Analise_Exploratoria_de_Dados_Netflix.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6a860290",
   "metadata": {},
   "source": [
    "# Sobre o Projeto\n",
    "### O objetivo do projeto é realizar uma Análise Exploratória de Dados (EDA) e tentar entender como funciona esse conjunto de dados e quais informações podemos retirar dele."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6bb7e024",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8732efa7",
   "metadata": {},
   "source": [
    "### Importando o Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f031002",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('netflix_daily_top_10.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e56c8a1",
   "metadata": {},
   "source": [
    "### Visualização do Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7ec45688",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>As of</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Year to Date Rank</th>\n",
       "      <th>Last Week Rank</th>\n",
       "      <th>Title</th>\n",
       "      <th>Type</th>\n",
       "      <th>Netflix Exclusive</th>\n",
       "      <th>Netflix Release Date</th>\n",
       "      <th>Days In Top 10</th>\n",
       "      <th>Viewership Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Tiger King: Murder, Mayhem …</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mar 20, 2020</td>\n",
       "      <td>9</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-</td>\n",
       "      <td>Ozark</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Jul 21, 2017</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>All American</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mar 28, 2019</td>\n",
       "      <td>9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-</td>\n",
       "      <td>Blood Father</td>\n",
       "      <td>Movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mar 26, 2020</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>The Platform</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mar 20, 2020</td>\n",
       "      <td>9</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        As of  Rank Year to Date Rank Last Week Rank  \\\n",
       "0  2020-04-01     1                 1              1   \n",
       "1  2020-04-01     2                 2              -   \n",
       "2  2020-04-01     3                 3              2   \n",
       "3  2020-04-01     4                 4              -   \n",
       "4  2020-04-01     5                 5              4   \n",
       "\n",
       "                          Title     Type Netflix Exclusive  \\\n",
       "0  Tiger King: Murder, Mayhem …  TV Show               Yes   \n",
       "1                         Ozark  TV Show               Yes   \n",
       "2                  All American  TV Show               NaN   \n",
       "3                  Blood Father    Movie               NaN   \n",
       "4                  The Platform    Movie               Yes   \n",
       "\n",
       "  Netflix Release Date  Days In Top 10  Viewership Score  \n",
       "0         Mar 20, 2020               9                90  \n",
       "1         Jul 21, 2017               5                45  \n",
       "2         Mar 28, 2019               9                76  \n",
       "3         Mar 26, 2020               5                30  \n",
       "4         Mar 20, 2020               9                55  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52489b70",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>As of</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Year to Date Rank</th>\n",
       "      <th>Last Week Rank</th>\n",
       "      <th>Title</th>\n",
       "      <th>Type</th>\n",
       "      <th>Netflix Exclusive</th>\n",
       "      <th>Netflix Release Date</th>\n",
       "      <th>Days In Top 10</th>\n",
       "      <th>Viewership Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7095</th>\n",
       "      <td>2022-03-11</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Worst Roommate Ever</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mar 1, 2022</td>\n",
       "      <td>10</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7096</th>\n",
       "      <td>2022-03-11</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Vikings: Valhalla</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Feb 25, 2022</td>\n",
       "      <td>14</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7097</th>\n",
       "      <td>2022-03-11</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>-</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>Movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aug 1, 2014</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7098</th>\n",
       "      <td>2022-03-11</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>Shrek 2</td>\n",
       "      <td>Movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mar 1, 2022</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7099</th>\n",
       "      <td>2022-03-11</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>-</td>\n",
       "      <td>Shrek</td>\n",
       "      <td>Movie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>May 1, 2018</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           As of  Rank Year to Date Rank Last Week Rank                Title  \\\n",
       "7095  2022-03-11     6                 5              1  Worst Roommate Ever   \n",
       "7096  2022-03-11     7                 7              2    Vikings: Valhalla   \n",
       "7097  2022-03-11     8                 8              -              Shooter   \n",
       "7098  2022-03-11     9                 9              7              Shrek 2   \n",
