DSA-Python-Cap06-09-Mini-Projeto1.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 6</font>\n",
    "\n",
    "## Download: http://github.com/dsacademybr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mini-Projeto 1 - Guia de Análise Exploratória de Dados com Python e Linguagem SQL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](imagens/mini-projeto1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neste Mini-Projeto vamos apresentar um guia básico de análise exploratória de dados usando Linguagem Python, Linguagem SQL e Banco de Dados SQLite. Usaremos dados reais disponíveis publicamente, dados sobre filmes no <a href=\"https://www.imdb.com/interfaces/\">IMDB</a>.\n",
    "\n",
    "Guias avançados de Análise Exploratória de Dados são fornecidos na <a href=\"https://www.datascienceacademy.com.br/bundle/formacao-cientista-de-dados\">Formação Cientista de Dados</a>.\n",
    "\n",
    "Para aprender mais sobre Linguagem SQL:\n",
    "\n",
    "- <a href=\"https://www.datascienceacademy.com.br/path-player?courseid=microsoft-power-bi-para-data-science\">SQL Analytics Básico</a>\n",
    "- <a href=\"https://www.datascienceacademy.com.br/course/modelagem-de-banco-de-dados-relacionais-nao-relacionais-e-data-stores\">SQL Analytics Avançado</a>\n",
    "\n",
    "Ao aplicar Análise Exploratória de Dados vamos responder a estas 10 perguntas:\n",
    "\n",
    "- 1- Quais São as Categorias de Filmes Mais Comuns no IMDB?\n",
    "- 2- Qual o Número de Títulos Por Gênero?\n",
    "- 3- Qual a Mediana de Avaliação dos Filmes Por Gênero?\n",
    "- 4- Qual a Mediana de Avaliação dos Filmes Em Relação ao Ano de Estréia?\n",
    "- 5- Qual o Número de Filmes Avaliados Por Gênero Em Relação ao Ano de Estréia?\n",
    "- 6- Qual o Filme Com Maior Tempo de Duração? Calcule os Percentis.\n",
    "- 7- Qual a Relação Entre Duração e Gênero?\n",
    "- 8- Qual o Número de Filmes Produzidos Por País?\n",
    "- 9- Quais São os Top 10 Melhores Filmes?\n",
    "- 10- Quais São os Top 10 Piores Filmes?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instalando e Carregando Pacotes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Versão da Linguagem Python Usada Neste Jupyter Notebook: 3.8.8\n"
     ]
    }
   ],
   "source": [
    "# Versão da Linguagem Python\n",
    "from platform import python_version\n",
    "print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://pypi.org/\n",
    "\n",
    "https://pypi.org/project/imdb-sqlite/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4275,
     "status": "ok",
     "timestamp": 1622091434937,
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    "id": "XEa1Oqd5t8tX",
    "outputId": "8ac987f5-8bd3-46f1-d5aa-626df31d650f"
   },
   "outputs": [],
   "source": [
    "# Instala o pacote\n",
    "!pip install -q imdb-sqlite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora executamos o pacote para download dos datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instala o pacote\n",
    "# https://pypi.org/project/pycountry/\n",
    "!pip install -q pycountry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "KwrZ9EWy02p7"
   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "import re\n",
    "import time\n",
    "import sqlite3\n",
    "import pycountry\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import cm\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "sns.set_theme(style = \"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yebFAbpYviBJ"
   },
   "source": [
    "## Carregando os Dados\n",
    "\n",
    "Primeiro temos que baixar os dados!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1126675,
     "status": "ok",
     "timestamp": 1622092561602,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "18JO-2JAwLf8",
    "outputId": "b4187b50-982a-4376-e43b-e9c6a2e56384"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-07-09 19:00:52,775 DB already exists: (imdb.db). Refusing to modify. Exiting\n",
      "CPU times: user 4.31 ms, sys: 9.77 ms, total: 14.1 ms\n",
      "Wall time: 236 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "!imdb-sqlite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "GCynwX5SFUt8"
   },
   "outputs": [],
   "source": [
    "# Conecta no banco de dados\n",
    "conn = sqlite3.connect(\"imdb.db\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "ODFg_X23F0Yu"
   },
   "outputs": [],
   "source": [
    "# Extrai a lista de tabelas\n",
    "tabelas = pd.read_sql_query(\"SELECT NAME AS 'Table_Name' FROM sqlite_master WHERE type = 'table'\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tipo do objeto\n",
    "type(tabelas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>crew</td>\n",
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       "      <td>episodes</td>\n",
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       "  Table_Name\n",
       "0     people\n",
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       "2       akas\n",
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       "4   episodes"
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   "source": [
    "# Visualiza o resultado\n",
    "tabelas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vamos converter o dataframe em uma lista\n",
    "tabelas = tabelas[\"Table_Name\"].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
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    "executionInfo": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Esquema da tabela: people\n"
     ]
    },
    {
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       "   cid       name     type  notnull dflt_value  pk\n",
       "0    0  person_id  VARCHAR        0       None   1\n",
       "1    1       name  VARCHAR        0       None   0\n",
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     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Esquema da tabela: titles\n"
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      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Esquema da tabela: akas\n"
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       "      <td>INTEGER</td>\n",
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       "6    6  is_original_title  INTEGER        0       None   0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Esquema da tabela: crew\n"
     ]
    },
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       "   cid        name     type  notnull dflt_value  pk\n",
