO8lDfVsn6j5QsoNu27luyQwR8Td/dO6eP4qpArfBKU5DO0VwQ0LlmUvUs3+MeDo/hWmMsZy0K9linq+3Hpf2UJExKnAqZLeGQU3TOmQ13P5Cqa2oK+0dPERpCug9vHZhS/Fq/Q75OaT1wIT+zVhrUGJdsk2T0raptWerLQm099K5FPLcsjqu75QWVNJk+k+EREPVMgHUrv7POBt9J09/QRpg48y/i0izs5NDbuR2tS/DRSZvFRHuX5HauaYQN8mpydI8xsKqTFYf4TUh7Ih6W9rrwh+s2Cxfk6qjP4F+Ez/wT9lOqvHctBvTVG/jApT1FXf4livyrWB9lmKn4yIfy2Yz+8kfYOB63WUmpFLWgvoTFLTwEdJl8KLymQkaTKpVr4DqYZ/OamdcuGQT0xWIrWXj6dvE9bjpNESZR0GnC3p/lymDYH3lMin6hUfUM/6QhHRsV+nZF7XA9dL+p8osU7LIFpXQHsBJ0XEzyQdW7Vc+TszpdtRSrlf7/9I2y3WoZZgnfsXvirpkIjoep+CQdS/3EiM3WUYViPV6CpNUZd0PakDr8/CbUU749RheV9J10REoWYL1biEas6vtQzuDa3hrJJ+HeW2AZxLGmHRPvPxfRHx5gJ5bBwl9p3tkM+rgXsj4k9Ki7TfC3gAAA5QSURBVFF9BHgHabepz0aBmau5We3QuprVJP2KNEmv0vpCdVK9+7WeRxpV9CbSCqt/A66KEksCKE1ofFsu03WkCsmvi5z4JG1Pqoy8jFS5GEeFCX81BevBmsH+vUIFrpXvNlXyGJNBXzWuDy7pyqhhTQ2ltXdeHXnCRB6fOy8Krn1eN0lXRMT2ki4kbW94P3BORGxaIq/rImLr4dKGyWMz4FMMvLIqdFJTWpTuTZEWw9qJtNXlIcDWwMsiotDVg6RLI2LnIs/pkMdL6LdgW/Z64L6IOLlK/lWo3v1aX0AaMn1jRNwhaQPgHyLiohJ5XZv75D5EquUf015B6TKPeaQ9bc8mDQE+gLSt4dFDPnHoPF/LwM9ooclZrb8jN4N9gdQM9i9V402ZymS7Mdm8E/WuD/5VpRl4pRbHavMj4GJJp+T7B5I2HClEaVGzdzLwA3dc0byyf5e0JmnY39dJ7ZNl23IfkvR+0hr6kDrOiwaNs0ntv9+j5Noh2bi22vx7gFm5T+XHkq4rkd9va2hW+wrpS92nPVlp7ZZjSFeUI+WxiPhlTXlNILXHI2mjnFZ2f4Tx+aTxbtqGbRYVEQskjYuI54BTJHU1wasTST8kbd94HUs/o0HxGbmVm8EGK2KVJ4/JoJ/VtT541cWxWq/7n7m2/ybSP+UC0qV0UT8j9dTPp8LmKW3laq3z8RjV2wc/CHyD1NEcpI60opuxLI6IkyqWA9IMzvERsZi0C1f7MgBlPtetIZ7tJ9ein4OpnYb2RcQ8SVNLlKlOte3XShqC2NqLYhVgE9K2l2Wuao8jTci6PCKuVlpI7I6CeTyVO9+vk/SfpM7d1YZ5zlCmA1tE9WaQ+yR9hxQTvpgrdCtUzBPSOkOljcnmHQBJHTdtyKNpiuRzG2mjkdKLiLXltTVpCNm7Sav8/TgivlEwj5sqjvzon19dHdV1ledY0pj8n9A3+BRaPVLS0aQhfw+RZrBuExGRm1hOjYgdait092VaEBEvKfpYL9TdV9Qv721IE9k+UuK56xT933fIY2PSZ2pF0lXsmqQlvxeUzO9sUh9PpZFTdTaD5fy2ZOD3uPDyLGM26MOS4YhERKnRKDmPM4FDIqLU5KDcRr0fS5s6zgQ+FRFlavmtdVe+HhFlF9bqn1/ljmpJX2eIpRKKXF0prZXUIYtSHYrbkzZluSjy0gn5/7F6tzVYDTIDuq1gXc9clnQ6aePr7/ZLP4i0G1OZUUVjQtl2Zkl3kJpRTgF+WUPturJ8gtya1BFfeO2kfnntCEyLiFNyvFo9Igov+yxpNmmRxJvpu99D4W1Px1zQVxqoegxpCrJIl0uLSYGycLt3Hj2wJVB2caznSTsbHdSqWUi6s2gQk3QT6Z85nrQBx51UWEiuLd/KHdX9rqo+R78ZoUWvrkYTLd1Ue3PSiJvWJJ+3ApdFRNc7EyktzPUT4FmWjj2fThpR8vYosNR23XLZ/gPYMCL2kLQF8Joyncv9TpQrkEalrBslNlHJ3+c3kZoJtyVVmr4fEX8okMdbSJM1+49MKjt6p5aNfvJnazppwcPNJG0InF3mKlTSLVFuSe2BeY3BoP8J0mX9zNYZM7cDnkQaHlVo2F3Vf7DSPpr7kdqELyCNIvleFNx7VdIjpNpFR1FymKMq7uLUIb8BQ1MLPr/OjSVqI+ki4J0R8US+/0LSF7Twwn5Ks8NbTXQ3R8Sv6itpOZJ+SapNHx0RW0kaT1pFsvCeFG0nSli6ftKPo9+CgyXyfQNpQMRqpOWpj4yI33fxvAWk4bo3joYrhZY8oOCVwDWt70zRkUlteZ0MfCkqLiUPQESMqR/SvrMTOqRPJH2Iy+S5Pmny0luA9UrmsRppvsB5pC3uTiJd0nf7/GuW0fv1BdJko1+TNne+hNQEUTa/SuUk1eSOAG7K91cFrhsFn6vbgJXb7q9MWrN8RMtV4993df59bVvaaHjf1yUt/z2P1EH8DlJtfTpwV5d5XAKsUGOZtidd+f+VdNX2HGl5lKL5XJV/X5N/r0aarV2mTDuRBmPcTpptfGPZvMbi6J0Vo8NKdRGxSGmSTiGS3g38F2nnHgFfl/TpiDhnyCcOfP0nSbNfT1PaH3df0qbo3XbarDdU+3KUX2Wz0i5Oy0BtG0vU7IfAVZJaO6i9neJD9EazJyWty9K13ben4JLb6ru+zQBRbnDA70nv/T7Rd2b3PEnf7jKPI4DzlTZ1qboBDqQRav3H/U8rkc9ZefTOWpI+TN79rGSZZpNGGfbpmytjLAb9oYJXmcB2NGlS1YOwpHP4f1m6kXhhkUYjfCf/dGscaZmCugNg5V2c1HfVyBdIerz1EMXbTitvLLEsRMTxuQnkdTnpwCiwgNgYcDipv2JTSb8lXRkXXf7iNaT9GU4nLclcx2d188hV2f4iotu1tI4n1cpXofpKsq3XLj3uX0s3tv8KaZj046Q+o89G+dUx74mIIU+63RqLQX+rtqDTrjVmuKgVou/Inb9Qz1jaoh6I8hOwhlJpF6d8bJ3LPR/LwI0l/rHG/AvJV2Utd+efJY9FxeGEo0VEXJP7r1rb7d0exdfieRHwZtJItfeSmmNOj4ibi5an/aqh04VewauGdSJi16JlGELVcf+DbWw/f6gnDeM2Sf9DWoCt/XvcrCGbdcgTVrZk6SzT95Dayj7T43JU6iAdIt9aRiLUKTcztDaWuKJTc10Py3IXSycawdIrmtJr04xGSkuX7MXAcd6lmkDyRKP9SU2jx0XBtWokLWKIq4Yin09JJ5D6qUqNf++Q38akPaVXosK4f/Xd2P41+afUxvZaOtO/XUQThmzWJU/iWT8ifqu0hd+OpA/eI6SF2/7Y4/Iss1plHq736nz3qig5J6GmsswhfdHnRPVtCa1Lks4nz2Kn73yNQrM7c7DfixTwp5KajGZHxH0F8xnH0quGLal21fAEqSb+DGn/gVJDNlVhZdRB8luTFOh3yL/XIo0wWlZbo3ZXrgYH/fPovE7KdOCY6LdN2VjVoaP6dUDhjuoay/N60tXUXqTJL2cC50XF4X4VytN/39c+ouKKiKNF2aGC/fI4lTQU9ZfAGRFxU01lq3TVUJf2CWaSfhwR7yyZT/+N7a8gXdEW3ti+zomRLWOxTb8uU/sHfBg166TUqfaO6iryZfuvc