tutorials/12/analysis_strategy_in_netflix.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0b71fd19",
   "metadata": {},
   "source": [
    "# 넷플릭스의 콘텐츠 보유 현황과 수급 전략 분석\n",
    "\n",
    "- 분석 목표 : 넷플릭스에서 보유한 콘텐츠 현황과 최근 집중하는 콘텐츠 유형을 파악해 넷플릭스의 시장 점유를 위한 콘텐츠 수급 전략을 분석한다.\n",
    "- 데이터 출처 : [Netflix Movies and TV Shows | Kaggle](https://www.kaggle.com/shivamb/netflix-shows)\n",
    "- 참고 사이트 : [영화, TV 관람 등급 표](https://www.primevideo.com/help/ref=atv_hp_nd_cnt?language=ko_KR&nodeId=GFGQU3WYEG6FSJFJ)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "374431cc",
   "metadata": {},
   "source": [
    "```\n",
    "Step 1. 가설 설정\n",
    "Step 2. 기본 전처리\n",
    "     2-1. 불필요한 컬럼 제거\n",
    "     2-2. 불필요한 행 제거\n",
    "     2-3. 데이터 타입 변환\n",
    "     2-4. 시계열 데이터 변환\n",
    "     2-5. 데이터 구간(시간) 분할\n",
    "Step 3. 가설 검정\n",
    "     3-1. 특정 콘텐츠 유형에 대한 집중 수급 여부 분석\n",
    "     3-2. 특정 국가를 겨냥한 콘텐츠 현황 분석\n",
    "     3-3. 콘텐츠 수급 대상 연도 분석\n",
    "     3-4. 넷플릭스의 주요 소비자 타겟(연령대) 분석\n",
    "     3-5. 콘텐츠 평균 재생 시간 분석\n",
    "     3-5. 넷플릭스에서 주로 수급하는 콘텐츠 장르 분석\n",
    "Step 4. 종합 결론\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd12fec1",
   "metadata": {},
   "source": [
    "---\n",
    "## 1. 가설 설정"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8e75ba9",
   "metadata": {},
   "source": [
    "각 컬럼별 유효 데이터와 결측치 분포를 확인하고, 이에 따라 분석 목표에 도달하기 위한 가설을 설정합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f59ed4c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1ffe93af",
   "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>show_id</th>\n",
       "      <th>type</th>\n",
       "      <th>title</th>\n",
       "      <th>director</th>\n",
       "      <th>cast</th>\n",
       "      <th>country</th>\n",
       "      <th>date_added</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>duration</th>\n",
       "      <th>listed_in</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s1</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>3%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>João Miguel, Bianca Comparato, Michel Gomes, R...</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>August 14, 2020</td>\n",
       "      <td>2020</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>4 Seasons</td>\n",
       "      <td>International TV Shows, TV Dramas, TV Sci-Fi &amp;...</td>\n",
       "      <td>In a future where the elite inhabit an island ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s2</td>\n",
       "      <td>Movie</td>\n",
       "      <td>7:19</td>\n",
       "      <td>Jorge Michel Grau</td>\n",
       "      <td>Demián Bichir, Héctor Bonilla, Oscar Serrano, ...</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>December 23, 2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>93 min</td>\n",
       "      <td>Dramas, International Movies</td>\n",
       "      <td>After a devastating earthquake hits Mexico Cit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s3</td>\n",
       "      <td>Movie</td>\n",
       "      <td>23:59</td>\n",
       "      <td>Gilbert Chan</td>\n",
       "      <td>Tedd Chan, Stella Chung, Henley Hii, Lawrence ...</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>December 20, 2018</td>\n",
       "      <td>2011</td>\n",
       "      <td>R</td>\n",
       "      <td>78 min</td>\n",
       "      <td>Horror Movies, International Movies</td>\n",
       "      <td>When an army recruit is found dead, his fellow...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s4</td>\n",
       "      <td>Movie</td>\n",
       "      <td>9</td>\n",
       "      <td>Shane Acker</td>\n",
       "      <td>Elijah Wood, John C. Reilly, Jennifer Connelly...</td>\n",
       "      <td>United States</td>\n",
       "      <td>November 16, 2017</td>\n",
       "      <td>2009</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>80 min</td>\n",
       "      <td>Action &amp; Adventure, Independent Movies, Sci-Fi...</td>\n",
       "      <td>In a postapocalyptic world, rag-doll robots hi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s5</td>\n",
       "      <td>Movie</td>\n",
       "      <td>21</td>\n",
       "      <td>Robert Luketic</td>\n",
       "      <td>Jim Sturgess, Kevin Spacey, Kate Bosworth, Aar...</td>\n",
       "      <td>United States</td>\n",
       "      <td>January 1, 2020</td>\n",
       "      <td>2008</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>123 min</td>\n",
       "      <td>Dramas</td>\n",
       "      <td>A brilliant group of students become card-coun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7782</th>\n",
       "      <td>s7783</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Zozo</td>\n",
       "      <td>Josef Fares</td>\n",
       "      <td>Imad Creidi, Antoinette Turk, Elias Gergi, Car...</td>\n",
       "      <td>Sweden, Czech Republic, United Kingdom, Denmar...</td>\n",
       "      <td>October 19, 2020</td>\n",
       "      <td>2005</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>99 min</td>\n",
       "      <td>Dramas, International Movies</td>\n",
       "      <td>When Lebanon's Civil War deprives Zozo of his ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7783</th>\n",
       "      <td>s7784</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Zubaan</td>\n",
       "      <td>Mozez Singh</td>\n",
       "      <td>Vicky Kaushal, Sarah-Jane Dias, Raaghav Chanan...</td>\n",
       "      <td>India</td>\n",
       "      <td>March 2, 2019</td>\n",
       "      <td>2015</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>111 min</td>\n",
       "      <td>Dramas, International Movies, Music &amp; Musicals</td>\n",
       "      <td>A scrappy but poor boy worms his way into a ty...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7784</th>\n",
       "      <td>s7785</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Zulu Man in Japan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nasty C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 25, 2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>44 min</td>\n",
       "      <td>Documentaries, International Movies, Music &amp; M...</td>\n",
       "      <td>In this documentary, South African rapper Nast...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7785</th>\n",
       "      <td>s7786</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Zumbo's Just Desserts</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adriano Zumbo, Rachel Khoo</td>\n",
       "      <td>Australia</td>\n",
       "      <td>October 31, 2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>TV-PG</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>International TV Shows, Reality TV</td>\n",
       "      <td>Dessert wizard Adriano Zumbo looks for the nex...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7786</th>\n",
       "      <td>s7787</td>\n",
       "      <td>Movie</td>\n",
       "      <td>ZZ TOP: THAT LITTLE OL' BAND FROM TEXAS</td>\n",
       "      <td>Sam Dunn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom, Canada, United States</td>\n",
       "      <td>March 1, 2020</td>\n",
       "      <td>2019</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>90 min</td>\n",
       "      <td>Documentaries, Music &amp; Musicals</td>\n",
       "      <td>This documentary delves into the mystique behi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7787 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     show_id     type                                    title  \\\n",
       "0         s1  TV Show                                       3%   \n",
       "1         s2    Movie                                     7:19   \n",
       "2         s3    Movie                                    23:59   \n",
       "3         s4    Movie                                        9   \n",
       "4         s5    Movie                                       21   \n",
       "...      ...      ...                                      ...   \n",
       "7782   s7783    Movie                                     Zozo   \n",
       "7783   s7784    Movie                                   Zubaan   \n",
       "7784   s7785    Movie                        Zulu Man in Japan   \n",
       "7785   s7786  TV Show                    Zumbo's Just Desserts   \n",
       "7786   s7787    Movie  ZZ TOP: THAT LITTLE OL' BAND FROM TEXAS   \n",
       "\n",
       "               director                                               cast  \\\n",
       "0                   NaN  João Miguel, Bianca Comparato, Michel Gomes, R...   \n",
       "1     Jorge Michel Grau  Demián Bichir, Héctor Bonilla, Oscar Serrano, ...   \n",
       "2          Gilbert Chan  Tedd Chan, Stella Chung, Henley Hii, Lawrence ...   \n",
       "3           Shane Acker  Elijah Wood, John C. Reilly, Jennifer Connelly...   \n",
       "4        Robert Luketic  Jim Sturgess, Kevin Spacey, Kate Bosworth, Aar...   \n",
       "...                 ...                                                ...   \n",
       "7782        Josef Fares  Imad Creidi, Antoinette Turk, Elias Gergi, Car...   \n",
       "7783        Mozez Singh  Vicky Kaushal, Sarah-Jane Dias, Raaghav Chanan...   \n",
       "7784                NaN                                            Nasty C   \n",
       "7785                NaN                         Adriano Zumbo, Rachel Khoo   \n",
       "7786           Sam Dunn                                                NaN   \n",
       "\n",
       "                                                country          date_added  \\\n",
       "0                                                Brazil     August 14, 2020   \n",
       "1                                                Mexico   December 23, 2016   \n",
       "2                                             Singapore   December 20, 2018   \n",
       "3                                         United States   November 16, 2017   \n",
       "4                                         United States     January 1, 2020   \n",
       "...                                                 ...                 ...   \n",
       "7782  Sweden, Czech Republic, United Kingdom, Denmar...    October 19, 2020   \n",
       "7783                                              India       March 2, 2019   \n",
       "7784                                                NaN  September 25, 2020   \n",
       "7785                                          Australia    October 31, 2020   \n",
       "7786              United Kingdom, Canada, United States       March 1, 2020   \n",
       "\n",
       "      release_year rating   duration  \\\n",
       "0             2020  TV-MA  4 Seasons   \n",
       "1             2016  TV-MA     93 min   \n",
       "2             2011      R     78 min   \n",
       "3             2009  PG-13     80 min   \n",
       "4             2008  PG-13    123 min   \n",
       "...            ...    ...        ...   \n",
       "7782          2005  TV-MA     99 min   \n",
       "7783          2015  TV-14    111 min   \n",
       "7784          2019  TV-MA     44 min   \n",
       "7785          2019  TV-PG   1 Season   \n",
       "7786          2019  TV-MA     90 min   \n",
       "\n",
       "                                              listed_in  \\\n",
       "0     International TV Shows, TV Dramas, TV Sci-Fi &...   \n",
       "1                          Dramas, International Movies   \n",
       "2                   Horror Movies, International Movies   \n",
       "3     Action & Adventure, Independent Movies, Sci-Fi...   \n",
       "4                                                Dramas   \n",
       "...                                                 ...   \n",
       "7782                       Dramas, International Movies   \n",
       "7783     Dramas, International Movies, Music & Musicals   \n",
       "7784  Documentaries, International Movies, Music & M...   \n",
       "7785                 International TV Shows, Reality TV   \n",
       "7786                    Documentaries, Music & Musicals   \n",
       "\n",
       "                                            description  \n",
       "0     In a future where the elite inhabit an island ...  \n",
       "1     After a devastating earthquake hits Mexico Cit...  \n",
       "2     When an army recruit is found dead, his fellow...  \n",
       "3     In a postapocalyptic world, rag-doll robots hi...  \n",
       "4     A brilliant group of students become card-coun...  \n",
       "...                                                 ...  \n",
       "7782  When Lebanon's Civil War deprives Zozo of his ...  \n",
       "7783  A scrappy but poor boy worms his way into a ty...  \n",
       "7784  In this documentary, South African rapper Nast...  \n",
       "7785  Dessert wizard Adriano Zumbo looks for the nex...  \n",
       "7786  This documentary delves into the mystique behi...  \n",
       "\n",
       "[7787 rows x 12 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_r = pd.read_csv('./data/netflix_titles.csv')\n",
    "netflix_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1b39d1e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "show_id            0\n",
       "type               0\n",
       "title              0\n",
       "director        2389\n",
       "cast             718\n",
       "country          507\n",
       "date_added        10\n",
       "release_year       0\n",
       "rating             7\n",
       "duration           0\n",
       "listed_in          0\n",
       "description        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_r.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "a76d31bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "sns.heatmap(netflix_r.isnull().T, cmap='YlOrBr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10308a88",
   "metadata": {},
   "source": [
    "- `show_id` : 해당 데이터셋 이외에 병합할 테이블이 없기 때문에 id 컬럼은 제거할 것입니다.\n",
    "- `director`: 감독에 대한 정보는 고려하지 않겠습니다.\n",
    "- `cast`: 배우 역시 고려하지 않겠습니다.\n",
    "\n",
    "\n",
    "본 데이터에서 검증하고자 하는 가설은 아래와 같습니다.(H1 = 연구가설)\n",
    "\n",
    "1. H1 : 최근 특정 콘텐츠 유형(TV show or Movie)이 늘고 있다. (주요 컬럼 : type)\n",
    "2. H1 : 최근 특정 국가의 콘텐츠 비중이 늘고 있다. (주요 컬럼 : country)\n",
    "3. H1 : 최근 옛날(오래된) 콘텐츠 수급 비중이 늘고 있다. (주요 컬럼 : release_year)\n",
    "4. H1 : 최근 타겟층(관람등급)이 명확한 콘텐츠 비중이 늘고 있다. (주요 컬럼 : rating)\n",
    "5. H1 : 최근 길이(시리즈, 시간)가 짧은 콘텐츠 비중이 늘고 있다. (주요 컬럼 : duration)\n",
    "6. H1 : 최근 유통하는 콘텐츠 장르가 더 다양해지고 있다. (주요 컬럼 : listed_in, description)\n",
    "\n",
    "\n",
    "위 순서로 가설을 검증해나가며 넷플릭스의 시장 점유 전략에 대한 최종 결론을 도출하고자 합니다.\n",
    "<br><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d339100b",
   "metadata": {},
   "source": [
    "---\n",
    "## 2. 기본 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39eeaeef",
   "metadata": {},
   "source": [
    "본격적인 분석에 앞서 미리 처리해야할 사항은 아래와 같습니다.\n",
    "\n",
    "1. 분석에 사용할 컬럼만 가져온다.\n",
    "2. 사용하지 않을 행은 제거한다.(시간 누락)\n",
    "3. 추세 확인을 위해 시계열 데이터로 변환한다.\n",
    "4. 과거와 현재를 상대적으로 비교하기 위해 구간(시간)을 분할한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e5a04041",