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     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Esquema da tabela: episodes\n"
     ]
    },
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       "   cid              name     type  notnull dflt_value  pk\n",
       "0    0  episode_title_id  INTEGER        0       None   0\n",
       "1    1     show_title_id  INTEGER        0       None   0\n",
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     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n",
      "Esquema da tabela: ratings\n"
     ]
    },
    {
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      "text/plain": [
       "   cid      name     type  notnull dflt_value  pk\n",
       "0    0  title_id  VARCHAR        0       None   1\n",
       "1    1    rating  INTEGER        0       None   0\n",
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     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Vamos percorrer a lista de tabelas no banco de dados e extrair o esquema de cada uma\n",
    "for tabela in tabelas:\n",
    "    consulta = \"PRAGMA TABLE_INFO({})\".format(tabela)\n",
    "    resultado = pd.read_sql_query(consulta, conn)\n",
    "    print(\"Esquema da tabela:\", tabela)\n",
    "    display(resultado)\n",
    "    print(\"-\"*100)\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Agora começamos a Análise Exploratória dos Dados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m6W_XSp3yBSL"
   },
   "source": [
    "## 1- Quais São as Categorias de Filmes Mais Comuns no IMDB?\n",
    "\n",
    "Quais são os principais tipos (categorias) dos títulos (filmes)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "72tvpOmJGTni"
   },
   "outputs": [],
   "source": [
    "# Cria a consulta SQL\n",
    "consulta1 = '''SELECT type, COUNT(*) AS COUNT FROM titles GROUP BY type''' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extrai o resultado\n",
    "resultado1 = pd.read_sql_query(consulta1, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 343
    },
    "executionInfo": {
     "elapsed": 524,
     "status": "ok",
     "timestamp": 1622092926334,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "HYgZRXHyRcK1",
    "outputId": "35c853a5-5780-41ab-f638-35575c8d69fc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>type</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>movie</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>radioSeries</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>short</td>\n",
       "      <td>819859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>5902653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tvMiniSeries</td>\n",
       "      <td>38221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tvMovie</td>\n",
       "      <td>132026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>tvSeries</td>\n",
       "      <td>210468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>tvShort</td>\n",
       "      <td>10139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>tvSpecial</td>\n",
       "      <td>33287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>video</td>\n",
       "      <td>313503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>videoGame</td>\n",
       "      <td>28511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            type    COUNT\n",
       "0          movie   581919\n",
       "1   radioEpisode        1\n",
       "2    radioSeries        1\n",
       "3          short   819859\n",
       "4      tvEpisode  5902653\n",
       "5   tvMiniSeries    38221\n",
       "6        tvMovie   132026\n",
       "7       tvSeries   210468\n",
       "8        tvShort    10139\n",
       "9      tvSpecial    33287\n",
       "10         video   313503\n",
       "11     videoGame    28511"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualiza o resultado\n",
    "display(resultado1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "oUsal5PCh701"
   },
   "outputs": [],
   "source": [
    "# Vamos calcular o percentual para cada tipo\n",
    "resultado1['percentual'] = (resultado1['COUNT'] / resultado1['COUNT'].sum()) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>radioEpisode</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>radioSeries</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>short</td>\n",
       "      <td>819859</td>\n",
       "      <td>10.158603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>5902653</td>\n",
       "      <td>73.137831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tvMiniSeries</td>\n",
       "      <td>38221</td>\n",
       "      <td>0.473584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tvMovie</td>\n",
       "      <td>132026</td>\n",
       "      <td>1.635891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>tvSeries</td>\n",
       "      <td>210468</td>\n",
       "      <td>2.607840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>tvShort</td>\n",
       "      <td>10139</td>\n",
       "      <td>0.125629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>tvSpecial</td>\n",
       "      <td>33287</td>\n",
       "      <td>0.412448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>video</td>\n",
       "      <td>313503</td>\n",
       "      <td>3.884513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>videoGame</td>\n",
       "      <td>28511</td>\n",
       "      <td>0.353270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            type    COUNT  percentual\n",
       "0          movie   581919    7.210367\n",
       "1   radioEpisode        1    0.000012\n",
       "2    radioSeries        1    0.000012\n",
       "3          short   819859   10.158603\n",
       "4      tvEpisode  5902653   73.137831\n",
       "5   tvMiniSeries    38221    0.473584\n",
       "6        tvMovie   132026    1.635891\n",
       "7       tvSeries   210468    2.607840\n",
       "8        tvShort    10139    0.125629\n",
       "9      tvSpecial    33287    0.412448\n",
       "10         video   313503    3.884513\n",
       "11     videoGame    28511    0.353270"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualiza o resultado\n",
    "display(resultado1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "oWJrmfjHyBpW"
   },
   "outputs": [],
   "source": [
    "# Vamos criar um gráfico com apenas 4 categorias:\n",
    "# As 3 categorias com mais títulos e 1 categoria com todo o restante\n",