03vjcCHSSMTSk2kqcGy2Pd1NPqlpF0rNoF8gLTO1WbAoW1t8WVr1v2vGr5GgV3YlsEJu72JqUqzXp0b28+rUI6Omhz0h+r0XbVnpVj2RktH9RJ59M5bSTX+bSixGmldYtns+zoaXQH8RGlnr1JNIBFR2+em31XD50peNdR9wo5BbhfLpMaN7WMZzHZvcvNOI9ZJGaSj+saIOGKEynMmaVu9C4CzgEsjotK447qohjXURytJdwL7MEpmrSotX9Lq06m0iXyNZXoul0mkit9TVcuktNvcDqTg/xbSchVrFXj+VyLiMEk/p8OJqMzciCYH/VG7Tkrd+nVUXxYRPxnBsuwOzM3jn0cNDbKGepk209FIaROdPUbLCbZuo+mErcE3tv8t6aTb9f9A0qsiYn6do/AaG/RbRuM6KctSbkvfLyJO6/HrvmOox8uMN66TpFupZw31UUnS90nt1L+knlmro8ZoO2FL+jJ5bH5UX5651hFF0Ow2fQAiorUezXJF0hqkjb0nkYbVzc33P036cvQ06JPa8AHWI9WCWifXN5BGFo1o0AduIk0+qvQlHcXuyj8rUdOs1VGkrk1PahE1bmwP/JTU71VpRFG7xgf95dgPSXMOfg98iBTsVwL2jogy2wlW0hqbnIfKbtGqASltLPHNXpenpa2t9IXALZIqr6E+GhUdjz/GLM8n7LpGFC3hoL/8enHkZXMlfY+8w1TkpYNH0NR+l7x/Ji0NMFLmkJaq+E2/9NeThtstF5Q2BunUEbg8DEmdwMATdkTE3iNYprrUMqKonYP+8mvJuiqRNpK/axQEfIBLc6fi6aQP8X7AxSNYnr0ZvZuZ1+lTbbdXAd5JWgt/eXBs222RBi3sPzJFqV1rrTEBq1Zc7DA9cZQ0g1nN2oafQd8haCM2JK6tbG8nrQ8OqQlq/Yg4eITKMuiexJJujBKbjIwVkn4dER1HhYw1Grg/9bkjNbN3tHNNfzkVEeNGugxDuIu0FsmSDeRHsCyNmKTXbzXRFYBXkdrBxyx13p9aDZhoV4mDvvXEKP6CXi3pw4NM0quyFO5oM5+lq4kuJp1sDxrRElV3G6kv5q2xdH/qT4xskUY/N+9YT6imDeSXQbkaM0lveaOa9qduGgd964nR/gVd3ifpSTqYtGT4o/n+2sD+EfGtkS1ZdZJWIy0xsT9pvZ1TgZ9UXFxuueWgbz3lL+jIkHRdRGzdL22ZbNwzkrR0f+r3LCfDUWvnoG8jxl/Q3pF0A7BVa9ZqXo7jhoh4+ciWzHrNQd+sAfJqq1OBb5M6dD8K3BsRnxzJclnvOeibNUBeR/8jwC6kETwXkfpURtVqp7bsOeibNUTeqHtzUk3/9oj4+zBPseWQg75ZA0jamdRpfjeppj8FmBERl41gsWwEOOibNYCk+cB7I+L2fH8z4PSIeNXIlsx6bUT3SjWznlmxFfABIuIPwIojWB4bIV6GwawZ5kk6mbTPAsD7WL6WmbAuuXnHrAEkrUzaOW3JXsnAtyLimSGfaMsdB32zhpA0ESAiFo10WWzkuE3fbDmm5FhJD5FWpbxd0iJJnx3pstnIcNA3W74dBuwAvDoi1o2IdYDtgB28DHEzuXnHbDkm6VrgzRHxUL/0icBFy9uCazY81/TNlm8r9g/4sKRd30M2G8hB32z59mzJx2w55eYds+WYpOeAJzs9BKwSEa7tN4yDvplZg7h5x8ysQRz0zcwaxEHfzKxBHPTNzBrEQd/MrEH+H3RkjZb+dNz9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df_new[\"Genre\"].value_counts().plot.bar()"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}