   "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>show_id</th>\n",
       "      <th>type</th>\n",
       "      <th>title</th>\n",
       "      <th>director</th>\n",
       "      <th>cast</th>\n",
       "      <th>country</th>\n",
       "      <th>date_added</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>duration</th>\n",
       "      <th>listed_in</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s1</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>3%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>João Miguel, Bianca Comparato, Michel Gomes, R...</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>August 14, 2020</td>\n",
       "      <td>2020</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>4 Seasons</td>\n",
       "      <td>International TV Shows, TV Dramas, TV Sci-Fi &amp;...</td>\n",
       "      <td>In a future where the elite inhabit an island ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s2</td>\n",
       "      <td>Movie</td>\n",
       "      <td>7:19</td>\n",
       "      <td>Jorge Michel Grau</td>\n",
       "      <td>Demián Bichir, Héctor Bonilla, Oscar Serrano, ...</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>December 23, 2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>93 min</td>\n",
       "      <td>Dramas, International Movies</td>\n",
       "      <td>After a devastating earthquake hits Mexico Cit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s3</td>\n",
       "      <td>Movie</td>\n",
       "      <td>23:59</td>\n",
       "      <td>Gilbert Chan</td>\n",
       "      <td>Tedd Chan, Stella Chung, Henley Hii, Lawrence ...</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>December 20, 2018</td>\n",
       "      <td>2011</td>\n",
       "      <td>R</td>\n",
       "      <td>78 min</td>\n",
       "      <td>Horror Movies, International Movies</td>\n",
       "      <td>When an army recruit is found dead, his fellow...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  show_id     type  title           director  \\\n",
       "0      s1  TV Show     3%                NaN   \n",
       "1      s2    Movie   7:19  Jorge Michel Grau   \n",
       "2      s3    Movie  23:59       Gilbert Chan   \n",
       "\n",
       "                                                cast    country  \\\n",
       "0  João Miguel, Bianca Comparato, Michel Gomes, R...     Brazil   \n",
       "1  Demián Bichir, Héctor Bonilla, Oscar Serrano, ...     Mexico   \n",
       "2  Tedd Chan, Stella Chung, Henley Hii, Lawrence ...  Singapore   \n",
       "\n",
       "          date_added  release_year rating   duration  \\\n",
       "0    August 14, 2020          2020  TV-MA  4 Seasons   \n",
       "1  December 23, 2016          2016  TV-MA     93 min   \n",
       "2  December 20, 2018          2011      R     78 min   \n",
       "\n",
       "                                           listed_in  \\\n",
       "0  International TV Shows, TV Dramas, TV Sci-Fi &...   \n",
       "1                       Dramas, International Movies   \n",
       "2                Horror Movies, International Movies   \n",
       "\n",
       "                                         description  \n",
       "0  In a future where the elite inhabit an island ...  \n",
       "1  After a devastating earthquake hits Mexico Cit...  \n",
       "2  When an army recruit is found dead, his fellow...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix_r.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a04c4bf",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 2-1. 불필요한 컬럼 제거\n",
    "\n",
    "사용하지 않을 아래 컬럼들을 제거합니다.\n",
    "- `show_id`\n",
    "- `director`\n",
    "- `cast`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "878dd76a",
   "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>type</th>\n",
       "      <th>title</th>\n",
       "      <th>country</th>\n",
       "      <th>date_added</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>duration</th>\n",
       "      <th>listed_in</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TV Show</td>\n",
       "      <td>3%</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>August 14, 2020</td>\n",
       "      <td>2020</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>4 Seasons</td>\n",
       "      <td>International TV Shows, TV Dramas, TV Sci-Fi &amp;...</td>\n",
       "      <td>In a future where the elite inhabit an island ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Movie</td>\n",
       "      <td>7:19</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>December 23, 2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>93 min</td>\n",
       "      <td>Dramas, International Movies</td>\n",
       "      <td>After a devastating earthquake hits Mexico Cit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Movie</td>\n",
       "      <td>23:59</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>December 20, 2018</td>\n",
       "      <td>2011</td>\n",
       "      <td>R</td>\n",
       "      <td>78 min</td>\n",
       "      <td>Horror Movies, International Movies</td>\n",
       "      <td>When an army recruit is found dead, his fellow...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      type  title    country         date_added  release_year rating  \\\n",
       "0  TV Show     3%     Brazil    August 14, 2020          2020  TV-MA   \n",
       "1    Movie   7:19     Mexico  December 23, 2016          2016  TV-MA   \n",
       "2    Movie  23:59  Singapore  December 20, 2018          2011      R   \n",
       "\n",
       "    duration                                          listed_in  \\\n",
       "0  4 Seasons  International TV Shows, TV Dramas, TV Sci-Fi &...   \n",
       "1     93 min                       Dramas, International Movies   \n",
       "2     78 min                Horror Movies, International Movies   \n",
       "\n",
       "                                         description  \n",
       "0  In a future where the elite inhabit an island ...  \n",
       "1  After a devastating earthquake hits Mexico Cit...  \n",
       "2  When an army recruit is found dead, his fellow...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nflx = netflix_r.copy()\n",
    "nflx.drop(['show_id','director','cast'], axis=1, inplace=True)\n",
    "nflx.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c026eaea",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 2-2. 불필요한 행 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ea701700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type              0\n",
       "title             0\n",
       "country         507\n",
       "date_added       10\n",
       "release_year      0\n",
       "rating            7\n",
       "duration          0\n",
       "listed_in         0\n",
       "description       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nflx.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a456b1e",
   "metadata": {},
   "source": [
    "모든 가설은 넷플릭스에 콘텐츠가 올라간 시간에 기준을 두고 있고, 최근 전략을 파악하고자 하는 것이 분석 목표이므로 시간 정보(date_added)가 없는 행은 과감하게 삭제하도록 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8977a218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nflx['date_added'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "523f9e8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nflx = nflx.loc[nflx['date_added'].isnull()==False]\n",
    "nflx['date_added'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10b1ef08",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 2-3. 데이터 타입 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9110ba4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type            object\n",
       "title           object\n",
       "country         object\n",
       "date_added      object\n",
       "release_year     int64\n",
       "rating          object\n",
       "duration        object\n",
       "listed_in       object\n",
       "description     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nflx.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "853b7067",
   "metadata": {},
   "source": [
    "날짜 데이터는 datetime 타입으로, 콘텐츠 길이(n부작, 시간)는 숫자로 변환합니다.\n",
    "- `date_added`, `release_year` -> datetime\n",
    "- `duration` -> int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "12fb5de9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2020-08-14\n",
       "1      2016-12-23\n",
       "2      2018-12-20\n",
       "3      2017-11-16\n",
       "4      2020-01-01\n",
       "          ...    \n",
       "7782   2020-10-19\n",
       "7783   2019-03-02\n",
       "7784   2020-09-25\n",
       "7785   2020-10-31\n",
       "7786   2020-03-01\n",
       "Name: date_added, Length: 7777, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nflx['date_added'] = nflx['date_added'].astype('datetime64')\n",
    "nflx['date_added']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fed15d71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2020-01-01\n",
       "1      2016-01-01\n",
       "2      2011-01-01\n",
       "3      2009-01-01\n",
       "4      2008-01-01\n",
       "          ...    \n",
       "7782   2005-01-01\n",
       "7783   2015-01-01\n",
       "7784   2019-01-01\n",
       "7785   2019-01-01\n",
       "7786   2019-01-01\n",
       "Name: release_year, Length: 7777, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 최초 출시일에 월/일 정보가 누락되어 있으므로 1월 1일로 일괄 대체한다.(넷플릭스에서 유통된 연도가 같은 경우 시간상 역행이 발생하지 않도록 하기 위함)\n",
    "nflx['release_year'] = pd.to_datetime(nflx['release_year'], format='%Y')\n",
    "nflx['release_year']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f4df27a",
   "metadata": {},
   "source": [
    "콘텐츠 길이는 콘텐츠 타입에 따라 형식이 다릅니다.\n",
    "- `type`이 TV Show라면 단일 시리즈인 경우 1 Season, 연속 시리즈인 경우 N Seasons로 표기된다.\n",
    "- `type`이 TV Show지만 `duration` 단위가 Season 혹은 Seasons가 아닌 예외 케이스는 없다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ee9c8507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       4 Seasons\n",
       "5        1 Season\n",
       "11       1 Season\n",
       "12       1 Season\n",
       "16       1 Season\n",
       "          ...    \n",
       "7767     1 Season\n",
       "7775     1 Season\n",
       "7777    2 Seasons\n",
       "7779     1 Season\n",
       "7785     1 Season\n",
       "Name: duration, Length: 2400, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nflx.loc[nflx['type']=='TV Show','duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1cfbfed3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1 Season 혹은 N Seasons로 표기되지 않은 예외케이스 갯수 확인\n",
    "(nflx.loc[nflx['type']=='TV Show','duration'].str.contains('Season')==False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0000ad32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       4\n",
       "5       1\n",
       "11      1\n",
       "12      1\n",
       "16      1\n",
       "       ..\n",
       "7767    1\n",
       "7775    1\n",
       "7777    2\n",
       "7779    1\n",
       "7785    1\n",
       "Name: duration, Length: 2400, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 단위(Seasons, Season) 제거\n",
    "nflx.loc[nflx['type']=='TV Show','duration'] = nflx.loc[nflx['type']=='TV Show','duration'].apply(lambda x : x.split(\" \")[0])\n",
    "nflx.loc[nflx['type']=='TV Show','duration']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "461c981b",
   "metadata": {},
   "source": [
    "- `type`이 Movie라면 모두 min으로 표기한다.\n",
    "- `type`이 Movie이지만 `duration` 단위가 min이 아닌 예외 케이스는 없다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f10847c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        93 min\n",
       "2        78 min\n",
       "3        80 min\n",
       "4       123 min\n",
       "6        95 min\n",
       "         ...   \n",
       "7781     88 min\n",
       "7782     99 min\n",
       "7783    111 min\n",
       "7784     44 min\n",
       "7786     90 min\n",
       "Name: duration, Length: 5377, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nflx.loc[nflx['type']=='Movie','duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "be6de277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# min으로 표기되지 않은 예외케이스 갯수 확인\n",
    "(nflx.loc[nflx['type']=='Movie','duration'].str.contains('min')==False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "388dc8f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        93\n",
       "2        78\n",
       "3        80\n",
       "4       123\n",
       "6        95\n",
       "       ... \n",
       "7781     88\n",
       "7782     99\n",
       "7783    111\n",
       "7784     44\n",
       "7786     90\n",
       "Name: duration, Length: 5377, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 단위(min) 제거\n",
    "nflx.loc[nflx['type']=='Movie','duration'] = nflx.loc[nflx['type']=='Movie','duration'].apply(lambda x : x.split(\" \")[0])\n",
    "nflx.loc[nflx['type']=='Movie','duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5edd431d",
   "metadata": {},
   "outputs": [],
   "source": [
    "nflx['duration'] = nflx['duration'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f7ecf1e0",
   "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>type</th>\n",
       "      <th>title</th>\n",
       "      <th>country</th>\n",
       "      <th>date_added</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>duration</th>\n",
       "      <th>listed_in</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TV Show</td>\n",
       "      <td>3%</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>2020-08-14</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>4</td>\n",
       "      <td>International TV Shows, TV Dramas, TV Sci-Fi &amp;...</td>\n",
       "      <td>In a future where the elite inhabit an island ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Movie</td>\n",
       "      <td>7:19</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>2016-12-23</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>93</td>\n",
       "      <td>Dramas, International Movies</td>\n",
       "      <td>After a devastating earthquake hits Mexico Cit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Movie</td>\n",
       "      <td>23:59</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>2018-12-20</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>R</td>\n",
       "      <td>78</td>\n",
       "      <td>Horror Movies, International Movies</td>\n",
       "      <td>When an army recruit is found dead, his fellow...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      type  title    country date_added release_year rating  duration  \\\n",
       "0  TV Show     3%     Brazil 2020-08-14   2020-01-01  TV-MA         4   \n",
       "1    Movie   7:19     Mexico 2016-12-23   2016-01-01  TV-MA        93   \n",
       "2    Movie  23:59  Singapore 2018-12-20   2011-01-01      R        78   \n",
       "\n",
       "                                           listed_in  \\\n",
       "0  International TV Shows, TV Dramas, TV Sci-Fi &...   \n",
       "1                       Dramas, International Movies   \n",
       "2                Horror Movies, International Movies   \n",