    "\n",
    "# Cria um dicionário vazio\n",
    "others = {}\n",
    "\n",
    "# Filtra o percentual em 5% e soma o total\n",
    "others['COUNT'] = resultado1[resultado1['percentual'] < 5]['COUNT'].sum()\n",
    "\n",
    "# Grava o percentual\n",
    "others['percentual'] = resultado1[resultado1['percentual'] < 5]['percentual'].sum()\n",
    "\n",
    "# Ajusta o nome\n",
    "others['type'] = 'others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'COUNT': 766157, 'percentual': 9.493199256361494, 'type': 'others'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualiza\n",
    "others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "H7WDZkbeyHz7"
   },
   "outputs": [],
   "source": [
    "# Filtra o dataframe de resultado\n",
    "resultado1 = resultado1[resultado1['percentual'] > 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Append com o dataframe de outras categorias\n",
    "resultado1 = resultado1.append(others, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "K5mJGmqMyMaf"
   },
   "outputs": [],
   "source": [
    "# Ordena o resultado\n",
    "resultado1 = resultado1.sort_values(by = 'COUNT', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>COUNT</th>\n",
       "      <th>percentual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>5902653</td>\n",
       "      <td>73.137831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>short</td>\n",
       "      <td>819859</td>\n",
       "      <td>10.158603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>others</td>\n",
       "      <td>766157</td>\n",
       "      <td>9.493199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>movie</td>\n",
       "      <td>581919</td>\n",
       "      <td>7.210367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        type    COUNT  percentual\n",
       "2  tvEpisode  5902653   73.137831\n",
       "1      short   819859   10.158603\n",
       "3     others   766157    9.493199\n",
       "0      movie   581919    7.210367"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualiza\n",
    "resultado1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ajusta os labels\n",
    "labels = [str(resultado1['type'][i])+' '+'['+str(round(resultado1['percentual'][i],2)) +'%'+']' for i in resultado1.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 559
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1621950718639,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "2_ugTEy_HmoQ",
    "outputId": "2ea6ea27-e942-4dbf-e358-3305cf88147a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "\n",
    "# Mapa de cores\n",
    "# https://matplotlib.org/stable/tutorials/colors/colormaps.html\n",
    "cs = cm.Set3(np.arange(100))\n",
    "\n",
    "# Cria a figura\n",
    "f = plt.figure()\n",
    "\n",
    "# Pie Plot\n",
    "plt.pie(resultado1['COUNT'], labeldistance = 1, radius = 3, colors = cs, wedgeprops = dict(width = 0.8))\n",
    "plt.legend(labels = labels, loc = 'center', prop = {'size':12})\n",
    "plt.title(\"Distribuição de Títulos\", loc = 'Center', fontdict = {'fontsize':20,'fontweight':20})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CWF1erMywd2w"
   },
   "source": [
    "## 2- Qual o Número de Títulos Por Gênero?\n",
    "\n",
    "Vamos calcular o número de filmes por gênero e entregar o resultado em valor percentual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "executionInfo": {
     "elapsed": 1092,
     "status": "ok",
     "timestamp": 1622093994891,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "1oY3-f_ulQmh",
    "outputId": "1c747b58-7a65-48e3-c88d-d4d4641eb983"
   },
   "outputs": [],
   "source": [
    "# Cria a consulta SQL\n",
    "consulta2 = '''SELECT genres, COUNT(*) FROM titles WHERE type = 'movie' GROUP BY genres''' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resultado\n",
    "resultado2 = pd.read_sql_query(consulta2, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>genres</th>\n",
       "      <th>COUNT(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>13236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Action,Adult</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Action,Adult,Adventure</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Action,Adult,Comedy</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action,Adult,Crime</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>Thriller,Western</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439</th>\n",
       "      <td>War</td>\n",
       "      <td>1249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>War,Western</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>Western</td>\n",
       "      <td>4070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>\\N</td>\n",
       "      <td>70459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1443 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      genres  COUNT(*)\n",
       "0                     Action     13236\n",
       "1               Action,Adult        11\n",
       "2     Action,Adult,Adventure         1\n",
       "3        Action,Adult,Comedy         5\n",
       "4         Action,Adult,Crime        10\n",
       "...                      ...       ...\n",
       "1438        Thriller,Western        36\n",
       "1439                     War      1249\n",
       "1440             War,Western         9\n",
       "1441                 Western      4070\n",
       "1442                      \\N     70459\n",
       "\n",
       "[1443 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualiza o resultado\n",
    "display(resultado2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "N_4ONbrYwEgW"
   },
   "outputs": [],
   "source": [
    "# Converte as strings para minúsculo\n",
    "resultado2['genres'] = resultado2['genres'].str.lower().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove valores NA (ausentes)\n",
    "temp = resultado2['genres'].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usamos o Countvectorizer para converter a coluna de gêneros em um vetor one-hot encoded para contar o número de filmes em cada gênero.\n",
    "\n",
    "![title](imagens/one-hot.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vamos criar um vetor usando expressão regular para filtrar as strings\n",
    "\n",
    "# https://docs.python.org/3.8/library/re.html\n",
    "padrao = '(?u)\\\\b[\\\\w-]+\\\\b'\n",
    "\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html\n",