       "7099  2022-03-11    10                10              -                Shrek   \n",
       "\n",
       "         Type Netflix Exclusive Netflix Release Date  Days In Top 10  \\\n",
       "7095  TV Show               Yes          Mar 1, 2022              10   \n",
       "7096  TV Show               Yes         Feb 25, 2022              14   \n",
       "7097    Movie               NaN          Aug 1, 2014               3   \n",
       "7098    Movie               NaN          Mar 1, 2022              10   \n",
       "7099    Movie               NaN          May 1, 2018               7   \n",
       "\n",
       "      Viewership Score  \n",
       "7095                81  \n",
       "7096               100  \n",
       "7097                 7  \n",
       "7098                33  \n",
       "7099                12  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "800bb79f",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "### Shape do Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "43a4347b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7100, 10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "982145ed",
   "metadata": {},
   "source": [
    "### Período da Análise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ca1a74d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-04-01\n"
     ]
    }
   ],
   "source": [
    "start = pd.to_datetime(dataset['As of']).dt.date.min()\n",
    "print(start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "62cad4a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-03-11\n"
     ]
    }
   ],
   "source": [
    "end = pd.to_datetime(dataset['As of']).dt.date.max()\n",
    "print(end)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "414aa94e",
   "metadata": {},
   "source": [
    "### Checando os valores Nulos e os Tipos de Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0100a526",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7100 entries, 0 to 7099\n",
      "Data columns (total 10 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   As of                 7100 non-null   object\n",
      " 1   Rank                  7100 non-null   int64 \n",
      " 2   Year to Date Rank     7100 non-null   object\n",
      " 3   Last Week Rank        7100 non-null   object\n",
      " 4   Title                 7100 non-null   object\n",
      " 5   Type                  7100 non-null   object\n",
      " 6   Netflix Exclusive     4599 non-null   object\n",
      " 7   Netflix Release Date  7100 non-null   object\n",
      " 8   Days In Top 10        7100 non-null   int64 \n",
      " 9   Viewership Score      7100 non-null   int64 \n",
      "dtypes: int64(3), object(7)\n",
      "memory usage: 554.8+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6845933c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "As of                   object\n",
       "Rank                     int64\n",
       "Year to Date Rank       object\n",
       "Last Week Rank          object\n",
       "Title                   object\n",
       "Type                    object\n",
       "Netflix Exclusive       object\n",
       "Netflix Release Date    object\n",
       "Days In Top 10           int64\n",
       "Viewership Score         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e83eb0",
   "metadata": {},
   "source": [
    "### Valores Nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1bb080cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "As of                      0\n",
       "Rank                       0\n",
       "Year to Date Rank          0\n",
       "Last Week Rank             0\n",
       "Title                      0\n",
       "Type                       0\n",
       "Netflix Exclusive       2501\n",
       "Netflix Release Date       0\n",
       "Days In Top 10             0\n",
       "Viewership Score           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a79d65e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    4599\n",
       "Name: Netflix Exclusive, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Netflix Exclusive'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30f63088",
   "metadata": {},
   "source": [
    "#### Nós podemos perceber que os valores nulos, na verdade, são os títulos que não são exclusivos da Netflix.\n",
    "#### Então eu vou alterar os valores nulos para \"Não\" e assim saberemos quais tipos não são exclusivos e poderemos fazer um gráfico para visualização. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "964ed8b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Last Week Rank</th>\n",
       "      <th>Title</th>\n",
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       "      <th>Netflix Exclusive</th>\n",
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       "      <th>Days In Top 10</th>\n",
       "      <th>Viewership Score</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Tiger King: Murder, Mayhem …</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mar 20, 2020</td>\n",
       "      <td>9</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-</td>\n",
       "      <td>Ozark</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Jul 21, 2017</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>All American</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>No</td>\n",
       "      <td>Mar 28, 2019</td>\n",
       "      <td>9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-</td>\n",
       "      <td>Blood Father</td>\n",
       "      <td>Movie</td>\n",
       "      <td>No</td>\n",
       "      <td>Mar 26, 2020</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>The Platform</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mar 20, 2020</td>\n",