       "\n",
       "                                         description  \n",
       "0  In a future where the elite inhabit an island ...  \n",
       "1  After a devastating earthquake hits Mexico Cit...  \n",
       "2  When an army recruit is found dead, his fellow...  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 확인\n",
    "nflx.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e963c6b",
   "metadata": {},
   "source": [
    "### 2-3. 시계열 데이터 변환"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d843b637",
   "metadata": {},
   "source": [
    "앞서 설계한 모든 가설은 '최근'이라는 시간 축을 기준으로 하고 있습니다.<br>\n",
    "모든 분석 방향이 과거와 현재를 비교하는 과정이 될 것이므로 콘텐츠가 넷플릭스에 추가된 날짜를 인덱스로 하여 시계열 데이터로 변환합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "95862c97",
   "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>type</th>\n",
       "      <th>title</th>\n",
       "      <th>country</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>duration</th>\n",
       "      <th>listed_in</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_added</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>Movie</td>\n",
       "      <td>To and From New York</td>\n",
       "      <td>United States</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>81</td>\n",
       "      <td>Dramas, Independent Movies, Thrillers</td>\n",
       "      <td>While covering a story in New York City, a Sea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-02-04</th>\n",
       "      <td>TV Show</td>\n",
       "      <td>Dinner for Five</td>\n",
       "      <td>United States</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1</td>\n",
       "      <td>Stand-Up Comedy &amp; Talk Shows</td>\n",
       "      <td>In each episode, four celebrities join host Jo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-05</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Just Another Love Story</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>104</td>\n",
       "      <td>Dramas, International Movies</td>\n",
       "      <td>When he causes a car accident that leaves a yo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-11-18</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Splatter</td>\n",
       "      <td>United States</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>29</td>\n",
       "      <td>Horror Movies</td>\n",
       "      <td>After committing suicide, a washed-up rocker r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Mad Ron's Prevues from Hell</td>\n",
       "      <td>United States</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>NR</td>\n",
       "      <td>84</td>\n",
       "      <td>Cult Movies, Horror Movies</td>\n",
       "      <td>This collection cherry-picks trailers, forgott...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Wish You</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>TV-PG</td>\n",
       "      <td>102</td>\n",
       "      <td>Dramas, International Movies, LGBTQ Movies</td>\n",
       "      <td>Singing and dreaming together, a talented sing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>TV Show</td>\n",
       "      <td>The Magicians</td>\n",
       "      <td>United States, Canada</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>5</td>\n",
       "      <td>TV Dramas, TV Sci-Fi &amp; Fantasy</td>\n",
       "      <td>When grad student Quentin Coldwater enters a c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Death of Me</td>\n",
       "      <td>United States, Thailand</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>R</td>\n",
       "      <td>94</td>\n",
       "      <td>Horror Movies</td>\n",
       "      <td>With no memory of the previous night, a vacati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Radium Girls</td>\n",
       "      <td>United States</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>103</td>\n",
       "      <td>Dramas, Independent Movies</td>\n",
       "      <td>When the women at a radium factory begin to fa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>Movie</td>\n",
       "      <td>A Monster Calls</td>\n",
       "      <td>United Kingdom, Spain, United States</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>108</td>\n",
       "      <td>Dramas, Sci-Fi &amp; Fantasy</td>\n",
       "      <td>Overwhelmed by his mother’s illness, a young b...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7777 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               type                        title  \\\n",
       "date_added                                         \n",
       "2008-01-01    Movie         To and From New York   \n",
       "2008-02-04  TV Show              Dinner for Five   \n",
       "2009-05-05    Movie      Just Another Love Story   \n",
       "2009-11-18    Movie                     Splatter   \n",
       "2010-11-01    Movie  Mad Ron's Prevues from Hell   \n",
       "...             ...                          ...   \n",
       "2021-01-15    Movie                     Wish You   \n",
       "2021-01-15  TV Show                The Magicians   \n",
       "2021-01-16    Movie                  Death of Me   \n",
       "2021-01-16    Movie                 Radium Girls   \n",
       "2021-01-16    Movie              A Monster Calls   \n",
       "\n",
       "                                         country release_year rating  \\\n",
       "date_added                                                             \n",
       "2008-01-01                         United States   2006-01-01  TV-MA   \n",
       "2008-02-04                         United States   2007-01-01  TV-MA   \n",
       "2009-05-05                               Denmark   2007-01-01  TV-MA   \n",
       "2009-11-18                         United States   2009-01-01  TV-MA   \n",
       "2010-11-01                         United States   1987-01-01     NR   \n",
       "...                                          ...          ...    ...   \n",
       "2021-01-15                                   NaN   2021-01-01  TV-PG   \n",
       "2021-01-15                 United States, Canada   2020-01-01  TV-MA   \n",
       "2021-01-16               United States, Thailand   2020-01-01      R   \n",
       "2021-01-16                         United States   2018-01-01  TV-14   \n",
       "2021-01-16  United Kingdom, Spain, United States   2016-01-01  PG-13   \n",
       "\n",
       "            duration                                   listed_in  \\\n",
       "date_added                                                         \n",
       "2008-01-01        81       Dramas, Independent Movies, Thrillers   \n",
       "2008-02-04         1                Stand-Up Comedy & Talk Shows   \n",
       "2009-05-05       104                Dramas, International Movies   \n",
       "2009-11-18        29                               Horror Movies   \n",
       "2010-11-01        84                  Cult Movies, Horror Movies   \n",
       "...              ...                                         ...   \n",
       "2021-01-15       102  Dramas, International Movies, LGBTQ Movies   \n",
       "2021-01-15         5              TV Dramas, TV Sci-Fi & Fantasy   \n",
       "2021-01-16        94                               Horror Movies   \n",
       "2021-01-16       103                  Dramas, Independent Movies   \n",
       "2021-01-16       108                    Dramas, Sci-Fi & Fantasy   \n",
       "\n",
       "                                                  description  \n",
       "date_added                                                     \n",
       "2008-01-01  While covering a story in New York City, a Sea...  \n",
       "2008-02-04  In each episode, four celebrities join host Jo...  \n",
       "2009-05-05  When he causes a car accident that leaves a yo...  \n",
       "2009-11-18  After committing suicide, a washed-up rocker r...  \n",
       "2010-11-01  This collection cherry-picks trailers, forgott...  \n",
       "...                                                       ...  \n",
       "2021-01-15  Singing and dreaming together, a talented sing...  \n",
       "2021-01-15  When grad student Quentin Coldwater enters a c...  \n",
       "2021-01-16  With no memory of the previous night, a vacati...  \n",
       "2021-01-16  When the women at a radium factory begin to fa...  \n",
       "2021-01-16  Overwhelmed by his mother’s illness, a young b...  \n",
       "\n",
       "[7777 rows x 8 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nflx = nflx.set_index('date_added').sort_index()\n",
    "nflx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "418230df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type                    object\n",
       "title                   object\n",
       "country                 object\n",
       "release_year    datetime64[ns]\n",
       "rating                  object\n",
       "duration                 int64\n",
       "listed_in               object\n",
       "description             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nflx.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dca6834",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 2-4. 데이터 구간(시간) 분할"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "f6393282",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "data = nflx['title'].resample('Q').count()\n",
    "\n",
    "plt.plot(data, linewidth=0.85)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c9b0e2b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_added\n",
       "2008-12-31       2\n",
       "2009-12-31       2\n",
       "2010-12-31       1\n",
       "2011-12-31      13\n",
       "2012-12-31       3\n",
       "2013-12-31      11\n",
       "2014-12-31      25\n",
       "2015-12-31      88\n",
       "2016-12-31     443\n",
       "2017-12-31    1225\n",
       "2018-12-31    1685\n",
       "2019-12-31    2153\n",
       "2020-12-31    2009\n",
       "2021-12-31     117\n",
       "Freq: A-DEC, Name: title, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nflx['title'].resample('Y').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa7ef78e",
   "metadata": {},
   "source": [
    "넷플릭스는 초창기부터 서서히 콘텐츠를 늘려왔고, 2016년~2016년을 기점으로 폭발적으로 성장했습니다.<br>\n",
    "현재 데이터는 시간 별 분포 차이가 심하기 때문에 시간 축을 그대로 사용하는 것은 바람직하지 않습니다.<br>\n",
    "전체 기간을 몇 개의 구간으로 분할하여 위와 같이 구간(Y) 별로 너무 많은 데이터가 몰리거나, 너무 적은 데이터만 취합되지 않도록 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f215fc6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7777\n"
     ]
    }
   ],
   "source": [
    "# 0부터 7까지, 각각 1111개씩 채워진 리스트 생성\n",
    "seps = []\n",
    "for i in range(1,7+1):\n",
    "    sep = np.ones(1111,dtype=np.int8)*i\n",
    "    seps.extend(sep)\n",
    "print(len(seps))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2f863e43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type                    object\n",
       "title                   object\n",
       "country                 object\n",
       "release_year    datetime64[ns]\n",
       "rating                  object\n",
       "duration                 int64\n",
       "listed_in               object\n",
       "description             object\n",
       "seps                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seps는 숫자로서의 의미가 없다. string으로 변환한다.\n",
    "nflx['seps'] = seps\n",
    "nflx['seps'] = nflx['seps'].astype('str')\n",
    "nflx.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "ff108f4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_seps = nflx.groupby('seps').count()['title']\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.bar(x=count_seps.index ,height=count_seps.values, width=0.44, color='pink')\n",
    "\n",
    "plt.ylabel('count')\n",
    "plt.xlabel('period(past --> now)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31199e10",
   "metadata": {},
   "source": [
    "총 7777개의 데이터를 시간 순으로 1111개씩 분할하였습니다. seps가 1에 가까울수록 최근으로부터 멀어집니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3a2dd6a",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "\n",
    "---\n",
    "## 3. 가설 검정"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adab30c9",
   "metadata": {},
   "source": [
    "아래 6가지 가설에 대하여 순차적으로 검증하고, 넷플릭스의 수급 전략을 분석합니다.\n",
    "\n",
    "1. H1 : 최근 특정 콘텐츠 유형(TV show or Movie)이 늘고 있다. (주요 컬럼 : type)\n",
    "2. H1 : 최근 특정 국가의 콘텐츠 수급 비중이 늘고 있다. (주요 컬럼 : country)\n",
    "3. H1 : 최근 옛날(오래된) 콘텐츠 수급 비중이 늘고 있다. (주요 컬럼 : release_year)\n",
    "4. H1 : 최근 어린이 콘텐츠 비중이 줄고 있다. (주요 컬럼 : rating)\n",
    "5. H1 : 최근 길이(시리즈, 시간)가 짧은 콘텐츠 비중이 늘고 있다. (주요 컬럼 : duration)\n",
    "6. H1 : 최근 유통하는 콘텐츠 장르가 더 다양해지고 있다. (주요 컬럼 : listed_in, description)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5b773ef",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 3-1. 특정 콘텐츠 유형에 대한 집중 수급 여부 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13baba51",
   "metadata": {},
   "source": [
    "최근 TV 시리즈물의 퀄리티나 제작 속도는 영화 산업의 발전 속도를 따라잡고 있습니다.\n",
    "따라서 넷플릭스가 TV 시리즈물과 영화 중 어떤 콘텐츠에 더 집중하고 있는지 확인하고자 합니다.\n",
    "\n",
    "- H0 : 콘텐츠 유형(TV show or Movie)별 점유율은 전과 동일하다. \n",
    "- H1 : 최근 특정 콘텐츠 유형(TV show or Movie)이 늘고 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a37cf82b",
   "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>seps</th>\n",
       "      <th>type</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Movie</td>\n",
       "      <td>744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Movie</td>\n",
       "      <td>802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Movie</td>\n",
       "      <td>829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>Movie</td>\n",
       "      <td>779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>Movie</td>\n",
       "      <td>741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>Movie</td>\n",
       "      <td>764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "      <td>Movie</td>\n",
       "      <td>718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seps     type  title\n",
       "0     1    Movie    744\n",
       "1     1  TV Show    367\n",
       "2     2    Movie    802\n",
       "3     2  TV Show    309\n",
       "4     3    Movie    829\n",
       "5     3  TV Show    282\n",
       "6     4    Movie    779\n",
       "7     4  TV Show    332\n",
       "8     5    Movie    741\n",
       "9     5  TV Show    370\n",
       "10    6    Movie    764\n",
       "11    6  TV Show    347\n",
       "12    7    Movie    718\n",
       "13    7  TV Show    393"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nflx.groupby(['seps','type'])['title'].count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e5fe3ee9",
   "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></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seps</th>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>Movie</th>\n",
       "      <td>744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Show</th>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>Movie</th>\n",
       "      <td>802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Show</th>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>Movie</th>\n",