    "vetor = CountVectorizer(token_pattern = padrao, analyzer = 'word').fit(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.feature_extraction.text.CountVectorizer"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(vetor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aplica a vetorização ao dataset sem valores NA\n",
    "bag_generos = vetor.transform(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scipy.sparse.csr.csr_matrix"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(bag_generos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retorna gêneros únicos\n",
    "generos_unicos =  vetor.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "executionInfo": {
     "elapsed": 838,
     "status": "ok",
     "timestamp": 1622094003105,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "C_HTLQkbl4r8",
    "outputId": "fe7d141d-a1d6-4dde-8072-ad4cae478ec8"
   },
   "outputs": [],
   "source": [
    "# Cria o dataframe de gêneros\n",
    "generos = pd.DataFrame(bag_generos.todense(), columns = generos_unicos, index = temp.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1443 entries, 0 to 1442\n",
      "Data columns (total 29 columns):\n",
      " #   Column       Non-Null Count  Dtype\n",
      "---  ------       --------------  -----\n",
      " 0   action       1443 non-null   int64\n",
      " 1   adult        1443 non-null   int64\n",
      " 2   adventure    1443 non-null   int64\n",
      " 3   animation    1443 non-null   int64\n",
      " 4   biography    1443 non-null   int64\n",
      " 5   comedy       1443 non-null   int64\n",
      " 6   crime        1443 non-null   int64\n",
      " 7   documentary  1443 non-null   int64\n",
      " 8   drama        1443 non-null   int64\n",
      " 9   family       1443 non-null   int64\n",
      " 10  fantasy      1443 non-null   int64\n",
      " 11  film-noir    1443 non-null   int64\n",
      " 12  game-show    1443 non-null   int64\n",
      " 13  history      1443 non-null   int64\n",
      " 14  horror       1443 non-null   int64\n",
      " 15  music        1443 non-null   int64\n",
      " 16  musical      1443 non-null   int64\n",
      " 17  mystery      1443 non-null   int64\n",
      " 18  n            1443 non-null   int64\n",
      " 19  news         1443 non-null   int64\n",
      " 20  reality-tv   1443 non-null   int64\n",
      " 21  romance      1443 non-null   int64\n",
      " 22  sci-fi       1443 non-null   int64\n",
      " 23  short        1443 non-null   int64\n",
      " 24  sport        1443 non-null   int64\n",
      " 25  talk-show    1443 non-null   int64\n",
      " 26  thriller     1443 non-null   int64\n",
      " 27  war          1443 non-null   int64\n",
      " 28  western      1443 non-null   int64\n",
      "dtypes: int64(29)\n",
      "memory usage: 338.2 KB\n"
     ]
    }
   ],
   "source": [
    "# Visualiza\n",
    "generos.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop da coluna n\n",
    "generos = generos.drop(columns = 'n', axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcula o percentual\n",
    "generos_percentual = 100 * pd.Series(generos.sum()).sort_values(ascending = False) / generos.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drama          18.503119\n",
       "comedy         16.770617\n",
       "documentary    16.008316\n",
       "adventure      14.899515\n",
       "action         14.622315\n",
       "romance        13.444213\n",
       "crime          13.028413\n",
       "mystery        12.543313\n",
       "thriller       12.474012\n",
       "fantasy        12.266112\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualiza\n",
    "generos_percentual.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.figure(figsize = (16,8))\n",
    "sns.barplot(x = generos_percentual.values, y = generos_percentual.index, orient = \"h\", palette = \"terrain\")\n",
    "plt.ylabel('Gênero')             \n",
    "plt.xlabel(\"\\nPercentual de Filmes (%)\")\n",
    "plt.title('\\nNúmero (Percentual) de Títulos Por Gênero\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jRKl5x8YxoXO"
   },
   "source": [
    "## 3- Qual a Mediana de Avaliação dos Filmes Por Gênero?\n",
    "\n",
    "Mediana de avaliação dos filmes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "H6UC4vtt0QQG"
   },
   "outputs": [],
   "source": [
    "# Consulta SQL\n",
    "consulta3 = '''\n",
    "            SELECT rating, genres FROM \n",
    "            ratings JOIN titles ON ratings.title_id = titles.title_id \n",
    "            WHERE premiered <= 2022 AND type = 'movie'\n",
    "            ''' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resultado\n",
    "resultado3 = pd.read_sql_query(consulta3, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>rating</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.5</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.1</td>\n",
       "      <td>Action,Adventure,Biography</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.2</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.5</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265680</th>\n",
       "      <td>3.6</td>\n",
       "      <td>Action,Adventure,Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265681</th>\n",
       "      <td>5.8</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265682</th>\n",
       "      <td>6.3</td>\n",
       "      <td>Adventure,Drama,History</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265683</th>\n",
       "      <td>3.8</td>\n",
       "      <td>Adventure,History,War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265684</th>\n",
       "      <td>8.3</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>265685 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        rating                      genres\n",
       "0          4.5                          \\N\n",
       "1          6.1  Action,Adventure,Biography\n",
       "2          5.2                       Drama\n",
       "3          4.5                       Drama\n",
       "4          3.8                       Drama\n",
       "...        ...                         ...\n",
       "265680     3.6   Action,Adventure,Thriller\n",
       "265681     5.8                    Thriller\n",
       "265682     6.3     Adventure,Drama,History\n",
       "265683     3.8       Adventure,History,War\n",
       "265684     8.3                       Drama\n",
       "\n",