       "      <td>9</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        As of  Rank Year to Date Rank Last Week Rank  \\\n",
       "0  2020-04-01     1                 1              1   \n",
       "1  2020-04-01     2                 2              -   \n",
       "2  2020-04-01     3                 3              2   \n",
       "3  2020-04-01     4                 4              -   \n",
       "4  2020-04-01     5                 5              4   \n",
       "\n",
       "                          Title     Type Netflix Exclusive  \\\n",
       "0  Tiger King: Murder, Mayhem …  TV Show               Yes   \n",
       "1                         Ozark  TV Show               Yes   \n",
       "2                  All American  TV Show                No   \n",
       "3                  Blood Father    Movie                No   \n",
       "4                  The Platform    Movie               Yes   \n",
       "\n",
       "  Netflix Release Date  Days In Top 10  Viewership Score  \n",
       "0         Mar 20, 2020               9                90  \n",
       "1         Jul 21, 2017               5                45  \n",
       "2         Mar 28, 2019               9                76  \n",
       "3         Mar 26, 2020               5                30  \n",
       "4         Mar 20, 2020               9                55  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdataset = dataset\n",
    "newdataset.fillna('No', inplace=True)\n",
    "newdataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dcb93901",
   "metadata": {},
   "outputs": [],
   "source": [
    "county = 0\n",
    "countn = 0\n",
    "for i in newdataset['Netflix Exclusive']:\n",
    "    if i == 'Yes':\n",
    "        county += 1\n",
    "    else:\n",
    "        countn += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "137f334a",
   "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>Netflix Exclusive</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>4599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>4599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>2501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>2501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>4599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7095</th>\n",
       "      <td>Yes</td>\n",
       "      <td>4599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7096</th>\n",
       "      <td>Yes</td>\n",
       "      <td>4599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7097</th>\n",
       "      <td>No</td>\n",
       "      <td>2501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7098</th>\n",
       "      <td>No</td>\n",
       "      <td>2501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7099</th>\n",
       "      <td>No</td>\n",
       "      <td>2501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Netflix Exclusive  Count\n",
       "0                  Yes   4599\n",
       "1                  Yes   4599\n",
       "2                   No   2501\n",
       "3                   No   2501\n",
       "4                  Yes   4599\n",
       "...                ...    ...\n",
       "7095               Yes   4599\n",
       "7096               Yes   4599\n",
       "7097                No   2501\n",
       "7098                No   2501\n",
       "7099                No   2501\n",
       "\n",
       "[7100 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdataset1 = newdataset['Netflix Exclusive']\n",
    "newdataset2 = pd.DataFrame(newdataset1)\n",
    "newdataset2['Count'] = 0\n",
    "for i , ne in enumerate(newdataset2['Netflix Exclusive']):\n",
    "    if ne == 'Yes':\n",
    "        newdataset2['Count'][i] = county\n",
    "    if ne == 'No':\n",
    "        newdataset2['Count'][i] = countn\n",
    "newdataset2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5e40b0f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=newdataset2, x='Netflix Exclusive', y='Count', palette = ['#fc1705', '#f73728']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cff0884",
   "metadata": {},
   "source": [
    "### Estatísticas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f91b325a",
   "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>Rank</th>\n",
       "      <th>Days In Top 10</th>\n",
       "      <th>Viewership Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7100.000000</td>\n",
       "      <td>7100.000000</td>\n",
       "      <td>7100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.500000</td>\n",
       "      <td>24.123662</td>\n",
       "      <td>122.790141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.872484</td>\n",
       "      <td>58.473789</td>\n",
       "      <td>213.861642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>128.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>428.000000</td>\n",
       "      <td>1474.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rank  Days In Top 10  Viewership Score\n",
       "count  7100.000000     7100.000000       7100.000000\n",
       "mean      5.500000       24.123662        122.790141\n",
       "std       2.872484       58.473789        213.861642\n",
       "min       1.000000        1.000000          1.000000\n",
       "25%       3.000000        3.000000         19.000000\n",
       "50%       5.500000        7.000000         50.000000\n",
       "75%       8.000000       18.000000        128.000000\n",
       "max      10.000000      428.000000       1474.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "533c1c65",
   "metadata": {},
   "source": [
    "#### Gráficos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc24484",
   "metadata": {},
   "source": [
    "#### Boxplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "58f1be92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color1 = '#fc1705'\n",