       "      <td>829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Show</th>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
       "      <th>Movie</th>\n",
       "      <td>779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Show</th>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>Movie</th>\n",
       "      <td>741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Show</th>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">6</th>\n",
       "      <th>Movie</th>\n",
       "      <td>764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Show</th>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">7</th>\n",
       "      <th>Movie</th>\n",
       "      <td>718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Show</th>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count\n",
       "seps type          \n",
       "1    Movie      744\n",
       "     TV Show    367\n",
       "2    Movie      802\n",
       "     TV Show    309\n",
       "3    Movie      829\n",
       "     TV Show    282\n",
       "4    Movie      779\n",
       "     TV Show    332\n",
       "5    Movie      741\n",
       "     TV Show    370\n",
       "6    Movie      764\n",
       "     TV Show    347\n",
       "7    Movie      718\n",
       "     TV Show    393"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_type = pd.DataFrame(nflx.groupby(['seps','type']).count()['title'])\n",
    "count_type.columns = ['count']\n",
    "count_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "93f3a342",
   "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>movie</th>\n",
       "      <th>tv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seps</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>744</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>802</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>829</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>779</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>741</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>764</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>718</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie   tv\n",
       "seps            \n",
       "1       744  367\n",
       "2       802  309\n",
       "3       829  282\n",
       "4       779  332\n",
       "5       741  370\n",
       "6       764  347\n",
       "7       718  393"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_type = pd.pivot_table(count_type, index='seps', columns='type')\n",
    "count_type.columns=['movie', 'tv']\n",
    "count_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6da275e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(np.arange(1,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "04e8b3f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "\n",
    "sns.lineplot(data=count_type, x=count_type.index, y=count_type['movie'], label='Movie', linewidth=0.85)\n",
    "sns.lineplot(data=count_type, x=count_type.index, y=count_type['tv'], label='TV Show', linewidth=0.85)\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.01,1))\n",
    "plt.ylabel('the number of contents')\n",
    "plt.xlabel('period(past.. 1--->7 ..now)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a70bb7f6",
   "metadata": {},
   "source": [
    "**결론 :** <br>\n",
    "**최근 영화보다 TV 시리즈가 늘고 있다. 귀무가설을 기각합니다.**<br>\n",
    "특히 넷플릭스 서비스 초기보다 현 시점에서 TV 시리즈가 늘고 있는 것에 의미가 있습니다.\n",
    "초기 영화 수급이 어려웠던 때보다 최근 자본이 받쳐주는 상황에서도 TV 시리즈 보유량을 늘리는 것을 보면 TV 시리즈에 대한 강한 시장 수요를 간접적으로 이해할 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e78eedec",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 3-2. 특정 국가를 겨냥한 콘텐츠 현황 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8132005",
   "metadata": {},
   "source": [
    "콘텐츠 플랫폼의 경우 특정 국가 진입 및 초기 점유율 확보를 위해 해당 국가에서 생산한 콘텐츠를 전략적으로 확보하는 경향이 있습니다.\n",
    "국가별 콘텐츠 비중을 살펴보고 최근 순위나 점유율의 변동이 있는지 살펴보도록 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be054e8b",
   "metadata": {},
   "source": [
    "- H0 : 국가별 콘텐츠 수급 비중은 전과 동일하다.\n",
    "- H1 : 최근 특정 국가의 콘텐츠 수급 비중이 늘고 있다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f6dd67ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States     385\n",
       "India             112\n",
       "United Kingdom     38\n",
       "Japan              31\n",
       "Canada             29\n",
       "Philippines        25\n",
       "South Korea        24\n",
       "Spain              16\n",
       "France             15\n",
       "Indonesia          15\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 가장 최근 확보한 1111개 콘텐츠의 제작 국가별 비중\n",
    "nflx.loc[nflx['seps']=='7','country'].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "aff21d82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['United States', 'India', 'United Kingdom', 'Japan', 'Canada',\n",
       "       'Philippines', 'South Korea', 'Spain', 'France', 'Indonesia'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10_countries = nflx.loc[nflx['seps']=='7','country'].value_counts()[:10].index\n",
    "top10_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "34b84501",
   "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>country</th>\n",
       "      <th>seps</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_added</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>United States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-02-04</th>\n",
       "      <td>United States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-11-18</th>\n",
       "      <td>United States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>United States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-27</th>\n",
       "      <td>United States</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>United States</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>United States</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>United States</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>United States</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>United States</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4843 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country seps\n",
       "date_added                    \n",
       "2008-01-01  United States    1\n",
       "2008-02-04  United States    1\n",
       "2009-11-18  United States    1\n",
       "2010-11-01  United States    1\n",
       "2011-09-27  United States    1\n",
       "...                   ...  ...\n",
       "2021-01-15  United States    7\n",
       "2021-01-15  United States    7\n",
       "2021-01-15  United States    7\n",
       "2021-01-15  United States    7\n",
       "2021-01-16  United States    7\n",
       "\n",
       "[4843 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seps_by_top10 = nflx.loc[nflx['country'].isin(top10_countries),['country','seps']]\n",
    "seps_by_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2c71440b",
   "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>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seps</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country\n",
       "seps         \n",
       "1         415\n",
       "2         332\n",
       "3         285\n",
       "4         336\n",
       "5         434\n",
       "6         362\n",
       "7         385"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seps_by_top10.loc[seps_by_top10['country'] == top10_countries[0]].groupby('seps').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6f4b8b95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['United States', 'India', 'United Kingdom', 'Japan', 'Canada',\n",
       "       'Philippines', 'South Korea', 'Spain', 'France', 'Indonesia'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "86109431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "for country in list(top10_countries):\n",
    "    count_country = seps_by_top10.loc[seps_by_top10['country'] == country].groupby('seps').count()\n",
    "    sns.lineplot(data=count_country, x=count_country.index, y=count_country['country'], label=country, linewidth=0.85)\n",
    "    \n",
    "plt.legend(bbox_to_anchor=(1.005,1))\n",
    "\n",
    "plt.ylabel('The Count of Countries')\n",
    "plt.xlabel('period(past.. 1--->7 ..now)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e6d7d8d",
   "metadata": {},
   "source": [
    "**결론 :**<br>\n",
    "**국가별 콘텐츠 수급 비중은 전과 다릅니다. 귀무가설을 기각합니다.**\n",
    "한 때, 인도 영화 수급 비중이 미국 본토 콘텐츠 수급 비중과 유사했던 적도 있으나 현재 미국 콘텐츠에 더 집중하고 있는 것으로 보입니다.\n",
    "동시에 영국 콘텐츠 수급 비중을 점차 줄여나가고 있습니다.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "868ec9d5",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 3-3. 콘텐츠 수급 대상 연도 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbfce6be",
   "metadata": {},
   "source": [
    "최근 국내에서는 과거 히트작이 재상영되거나 유튜브 등을 통해 다시 회자되는 시청 트랜드가 생겨나고 있습니다.\n",
    "전 세계적으로 이러한 현상이 나타나고 있다면 넷플릭스에서도 최신 영화, 최신 드라마만 집중하지 않고 더 넓은 범위의 연도를 고려할 수 있겠습니다.\n",
    "\n",
    "- H0 : 최근 옛날(오래된) 콘텐츠 수급 비중이 늘고 있다.\n",
    "- H1 : 최근 옛날(오래된) 콘텐츠 수급 비중이 늘고 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ed3851f4",
   "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>type</th>\n",
       "      <th>title</th>\n",
       "      <th>country</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>duration</th>\n",
       "      <th>listed_in</th>\n",
       "      <th>description</th>\n",
       "      <th>seps</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_added</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>Movie</td>\n",
       "      <td>To and From New York</td>\n",
       "      <td>United States</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>81</td>\n",
       "      <td>Dramas, Independent Movies, Thrillers</td>\n",
       "      <td>While covering a story in New York City, a Sea...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-02-04</th>\n",
       "      <td>TV Show</td>\n",
       "      <td>Dinner for Five</td>\n",
       "      <td>United States</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1</td>\n",
       "      <td>Stand-Up Comedy &amp; Talk Shows</td>\n",
       "      <td>In each episode, four celebrities join host Jo...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-05</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Just Another Love Story</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>104</td>\n",
       "      <td>Dramas, International Movies</td>\n",
       "      <td>When he causes a car accident that leaves a yo...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-11-18</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Splatter</td>\n",
       "      <td>United States</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>29</td>\n",
       "      <td>Horror Movies</td>\n",
       "      <td>After committing suicide, a washed-up rocker r...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Mad Ron's Prevues from Hell</td>\n",
       "      <td>United States</td>\n",
       "      <td>1987-01-01</td>\n",
       "      <td>NR</td>\n",
       "      <td>84</td>\n",
       "      <td>Cult Movies, Horror Movies</td>\n",
       "      <td>This collection cherry-picks trailers, forgott...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Wish You</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>TV-PG</td>\n",
       "      <td>102</td>\n",
       "      <td>Dramas, International Movies, LGBTQ Movies</td>\n",
       "      <td>Singing and dreaming together, a talented sing...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>TV Show</td>\n",
       "      <td>The Magicians</td>\n",
       "      <td>United States, Canada</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>5</td>\n",
       "      <td>TV Dramas, TV Sci-Fi &amp; Fantasy</td>\n",
       "      <td>When grad student Quentin Coldwater enters a c...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Death of Me</td>\n",
       "      <td>United States, Thailand</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>R</td>\n",
       "      <td>94</td>\n",
       "      <td>Horror Movies</td>\n",
       "      <td>With no memory of the previous night, a vacati...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>Movie</td>\n",
       "      <td>Radium Girls</td>\n",
       "      <td>United States</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>103</td>\n",
       "      <td>Dramas, Independent Movies</td>\n",
       "      <td>When the women at a radium factory begin to fa...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>Movie</td>\n",
       "      <td>A Monster Calls</td>\n",
       "      <td>United Kingdom, Spain, United States</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>108</td>\n",
       "      <td>Dramas, Sci-Fi &amp; Fantasy</td>\n",
       "      <td>Overwhelmed by his mother’s illness, a young b...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7777 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               type                        title  \\\n",
       "date_added                                         \n",
       "2008-01-01    Movie         To and From New York   \n",
       "2008-02-04  TV Show              Dinner for Five   \n",
       "2009-05-05    Movie      Just Another Love Story   \n",
       "2009-11-18    Movie                     Splatter   \n",
       "2010-11-01    Movie  Mad Ron's Prevues from Hell   \n",
       "...             ...                          ...   \n",
       "2021-01-15    Movie                     Wish You   \n",
       "2021-01-15  TV Show                The Magicians   \n",
       "2021-01-16    Movie                  Death of Me   \n",
       "2021-01-16    Movie                 Radium Girls   \n",
       "2021-01-16    Movie              A Monster Calls   \n",
       "\n",
       "                                         country release_year rating  \\\n",
       "date_added                                                             \n",
       "2008-01-01                         United States   2006-01-01  TV-MA   \n",
       "2008-02-04                         United States   2007-01-01  TV-MA   \n",
       "2009-05-05                               Denmark   2007-01-01  TV-MA   \n",
       "2009-11-18                         United States   2009-01-01  TV-MA   \n",
       "2010-11-01                         United States   1987-01-01     NR   \n",
       "...                                          ...          ...    ...   \n",
       "2021-01-15                                   NaN   2021-01-01  TV-PG   \n",
       "2021-01-15                 United States, Canada   2020-01-01  TV-MA   \n",
       "2021-01-16               United States, Thailand   2020-01-01      R   \n",
       "2021-01-16                         United States   2018-01-01  TV-14   \n",
       "2021-01-16  United Kingdom, Spain, United States   2016-01-01  PG-13   \n",
       "\n",
       "            duration                                   listed_in  \\\n",
       "date_added                                                         \n",
       "2008-01-01        81       Dramas, Independent Movies, Thrillers   \n",