       "[265685 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualiza\n",
    "display(resultado3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "W0JWWH6Y19SU"
   },
   "outputs": [],
   "source": [
    "# Vamos criar uma função para retornar os genêros\n",
    "def retorna_generos(df):\n",
    "    df['genres'] = df['genres'].str.lower().values\n",
    "    temp = df['genres'].dropna()\n",
    "    vetor = CountVectorizer(token_pattern = '(?u)\\\\b[\\\\w-]+\\\\b', analyzer = 'word').fit(temp)\n",
    "    generos_unicos =  vetor.get_feature_names()\n",
    "    generos_unicos = [genre for genre in generos_unicos if len(genre) > 1]\n",
    "    return generos_unicos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aplica a função\n",
    "generos_unicos = retorna_generos(resultado3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['action',\n",
       " 'adult',\n",
       " 'adventure',\n",
       " 'animation',\n",
       " 'biography',\n",
       " 'comedy',\n",
       " 'crime',\n",
       " 'documentary',\n",
       " 'drama',\n",
       " 'family',\n",
       " 'fantasy',\n",
       " 'film-noir',\n",
       " 'game-show',\n",
       " 'history',\n",
       " 'horror',\n",
       " 'music',\n",
       " 'musical',\n",
       " 'mystery',\n",
       " 'news',\n",
       " 'reality-tv',\n",
       " 'romance',\n",
       " 'sci-fi',\n",
       " 'short',\n",
       " 'sport',\n",
       " 'talk-show',\n",
       " 'thriller',\n",
       " 'war',\n",
       " 'western']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualiza\n",
    "generos_unicos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cria listas vazias\n",
    "genero_counts = []\n",
    "genero_ratings = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "Iggq_inu9QJz"
   },
   "outputs": [],
   "source": [
    "# Loop\n",
    "for item in generos_unicos:\n",
    "    \n",
    "    # Retorna a contagem de filmes por gênero\n",
    "    consulta = 'SELECT COUNT(rating) FROM ratings JOIN titles ON ratings.title_id=titles.title_id WHERE genres LIKE '+ '\\''+'%'+item+'%'+'\\' AND type=\\'movie\\''\n",
    "    resultado = pd.read_sql_query(consulta, conn)\n",
    "    genero_counts.append(resultado.values[0][0])\n",
    "  \n",
    "     # Retorna a avaliação de filmes por gênero\n",
    "    consulta = 'SELECT rating FROM ratings JOIN titles ON ratings.title_id=titles.title_id WHERE genres LIKE '+ '\\''+'%'+item+'%'+'\\' AND type=\\'movie\\''\n",
    "    resultado = pd.read_sql_query(consulta, conn)\n",
    "    genero_ratings.append(np.median(resultado['rating']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "E4lGTLpfQStn"
   },
   "outputs": [],
   "source": [
    "# Prepara o dataframe final\n",
    "df_genero_ratings = pd.DataFrame()\n",
    "df_genero_ratings['genres'] = generos_unicos\n",
    "df_genero_ratings['count'] = genero_counts\n",
    "df_genero_ratings['rating'] = genero_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>genres</th>\n",
       "      <th>count</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>action</td>\n",
       "      <td>28481</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>adult</td>\n",
       "      <td>4073</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adventure</td>\n",
       "      <td>17508</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>animation</td>\n",
       "      <td>4616</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>biography</td>\n",
       "      <td>7958</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>comedy</td>\n",
       "      <td>66329</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>crime</td>\n",
       "      <td>22920</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>documentary</td>\n",
       "      <td>38804</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>drama</td>\n",
       "      <td>117974</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>family</td>\n",
       "      <td>10112</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>fantasy</td>\n",
       "      <td>7873</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>film-noir</td>\n",
       "      <td>760</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>game-show</td>\n",
       "      <td>3</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>history</td>\n",
       "      <td>7295</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>horror</td>\n",
       "      <td>18693</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>music</td>\n",
       "      <td>12230</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>musical</td>\n",
       "      <td>5918</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>mystery</td>\n",
       "      <td>9878</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>news</td>\n",
       "      <td>657</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>reality-tv</td>\n",
       "      <td>35</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         genres   count  rating\n",
       "0        action   28481     5.8\n",
       "1         adult    4073     5.8\n",
       "2     adventure   17508     6.0\n",
       "3     animation    4616     6.4\n",
       "4     biography    7958     7.0\n",
       "5        comedy   66329     6.0\n",
       "6         crime   22920     6.1\n",
       "7   documentary   38804     7.3\n",
       "8         drama  117974     6.3\n",
       "9        family   10112     6.3\n",
       "10      fantasy    7873     6.0\n",
       "11    film-noir     760     6.6\n",
       "12    game-show       3     7.2\n",
       "13      history    7295     6.9\n",
       "14       horror   18693     5.0\n",
       "15        music   12230     6.6\n",
       "16      musical    5918     6.3\n",
       "17      mystery    9878     6.0\n",
       "18         news     657     7.3\n",
       "19   reality-tv      35     6.7"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualiza\n",
    "df_genero_ratings.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "h0voA1yQQ_80"
   },
   "outputs": [],
   "source": [
    "# Drop do índice 18 (news)\n",
    "# Não queremos essa informação como gênero\n",
    "df_genero_ratings = df_genero_ratings.drop(index = 18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "C2d5HNJITbaG"
   },
   "outputs": [],
   "source": [
    "# Ordena o resultado\n",
    "df_genero_ratings = df_genero_ratings.sort_values(by = 'rating', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "executionInfo": {