    "plt.figure(figsize=(5, 7))\n",
    "sns.boxplot(data=newdataset['Rank'],color=color1)\n",
    "plt.xticks(ticks=[])\n",
    "plt.xlabel('Rank');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ea6afb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color2 ='#f73728'\n",
    "plt.figure(figsize=(5, 7))\n",
    "sns.boxplot(data=newdataset['Days In Top 10'],color=color2)\n",
    "plt.xticks(ticks=[])\n",
    "plt.xlabel('Days In Top 10');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aa097a29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color3 = '#f56a5f'\n",
    "plt.figure(figsize=(5, 7))\n",
    "sns.boxplot(data=newdataset['Viewership Score'], color=color3)\n",
    "plt.xticks(ticks=[])\n",
    "plt.xlabel('Viewership Score');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c424b16",
   "metadata": {},
   "source": [
    "##### Analisando os Boxplots podemos perceber um grande número de Outliers, vamos tentar entendê-los."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7c987f4",
   "metadata": {},
   "source": [
    "#### Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ea2a52e0",
   "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>As of</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Year to Date Rank</th>\n",
       "      <th>Last Week Rank</th>\n",
       "      <th>Title</th>\n",
       "      <th>Type</th>\n",
       "      <th>Netflix Exclusive</th>\n",
       "      <th>Netflix Release Date</th>\n",
       "      <th>Days In Top 10</th>\n",
       "      <th>Viewership Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2886</th>\n",
       "      <td>2021-01-14</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>Cocomelon</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>No</td>\n",
       "      <td>Jun 1, 2020</td>\n",
       "      <td>100</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2896</th>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Cocomelon</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>No</td>\n",
       "      <td>Jun 1, 2020</td>\n",
       "      <td>101</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>2021-01-16</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>Cocomelon</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>No</td>\n",
       "      <td>Jun 1, 2020</td>\n",
       "      <td>102</td>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>Cocomelon</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>No</td>\n",
       "      <td>Jun 1, 2020</td>\n",
       "      <td>103</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3019</th>\n",
       "      <td>2021-01-27</td>\n",
       "      <td>10</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Cocomelon</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>No</td>\n",
       "      <td>Jun 1, 2020</td>\n",
       "      <td>104</td>\n",
       "      <td>294</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6674</th>\n",
       "      <td>2022-01-28</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>-</td>\n",
       "      <td>Cocomelon</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>No</td>\n",
       "      <td>Jun 1, 2020</td>\n",
       "      <td>424</td>\n",
       "      <td>1466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6687</th>\n",
       "      <td>2022-01-29</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>Cocomelon</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>No</td>\n",
       "      <td>Jun 1, 2020</td>\n",
       "      <td>425</td>\n",
       "      <td>1469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6718</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>9</td>\n",
       "      <td>-</td>\n",
       "      <td>7</td>\n",
       "      <td>Cocomelon</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>No</td>\n",
       "      <td>Jun 1, 2020</td>\n",
       "      <td>426</td>\n",
       "      <td>1471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6959</th>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>10</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Cocomelon</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>No</td>\n",
       "      <td>Jun 1, 2020</td>\n",
       "      <td>427</td>\n",
       "      <td>1472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6998</th>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>9</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Cocomelon</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>No</td>\n",
       "      <td>Jun 1, 2020</td>\n",
       "      <td>428</td>\n",
       "      <td>1474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>329 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           As of  Rank Year to Date Rank Last Week Rank      Title     Type  \\\n",
       "2886  2021-01-14     7                 6              8  Cocomelon  TV Show   \n",
       "2896  2021-01-15     7                 7             10  Cocomelon  TV Show   \n",
       "2909  2021-01-16    10                 7              9  Cocomelon  TV Show   \n",
       "2919  2021-01-17    10                10              9  Cocomelon  TV Show   \n",
       "3019  2021-01-27    10                 -              -  Cocomelon  TV Show   \n",
       "...          ...   ...               ...            ...        ...      ...   \n",
       "6674  2022-01-28     5                 6              -  Cocomelon  TV Show   \n",
       "6687  2022-01-29     8                 5              8  Cocomelon  TV Show   \n",
       "6718  2022-02-01     9                 -              7  Cocomelon  TV Show   \n",
       "6959  2022-02-25    10                 -              -  Cocomelon  TV Show   \n",