       "2008-02-04         1                Stand-Up Comedy & Talk Shows   \n",
       "2009-05-05       104                Dramas, International Movies   \n",
       "2009-11-18        29                               Horror Movies   \n",
       "2010-11-01        84                  Cult Movies, Horror Movies   \n",
       "...              ...                                         ...   \n",
       "2021-01-15       102  Dramas, International Movies, LGBTQ Movies   \n",
       "2021-01-15         5              TV Dramas, TV Sci-Fi & Fantasy   \n",
       "2021-01-16        94                               Horror Movies   \n",
       "2021-01-16       103                  Dramas, Independent Movies   \n",
       "2021-01-16       108                    Dramas, Sci-Fi & Fantasy   \n",
       "\n",
       "                                                  description seps  \n",
       "date_added                                                          \n",
       "2008-01-01  While covering a story in New York City, a Sea...    1  \n",
       "2008-02-04  In each episode, four celebrities join host Jo...    1  \n",
       "2009-05-05  When he causes a car accident that leaves a yo...    1  \n",
       "2009-11-18  After committing suicide, a washed-up rocker r...    1  \n",
       "2010-11-01  This collection cherry-picks trailers, forgott...    1  \n",
       "...                                                       ...  ...  \n",
       "2021-01-15  Singing and dreaming together, a talented sing...    7  \n",
       "2021-01-15  When grad student Quentin Coldwater enters a c...    7  \n",
       "2021-01-16  With no memory of the previous night, a vacati...    7  \n",
       "2021-01-16  When the women at a radium factory begin to fa...    7  \n",
       "2021-01-16  Overwhelmed by his mother’s illness, a young b...    7  \n",
       "\n",
       "[7777 rows x 9 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nflx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c5acfe",
   "metadata": {},
   "source": [
    "'출시일로부터 얼마나 지난 콘텐츠를 수급했는가?' 에 대한 데이터를 확보합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "25767b4d",
   "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>days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_added</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>730 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-02-04</th>\n",
       "      <td>399 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-05</th>\n",
       "      <td>855 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-11-18</th>\n",
       "      <td>321 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>8705 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>14 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>380 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>381 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>1111 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>1842 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7777 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                days\n",
       "date_added          \n",
       "2008-01-01  730 days\n",
       "2008-02-04  399 days\n",
       "2009-05-05  855 days\n",
       "2009-11-18  321 days\n",
       "2010-11-01 8705 days\n",
       "...              ...\n",
       "2021-01-15   14 days\n",
       "2021-01-15  380 days\n",
       "2021-01-16  381 days\n",
       "2021-01-16 1111 days\n",
       "2021-01-16 1842 days\n",
       "\n",
       "[7777 rows x 1 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_from_release = pd.DataFrame(nflx.index - nflx['release_year'])\n",
    "add_from_release.columns = ['days']\n",
    "add_from_release"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "16e63d09",
   "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>days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_added</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-02-04</th>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-05</th>\n",
       "      <td>855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-11-18</th>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>8705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>1111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>1842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7777 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            days\n",
       "date_added      \n",
       "2008-01-01   730\n",
       "2008-02-04   399\n",
       "2009-05-05   855\n",
       "2009-11-18   321\n",
       "2010-11-01  8705\n",
       "...          ...\n",
       "2021-01-15    14\n",
       "2021-01-15   380\n",
       "2021-01-16   381\n",
       "2021-01-16  1111\n",
       "2021-01-16  1842\n",
       "\n",
       "[7777 rows x 1 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 숫자만 남긴다.\n",
    "add_from_release['days'] = add_from_release['days'].apply(lambda x : str(x).split(' ')[0])\n",
    "add_from_release['days'] = add_from_release['days'].astype('int64')\n",
    "add_from_release"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>days</th>\n",
       "      <th>seps</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_added</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>730</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-02-04</th>\n",
       "      <td>399</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-05</th>\n",
       "      <td>855</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-11-18</th>\n",
       "      <td>321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>8705</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>380</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>381</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>1111</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>1842</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7777 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            days seps\n",
       "date_added           \n",
       "2008-01-01   730    1\n",
       "2008-02-04   399    1\n",
       "2009-05-05   855    1\n",
       "2009-11-18   321    1\n",
       "2010-11-01  8705    1\n",
       "...          ...  ...\n",
       "2021-01-15    14    7\n",
       "2021-01-15   380    7\n",
       "2021-01-16   381    7\n",
       "2021-01-16  1111    7\n",
       "2021-01-16  1842    7\n",
       "\n",
       "[7777 rows x 2 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_from_release"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "a9549720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 시계열 그래프 확인\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.plot(add_from_release['days'], linewidth=0.64)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b1fa4bd",
   "metadata": {},
   "source": [
    "역시나 데이터가 '최근'에 밀집되어 있어서 시계열 그대로 데이터 분포를 분석하기는 어렵습니다.\n",
    "이번에도 `seps` 컬럼을 붙여서 사용하도록 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "47a2f32d",
   "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>days</th>\n",
       "      <th>seps</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_added</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>730</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-02-04</th>\n",
       "      <td>399</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-05</th>\n",
       "      <td>855</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-11-18</th>\n",
       "      <td>321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>8705</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-15</th>\n",
       "      <td>380</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>381</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>1111</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-16</th>\n",
       "      <td>1842</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7777 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            days seps\n",
       "date_added           \n",
       "2008-01-01   730    1\n",
       "2008-02-04   399    1\n",
       "2009-05-05   855    1\n",
       "2009-11-18   321    1\n",
       "2010-11-01  8705    1\n",
       "...          ...  ...\n",
       "2021-01-15    14    7\n",
       "2021-01-15   380    7\n",
       "2021-01-16   381    7\n",
       "2021-01-16  1111    7\n",
       "2021-01-16  1842    7\n",
       "\n",
       "[7777 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_from_release['seps'] = nflx['seps']\n",
    "add_from_release"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "8be5e008",
   "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>days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seps</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1620.840684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1533.819082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1693.895590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1470.170117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2354.061206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2468.386139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1818.285329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             days\n",
       "seps             \n",
       "1     1620.840684\n",
       "2     1533.819082\n",
       "3     1693.895590\n",
       "4     1470.170117\n",
       "5     2354.061206\n",
       "6     2468.386139\n",
       "7     1818.285329"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "b2ed965c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 각 시계열 구간 별 평균 집계\n",
    "plt.figure(figsize=(12,4))\n",
    "\n",
    "data = add_from_release.groupby('seps').mean()\n",
    "plt.plot(data, linewidth=0.85)\n",
    "plt.bar(height=data['days'], x=data.index, width=0.2, alpha=0.6, color='pink')\n",
    "plt.axvline(x=3, color='r', linestyle='--', linewidth=0.85)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "108e5833",
   "metadata": {},
   "source": [
    "**결론 :**<br>\n",
    "넷플릭스는 과거(1,2,3,4)에 비해 최근(5,6,7) 출시일이 오래 지난 콘텐츠를 많이 수급한 경향이 있습니다. 따라서 귀무가설을 기각합니다.  \n",
    "넷플릭스 수급 현황을 보면서 국내와 마찬가지로 세계적으로 시간이 흐른 명작들을 다시 보고자 하는 소비자 수요도 확인해볼 수 있었습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ce406a",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 3-4. 넷플릭스의 주요 소비자 타겟(연령대) 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fee49301",
   "metadata": {},
   "source": [
    "넷플릭스 사용자가 늘어나면서, 이탈율 관리를 위해 주 타겟층을 확실하게 확보하고자 하는 움직임을 보일 수 있습니다.  \n",
    "콘텐츠 플랫폼은 2~30대 사용자 수가 가장 많습니다.  \n",
    "따라서 어린이 연령대의 비중이 낮아지는지, 반대로 성인 연령대 콘텐츠 비중이 높아지는지 함께 확인하는 것을 통해 넷플릭스가 집중하고 있는 타겟층을 분석할 수 있겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bdff416",
   "metadata": {},
   "source": [
    "- H0 : 어린이 콘텐츠 비중은 일정하게 유지되고 있다.\n",
    "- H1 : 최근 어린이 콘텐츠 비중이 줄고 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "990e0c1d",
   "metadata": {},
   "source": [
    "[(참고) 영화, TV 관람 등급 표](https://www.primevideo.com/help/ref=atv_hp_nd_cnt?language=ko_KR&nodeId=GFGQU3WYEG6FSJFJ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "878739b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TV-MA', 'NR', 'TV-PG', 'TV-14', 'PG', 'R', 'TV-G', 'G', 'TV-Y',\n",
       "       'TV-Y7-FV', 'TV-Y7', 'PG-13', nan, 'NC-17', 'UR'], dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nflx['rating'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b4838ef6",
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_dict = {\n",
    "    'kids' : ['G','TV-G','TV-Y'], # 전체관람가\n",
    "    'elem' : ['PG','TV-Y7','TV-Y7-FV','TV-PG'], # 7세 이상\n",
    "    'teen' : ['PG-13'], # 13세 이상\n",
    "    'youth' : ['TV-14'], # 16세 이상\n",
    "    'adult' : ['NC-17','R','TV-MA','NR','UR'] # 18세 이상, 청불 포함\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "720dd0e3",
   "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>kids</th>\n",
       "      <th>elem</th>\n",
       "      <th>teen</th>\n",
       "      <th>youth</th>\n",
       "      <th>adult</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>316</td>\n",
       "      <td>253</td>\n",
       "      <td>16</td>\n",
       "      <td>271</td>\n",
       "      <td>518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>307</td>\n",
       "      <td>248</td>\n",
       "      <td>30</td>\n",
       "      <td>298</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>285</td>\n",
       "      <td>215</td>\n",
       "      <td>36</td>\n",
       "      <td>316</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>255</td>\n",
       "      <td>219</td>\n",
       "      <td>51</td>\n",
       "      <td>278</td>\n",
       "      <td>578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>358</td>\n",
       "      <td>282</td>\n",
       "      <td>93</td>\n",
       "      <td>237</td>\n",
       "      <td>516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>316</td>\n",
       "      <td>233</td>\n",
       "      <td>79</td>\n",
       "      <td>282</td>\n",
       "      <td>513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>388</td>\n",
       "      <td>263</td>\n",
       "      <td>81</td>\n",
       "      <td>246</td>\n",
       "      <td>477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   kids  elem  teen  youth  adult\n",
       "0   316   253    16    271    518\n",
       "1   307   248    30    298    505\n",
       "2   285   215    36    316    510\n",
       "3   255   219    51    278    578\n",
       "4   358   282    93    237    516\n",
       "5   316   233    79    282    513\n",
       "6   388   263    81    246    477"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = rating_dict.keys()\n",
    "rating_df = pd.DataFrame(columns=ratings)\n",
    "rating_list = []\n",
    "\n",
    "for i in range(1,8): # seps : 1~7\n",
    "    rating_list = []\n",
    "    for key in rating_dict.keys(): # rating : kids / elem / teen / youth / adult\n",
    "        rating_list.append(nflx.loc[nflx['seps']==str(i),'rating'].str.contains('|'.join(rating_dict[key])).sum())\n",
    "    rating_df = rating_df.append(pd.Series(rating_list,index=rating_df.columns), ignore_index=True)\n",
    "rating_df = rating_df.astype('int64')\n",