     "elapsed": 2327,
     "status": "ok",
     "timestamp": 1622094283855,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "MSrgVMHuUAAV",
    "outputId": "cd8a7363-3fab-42c7-eea3-c734e621904b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "\n",
    "# Figura\n",
    "plt.figure(figsize = (16,10))\n",
    "\n",
    "# Barplot\n",
    "sns.barplot(y = df_genero_ratings.genres, x = df_genero_ratings.rating, orient = \"h\")\n",
    "\n",
    "# Textos do gráfico\n",
    "for i in range(len(df_genero_ratings.index)):\n",
    "    \n",
    "    plt.text(4.0, \n",
    "             i + 0.25, \n",
    "             str(df_genero_ratings['count'][df_genero_ratings.index[i]]) + \" filmes\")\n",
    "    \n",
    "    plt.text(df_genero_ratings.rating[df_genero_ratings.index[i]],\n",
    "             i + 0.25,\n",
    "             round(df_genero_ratings[\"rating\"][df_genero_ratings.index[i]],2))\n",
    "\n",
    "plt.ylabel('Gênero')             \n",
    "plt.xlabel('Mediana da Avaliação')\n",
    "plt.title('\\nMediana de Avaliação Por Gênero\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V4RFdmlvsbgQ"
   },
   "source": [
    "## 4- Qual a Mediana de Avaliação dos Filmes Em Relação ao Ano de Estréia?\n",
    "\n",
    "Mediana de avaliação dos filmes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "executionInfo": {
     "elapsed": 1283,
     "status": "ok",
     "timestamp": 1622094463416,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "D7ZoVPJip0eA",
    "outputId": "8ab5db2f-ccd4-49c0-8eba-0da45d2e9812"
   },
   "outputs": [],
   "source": [
    "# Consulta SQL\n",
    "consulta4 = '''\n",
    "            SELECT rating AS Rating, premiered FROM \n",
    "            ratings JOIN titles ON ratings.title_id = titles.title_id \n",
    "            WHERE premiered <= 2022 AND type = 'movie'\n",
    "            ORDER BY premiered\n",
    "            ''' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resultado\n",
    "resultado4 = pd.read_sql_query(consulta4, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Rating</th>\n",
       "      <th>premiered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.4</td>\n",
       "      <td>1896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.4</td>\n",
       "      <td>1897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.9</td>\n",
       "      <td>1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.7</td>\n",
       "      <td>1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265680</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265681</th>\n",
       "      <td>4.8</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265682</th>\n",
       "      <td>8.3</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265683</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265684</th>\n",
       "      <td>7.9</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>265685 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Rating  premiered\n",
       "0          6.4       1896\n",
       "1          4.4       1897\n",
       "2          3.9       1899\n",
       "3          3.7       1899\n",
       "4          6.0       1900\n",
       "...        ...        ...\n",
       "265680     6.6       2021\n",
       "265681     4.8       2021\n",
       "265682     8.3       2021\n",
       "265683     6.0       2021\n",
       "265684     7.9       2021\n",
       "\n",
       "[265685 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(resultado4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "MC6R8jw-XWZj"
   },
   "outputs": [],
   "source": [
    "# Calculamos a mediana ao longo do tempo (anos)\n",
    "ratings = []\n",
    "for year in set(resultado4['premiered']):\n",
    "    ratings.append(np.median(resultado4[resultado4['premiered'] == year]['Rating']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4.4, 3.8, 6.15, 4.5, 3.5, 4.6, 5.4, 4.3, 3.6]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "VV_YECfDYLkU"
   },
   "outputs": [],
   "source": [
    "# Lista de anos\n",
    "anos = list(set(resultado4['premiered']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1897, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anos[1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "executionInfo": {
     "elapsed": 830,
     "status": "ok",
     "timestamp": 1622094760127,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "o-DcxevEspQQ",
    "outputId": "e3770997-6254-4e5c-a60f-e8cc665e9674"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.figure(figsize = (16,8))\n",
    "plt.plot(anos, ratings)\n",
    "plt.xlabel('\\nAno')\n",
    "plt.ylabel('Mediana de Avaliação')\n",
    "plt.title('\\nMediana de Avaliação dos Filmes Em Relação ao Ano de Estréia\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2F2Dmji2xR9v"
   },
   "source": [
    "## 5- Qual o Número de Filmes Avaliados Por Gênero Em Relação ao Ano de Estréia?\n",
    "\n",
    "Número de filmes por gênero. Vamos listar os Top 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "eHdCbIyDm10W"
   },
   "outputs": [],
   "source": [
    "# Consulta SQL\n",
    "consulta5 = '''SELECT genres FROM titles ''' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resultado\n",
    "resultado5 = pd.read_sql_query(consulta5, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Documentary,Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Animation,Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Animation,Comedy,Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Animation,Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Comedy,Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8070583</th>\n",
       "      <td>Action,Drama,Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8070584</th>\n",
       "      <td>Action,Drama,Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8070585</th>\n",
       "      <td>Action,Drama,Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8070586</th>\n",
       "      <td>Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8070587</th>\n",
       "      <td>Animation,Comedy,Family</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8070588 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           genres\n",
       "0               Documentary,Short\n",
       "1                 Animation,Short\n",
       "2        Animation,Comedy,Romance\n",
       "3                 Animation,Short\n",