       "6998  2022-03-01     9                 -              -  Cocomelon  TV Show   \n",
       "\n",
       "     Netflix Exclusive Netflix Release Date  Days In Top 10  Viewership Score  \n",
       "2886                No          Jun 1, 2020             100               287  \n",
       "2896                No          Jun 1, 2020             101               291  \n",
       "2909                No          Jun 1, 2020             102               292  \n",
       "2919                No          Jun 1, 2020             103               293  \n",
       "3019                No          Jun 1, 2020             104               294  \n",
       "...                ...                  ...             ...               ...  \n",
       "6674                No          Jun 1, 2020             424              1466  \n",
       "6687                No          Jun 1, 2020             425              1469  \n",
       "6718                No          Jun 1, 2020             426              1471  \n",
       "6959                No          Jun 1, 2020             427              1472  \n",
       "6998                No          Jun 1, 2020             428              1474  \n",
       "\n",
       "[329 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdataset[newdataset['Days In Top 10'] >= 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "60b07c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cocomelon                       428\n",
       "Ozark                            85\n",
       "Cobra Kai                        81\n",
       "Manifest                         80\n",
       "The Queenâs Gambit             73\n",
       "                               ... \n",
       "The Office                        1\n",
       "Animals on the Loose: A You…      1\n",
       "Dark                              1\n",
       "The Secret Life of Pets 2         1\n",
       "Step Up Revolution                1\n",
       "Name: Title, Length: 645, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdataset.Title.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3372719",
   "metadata": {},
   "source": [
    "#### Nós podemos concluir que o grande número de Outliers era por conta do título Cocomelon que foi um grande sucesso."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d70017c6",
   "metadata": {},
   "source": [
    "### Analisando os Tipos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "990dbc5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV Show            4446\n",
       "Movie              2611\n",
       "Stand-Up Comedy      41\n",
       "Concert/Perf…         2\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdataset.Type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "756a3f11",
   "metadata": {},
   "outputs": [],
   "source": [
    "ntype = {'TV Show':[4446], 'Movie':[2611], 'Stand-Up Comedy':[41],'Concert/Perfomance':[2]} \n",
    "ntype1 = pd.DataFrame(ntype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "37f626ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colorbar = ['#8f1128', '#b31532', '#cc1b3b', '#eb2145']\n",
    "plt.figure(figsize=(7, 5))\n",
    "sns.barplot(data=ntype1, palette = ['#fc1705', '#f73728', '#f56a5f'])\n",
    "plt.xticks(rotation=45);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c14a5c",
   "metadata": {},
   "source": [
    "### Analisando a pontuação \"Viewership Score\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d425a8cf",
   "metadata": {},
   "source": [
    "#### Acessando o site: www.the-numbers.com/netflix-top-10 (de onde o conjunto de dados foi retirado). Podemos ver esta explicação sobre a pontuação de audiência: \"A pontuação de audiência é uma pontuação atribuída a cada programa com base em sua classificação diária, atribuindo 10 pontos para cada dia no TOP 1, 9 pontos para cada dia  no TOP 2, etc.\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d2f9c774",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 5))\n",
    "sns.histplot(newdataset['Viewership Score'], bins=25, element='step', color='#fc1705')\n",
    "plt.ylabel(ylabel=''); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "69185476",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>As of</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Year to Date Rank</th>\n",
       "      <th>Last Week Rank</th>\n",
       "      <th>Title</th>\n",
       "      <th>Type</th>\n",
       "      <th>Netflix Exclusive</th>\n",
       "      <th>Netflix Release Date</th>\n",
       "      <th>Days In Top 10</th>\n",
       "      <th>Viewership Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6998</th>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>9</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Cocomelon</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>No</td>\n",
       "      <td>Jun 1, 2020</td>\n",
       "      <td>428</td>\n",
       "      <td>1474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           As of  Rank Year to Date Rank Last Week Rank      Title     Type  \\\n",
       "6998  2022-03-01     9                 -              -  Cocomelon  TV Show   \n",
       "\n",
       "     Netflix Exclusive Netflix Release Date  Days In Top 10  Viewership Score  \n",
       "6998                No          Jun 1, 2020             428              1474  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdataset[newdataset['Viewership Score'] == newdataset['Viewership Score'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4f32b8a",
   "metadata": {},
   "source": [
    "#### Podemos concluir que a maioria dos Títulos não ficam muito tempo no TOP 10 e tem pontuações mais baixas e o Título com maior pontuação é  a animação Cocomelon."
   ]
  }
 ],
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