    "rating_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "68e480ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxoAAAEHCAYAAAAplzA+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABgXElEQVR4nO3deZgU1b0+8Lequqv3bXZgGDZBjQZxBRdQ1IiJUYEgKmbMFY2RG6OSSBAX5MYVMV5vMC5ZzIJGXHD95boENOJ1QUOixgVRBIZ1Fqb3pbq7qn5/dHdN9ywMy8x0z8z7eZ55uru6u/rMnOmZevucbx1B13UdREREREREPUgsdgOIiIiIiGjgYdAgIiIiIqIex6BBREREREQ9jkGDiIiIiIh6HIMGERERERH1OAYNIiIiIiLqcabe3PkjjzyC119/HalUChdffDFOOOEE3HDDDRAEAWPHjsWtt94KURTx1FNPYeXKlTCZTJg3bx6mTp3am80iIiIiIqJeJvTWOhrr1q3DH/7wBzz44IOIx+N49NFH8emnn+Kyyy7DxIkTsXjxYkyePBkTJkzA3LlzsWrVKiiKgjlz5mDVqlWQZbnLfTc3h3ujyfvE57PD748V7fWpI/ZJ6WGflCb2S+lhn5Qm9kvpKWafVFa6ivK6A0GvTZ36v//7P4wbNw4//vGPcdVVV+G0007Dp59+ihNOOAEAMGXKFLzzzjv4+OOPcfTRR0OWZbhcLtTV1WHDhg291ayDZjJJxW4CtcM+KT3sk9LEfik97JPSxH4pPeyT/qnXpk75/X7s3LkTDz/8MLZv34558+ZB13UIggAAcDgcCIfDiEQicLnakqLD4UAkEtnrvn0+e1F/4ZhsSw/7pPSwT0oT+6X0sE9KE/ul9LBP+p9eCxperxejR4+GLMsYPXo0LBYLdu/ebdwfjUbhdrvhdDoRjUYLtucHj84UczizstJV1Klb1BH7pPSwT0oT+6X0sE9KE/ul9BSzTxhwDlyvTZ069thj8dZbb0HXdTQ2NiIej+PEE0/EunXrAABr167Fcccdh/Hjx2P9+vVQFAXhcBibNm3CuHHjeqtZRERERETUB3ptRGPq1Kn44IMPMGvWLOi6jsWLF6O2tha33HIL7rvvPowePRrTpk2DJEmor6/HnDlzoOs65s+fD4vF0lvNIiIiIiKiPtBrZ53qTcUczuRwaulhn5Qe9klpYr+UHvZJaWK/lB5OneqfuGAfERERERH1OAYNIiIiIiLqcb26MjgRERWfrutIJ/1IxtPQdck4zTgREVFvYtAgIhpgdF1DMr4bSqQBSqQBqXgjJNmL8E4gEQvAbKuCbB8Gi2MYZPtQiBJPwEFENFA8++yz+Prrr3H99dcDyJzpddeuXbjwwgsLHjd79mzcd999qK2t7bW2MGgQEfVzmppEMrYjEyyiDUgrAZht1bA46+CuOQWyrRqCIKGy0oWmpiBS8SYosR2Itv4b/u2vAgBk+9Bs8BgGs60KgsCZtUREA8GUKVOK9toMGkRE/YyaikKJbjOChaYlYbHXwuIcDp/vOzBZyrqcHiUIImR7DWR7DVBxLABAU5VMUInuQHD335GKN0OSXdlRj1rI9mEwye6+/BaJiOggtba24j//8z/xve99D1u3bsX111+P//7v/8Zbb72Fmpoa+P1+AMD69euxdOlSmEwmuN1u3HvvvXA6nT3SBgYNIqISlquvUCLboEQbkIxuhyCaYXEMz4xYVJ8EyXxw/xBEyQKrazSsrtHGa6qpIJToDiiRbQg3vQc1HYXZWmWMemSmXMk98S0SEQ1YS//8Ab7eEeyx/Y0e5sHCS4/v9nF79uzBvHnzcOONN2LTpk0AgI0bN+KDDz7AM888g1gshrPOOgsAsHr1anzrW9/C5Zdfjtdffx2hUIhBg4hoINJ1Dal4Y3a0YhuS8d2QzG5YnHWwe78B37Czer2mQhAEmGQvTLIXDt8R2XapmSlX0R2Itn4E//aXAUHMq/UYBrO1glOuiIjy7Eso6A1vvfUWKisroWmase2rr77CkUceCVEU4XQ6MW7cOADAVVddhYcffhg/+MEPUF1djfHjx/dYOxg0iIiKSNNSSEZ3QIlmCrfTih9mWxUszjq4qk6EbK+BIEjFbiYEQYJsHwLZPgTAcQAALZ2AEtuBZGwHAjvXIJ1ogSR7jOBhcQyDZOZCV0REfW369OmYPn06rr32WsyZMwcAMGrUKPz5z3+GpmlIJBL46quvAAAvvfQSZsyYgYULF+KRRx7BU089hauvvrpH2sGgQUTUh9R0zJgGpUQaoKkKLI5hsDiGw1d7NkyW8n5z+lnRZIXNPQY29xgA2SlXyQCU2A4oka0INb0DLR3LFKbbh0HOneVKNBe55UREA98hhxyC8847D3fddRf+4z/+A4cffjjOPvtszJo1C1VVVSgvLwcAfPOb38QNN9wAu90Os9mMX/ziFz3WBkHXdb3H9tZHirUEPZBZhr6Yr08dsU9KD/skwzjwjm5DIpKpr4AgweIcDqujDrJzOEx9+Il/MfpF11QkE43ZUZsdSMZ3QhBMRvCw2IfBZK3oN+Gqp/G9UprYL6WnmH1SWcmR2QPFEQ0ioh6Sqa9oyo5WbEMyvguS2QWLow527+HwDfvWoFuzQhAlWOxDYbEPhasyM1dZTceRjO1AMroD/sBnSCf2QJK9mSlX2fBxsAXuRERUfAwaREQHSNNSSMZ2GgvjpZXWTH2FYzhcVRMh24ZAEItfX1FqJJMNNvchsLkPAZC3cnl0BxLhzQjtfguaqkC2VRvBw2wfwilXRET9DIMGEdE+UtNxY7RCiTZAS8ch24fB6hwOX+1ZMFkG7xSggyEIAsyWMpgtZXCUfRMAoGtpJOO7kYzuQLjlH0jGdkMUzdk6j0yheX+qZyEiGowYNIiIupBOBjKhInuqWQiisX6Fq/IELmLXiwTRBIujFhZHLXKzo9V0LFPrEduBmP8TpJVWmCy+glPsSmZHUdtNRERtGDSIiJCZvpNKNLWtXxHbCcnshMVRB5v3UHiHnQFRsha7mYOaZLLD5hkLm2csgOyUK6UVydgOxEObENy9NjvlqiYz5coxLDt9jf/qiIiKgX99iWhQ0rU0lFx9RbQB6cQemKwVsDrr4Ko4HrKdB6ilThAEmK3lMFvL4SjLLDCVmXK1C0p0B8JN7yMZ3w1RshSMepgsZZxyRUTUB/hflIgGBS2dyNRXRDNTodR0DLJ9KCzO4fAOPRNmayUPPgeAzJSr4bA4hhvb1FQUydiO7KrmHyOt+GGylBmF5rJjGCSTvYitJiLqXaeffjpefvllWCx9e+ZDBg0iGpDSyWDbwnjRbQCQPQCtg7PiOJhkT5FbSH1FMjtg84yDzTMOQG7K1R4o0R2Ih75EcNffoekpyLYhxqiHbKvmiBYR0UHiX1Ei6vcy9RXNbetXxHZCMtlhcdbB5h4L75CpEE22YjeTSkRmylUFzNYKoPwoAJlTFadiu6DEdiDc9C6S8UaIkrVg1MMk+zjqRUQlL5VK4dZbb8XWrVuhaRquu+46475du3bhlltugaIosFgsuO2226CqKubPn48hQ4Zg+/btOOecc/Dll1/is88+w2mnnYaf/vSnB9wWBg0i6nd0LY1kbBeUaAMSkQakEy0wWStgcQyHs+JYWOzn8tNo2i+iaIbFWQeLs87YpqYiULILC0b2fAg1GYDJWl6wqjkDLBHtzX+/8zts8W/rsf2N9A3H/JOu2Otjnn76afh8Ptx5553w+/34/ve/b9y3dOlS1NfX49RTT8W7776Le++9F/Pnz8e2bdvw6KOPIpFI4IwzzsDatWths9kwdepUBg0iGtg0NZGdBpWZCqWmIpn6CsdweIeeDrO1ip80U4+TzE7YPYfC7jkUQHbl90RL5ixXgQ0I7Hwd0FWYc1OuHMMgW6u5SCMRGboLBb1h48aNWL9+PT7++GMAQDqdht/vN+575JFH8Lvf/Q66rsNsziyEOnz4cLhcLsiyjIqKCni9XgA46P+tDBpEVHLSyZBRtK1EtwG6DouzNltfcQxMsrfYTaRBSBBEyLYqyLYqoPxoAICmJpGM70IyugOhxreRijdCNNkLRj0k2csgTER9ZvTo0aipqcFVV12FRCKBhx56CC+88IJx39y5c3HMMcdg06ZN+OCDDwAcfKDoCoPGftDScaQUAZqahCCa+Y+DqAdkCnNboEQakIhsQzK2A6LJllm/wj0GniGnQeL0FCpRoiTD6hwBq3OEsS2dCiMZ3YFkdDsiLeuhJkPZqX1tq5pzTRYi6i0XXXQRbr75Znz/+99HJBLBnDlzIIoiAGDhwoVYsmQJFEVBIpHATTfd1KttEXRd13v1FXpBc3O4KK/r3/4atNQuJJUEdC1VcJ8gmCFIZoiiDEGUIYhmiFLmUhBlY3vhtsylIOXuz95mgNkvlZWuov1OUOf21ie6pmbWOciuX5GKN2fmvWdX3JbtQyGK5j5u8eDA90pxZKZcNRurmidjOwFdg2wfCl9lHaLRNCCIECAAggBAzP4fEDpshwAIELO3BQiCmH2csO/b2+87d72L1xyM/5P4Xik9xeyTykpXUV53IOCIxn7w1Z7V6S+6ruvQ9TR0NQldS0LTktC1FHQ1CU1LQdfatqeTMWhq9v7cY43npbIBpi37CYKpLZhkQ4oRSiQ5L9hkg0vBY3IhJhdgxD7+iREBmqpAiW43goWaCmdOI+qsg6fmNJhtVfzdpAEtM+WqGrKtGk4cAyA75Sq2E4IQgq4rgJaGDg2Zz/50QNegQwd0Hehy+74+rnA7oGdva4CuZx+nAciEoty+dbRdP4jvviD0FIao/QhH3YWwgtAkZLd3so92Yavz1xQhoxypuAWS7IEoyQfx/RMNbgwaPUAQBAiCGRDNABw9tl9d1wFdzQaXJHQ1ZVxvH2a0VAjpXFjpIvAAWl6bpXajLeYOIyuZYFMYbtq25QUbgYWPA4VuHJRomQMV41Jtt10tvB8aoKvG7da0iNbdX0GJbAN0FRZHLSzOOjjLj4YkewblJ6RE+URJhtU1EpWVLggD9JPztnDTLrQcVDja9xCmZ58HPe96+8fl/n5pel7Q0xDc04JIoBnpZAC6loJossEkeyHJHphkr/HFIEK0d70aNKZPnw6XKzPcVFtbi6uuugo33HADBEHA2LFjceutt0IURTz11FNYuXIlTCYT5s2bh6lTp/Zms/oNQRAAwQRJNAHo2VVrdU3NG0XJBpOCkZVMSFFTEeN62+hLW+DRtVT2IBTIfVrU9fQwc1tIaT86kws8edshSCVzQFrwj0zPO6hud/Dd1YF3wSU6OVDP7RNtt3OPMW5D6/j6Bdvb71OF8c/WaK/xHWUvO/58M5/widlP/fIvpeynhvnbpXaPkyBAgKQ7YXWNgqdmCldcJhqk2kYeAAH96wOp/NkLuq5DUxNQkwGks1/x8CaoSgDpZBC6lmQQIepCrwUNRVEAACtWrDC2XXXVVbjuuuswceJELF68GGvWrMGECROwYsUKrFq1CoqiYM6cOTj55JMhy3xj9iZBlCCINojo2SJbXdfawspeppJp6Vg2uKQKwk0u7GQCTDq/xV1OD0vssSAWU4yD6dzBfIeDf10D0PXBf2cH3YU/tNzQupQdxcoceBcelLc78EbeAXr7A3d0PFAXBLmTA30p89qC1O4gv92+u3yt/Of1TXDj/GYiGigEQYBkskEy2SDbh3S4X9d16GoiG0KCbUEkGUBaaQsihSHEAyl7nUGEBrJeCxobNmxAPB7H3LlzkU6n8dOf/hSffvopTjjhBADAlClT8Pbbb0MURRx99NGQZRmyLKOurg4bNmzA+PHje6tp1IsEQYQgWTNnVOnBel5d1zupa8mMrLjdFuhmpeAT9n0/8M4PBURERPtHEAQIJhvk/QoiXzOI0KDQa0HDarXi8ssvxwUXXIAtW7bghz/8IXRdNz5RdTgcCIfDiEQixvSq3PZIJNJbzaJ+ShAECJIMSHKHAXhPpQtJ8NNzIiIqPQcSRBLhzcY0LV1lEKH+q9eCxqhRozBixAgIgoBRo0bB6/Xi008/Ne6PRqNwu91wOp2IRqMF2/ODR2d8PjtMpuLN9+RpzkoP+6T0sE9KE/ul9LBPSlPf9osbQFWX96ZTcSTjrUgm/FDifiTj2xEP/xvJRCvUtAKT2Q7Z6oPF5oNsK4Ns88FiLYNs80IyDZw1W/he2TeKouDFF1/EBRdcUOym9F7QeOaZZ7Bx40YsWbIEjY2NiEQiOPnkk7Fu3TpMnDgRa9euxaRJkzB+/Hjcf//9UBQFyWQSmzZtwrhx4/a6b78/1lvN7hbnnpce9knpYZ+UJvZL6WGflKbS7Bc3ILgh2kfAagfy44OWThgjIOFwAOqeXW0jIloKomiBZMmMhLQVqmdui5KlaN/R/uA6GvuuubkZTz/99MAOGrNmzcKiRYtw8cUXQxAE3HnnnfD5fLjllltw3333YfTo0Zg2bRokSUJ9fT3mzJkDXdcxf/58WCz945eeiIiIqNhEkxWyqQayvabT+9uCSPupWdkaEdHSNjXLUnj2rP4SRKjNww8/jK+++goPPPAANm7cCL/fDwC4+eabceihh+Lll1/GH//4R4iiiGOPPRbXX389li9fju3bt2PPnj3YuXMnFi1ahMmTJx90W3otaMiyjF/+8pcdtj/22GMdts2ePRuzZ8/uraYQERERDVr7G0SU8BZEc0FEVSBI1rbREAaR/bLhnl8iunlzj+3PMWoUDvv5z/b6mKuuugobN25EPB7HpEmTMGfOHGzZsgWLFi3CQw89hOXLl2PVqlWw2WxYsGAB3n77bQCZY/ff/e53ePvtt/Hoo4+WdtAgIiIiotK3P0FENYJIMDs1KwlBtDCIdKG7UNCbNm7ciPfeew8vv/wyACAUCqGhoQGtra248sorAWRqo7dt2wYAOPzwwwEANTU1SCaTPdIGBg0iIiIi6lK3QURNGKMhqtIuiKgKBMlinC3LJHuz9SIMIr1FFEVomobRo0fjvPPOw7nnnos9e/bg6aefRm1tLYYMGYJHH30UZrMZzz77LA4//HCsXr26V9baYtAgIiIiogMmSlbINitkW3Wn9zOI9K3y8nKkUilEo1G8/PLLeOqppxCJRHD11VejrKwM//Ef/4H6+nqoqophw4bh29/+dq+1RdB1Xe+1vfeSYp4JojTPRDG4sU9KD/ukNLFfSg/7pDSxX/qWpirGWbLUZBBpJZBXrK5AEGXUjJgIwXZUUdrX3846VUo4okFERERERSNKFsi26r2MiCgor3Ci1Z/q45bRwRKL3QAiIiIioq6IkmVALTw4mDBoEBERERFRj2PQICIiIiKiHsegQUREREREPY5Bg4iIiIiIehyDBhERERERAQCefPJJpFIprFu3DvPnzz+ofTFoEBERERERAOCRRx6Bpmk9si8GDSIiIiKiAeJnP/sZ/v73vwMANm3ahCuvvBILFizARRddhAsuuAD/+7//CwCor6/Hpk2bAABPPPEEli9fjqeffhrNzc3GSMbWrVtxxRVXYObMmVi+fPl+t4UL9hERERER9YJn/rweu3cEe2x/NcM8mHXpsXt9zAUXXIAnnngCp512Gp555hmMHz8eoVAIy5YtQyQSwcyZMzFp0qQun/vQQw/hv//7v/Hhhx9CURQ8+OCDUFUVp512Gn7yk5/sV3sZNIiIiIiIekF3oaA3TJw4EXfccQf27NmDt99+G8ceeyxOOukkAIDT6cSYMWOwbdu2gufout7pvsaOHQtZlgEAJtP+xwZOnSIiIiIiGiAEQcC5556LO+64AyeffDLGjBmDf/zjHwCASCSCjRs3ora2FrIso7m5GQDw2WefFTw/V6MhCMJBtaXboBEIBPDOO+8AyBSHXHPNNWhoaDioFyUiIiIiot4xc+ZMvPbaa5g1axZmz56NQCCAiy++GJdeeimuvvpqlJeX49JLL8UvfvELXH755VBV1XjucccdhyuvvLLLUY79Iejd7OXyyy/HSSedhMMPPxzLli3DD37wA6xatQorVqw46Bc/UM3N4aK9dmWlq6ivTx2xT0oP+6Q0sV9KD/ukNLFfSk8x+6Sy0lWU1z0YjY2N+PnPf44//elPRW1HtyMawWAQl19+OdasWYMZM2Zg+vTpiEajfdE2IiIiIiLaD6+++iquuOIK/OxnPyt2U7ovBtc0DZ988glWr16Nxx57DJ9//nnB8AoREREREZWGadOmYdq0acVuBoB9CBoLFizAPffcg8suuwzDhw/H7NmzccMNN/RF24iIiIiIqJ/qNmiceOKJOPHEE43bTz31VK82iIiIiIiI+r9ug8app56KpqYmuN1u6LqOcDgMt9uN2tpa3H777Tj88MP7op1ERERERNSPdBs0jj/+eJx99tk488wzAQBvvvkmXnnlFdTX1+O//uu/sHLlyl5vJBERERER9S/dnnXqyy+/NEIGkBnh+OKLL/CNb3wDiqL0auOIiIiIiKjnPPHEE1i+fHmX9y9fvhxPPPEEAOCxxx47qNfqNmi43W6sXLkSsVgMkUgETzzxBDweDzZt2mSsGkhERERERAPLQw89dFDP7zZo3HvvvXjnnXcwefJknH766Vi3bh2WLl2Kd955pyTOz0tERERERBmRSATXXnst5s6dixkzZuAvf/kL/vGPf2DmzJm47LLLsHr1agDA9u3bMXv2bON5s2fPxvbt243bDz30EILBIJYsWXLAbem2RqO6uhq/+tWvOmyvr68/4BclIiIiIhrovv7oMcTCO3psf3bXMIw+6vt7fczWrVtxzjnn4KyzzkJjYyPq6+vhcrnwy1/+EqNGjcKtt966T681b948PPbYY70bNN566y3cf//9CAaD0HXd2L5mzZpud75nzx7MnDkTjz76KEwmE2644QYIgoCxY8fi1ltvhSiKeOqpp7By5UqYTCbMmzcPU6dOPeBvhoiIiIioVHQXCnpDRUUF/vSnP+G1116D0+lEOp1GY2MjRo0aBQA45phj0NDQ0OF5+cf5PaXboHH77bfjhhtuwNixYyEIwj7vOJVKYfHixbBarQCAu+66C9dddx0mTpyIxYsXY82aNZgwYQJWrFiBVatWQVEUzJkzByeffDJkWT7w74iIiIiIaJB69NFHMWHCBMyZMwfvvfce3nzzTVRWVmLTpk0YM2YM/v3vf8Pj8cBisWDPnj1QVRXRaLRg2lTOwYaPboOGz+c7oFGGpUuX4qKLLsJvfvMbAMCnn36KE044AQAwZcoUvP322xBFEUcffTRkWYYsy6irq8OGDRswfvz4/X49IiIiIqLBburUqViyZAleeukleL1eSJKEu+66CwsXLoTD4YDD4YDH40FlZSVOPvlkzJo1C3V1dRgxYkSHfY0ZMwbXX3897r333gNqS7dB49hjj8Vdd92FyZMnw2KxGNuPP/74Lp/z7LPPoqysDJMnTzaChq7rxoiIw+FAOBxGJBKBy+UynudwOBCJRLpttM9nh8kkdfu43lJZ6er+QdSn2Celh31SmtgvpYd9UprYL6WHfbJvJk2ahFdeeaXD9meeeabDtl/84hcdtv3kJz8xrq9YseKg2tJt0Pj4448BAJ999pmxTRAE/PnPf+7yOatWrYIgCHj33Xfx+eefY+HChWhtbTXuj0ajcLvdcDqdiEajBdvzg0dX/P5Yt4/pLZWVLjQ3h4v2+tQR+6T0sE9KE/ul9LBPShP7pfQUs08YcA5ct0HjQJLM448/blyvr6/HkiVLsGzZMqxbtw4TJ07E2rVrMWnSJIwfPx73338/FEVBMpnEpk2bMG7cuP1+PSIiIiIiKi1dBo1bbrkFt912G+rr6zstAt/biEZnFi5ciFtuuQX33XcfRo8ejWnTpkGSJNTX12POnDnQdR3z588vmJ5FRERERINXWtWwdXcY/ngaPlu3n49TiRH0LsrJP/nkExx55JF4//33O31irrC7GIo5nMnh1NLDPik97JPSxH4pPeyT0sR+KZ5csNjQ4McXDQE0+eMYOcSFc04Zg+HltqK0iVOnDlyX0fDII48EALz66qu45ZZbCu5buHBhUYMGEREREfV/aVXDlt1hfNHgx4aGAJqzweLQOh8uPnMsasrsEASB4a+f6jJo3HTTTdi2bRs++eQTfPnll8Z2VVURCoX6pHFERERENHB0CBaBOEbWuHBYnQ9z8oIFDQxdBo158+Zhx44duOOOO3D11Vcb2yVJwpgxY/qkcURERETUfzFYDG5dBo3a2lrU1tbixRdfRCAQQDweh67rUFUVn3/+OU488cS+bCcRERERlbi0qmHLrlyNhR/NwQRGDXHj0OFeBotBqNvy/eXLl+OPf/wj0uk0vF4vmpqacOSRR+Lpp5/ui/YRERERUYnqMljUeXHJWYei2mdjsBjEug0azz33HN58803ccccdmDdvHr7++mv85S9/6Yu2EREREVEJSasaNu8KYUNDAF80+NHCYEF70W3QqKqqgtPpxNixY7FhwwacddZZ+OUvf9kXbSMiIiKiIuosWIzOBov6sw5FFYMF7UW3QcPpdOL555/HEUccgcceewxVVVVIJBJ90TYiIiIi6kMMFtSTug0ad9xxB/76179i+vTpeOONN7B48WLMnz+/L9pGRERERL0olc4Ei9xZoVpDCYwamineZrCgg9Vt0KiursbcuXMBADfccAMA4P/9v//Xu60iIiIaoHRdx5bdYbz7yW58vSsMt92M6jIbqn12VJfZUe2zweuyQOTBHfWCroLFYXU+XHr2oajyMlhQz+kyaKxevRq33norvF4vHnzwQYwYMQIfffQR7rjjDmzfvh3f/e53+7KdRERE/VqTP4b3Pm3EBxuaUOm1YdIR1Zg7/ZvYtGUPGv1xNLbGsO6z3WhsjcMfUSCbpLYA4rNlQkiZHW67mQeCtM9ywSJzVigGC+pbXQaNZcuW4b/+67+wc+dOPPTQQxg5ciQeeeQRfP/738ejjz7al20kIiLql0KxJD74vAnvfbobgijgxG9U4+dzjobLLgMAXHYZwyqdGFbp7PDcRDKNJn8cjf44drfGsHFbAI3+OELRJGwWU94oSDaE+Oxw2sx9/S1SieksWIwe6sahdT784OxDUclgQX2oy6AhyzLOPPNMAMApp5yC7du346WXXkJtbW2fNY6IiKi/UZIq/vVlM977rBGtoQSOP7waV553BCq9tv3aj1U2oa7ahbpqV4f7YomUMQqyuzWGT75uRaM/hkg8BZddbgsgeZc2S7ezpakfYrCgUtblXx1JkozrVqsVjzzyCBwOR580ioiIqD9RNQ2fbfHjvU934+udIRx1SAVmTB6Numpnrxzk2a1mjBpixqgh7oLtuq4jHE+hqTWORn8M25sjWL+xGU2tMSSSKjxO2agDydWEVPlssJilLl6JSk0qreHrnUF80RDAhgY//GEFo4d6cGidl8GCSk6XQSP/l9TlcjFkEBER5dF1HZt3hfHep7vx8dd7MHaYByd9cwguP+cbEMXiHOgJggC3XYbbLuOQWk+H9gYiSTT5Y2j0x/H1rhDe/XQ3mgNxpNIafC4LasrsqMobBan02mA2iUX5Xiijq2BxWJ0X//Gdw1HpsTJYUMnqMmjs3LkTixYt6nA956677urdlhEREZWgxmxR9z/yirq/d9qYkh8VEAQBPpcFPpcFh9b5Cu7TdB2toQQa/XE0tcbwRUMAb364E82BOHQdKPdYUeOzoypvOlaFxwpJZAjpaam0iq93tq1j4Y8kMXqIm8GC+qUug0buVLYAcMIJJ/RJY4iIiEpRd0Xd/Z0oCKjw2FDhseGIkWUF96mahpZgAo3Z6Vj/3rQHf/tHDHuCCQgCUOnNhI+aMhuqstOyytxWnp53H3UWLMYMzSyQd9l3DkcFgwX1Y10GjRkzZvRlO4iIiEpKTxV193eSKGZPsWsHUF5wXyqtoSWYOStWY2sc6zc0YXdrDP6wArNJzISQMjtqsgGkymeH1ykP6gNnBgsaTHgKCiIioqy+Luru78wmEUPKHRhS3rGOU0mpaM6emrfRH8NXO4JobI0hGE3CapaM0Y+a7Kl5q8pscNkG3hohqbSKTTvazgoViDJY0ODRZdCIxWKw2+192RYiIqI+V4pF3QOBxSyhtsqJ2qqOa4TElcwaIbkQ8tkWf+b0vLEUHDZTJnjkQkg2kNit/WONkM6CxSHZ082ecs4QVAyyETEa3LoMGpdccgmee+45LFmyBEuWLOnDJhEREfW+/lrUPRDYLCaMqHFhRE3HNUIi8RQa/TE0tsawa08MH37VgkZ/HPFEGm6HnJmCVTAdywarXLwJGsmUik07Q/giGyyCxogFgwVRl+/MeDyO66+/Hm+99RYURelwP886RURE/c1AL+oeCJw2M5w2D8YM7Xh63lAshcbWTAjZujuM9z9vRGNrHMmUCp/LYpyWtypXnO6zwWzq2eCYHyw2NAQQiiYxZpgbh9X5cMo3GSyI8nUZNP7whz9g3bp1WL9+Pc86RURE/RaLugcGQRDgccjwOGSMG+4tuE/TdQTCirFa+lc7Anj737vQHIgjrWoo91izBe1toyEVHitMUven52WwIDpwgq7r+t4esGHDBowZMwabN2+GqqoYO3YsTKbi1pA3N4eL8rrJlIqycieCgSjPHV5CKitdRfudoI5UVYPPa0cwFGeBY4kZTO+Vzoq6TzyipuSKugdTnxSLpunYE0pkp2NlgkijP46WYBwAUOGxZVZKz5uKlRZErPt4J75o8CMUSxnB4tDhXgaLIinme6WysuMUP9o33SaGVCqFadOmwev1QtM0tLS04Ne//jWOOuqovmhfSXnqja+wtSmCeCINVWuXz3QdJkmEySTCbBJhlrKXe70tdbi/w/PznmfKuy2JQkn9s6SBQ1U1JJU0lES6w6WipJHs4lJTM+8JURRgsZoQiyZhliW4vTa4vVa4PTa4vFa4vTa4PBaYeng6A1GHou5aFnVT5m9SpdeGSq8NR44qvC+t5tYIyYSPf33ZgiZ/DEMqXRhR5cDko4agwsNgQXSguh3RuOiii7Bo0SIjWHz44Ye4/fbb8cwzz/RJAztTzE9/ukrUuq5D1XSk0hpSqoZ0Wstcz95OdbitdnN/3j46uV9VtcLXB2CSug44+xaApL3eX6pBh58IFjrooCAJsFhMkK2mzi8tJlis7S4tJkimtlG+XJ8klTTCwQRCgQRCgThCgQTCwThCwQQ0VYfdIcPttcKVCyPZS7tjcJ9nv7cM1PdKZ0XdEw6pgNwPiroHap/0d+yX0sMRjf6p2xGNWCxWMHoxYcKETovDBztBEGCSBJgkEcX47ONggk5MSSMVLU7QMXUycrO3EZ/cVykFnZ6mqlqnISGp9ExQcHusnYYFaR/mKu8v2WJCeZUT5Z2c3lLXdcQiSYSCbSFkxxY/QoEEYtEkREmA25MbAWkLIW6vFeYinmGGSkMomsT7nzdi3WeNLOomIipR3f639ng8WL16Nc4880wAwOrVq+H1ervdsaqquPnmm7F582ZIkoS77roLuq7jhhtugCAIGDt2LG699VaIooinnnoKK1euhMlkwrx58zB16tSD/sYGm/4cdOJKGqH9HNFJ54KODkgmEbIkwmkzwWmXs2csyfuym+G0muGym+GwmXv91JV7DQp7GVXQtNILCr1JEAQ4XBY4XBYMqfV0uD+dVhEOKggF4ggHEti1PYAvPsmEklRShdVuzoSPghBig8Nl4TSZASpX1P3up43wh1nUTURU6roNGrfddhsWLFiAm266CQAwfPhwLFu2rNsdv/HGGwCAlStXYt26dUbQuO666zBx4kQsXrwYa9aswYQJE7BixQqsWrUKiqJgzpw5OPnkkyHL/FSqPylm0KmocGLbjgDC8RSi8RTCsexlPIUdLVFE4qnMVyyJSDyNZFoFAFjNEhy2tgDiygYTuyzBapZgEQWYBQESAC2tIZVU9z0odBESLDYT3F5rh5DQH4NCbzOZJPjK7fCVd1w4VNd1KIm0MRISCsTRuDOEUCCOSFiBAMDpthZMx8qNjFispgE7GjYQdVbUPXMKV+omIuoPug0aI0eOxNNPP41YLAZN0+B0dpwC0ZkzzzwTp512GgBg586dqKiowN///nfjVLlTpkzB22+/DVEUcfTRR0OWZciyjLq6OmzYsAHjx48/8O+KBhVBEGCzmGCzmACvDWpag9LFaIIxqpBIIR5PIR5LIdESh6KE0apqaNEz08A0IfOV0oGUpiOl69AEQJAkWKwSrDYzbHYzHB4LXE43alwWuJyFoylWWeKBUC8RBAFWmxlWmxlVQ9wd7tc0DZGQYoSQ5sYIvv6iGaFAAkoinS1St8LlKawNcbmtBbUmVBws6iYiGhj2eaKz3d7xU8Vud24yYeHChfjb3/6GX/3qV3jjjTeMAy+Hw4FwOIxIJAKXq63IxuFwIBKJ7HW/Pp+9qGesYVFQ30oqaUTCCiJhBdFwApGQYtyOhBXEokko8ZQxomAyibBkD0KtNhOsVrNxUOrx2GCxmozbVpsZVqsJFpt5n0YUEsk0wtEUQlEF4VgSoWgS4WjmcvfOUNvtWBLxRBo6ANkkwuWQ4XbIcNkzl+52tzP3W+AYQJ+2F/t9Ul3d9X1KIgV/awyBPTH4W2No2hHCFx/vRtAfg6rqcHms8JZlRlN8ZXZ4s5cOl6Xf90+x+2VvdrZE8Ob67fi/j3eipsyB046txVUXTBjwK3WXcp8MZuyX0sM+6X96vaJy6dKluP766zF79uyCIvJoNAq32w2n04loNFqwPT94dMbvj/Vae7vDM1H0DFXVEI8mEcv7ikfyb6eQiCWh64DZLMHmlGF3tH3ZHGb4Ku2wO2XU1voQiiQOaOqRqmuIxpOIxpP79Ty3RYLbYsMwX/cTxVJpFZF4GuFY0pjSFY2nsNkfa5vWFU8hEkshnlQBXYckiXBaczUnJjhtcse6k9xUL6sJYokd/PaH94lkFlFe40R5TeEora7riEaSRm3Itq2t+PSjnQgF44hHU5BMIlztpmW5sqfvNculfUBciv3Soaj7iBpcf+EEOG3mzP2B4v297wul2CfEfilFPOtU/9Rt0Pjyyy8xduzYgm0ffvghJkyYsNfnPf/882hsbMSPfvQj2Gw2CIKAI488EuvWrcPEiROxdu1aTJo0CePHj8f9998PRVGQTCaxadMmjBs37qC+KSoOXdeRiKcyoSGaRCxSGCRytzVVhygKsOUFB7tThsNtReUQt3HbajPv0zQJu9Oy30GhL5lNEnwuCT6XZZ+fk1a1ggCSux6IKNjeHCkIKNFEGtAzP9NcrUl+zUln4cRh3bef7WAkCAKcLgucLgswvOP96ZTadsreYBw7GgIIfZwJJem0lilS93SsDWGRehsWdRMRDQ5dBo3169dD0zTcfPPNuOOOO5BbbiOdTmPJkiV49dVX97rjs846C4sWLcIll1yCdDqNG2+8EWPGjMEtt9yC++67D6NHj8a0adMgSRLq6+sxZ84c6LqO+fPnw2LZ9wMy6l26riOVVNtGHdoFh9ztpKICAmC1mjMjDnkjEEN8mZGH3EgEF2rrnkkS4XVa4HXuXziJJtIFhe+ReGYa18490bbQkkghGk9D1/XMmZ9sJrhscuGlXYbDWlgo77CZIImsXzCZJfgqHPBVODrclwvb+euG7NoeRCiQQDSiQBAEuNyWtnVDPG2n7LVYzUX4bvoOi7qJiAafLhfsW758Od5//3188sknOPLII43tJpMJkydPxty5c/uske2V4oJ9/U3B1KVORh7i0STisRQAwCxLbVOW2k1hygUIcxELnwdKnxSDpumIJlKdjp5E8qZ55S41TQcEAQ6raa+nER4+zAs9lYbdMnBqTnqCqmaK1MPBeEEYCQUSSCrpwpXU89YNcbqtPXJWsr5+r3RW1D3piBocXufj6E4W/36VJvZL6eHUqf6p25XBn3/+eUyfPr2PmrNvGDQ6Z0xdiuyt9iE7dUkSYLO3BYX2wcHm2PepS8VWyn0yEGm6jlhu5KSTcBKJJ6GkdTS1xhBLpCAIAtwOGV6nDJ/LAp/TAq8rM1rjdVngc8owc5QLQKZIPbN6el4ICSYQCSag6TocTkvBdKzcdZvdvE+Brq/eK/15pe6+pOka7B4J8aDGQF5ClEQKVVVuBEPxYjeF8jBo9E/dBo0dO3bgscceQzAYRP5D77rrrl5vXFcGU9AomLq0lwCRVNIQRAE2mzlT+9CheLrt+kA7fSeDRunJ7xNN0xGKJeEPKwiEFQQiCvwRBYFwMnMZUZBOazCbxGzwyIQQnyvv0mWBy24uuaL3vqRpOmIRpWAkJBdI4rFMkXp+bYgrL5CYswf4vfleyS/qFkUBk46owfGHVRlF3QREUzFsCW3DluBWbA41oDnWAo/NhWA8DI/FjSGOagxx1mCooxpDHDVwyft2Onnaf5qmIxJKwL8ne/a57GU0ohhr7STiKVhtZrg81o5fbmvJn/xhoGHQ6J+6DRoXXHABjjvuOIwdO7bgE5cZM2b0euO6MhCChprWCkNDJ0EikT91qZORh/zwUMypS8XGoFF6DqRPlJSKQCQTRnJBJBBRMgEloiAcS0HXdThtZmM0pLNREpul10+mV5JSRpF627SscHZERE1rsNnNqKpxw2I3weOzGV8HUxvSWVH3pG9Us6gbgKqp2BltxJbQVmwONmBreDvMgoQRnjqMcme+Ku0VqK7yoKkphGAyhF3RRuyK7MauaCN2RhsRSUXhs3gw1FmTCSGOTAixm/f/dPODVSqZRqA1XhgoWmNQVQ3u7GmsveV243TWdqcMQRBQWelCU1MISiKNcDBR8JUbZUylVAaRPsSg0T91GzRmzJiB5557rq/as09KNWhoWsepS0aAiGVrH2JtU5cKRhw6CRJW275NhxjsGDRKT2/1ia7riCbS8IfbAogxSpINKEpShSgK2WJ6ucMoic9lgdshwzSIVmLXdR3xWAqiLmDr5j0I+uPZrxiURBo2uwyPzwZ3XgDxlnUeQnJF3e9+uhubs0XdJx5RM+iLuoNKCJtDDdgSbMDm0FYElRCGOodglLsOI911qHPXwiLJHZ63t/eKrutoTQSwK5oJH5kAshuJdALl1rKCADLEUQWrydrb32ZJyp2SOtAuTISDCZjMohEmfNlA4S2zdxsC9uVvmK7rSCppY3Sx/VcqpcJibQsi7oIwYoFZHpwfihwoBo3+qdugcfvtt+Okk07CKaecAlnu+EeyGIr1i7anKQI1qWH3rlCHKUzJZHbqkt281wBhc8g9UtRJbRg0Sk+x+yStaghGkgUBpC2UZLarmg6rLMGXN03L6yqcsjWQFlAEOu+XXG1XW/jIfIX8cWPqiMdng2YSsTOUwNd7oqir9eDEo4YO2qLulJrCtshOYwrU9vBOOMwOjPJkQsUoTx18Fm+v1c1ouoaWeGs2fLSFkJSWQqWtAkMd2QDirEaNvRqyNDCmr6lpDQF/JkwEWuNGqFASKThclrYgkQ0VTrf1gH8/e+JvGINIz2LQ6J+6DRqnnHIKWlpaCp8kCPj88897tWF7U6xftI/e3wZRFAFB7+SsS/yDUCzFPqiljvpLn8SVdMH0rEwdSdKoI4nGU4AgwG03F9SNeF2yMV3L57T0myLn/e2X3a1RvPvRLvx7QzN8VhOGe22wCwLCwQSURBoWq6lgFCT3NZBGY3Vdx55EKzYHGzIjFqEGJNIKhruGGqFimHMozOKB/Q/oyfeKqqlojrdgZ94UrN2xJmi6jmp7Zbb2I1MHUmWvPOA297Z4LDM64W+NGaMUQX8cgihkR9uyoxPZYGHthTqgvvgb1iGIhNoFkSSDSD4Gjf6p26BRikp16hQVB/uk9AykPtF0HeFoEoFIsm10JO8yEFGQzBWzO3P1IrIxXSs3SuK2y0X/9H9f+mV/irrbj4SE/HEE/DEkYilYrOYOAcRTVvohJJFOYGtoezZUbMWuaBPKrL5MXUV2xKIni7T74r2S0tJoijVjV2R3JoREG9EYa4YoCKixV2FIdgrWUEc1Km0VkMTeD86apiEcTHSonYhFkrDazfDlT3cqt8PttfXpbIBS+RumJFIFtSEdg4ip0xoRt8c64IIIg0b/1G3QeOCBBzrdfvXVV/dKg/YFgwblY5+UnsHYJ6m0Cn8kWVgzkldHEsoWs9utZqOIPf+sWrmQYrP03okduuqX3ijqVhJ5IaQ1exmIIx5NQraY4C3L1oR4bfCU2eHx7ftpenuKpmtojDVjczATKraEtkHXdYxwD8/UVnjqMMRRDVHovQPcYr5XkmoSu6NNRu3HrmgjmuMtMIvmbO1Hrv6jGhW2sgP6OSSVNAKtsYJAEfTH24qx82onfOV22BxySQTR/vI37ECDiMtthVziJ85Q1CQCShBBJYSAEsTomqGoQE1R2sKgceD267cslUrhrbfewlFHHdVb7SEi6pfMJglVXhuq9nJwnitmN86uFVbQGkpg086QMUoSV9KQRAEeh5y31kj+ZWb7wRazd1bU3ZMrdVusZlQNMaNqiLvDffkhJOSPY0dDACF/HLFsCMkfBXH7bPD6bD1yABpJRrEl1GAUbbfE96DGUYWR7hE4puoozDjkHNhMg+eMWbIko85dizp3bcH2RDqBXdEm7IruxleBr7F2+zvYk2iF1WRtq//IhpAyqxcAEA0rbWEiO+UpElJgNkuZKU7ldpRVOjDmsEp4yuzGKZfp4FisZlisZlRUd34gnAkiihE+dmwJFNSIyBapQwDJXe+tIKJqKkLJMILJEALZEBFUQsZXIBmCqqUhSzK8Fg+8Fjc8shuj9KFA8TMo7af9njqVTCYxd+5cPPbYY73Vpm5xRIPysU9KD/vk4Khappi9s9P8+sMKgtEk0qoGi1lqqxvJneo3r7Dd1W6aUkWFE+9/vLOkV+pWEmmEAnmF6a2x7EhICmaz1LEmpMwGeychRNVU7IjswuZQAzYHG7AtvB2yZMZI9whjClSlrbzon573p/dKKBbBpl07sGN3M1pawoj4k9AiJkiqCWaHAKdXRmWlG7U1lRhWXQGXx1r0n++B6k/9cjDaB5H8r2QyDdli6qQ+pPMgous6Yum4MQKRuQwhkMwFiSDi6QREQYJbdsJjcWeDhCd7PRMo3BZ3p/VDnDrVP+13XI1Go9i5c2dvtIWIiABIoogytxVl7r2frjSRTBu1I7kpWzuao8ZZtiLZtXhcdhkep4zmYAJlTgsmHVGNWaeNKckidovVhMoaFyprOv5jTyrpgpqQ3duDCPjjiEUUiCZAcKpQLFEEpBZEzUFUVngwsmooThk2EXWumZA7Ob0sFcqdCjmQrZnIjVIEA/HMaaN9dlSUD8Uho3NTnmxIigp2RXdjZ6QRu6Kb8XHkPYT2hDKLEBr1H5lLLkJYWtpGRDrvl1wQCfgjaG4NomFjI8LBBGLhFNJJDZopjbQlAUWOQbUqkOw6nG4ZHq8dXqcbPqsHozx1RpCwSv03fNKB6TZonH766cYvha7rCAaDuOKKK3q9YaXo8c+fxuYPtkJVNWObKEiQBBGSIBrXRUGEJEgQxcyllLu9j/dJglhwv5i378x9hc8zLsVOtnV6X2Fb+aYn6p+ssgk1ZSbUlHW9gJum64jEUvCHFYwbXQElpvRhC3uWbMmEEE+lBQ3hKPyhRrQEG7AjshNu0Y1h0ggMU6twiDICyYiO4MY4mv+ZRKupBV/5oh2K0x0uy6D9+6eqGkKBhBEoclOe4tEkbA7ZKMYeWufFEUcPhctj7bIY2wIzXPIhGOc7xNim63pmEcJIpv7j/d3/xM7obkRTMZRZvBjibKv/4CKExaPpGsLJKILJYMeRiOz1lJaCWTTDY3fD43PDK7sxNDsK4YADJsUKLSYhEsqOjDQlEP4ygdakirAMBDwKXJ4wXJ5UwehIqdeIUM/odurUjh072h4sCHC73XA6i/uJRKlMndJ1HZquQdU1qLoKTdeyt1WoWtu2fblP1TVomprdlwZNV7OX2cdoha+Tf5n/3M7va/c6euHrZL6Z7DfYyf9cAUIX4aVdsMoPMaK4lxC1t4BVGNxy+5Sy++wQ5gQRtdUVSEfEXi3YpP0zWKYd9Df9sV90XUdzvAVbQtuMou2kmsJwVy1GZVfZHuYcstczJaWSaQT9iUxNSCCOQGsMIX8c0UgSkkmEx5utBSmzwe3NXPZVCOntPlESqYKVsQN7Ygj4Y9A0HR6vrXAhu3J7rxfka7oGfyJQUIC+K7IbcVVBpa28oAC9mIsQ9sf3SnvxdKIgPATzpjEFlBCiqSgEQYDL3DaNyWPJBAlP3nSmgxkJVBJpREIdC9XDwQSSStvUrPZnzOosiHDqVP/UbdDQdR1PPPEE3nvvPaTTaUyaNAnf//73M+tJFEmpBI3BIheS8sOLquWHIbUgcGXu6zxMqVoXQanT+wpDVvvwlQtYKSGJlqgfuq7DJbvgs3hQZvXBa/XAZ/HCZ/WgzOKDw2wftJ9e9rXB+D7pD/pDv8TTcWwJbcuusN2AxmgTKmzlRl3FSHcdnLKjx14vlVQLa0KyX9GwAskkwu21ZkdA7MZIiNPdcyGkpxaGi4Q6K8ZOZM7wVW4vOF2sx2eDqcSmzWUWIdyTCSCRtoUI01oaVfaKtvDRR4sQlvJ7RdVUo5C6/ShEUAkilAxD1VVYJQs8uWJqI0i03XaYiv8/MT+IRNqdPSuVVGGW24rVxx9TC5eveMGTDky3QWPp0qXYunUrvve970HXdTz77LMYOnQobr755r5qYwcMGpQv1ye5IeCAEoA/EUBr9tKvBOFPBBBNRSEKErwWtxFAMpfZL4unaJ+eDTR8n5SmUusXTdewK9pohIqtoW0QBAEj3cONou1qe2XRRitTqUwIyawPkr1szYQQURIyox++wtGQ/V2Nen/6JJVSEcyrm8hMeYojlVLhdFs6BIqBMDVM1VQ0xVuyAaRtEUJd11FjrzQWIBziqEa1vRKmHlqEsBjvFV3XEU3FsiEi2CFABJUQEqoCSZDgzhZOe/NHIrLX3bKrT9ZC6QtJJW2MgAwZ5oXFXpzpVgwaB67boHHeeefh+eefN0Yw0uk0zj33XLz88st90sDOMGhQvv3pE1VTEVCCRvjwG2EkAH8iiISqQBbNRvDwWX3ZSy/KrF54LJ6SXU23lPB9UpqK3S/hZASbg1uzi+FtQ2vCjyGO6syaFe46jHDX9puwn06pCAUS2RGQmDESEgllQ4in7axYbSMhHUNI+z7RdR3xaNJYwC4XKkKBBERJKFgVOzflaTDOdU+pKTTGmgumYDXFmiEKImqyp9/NFaBX2sr3+8C7p98rSTVpBIaAEjLCRC5IRJIRAIDDbIfHkpm6lJnCVBgk+sv7ozdw6lT/1O1fJ1VVkU6nIcuycVuSBkZSpsFHEiWU28pQbivr8jGKmkQgOxLSmgigJdGKrwJfozURQCAZQlpLw2GyGyMiXmtmqlYukLhlF+tFaNBLa2lsj+zM1lU0YFt4B6wma2aFbXcdTh46EeVWX7/9xN1kllBW6UBZZcdpXOm0inAgYYyCfL2xBSF/HOFQAqIgwO21Gqfpra7xYHtDqzHlKRFLwe6UjZqJ2hE+HHnMMLi91qJOWS41ZsmMWtdQ1LqGFmxX1CR2Z1c/3xzaind2vY+W2B6YJXNB/cdQRw3Kbb6D/lut6VpmTYi8kYeCKU3Z/xlm0dy2JkQ2SBzqO8QIEk6zg/83aEDqNmice+65uPTSS3HOOecAAP7617/iu9/9bq83jKhYLJKMakcVqh1Vnd6v6zri6Tha80ZCtoV34OPmT+FXAgglw4AOuGRndmTEWzhCYvWUxNxYop6i6zr8SsAIFZuDDYimo6h1DsUodx1OrT0Zw51DYe7lefWlwmSS4KtwwFfRMYSoaQ2hYNtq6bt3BGG1m3F47ZBsMTZPwXswLJKMEe7hGOEeXrA9nk5gd3b048vAJqzd/g5aEq2wm6x5xefVGOqsgc/izawJkYobYSFgLCjXFiRi6RhEQYRLdmanMWWCxDBnDb5RfqixLsRg+b0n6sw+Ldi3du1avPvuu9B1HZMmTcJpp53WB03rGqdOUb5S7JNMvUgEfiWQGQnJGyHxKwHEUjGIggSfxWMUrZcZocQDr8ULq8lS7G/jgJVin1DP9YuiJtEQ2massL0juhs+iwcj3XVG0bbH0nFFcOqI75XiiqZiBfUfu6K74VeCMEkiZEHOFk97Oq2HsJls/MCoD3HqVP+01xGNYDAIVVUxZcoUTJkyBevWrcPYsWP7qm1E/ZYoiNnhcTdGuus6fUxaSyOghApqRRrCO4zbSTUJWZI71IrkRki8FnePFT4SdUXTNTTHWjIrbIcasDXYgLSuoi57etlvj/oWhjqqB0zxKQ0uDrMdh3hH4RDvqILtDIBEPaPLo5TPPvsMV155Je68805MmTIFAPDOO+/g+uuvx29/+1scdthhfdZIooHIJJpQYStDRTf1Iv5EW8F6c6wFG/2b4E8EEFCCSOsqHGZ73ml8vfDmjYywXoT2VzQVy55eNlO03RxrQZW9EiPdwzG+4gicN/psOLi4GhER7YMug8bSpUvxy1/+EhMnTjS2zZ8/H8cddxzuvvtu/PGPf+yL9hENahZJRo2jCjV7qReJpmPwJ4LGaX0bQtvxkfIJ/IkgwsnMJ3Iu2ZWZmmXUirSNjNg5/D9oqZqKndFGbAltxeZgA7aGt8MsSBiRXQjvuOoJqLRXMKwSEdEB6TJohEKhgpCRM3nyZNx777292igi2jeCIMBpdsBpdmB4u7Ov5OTOiuJPBI0pWl/4v8qOlAQRTcdgEiR4cwEkb0QkF0YsB7EyLJUOfzyIj5o/M4q2A0oQQx01GOmpw4lDjsPscdP7dW0QERGVli6DRjqdhqZpHU6np2kaUqlUrzeMiHqGKIjZs6F4MAqd14uktDSCxtoimaL1reFtCCQyxewpLQVZktsK1tstdOi1eEpqjr6u69Ch561or0HXcyvM69nV7LP3QzMel/+V/xxd140V67W8/Xb+pRur2Gee1/VrtH+eqmvQ0f61c+1u/xqd7zdHQOEolQ4dXrsbw+21GOmuw5TaE+GzeDmaRUREvabLoHH88cfjgQcewDXXXFOw/cEHH8SRRx7Z6w0jor5jFk2osJWjwlbe5WMSaQWB7Fm0/EoAjbFmbGj9En4lgKASgqqrcJgd8Fk8qPaWIxFPdThwNw7yuz1YL3xs7oBb07UOB9BdEQQBoiAWfiF3XYAkSBAEAZIgQhBESHt5rChI2Uux8+dBhCiKEJF5rEk0QRbEguflf0mCCAGFbejqsfltkUQRAnJt7fhYAcJegwMLXImIqC91GTR++tOf4sorr8Tzzz+Pww47DBaLBZ999hnKysrw0EMP9WUbiagEWE0W1JiqUeOo7vR+XdcRTcXgVwKANY1QKJ53sJ7/1Xbgvr8H+awVICIi6j+6DBpOpxOPP/443nvvPXz++ecQRRGXXHIJjjvuuL5sHxH1E4IgwCk74JQdmU/OzfzknIiIaDDb60n4BUHAiSeeiBNPPHG/d5xKpXDjjTdix44dSCaTmDdvHg455BDccMMNEAQBY8eOxa233gpRFPHUU09h5cqVMJlMmDdvHqZOnXrA3xARERERERVfr6329eKLL8Lr9WLZsmXw+/2YMWMGDjvsMFx33XWYOHEiFi9ejDVr1mDChAlYsWIFVq1aBUVRMGfOHJx88smQZZ7lhoiIiIiov+q1oHH22Wdj2rRpxm1JkvDpp5/ihBNOAABMmTIFb7/9NkRRxNFHHw1ZliHLMurq6rBhwwaMHz++t5pGRERERES9bJ+Cxvr167Fx40Z873vfw0cffYTjjz++2+c4HA4AQCQSwTXXXIPrrrsOS5cuNc6I4nA4EA6HEYlE4HK5Cp4XiUT2um+fzw6TqXin0qysdHX/IOpT7JPSwz4pTeyX0sM+KU3sl9LDPul/ug0af/rTn7B69Wo0NTXh7LPPxuLFizFr1ixcfvnl3e58165d+PGPf4w5c+bg3HPPxbJly4z7otEo3G43nE4notFowfb84NEZvz/W7Wv3Fp4esvSwT0oP+6Q0sV9KD/ukNLFfSk8x+4QB58B1e67I5557Dr///e9hs9ng8/nwzDPPYNWqVd3uuKWlBXPnzsWCBQswa9YsAMA3vvENrFu3DgCwdu1aHHfccRg/fjzWr18PRVEQDoexadMmjBs37iC/LSIiIiIiKqZuRzREUSwozLZYLJCk7qctPfzwwwiFQnjwwQfx4IMPAgBuuukm3H777bjvvvswevRoTJs2DZIkob6+HnPmzIGu65g/fz4sFstBfEtERERERFRsgq7r+t4ecPfdd0MQBLz++utYsGABnnzySYwcORI33XRTX7Wxg2IOZ3I4tfSwT0oP+6Q0sV9KD/ukNLFfSg+nTvVP3U6d+vnPf44RI0bg0EMPxfPPP49TTz0VCxcu7Iu2ERERERFRP7VPU6fOO+88nHrqqcgNfjQ1NWHo0KG93jgiIiIiIuqfug0aDzzwAH7/+9/D5/NBEAToug5BELBmzZq+aB8REREREfVD3QaNZ599Fq+//jp8Pl9ftIeIiIiIiAaAbms0qqqqul3XgoiIiIiIKF+XIxoPPPAAAMDtduPCCy/ElClTCk5re/XVV/d+64iIiIiIqF/qdurU+PHj+6IdREREREQ0gHQZNIYNG4YZM2b0ZVuIiIiIiGiA6LJG489//nNftoOIiIiIiAaQbovBiYiIiIiI9leXU6e+/PJLnHHGGR22cx0NIiIiIiLqTpdBY8SIEfjNb37Tl20hIiIiIqIBosugYTabMWzYsL5sCxERERERDRBd1mgcc8wxfdkOIiIiIiIaQLoMGosXL+7LdhARERER0QDCs04REREREVGPY9AgIiIiIqIex6BBREREREQ9jkGDiIiIiIh6HIMGERERERH1OAYNIiIiIiLqcQwaRERERETU4xg0iIiIiIioxzFoEBERERFRj2PQICIiIiKiHsegQUREREREPY5Bg4iIiIiIelyvBo2PPvoI9fX1AICtW7fi4osvxpw5c3DrrbdC0zQAwFNPPYWZM2di9uzZeOONN3qzOURERERE1Ed6LWj89re/xc033wxFUQAAd911F6677jr85S9/ga7rWLNmDZqbm7FixQqsXLkSv//973HfffchmUz2VpOIiIiIiKiP9FrQqKurw/Lly43bn376KU444QQAwJQpU/DOO+/g448/xtFHHw1ZluFyuVBXV4cNGzb0VpOIiIiIiKiPmHprx9OmTcP27duN27quQxAEAIDD4UA4HEYkEoHL5TIe43A4EIlEut23z2eHyST1fKP3UWWlq/sHUZ9in5Qe9klpYr+UHvZJaWK/lB72Sf/Ta0GjPVFsGzyJRqNwu91wOp2IRqMF2/ODR1f8/livtHFfVFa60NwcLtrrU0fsk9LDPilN7JfSwz4pTeyX0lPMPmHAOXB9FjS+8Y1vYN26dZg4cSLWrl2LSZMmYfz48bj//vuhKAqSySQ2bdqEcePG9VWTiIiI+oyu61AjYaQaG5FsbESqqRGBeASKJkC0WCBarRAtVohWKwRr3u3sNjG7TZAtEESeNJKISl+fBY2FCxfilltuwX333YfRo0dj2rRpkCQJ9fX1mDNnDnRdx/z582GxWPqqSURERD1OjUUzYaKpEcndu5FqygQLLRaD5HTCXF0NuaoalmG1qBhdC39zEFoiDi2hQEskoMai0Fr3QFMU6EoCWiLvS1GgKQlA1wFkpiOLspwJIJZsGMkFk2x4EYwAk39fXqCxWCGYzcb0ZiKiniLouq4XuxH7q5jDmRxOLT3sk9LDPilN7JeeoyUSSDZlRiXyw4QaDkO02SBXV8NcVQ25usYIFpLT2WE/B9snuq5DTybbQkgiDl1RsrcTRnjRsoHFCC65x2Sv66m2Mz4KgpgJJ3lhxQgmudvGfba2QGNpG3URLVYIUvFqKQ8W3yulh1On+qc+G9EgIiLqT7RUEqmmZiQbd2dHKDKX6UAAokWGuaotTNgPPQzmqmpIbnefjgwIgpA5+O/B2QC6qhpBpEMwMcJLHGowWBBi8sOLrijQNbWtnSZzu4CSCyZ5YaazQGOEFxsEWeaoC1E/w6BBRESDlp5OI9XSkhcmGpFqbESqtQWCZIK5qgpyVTXM1TVwTzwR5qpqmLzeAV0jIUgSJLsdkt3eI/vTdR16OpUJIAmlXTDJThlTElBDIaSU5sLwksibPpZMAshNwhDaBRRLuylitraaFku7MJMfaEz98zBI1/Xs9DkAmtZ2O/uVua0Bmp59fPa6rgPQoeeu6xqgo+3xevY+ZO/X9Mxzgezzu3it7CLMmedr7drR9lqZl9fa9p33OpnHZ9rT2etYjjwUqKzt4580Haz++Q4jIiLaR7qmIb1nD5KNu40gkWxsRLqlGQBgqqiEXJOZ3uQ8+hjIVdUwlZcP6DDRlwRBgGCWIZploIdmoOiaBj2pGGHECCe5KWLZbalIS0FtS1vYyQQcPZ3ONjITsHIhpMliRlJJA8g7kM4e2O/1QDp7YJ87yDfu75WBGCEzwiMKgJD5XRVy1wUBELL3567nfp8FARDF7OhQ7vmFj83cFjP3ZXac3bfQbt+518pMuUO2PYX7zr6WAOPxHV5LzD4XaLve7nvQc2GG+hUGDSIi6vd0TUM6EEAqP0xkayh0VYO5rBzmbJiwH3EEvFPPgLmiot9+oj3YCaIIwWqDaLX12D71dNoILGU+O1pbo20HxmL2wNk4kG47wG47kG53cCzmHVTTQfOwbqZf4l9YIiLqF3RdhxoKGqeGTTY2ZoNFE/RUEiaPF3JNDcxV1bCNHQv3yZNhrqrMfJJO1A3BZILkdEJyOmGtdMEs8qCW6GAxaBARUUlRI5FMzUR+mGhshJZIQHK5jDM5WUeMgOv4EyBXVUO0WovdbCIiaodBg4iI+pwajxecySkXLNRwBKLDAbm6BnJ1NeQhQ+A8agLM1dWQ7I5iN5uIiPYDgwYREfUKTVGQamrKCxOZKU/pQACi1QpzNkyYq6phP/wbkKtrIDqdnNNORDRAMGgQEdEB01IppJqbs9Oc2k4Rm25thWA2Z08NmynCdh8yNrNwncfDMEFENAgwaBAR0V7pqopUS4sRJnIjE6mWFgiiCHNlZWZ0oqoaruNPyKw14fPx9LBERIMcgwYREWVOD+tvRXJ3XhF2UyNSTU3QocNcXpGd5lQD5/ijYK6ugbm8HIIkFbvpRERUohg0iIgGCV3XkQ74kdzdfq2JJujpFEy+sraaicMOh3zqaTBVVEI0m4vddCIi6ocYNIiI+gFdVbOrICegJeJt1+PxrrcriYL7GgAILrdRM2EdPQbuE0+CubIKosVS7G+RiIgGGAYNIqJeoqVS0BMJqIk49OwBvxpvu94hHLQPCknF2JcgiBBtNohWKwSrFZLVBsFqhZj7stlg8pUZ143t1rbrVdUerqxLRER9hkGDiChL13XoyeTeRwe6GkHI3qenU8b+BJM574C/8KA/d93scnUZDARZZkE1ERH1WwwaRNSv6ZoGPal0P41oLyMIuqoB0AEAoixDtNogWKyQbNbsqEFeELDZMyMHtvzteeHAbOapW4mIiMCgQURFoGta5iA/3v7gv5NwoLQPCgnoiQR0XcvsTBAgypZOpgy1BQDJ5YK5qiqzzWLLhoS2+wUT/xQSERH1NP53JRoEdE2Drqahp1VAVY3ruqoCedd1NZ3dlv+Y7La867ntmcepHfYdNIuI+cOFwUFRkBs1EEQJgqWzcNB23ag3yKtBMK5brDytKhERUYlj0CDqhq7rgKZlD6hVIJ02ruvp/APzdgfqxuM6P8Dv7EC90wP//Ofm2lCw/+y+dB2AgNzBfP51QRABSYJgMkGQpMyXyZTZlrc9c3vvjxEkCTCZIJqt2dt5z80+xlvuQljRCwIE6w2IiIgGFwYNKll6Ot02dSahGMW4unHKzsy2uFlANBQrOPhHp0EgvffHGAfoHfX1gfoB7b+EDuK9lS6keHYjIiKiQY1Bg3pEQUFuXigwwkFCabutKB3vU+LQEkomAGTraAVJgmjJFtjmptlYrBCtlswUG4sFot0Oa5kLaVd6QB2oExEREfV3DBqDkK7r0NOpvIP8tjn0mTn1mW16omMo0BJK24hC3jn+Cwpys6FAsOSmzVgyBbhWC8zOisxZfHLBwbhuy4SJA1iBuLLSBYGfnhMRERGVFAaNfkBXVWP6kF4QChIFK//q7bflbmfv0zXV2GfB+f0tlkxxbbsAILndMFvyRhPyRxQsPMc/EREREXWNQaOHFSz4lR0h6BAAOqs3yAsFmqJATyWNfQqC2LYCcP4UIoulcGVgj8eYUlQwmmC1QpQtPIUnEREREfUZHnnuh8DrqxFs3o1YMFwQCjQlkT3jDwAI2QW/2tcV5E0pslph8nrb3WeFYG17PBf9IiIiIqL+jEFjP1jHHAL3EeMQimvGaIGQG1ngFCIiIiIiIgODxn6wjhgJT6ULSRYeExERERHtVUkEDU3TsGTJEnzxxReQZRm33347RowYUexmERERERHRASqJ+T6rV69GMpnEk08+iZ/97Ge4++67i90kIiIiIiI6CCURNNavX4/JkycDACZMmIBPPvmkyC0iIiIiIqKDURJTpyKRCJxOp3FbkiSk02mYujgdq89nh8kk9VXzOqisdBXttalz7JPSwz4pTeyX0sM+KU3sl9LDPul/SiJoOJ1ORKNR47amaV2GDADw+2N90axOVVa60Mxi8JLCPik97JPSxH4pPeyT0sR+KT3F7BMGnANXElOnjjnmGKxduxYA8OGHH2LcuHFFbhERERERER2MkhjR+Na3voW3334bF110EXRdx5133lnsJhERERER0UEoiaAhiiJ+8YtfFLsZRERERETUQwRd1/ViN4KIiIiIiAaWkqjRICIiIiKigYVBg4iIiIiIehyDBhERERER9TgGDSIiIiIi6nEMGkRERERE1OMYNIiIiIiIqMcxaOwDTdOwePFiXHjhhaivr8fWrVuL3STK+uijj1BfX1/sZlBWKpXCggULMGfOHMyaNQtr1qwpdpMIgKqqWLRoES666CJccsklaGhoKHaTKGvPnj049dRTsWnTpmI3hQBMnz4d9fX1qK+vx6JFi4rdHMp65JFHcOGFF2LmzJl4+umni90c2g8lsWBfqVu9ejWSySSefPJJfPjhh7j77rvx0EMPFbtZg95vf/tbvPjii7DZbMVuCmW9+OKL8Hq9WLZsGfx+P2bMmIEzzjij2M0a9N544w0AwMqVK7Fu3Trcdddd/BtWAlKpFBYvXgyr1VrsphAARVEAACtWrChySyjfunXr8K9//QtPPPEE4vE4Hn300WI3ifYDRzT2wfr16zF58mQAwIQJE/DJJ58UuUUEAHV1dVi+fHmxm0F5zj77bFx77bXGbUmSitgayjnzzDNx2223AQB27tyJioqKIreIAGDp0qW46KKLUFVVVeymEIANGzYgHo9j7ty5uPTSS/Hhhx8Wu0kE4P/+7/8wbtw4/PjHP8ZVV12F0047rdhNov3AEY19EIlE4HQ6jduSJCGdTsNk4o+vmKZNm4bt27cXuxmUx+FwAMi8Z6655hpcd911xW0QGUwmExYuXIi//e1v+NWvflXs5gx6zz77LMrKyjB58mT85je/KXZzCIDVasXll1+OCy64AFu2bMEPf/hDvPLKK/xfX2R+vx87d+7Eww8/jO3bt2PevHl45ZVXIAhCsZtG+4AjGvvA6XQiGo0atzVN4x8eoi7s2rULl156Kc4//3yce+65xW4O5Vm6dCleffVV3HLLLYjFYsVuzqC2atUqvPPOO6ivr8fnn3+OhQsXorm5udjNGtRGjRqF8847D4IgYNSoUfB6veyTEuD1enHKKadAlmWMHj0aFosFra2txW4W7SMGjX1wzDHHYO3atQCADz/8EOPGjStyi4hKU0tLC+bOnYsFCxZg1qxZxW4OZT3//PN45JFHAAA2mw2CIHBaW5E9/vjjeOyxx7BixQocfvjhWLp0KSorK4vdrEHtmWeewd133w0AaGxsRCQSYZ+UgGOPPRZvvfUWdF1HY2Mj4vE4vF5vsZtF+4gfy++Db33rW3j77bdx0UUXQdd13HnnncVuElFJevjhhxEKhfDggw/iwQcfBJAp2mexa3GdddZZWLRoES655BKk02nceOONsFgsxW4WUUmZNWsWFi1ahIsvvhiCIODOO+/k7IUSMHXqVHzwwQeYNWsWdF3H4sWL+UFJPyLouq4XuxFERERERDSwcOoUERERERH1OAYNIiIiIiLqcQwaRERERETU4xg0iIiIiIioxzFoEBERERFRj2PQICLaR//+979x00037ddzDj30UOP67t27sWjRoh5rTzgcxo9//OMDeu7999+P5cuX91hbAODNN9/E+eefb3xNmjQJP/rRj3r0NfbHa6+9hscee6xor09ENNgxaBAR7aNvfvObuOOOOw74+XfeeSeuuOKKHmtPMBjE559/vl/PCYfDuPHGG/GHP/zhgF/3ueeewwcffNBh+6mnnooXXngBL7zwAn73u9/B6XT2aLDaX2eddRZee+017Nmzp2htICIazBg0iGjQWLduHX7wgx/g8ssvx7Rp07BgwQIkk0kAmdWzZ8yYgfPPPx833ngjFEUBAEyaNAlXXHEFzj//fLz99tuor68HAGzevBn19fU499xzceGFF+Ljjz8GAGzfvh0XX3wxzj//fCxevNh47YaGBjQ1NWHMmDEAgNNPPx333HMPpk+fjunTp+Ozzz4DALz//vu4+OKLMWPGDJxxxhlYvXo1AOCll17C+eefj5kzZ+Kaa66Boii4/fbb0dTUtF+jGmvWrMHIkSNx2WWXHfDPcfjw4XjwwQcxc+ZMPP/888bPMN8999yDiy66CCNHjtzn/dbX1+Oee+7BhRdeiG9961t48803AWRWnP/Rj36Ec889FzNmzMDatWvR2tqKyZMnG8+dPHky/vd//xcA8Mgjj+B3v/sdgEzYePzxxw/4eyUiogPHoEFEg8q//vUv3HTTTXjllVegKAoef/xxfPnll3jqqaewcuVKvPDCCygvL8fvf/97AIDf78cPf/hDvPDCCwWrBC9YsAD19fV46aWXsGjRIlx77bVIJpO47bbbMHPmTLzwwgs45phjjMe//vrrBbcBwG634/nnn8c111yDhQsXAgAee+wx3H777Xjuuedw++2343/+538AZKY6Pfroo3j22WcxbNgwfP3117j55ptRVVWFX//61/v8/U+fPh1XXnnlQa2se9xxx+EPf/gDli1bho8++gjf/e538corrxj3b9myBe+//z4uvfTS/d53KpXCk08+iUWLFhnf+2233YZJkybhpZdewq9+9SvceOON0DQNQ4YMwcaNG7Fp0yaoqor3338fAPDWW29h6tSpRltff/31A/5eiYjowDFoENGgcvzxx2P06NEQBAHnn38+3nvvPaxbtw5bt27F7Nmzcf7552PNmjX4+uuvjeccddRRBfuIRqNoaGjAWWedBQCYMGECPB4Pvv76a7z//vv49re/DQA477zzYDabAQBbt25FTU1NwX5mz54NIDO60djYiNbWVixbtgxffvklfv3rX+MPf/gDotEoAGDq1Km4+OKLcc8992DatGk4/PDDe+cHtB9EUYQoihAEAYIgGNuffPJJzJkzB7Is7/c+c6MUY8eORSAQAAC89957mDVrFoDMaMpRRx2Fjz76CFOmTMG7776L9957D5deeinWr1+PcDiMlpYWY+Ro2LBh2Lp160F+p0REdCBM3T+EiGjgyP8kX9d1SJIEVVXx7W9/GzfffDOATJBQVdV4nNVqLdiHrusd9qvruvGc3P2CIEAUReN6/ogIgILbmqZBkiTMmTMHEydOxMSJE3HiiSfi+uuvBwDcfPPN2LBhA958800sWLAAV199NY499tgD/jnka2xsxJVXXgkAqKqqwkUXXYRf/epXADIhCIAxKnDNNdfA5/PhwQcfRGtrKy699FIsXLiwIFSsWbPGGBHaXxaLBQAKgkv7n3fuZ33aaafhgQcegCzLuPbaa/Hyyy/jpZdewimnnGI81mQyFeyLiIj6DoMGEQ0q69evR2NjIyorK/H8889jypQpmDBhAh599FHMmzcPZWVlWLJkCerq6vCTn/yk0304nU7U1tbitddew1lnnYUPP/wQLS0tGDt2LE466SS8+OKLuOSSS/Daa68ZtR51dXXYsWNHwX7++te/or6+Hn/7298wZswY6LqOLVu24C9/+QtkWca9994LVVWRTqfxne98BytWrMCPfvQjpFIpfP7555g4cSLS6fRB/0yqq6vxwgsvFGw744wzCm5fe+21xvVnnnkGV1xxBSZNmtRhX62trUgkEhg+fPhBtytn0qRJeOaZZ3DZZZdh27Zt+Oc//4klS5agsrISmzdvhsViwZgxYzBx4kQ89NBDuPvuu43nbt++HSNGjOixthAR0b7j1CkiGlSqqqrw85//HN/5zndQXV2NCy64AIcddhiuvvpq/OAHP8A555wDTdOMT/i7smzZMqxYsQLnnnsufvGLX2D58uWQZRmLFy/Gq6++ivPOOw9vvvkmHA4HgMzUp1wNQc4///lPnH/++fj973+Pu+++G16vF7NmzcI555yDb3/724hGo0gkEkgmk7jmmmswd+5czJw5Ex999BF++MMfory8HEOHDjUK1H/4wx/i3//+NwDgpptuwpo1a3rhJwjMmjWr05ABZA7s208Ray+/bf/zP/+DJ554otvHv/feezj33HPxn//5n7j99ttRVVUFQRBw7LHHGtOkJk2ahEgkguOPP9547rp16zqEJiIi6huC3tkcACKiAWjdunV44IEHsGLFiqK8/tVXX41rrrkG48aNw+mnn44///nPqK2tLUpbBouLL74YDzzwAMrLy4vdFCKiQYcjGkREfWTRokUHXLtA+++VV17BtGnTGDKIiIqEIxpERERERNTjOKJBREREREQ9jkGDiIiIiIh6HIMGERERERH1OAYNIiIiIiLqcQwaRERERETU4xg0iIiIiIiox/1/eEj0IuC7G2kAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "for col in list(rating_df.columns):\n",