       "4                    Comedy,Short\n",
       "...                           ...\n",
       "8070583       Action,Drama,Family\n",
       "8070584       Action,Drama,Family\n",
       "8070585       Action,Drama,Family\n",
       "8070586                     Short\n",
       "8070587   Animation,Comedy,Family\n",
       "\n",
       "[8070588 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(resultado5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "3l9aiHCLvI5S"
   },
   "outputs": [],
   "source": [
    "# Retorna gêneros únicos\n",
    "generos_unicos = retorna_generos(resultado5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['action',\n",
       " 'adult',\n",
       " 'adventure',\n",
       " 'animation',\n",
       " 'biography',\n",
       " 'comedy',\n",
       " 'crime',\n",
       " 'documentary',\n",
       " 'drama',\n",
       " 'family',\n",
       " 'fantasy',\n",
       " 'film-noir',\n",
       " 'game-show',\n",
       " 'history',\n",
       " 'horror',\n",
       " 'music',\n",
       " 'musical',\n",
       " 'mystery',\n",
       " 'news',\n",
       " 'reality-tv',\n",
       " 'romance',\n",
       " 'sci-fi',\n",
       " 'short',\n",
       " 'sport',\n",
       " 'talk-show',\n",
       " 'thriller',\n",
       " 'war',\n",
       " 'western']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualiza o resultado\n",
    "generos_unicos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "IvXAi1HGf7X8"
   },
   "outputs": [],
   "source": [
    "# Agora fazemos a contagem\n",
    "genero_count = []\n",
    "for item in generos_unicos:\n",
    "    consulta = 'SELECT COUNT(*) COUNT FROM  titles  WHERE genres LIKE '+ '\\''+'%'+item+'%'+'\\' AND type=\\'movie\\' AND premiered <= 2022'\n",
    "    resultado = pd.read_sql_query(consulta, conn)\n",
    "    genero_count.append(resultado['COUNT'].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "6ASveNmFf9aI"
   },
   "outputs": [],
   "source": [
    "# Prepara o dataframe\n",
    "df_genero_count = pd.DataFrame()\n",
    "df_genero_count['genre'] = generos_unicos\n",
    "df_genero_count['Count'] = genero_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "938c_uY-kDiW"
   },
   "outputs": [],
   "source": [
    "# Calcula os top 5\n",
    "df_genero_count = df_genero_count[df_genero_count['genre'] != 'n']\n",
    "df_genero_count = df_genero_count.sort_values(by = 'Count', ascending = False)\n",
    "top_generos = df_genero_count.head()['genre'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 499
    },
    "executionInfo": {
     "elapsed": 4708,
     "status": "ok",
     "timestamp": 1621951784338,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "8aP0rOP0mBh_",
    "outputId": "8604eb15-1809-48e6-9772-6e5c34e0624c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "\n",
    "# Figura\n",
    "plt.figure(figsize = (16,8))\n",
    "\n",
    "# Loop e Plot\n",
    "for item in top_generos:\n",
    "    consulta = 'SELECT COUNT(*) Number_of_movies, premiered Year FROM  titles  WHERE genres LIKE '+ '\\''+'%'+item+'%'+'\\' AND type=\\'movie\\' AND Year <=2022 GROUP BY Year'\n",
    "    resultado = pd.read_sql_query(consulta, conn)\n",
    "    plt.plot(resultado['Year'], resultado['Number_of_movies'])\n",
    "\n",
    "plt.xlabel('\\nAno')\n",
    "plt.ylabel('Número de Filmes Avaliados')\n",
    "plt.title('\\nNúmero de Filmes Avaliados Por Gênero Em Relação ao Ano de Estréia\\n')\n",
    "plt.legend(labels = top_generos)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9U6CKRVVzF5O"
   },
   "source": [
    "## 6- Qual o Filme Com Maior Tempo de Duração? Calcule os Percentis.\n",
    "\n",
    "Percentis e filme mais longo da história."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "0emEVWQcZAkB"
   },
   "outputs": [],
   "source": [
    "# Consulta SQL\n",
    "consulta6 = '''\n",
    "            SELECT runtime_minutes Runtime \n",
    "            FROM titles \n",
    "            WHERE type = 'movie' AND Runtime != 'NaN'\n",
    "            ''' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resultado\n",
    "resultado6 = pd.read_sql_query(consulta6, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366324</th>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366325</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366326</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366327</th>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366328</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366329 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Runtime\n",
       "0           100\n",
       "1            70\n",
       "2            90\n",
       "3           120\n",
       "4            58\n",
       "...         ...\n",
       "366324      123\n",
       "366325       57\n",
       "366326      100\n",
       "366327      116\n",
       "366328       49\n",
       "\n",
       "[366329 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(resultado6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1622095194538,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "TTu1QGec4wgb",
    "outputId": "3d8b9dc8-0d36-4e76-bbed-706aacb02b0d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 percentil da duração (runtime) é: 1.0\n",
      "1 percentil da duração (runtime) é: 45.0\n",
      "2 percentil da duração (runtime) é: 48.0\n",
      "3 percentil da duração (runtime) é: 50.0\n",
      "4 percentil da duração (runtime) é: 50.0\n",
      "5 percentil da duração (runtime) é: 52.0\n",
      "6 percentil da duração (runtime) é: 52.0\n",
      "7 percentil da duração (runtime) é: 54.0\n",
      "8 percentil da duração (runtime) é: 55.0\n",
      "9 percentil da duração (runtime) é: 56.0\n",
      "10 percentil da duração (runtime) é: 58.0\n",
      "11 percentil da duração (runtime) é: 59.0\n",
      "12 percentil da duração (runtime) é: 60.0\n",
      "13 percentil da duração (runtime) é: 60.0\n",
      "14 percentil da duração (runtime) é: 60.0\n",
      "15 percentil da duração (runtime) é: 62.0\n",
      "16 percentil da duração (runtime) é: 63.0\n",
      "17 percentil da duração (runtime) é: 65.0\n",
      "18 percentil da duração (runtime) é: 66.0\n",
      "19 percentil da duração (runtime) é: 68.0\n",
      "20 percentil da duração (runtime) é: 70.0\n",
      "21 percentil da duração (runtime) é: 70.0\n",
      "22 percentil da duração (runtime) é: 71.0\n",
      "23 percentil da duração (runtime) é: 72.0\n",