    "    sns.lineplot(data=rating_df, x=rating_df.index, y=rating_df[col], label=col, linewidth=0.85)\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.01,1))\n",
    "\n",
    "plt.ylabel('The Count of Ratings')\n",
    "plt.xlabel('period(past.. 1--->7 ..now)')\n",
    "\n",
    "plt.show()    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd43a95d",
   "metadata": {},
   "source": [
    "**결론 :**<br>\n",
    "**adult 등급의 콘텐츠 수급은 줄어드는 반면 kids와 teen 등급 수급은 늘고 있습니다. 연구가설을 기각하고 귀무가설을 채택합니다.  \n",
    "- 가장 활발한 콘텐츠 이용자가 2~30대라는 점에서 전략적으로 해당 연령층에 집중할 것이라는 예상을 했으나 예상과 다른 결과를 확인했습니다.  \n",
    "- 넷플릭스는 2-30대 뿐만아니라 더 젋은 세대까지 확보하고자 합니다.  \n",
    "- 이는 IT 기술과 영상 콘텐츠가 익숙한 Z세대를 고려했을때 충분히 납득가능한 결과입니다.\n",
    "- (국내에서도 초등학생들이 네이버보다 유튜브로 먼저 검색한다네요.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed4e5b64",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 3-5. 콘텐츠 평균 재생 시간 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f9ec0cd",
   "metadata": {},
   "source": [
    "세계적으로 틱톡과 같은 숏폼 콘텐츠가 유행입니다.  \n",
    "또한, 왕좌의게임과 같은 드넓은 세계관의 장기 시리즈가 과거 인기였다면 최근에는 킹덤, 스위트홈처럼 짧고 자극적인 시리즈가 주목을 받고 있습니다.  \n",
    "이에 따라 최근 넷플릭스에서 콘텐츠를 수급할 때 재생 시간을 고려하는 경향이 있는지 여부를 확인하고자 합니다.\n",
    "\n",
    "- H0 : 평균 콘텐츠 재생 시간은 전과 동일하거나 더 길어지고 있다.\n",
    "- H1 : 최근 재생 시간이 짧은 콘텐츠 비중이 늘고 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0603fc11",
   "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></th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seps</th>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>Movie</th>\n",
       "      <td>88.951613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Show</th>\n",
       "      <td>1.662125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>Movie</th>\n",
       "      <td>99.214464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Show</th>\n",
       "      <td>1.915858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>Movie</th>\n",
       "      <td>102.677925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Show</th>\n",
       "      <td>1.627660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
       "      <th>Movie</th>\n",
       "      <td>98.812580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Show</th>\n",
       "      <td>1.617470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>Movie</th>\n",
       "      <td>98.291498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Show</th>\n",
       "      <td>1.624324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">6</th>\n",
       "      <th>Movie</th>\n",
       "      <td>106.675393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Show</th>\n",
       "      <td>1.878963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">7</th>\n",
       "      <th>Movie</th>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Show</th>\n",
       "      <td>1.972010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                duration\n",
       "seps type               \n",
       "1    Movie     88.951613\n",
       "     TV Show    1.662125\n",
       "2    Movie     99.214464\n",
       "     TV Show    1.915858\n",
       "3    Movie    102.677925\n",
       "     TV Show    1.627660\n",
       "4    Movie     98.812580\n",
       "     TV Show    1.617470\n",
       "5    Movie     98.291498\n",
       "     TV Show    1.624324\n",
       "6    Movie    106.675393\n",
       "     TV Show    1.878963\n",
       "7    Movie    100.000000\n",
       "     TV Show    1.972010"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration_df = pd.DataFrame(nflx.groupby(['seps','type'])['duration'].mean())\n",
    "duration_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "46b10584",
   "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>movie</th>\n",
       "      <th>tv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seps</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88.951613</td>\n",
       "      <td>1.662125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99.214464</td>\n",
       "      <td>1.915858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102.677925</td>\n",
       "      <td>1.627660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98.812580</td>\n",
       "      <td>1.617470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>98.291498</td>\n",
       "      <td>1.624324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>106.675393</td>\n",
       "      <td>1.878963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.972010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           movie        tv\n",
       "seps                      \n",
       "1      88.951613  1.662125\n",
       "2      99.214464  1.915858\n",
       "3     102.677925  1.627660\n",
       "4      98.812580  1.617470\n",
       "5      98.291498  1.624324\n",
       "6     106.675393  1.878963\n",
       "7     100.000000  1.972010"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration_df = pd.pivot_table(duration_df, index='seps', columns='type')\n",
    "duration_df.columns = ['movie','tv']\n",
    "duration_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "19db178d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(2,1, figsize=(12,6))\n",
    "\n",
    "sns.lineplot(data=duration_df, x=duration_df.index, y=duration_df['movie'], ax=axes[0], linewidth=0.85)\n",
    "sns.lineplot(data=duration_df, x=duration_df.index, y=duration_df['tv'], ax=axes[1], linewidth=0.85)\n",
    "\n",
    "axes[0].set_title('Movie')\n",
    "axes[1].set_title('TV Show')\n",
    "\n",
    "axes[0].set_ylabel('The Mean of Durations')\n",
    "axes[1].set_ylabel('The Mean of Durations')\n",
    "\n",
    "axes[0].set_xlabel('Period')\n",
    "axes[1].set_xlabel('Period')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cf5925da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     1608\n",
       "2      378\n",
       "3      183\n",
       "4       86\n",
       "5       57\n",
       "6       30\n",
       "7       19\n",
       "8       18\n",
       "9        8\n",
       "10       5\n",
       "12       2\n",
       "11       2\n",
       "15       2\n",
       "13       1\n",
       "16       1\n",
       "Name: duration, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 급격히 늘고 있는 TV Show 방영 차수 빈도 확인\n",
    "nflx.loc[nflx['type']=='TV Show','duration'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad13cf42",
   "metadata": {},
   "source": [
    "**결론 :**<br>\n",
    "오히려 최근 평균 콘텐츠 재생 시간이 증가하고 있다. 연구가설을 기각하고 귀무가설을 채택합니다.  \n",
    "특히 TV Show의 경우 시리즈물에 대한 수요가 더 커지는 것을 확인할 수 있습니다. 다만, 수치상 큰 차이가 없기 때문에 넷플릭스에서 전략적으로 의도했다기보다는 최근 제작되는 콘텐츠들이 더 풍성하고 퀄리티있게 제작되면서 자연스럽게 시리즈물이 많아지고, 영화 시간이 길어진 것으로 보입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3e057fe",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### 3-6. 넷플릭스에서 주로 수급하는 콘텐츠 장르 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05f9c0d8",
   "metadata": {},
   "source": [
    "콘텐츠, OTT시장의 고도화된 개인화 추천서비스로 대중적인 취향보다 개별적 취향을 더 심도깊게 고민하고 기술적으로 분석하고 있습니다.\n",
    "넷플릭스는 추천서비스를 비즈니스에 적극적으로 활용하면서 가파른 성장을 이뤄낸 경험이 있다.\n",
    "따라서 넷플릭스는 고객을 더 세분화하고 이에 따라 콘텐츠 장르를 더 다양하게 분류하고 수급하는 전략을 취할 수 있습니다. 이에 따라 다음과 같은 가설을 설정합니다.\n",
    "\n",
    "- H0 : 최근 유통하는 콘텐츠 장르는 그 수가 과거와 같거나 적다.\n",
    "- H1 : 최근 유통하는 콘텐츠 장르가 더 다양해지고 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c330a351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Documentaries                                                   334\n",
       "Stand-Up Comedy                                                 321\n",
       "Dramas, International Movies                                    320\n",
       "Comedies, Dramas, International Movies                          243\n",
       "Dramas, Independent Movies, International Movies                215\n",
       "                                                               ... \n",
       "Action & Adventure, Sports Movies                                 1\n",
       "Anime Series, Crime TV Shows, TV Thrillers                        1\n",
       "Comedies, Dramas, Sports Movies                                   1\n",
       "International TV Shows, Spanish-Language TV Shows, TV Horror      1\n",
       "TV Action & Adventure, TV Comedies, TV Sci-Fi & Fantasy           1\n",
       "Name: listed_in, Length: 491, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 전체 기간 중 장르별 콘텐츠 수\n",
    "nflx['listed_in'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87cca0f1",
   "metadata": {},
   "source": [
    "넷플릭스에는 다큐멘터리가 가장 많고, 스탠드업코디미가 그 다음 순이네요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f87130fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "nflx.groupby('seps')['listed_in'].nunique().plot(linewidth=0.85)\n",
    "\n",
    "plt.xlabel('Period')\n",
    "plt.ylabel('Count of Contents')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95a352bf",
   "metadata": {},
   "source": [
    "**최근으로 올수록 더 다양한 장르를 보유하는 추세를 보입니다. 콘텐츠 장르가 더 다양하지고 있으므로(1,2,3,4 Period < 5,6,7 Period) 귀무가설을 기각합니다.**  \n",
    "해당 데이터에서는 추가로 더 많은 정보를 얻어낼 수 있을 것 같습니다. 과거에는 어떤 장르를 주로 수급했고, 최근 어떤 장르를 주로 수급하는지 알아보도록 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "616d303a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "docum_count = nflx.loc[nflx['listed_in']=='Documentaries'].groupby('seps').count()['listed_in']\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "docum_count.plot(linewidth=0.85)\n",
    "plt.ylabel('the count of documentary genre')\n",
    "plt.xlabel('Period')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "635c7b36",
   "metadata": {},
   "source": [
    "전체 콘텐츠 수에서 다큐멘터리 장르가 차지하는 비중이 가장 높았는데, 이는 넷플릭스 초기에 많이 수급했던 것에 원인이 있었습니다. \n",
    "최근 넷플릭스는 다큐멘터리 수급에 집중하지 않고 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c4a6e6c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dramas, International Movies                            45\n",
       "Kids' TV                                                41\n",
       "Children & Family Movies, Comedies                      38\n",
       "Children & Family Movies                                35\n",
       "Comedies, Dramas, International Movies                  34\n",
       "                                                        ..\n",
       "Cult Movies, Dramas, International Movies                1\n",
       "Crime TV Shows, International TV Shows, TV Mysteries     1\n",
       "Crime TV Shows, International TV Shows, TV Comedies      1\n",
       "Classic Movies, Thrillers                                1\n",
       "TV Dramas, TV Sci-Fi & Fantasy                           1\n",
       "Name: listed_in, Length: 229, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 최근 집중적으로 수급하는 장르 확인\n",
    "nflx.loc[nflx['seps']=='7','listed_in'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8d772dfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top5_genre = nflx.loc[nflx['seps']=='7','listed_in'].value_counts().index[:5]\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "for genre in list(top5_genre):\n",
    "    genre_count = nflx.loc[nflx['listed_in']==genre].groupby('seps').count()['listed_in']\n",
    "    sns.lineplot(x=genre_count.index, y=genre_count.values, label=genre, linewidth=0.85)\n",
    "    \n",
    "plt.xlabel('Period')\n",
    "plt.ylabel('The Count of Each Genre')\n",
    "plt.axvline(x=3, linestyle='--', color='r', linewidth=0.85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8360e36f",
   "metadata": {},
   "source": [
    "\n",
    "키즈 콘텐츠, 가족 콘텐츠의 비중이 4 `Period` 부터 꾸준히 상승하고 있네요.\n",
    "\n",
    "**결론 :**<br>\n",
    "과거 대비 다양한 장르를 확보하면서 다큐멘터리와 같은 콘텐츠 점유율이 높았던 장르의 비중이 낮아지고,\n",
    "더 다양한 고객층의 니즈를 반영하고자 하는 것을 확인할 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6401280c",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "\n",
    "---\n",
    "## 4. 종합 결론"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f1dcca0",
   "metadata": {},
   "source": [
    "아래 6가지 가설 검정 과정을 통해 **넷플릭스가 시장 점유를 위해 취하고 있는 콘텐츠 수급 전략**을 분석해보았습니다.\n",
    "\n",
    "1. H1 : 최근 특정 콘텐츠 유형(TV show or Movie)이 늘고 있다. (주요 컬럼 : type)\n",
    "2. H1 : 최근 특정 국가의 콘텐츠 수급 비중이 늘고 있다. (주요 컬럼 : country)\n",
    "3. H1 : 최근 옛날(오래된) 콘텐츠 수급 비중이 늘고 있다. (주요 컬럼 : release_year)\n",
    "4. H1 : 최근 어린이 콘텐츠 비중이 줄고 있다. (주요 컬럼 : rating)\n",
    "5. H1 : 최근 길이(시리즈, 시간)가 짧은 콘텐츠 비중이 늘고 있다. (주요 컬럼 : duration)\n",
    "6. H1 : 최근 유통하는 콘텐츠 장르가 더 다양해지고 있다. (주요 컬럼 : listed_in, description)\n",
    "\n",
    "종합하면,\n",
    "\n",
    "- 넷플릭스는 영화보다 **TV 시리즈 수급**을 늘리고 있고, **자국(미국) 콘텐츠 확보**에 힘을 싣고 있습니다.\n",
    "- 또한 소비자 수요를 반영해 최신 콘텐츠뿐만 아니라 **시간이 흐른 콘텐츠**들을 함께 확보하고 있고,\n",
    "- 2~30대 주요 고객층 이외 **어린이를 포함해 더 다양한 고객을 타겟**으로 하고 있었습니다.\n",
    "- 숏폼 콘텐츠가 유행하고 있지만 영화 산업이나 TV 드라마 산업에서 자체 콘텐츠의 길이를 줄이려는 시도를 하는 경향은 없으며,\n",
    "- 넷플릭스 역시 영상물의 **재생 시간에 대해서는 수급 기준으로 고려하지 않는** 것으로 보입니다.\n",
    "- 마지막으로 다양한 고객들의 수요를 맞추기 위해 **다양한 콘텐츠 장르**를 선보이면서,\n",
    "- 특히 **어린이 및 가족 콘텐츠** 보유량을 늘려 다양한 고객층의 니즈를 반영하는 모습을 보입니다."
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "946e70462398da2b7d5146f365841ff0dae0fe6e2f7c4e5ed0a8dd78c4677798"
  },
  "kernelspec": {
   "display_name": "datascience",
   "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": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "toc-autonumbering": false,
  "toc-showcode": false,
  "toc-showmarkdowntxt": false,
  "toc-showtags": false,
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}