      "24 percentil da duração (runtime) é: 73.0\n",
      "25 percentil da duração (runtime) é: 74.0\n",
      "26 percentil da duração (runtime) é: 75.0\n",
      "27 percentil da duração (runtime) é: 75.0\n",
      "28 percentil da duração (runtime) é: 76.0\n",
      "29 percentil da duração (runtime) é: 77.0\n",
      "30 percentil da duração (runtime) é: 78.0\n",
      "31 percentil da duração (runtime) é: 79.0\n",
      "32 percentil da duração (runtime) é: 80.0\n",
      "33 percentil da duração (runtime) é: 80.0\n",
      "34 percentil da duração (runtime) é: 80.0\n",
      "35 percentil da duração (runtime) é: 81.0\n",
      "36 percentil da duração (runtime) é: 82.0\n",
      "37 percentil da duração (runtime) é: 82.0\n",
      "38 percentil da duração (runtime) é: 83.0\n",
      "39 percentil da duração (runtime) é: 84.0\n",
      "40 percentil da duração (runtime) é: 84.0\n",
      "41 percentil da duração (runtime) é: 85.0\n",
      "42 percentil da duração (runtime) é: 85.0\n",
      "43 percentil da duração (runtime) é: 85.0\n",
      "44 percentil da duração (runtime) é: 86.0\n",
      "45 percentil da duração (runtime) é: 86.0\n",
      "46 percentil da duração (runtime) é: 87.0\n",
      "47 percentil da duração (runtime) é: 87.0\n",
      "48 percentil da duração (runtime) é: 88.0\n",
      "49 percentil da duração (runtime) é: 88.0\n",
      "50 percentil da duração (runtime) é: 89.0\n",
      "51 percentil da duração (runtime) é: 90.0\n",
      "52 percentil da duração (runtime) é: 90.0\n",
      "53 percentil da duração (runtime) é: 90.0\n",
      "54 percentil da duração (runtime) é: 90.0\n",
      "55 percentil da duração (runtime) é: 90.0\n",
      "56 percentil da duração (runtime) é: 90.0\n",
      "57 percentil da duração (runtime) é: 90.0\n",
      "58 percentil da duração (runtime) é: 91.0\n",
      "59 percentil da duração (runtime) é: 91.0\n",
      "60 percentil da duração (runtime) é: 92.0\n",
      "61 percentil da duração (runtime) é: 92.0\n",
      "62 percentil da duração (runtime) é: 93.0\n",
      "63 percentil da duração (runtime) é: 93.0\n",
      "64 percentil da duração (runtime) é: 94.0\n",
      "65 percentil da duração (runtime) é: 94.0\n",
      "66 percentil da duração (runtime) é: 95.0\n",
      "67 percentil da duração (runtime) é: 95.0\n",
      "68 percentil da duração (runtime) é: 96.0\n",
      "69 percentil da duração (runtime) é: 96.0\n",
      "70 percentil da duração (runtime) é: 97.0\n",
      "71 percentil da duração (runtime) é: 97.0\n",
      "72 percentil da duração (runtime) é: 98.0\n",
      "73 percentil da duração (runtime) é: 99.0\n",
      "74 percentil da duração (runtime) é: 100.0\n",
      "75 percentil da duração (runtime) é: 100.0\n",
      "76 percentil da duração (runtime) é: 100.0\n",
      "77 percentil da duração (runtime) é: 101.0\n",
      "78 percentil da duração (runtime) é: 102.0\n",
      "79 percentil da duração (runtime) é: 103.0\n",
      "80 percentil da duração (runtime) é: 104.0\n",
      "81 percentil da duração (runtime) é: 105.0\n",
      "82 percentil da duração (runtime) é: 106.0\n",
      "83 percentil da duração (runtime) é: 107.0\n",
      "84 percentil da duração (runtime) é: 108.0\n",
      "85 percentil da duração (runtime) é: 110.0\n",
      "86 percentil da duração (runtime) é: 110.0\n",
      "87 percentil da duração (runtime) é: 112.0\n",
      "88 percentil da duração (runtime) é: 114.0\n",
      "89 percentil da duração (runtime) é: 116.0\n",
      "90 percentil da duração (runtime) é: 119.0\n",
      "91 percentil da duração (runtime) é: 120.0\n",
      "92 percentil da duração (runtime) é: 122.0\n",
      "93 percentil da duração (runtime) é: 126.0\n",
      "94 percentil da duração (runtime) é: 130.0\n",
      "95 percentil da duração (runtime) é: 134.0\n",
      "96 percentil da duração (runtime) é: 139.0\n",
      "97 percentil da duração (runtime) é: 145.0\n",
      "98 percentil da duração (runtime) é: 153.0\n",
      "99 percentil da duração (runtime) é: 168.0\n",
      "100 percentil da duração (runtime) é: 51420.0\n"
     ]
    }
   ],
   "source": [
    "# Loop para cálculo dos percentis\n",
    "for i in range(101): \n",
    "    val = i\n",
    "    perc = round(np.percentile(resultado6['Runtime'].values, val), 2)\n",
    "    print('{} percentil da duração (runtime) é: {}'.format(val, perc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "id": "QsG590nIag_h"
   },
   "outputs": [],
   "source": [
    "# Refazendo a consulta e retornando o filme com maior duração\n",
    "consulta6 = '''\n",
    "            SELECT runtime_minutes Runtime, primary_title\n",
    "            FROM titles \n",
    "            WHERE type = 'movie' AND Runtime != 'NaN'\n",
    "            ORDER BY Runtime DESC\n",
    "            LIMIT 1\n",
    "            ''' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultado6 = pd.read_sql_query(consulta6, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 77
    },
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1622095429610,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "GiXWo7tBaqhZ",
    "outputId": "ae7656fe-6ba5-43dd-d4ae-5a39cac5b120"
   },
   "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>Runtime</th>\n",
       "      <th>primary_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51420</td>\n",
       "      <td>Logistics</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Runtime primary_title\n",
       "0    51420     Logistics"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultado6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ho17U3IbcieL"
   },
   "source": [
    "'Logistics' é o filme mais longo da história (até este momento), com 857 horas (51420 minutos). Dirigido por Daniel Andersson e Erika Magnusson, o filme acompanha todo o ciclo de vida de um pedômetro (dispositivo de rastreamento de passos) em ordem cronológica reversa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z4O4rDdHy7oO"
   },
   "source": [
    "## 7- Qual a Relação Entre Duração e Gênero?\n",
    "\n",
    "Relação entre duração do filme e gênero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "id": "0kc8MHtUledj"
   },
   "outputs": [],
   "source": [
    "# Consulta SQL\n",
    "consulta7 = '''\n",
    "            SELECT AVG(runtime_minutes) Runtime, genres \n",
    "            FROM titles \n",
    "            WHERE type = 'movie'\n",
    "            AND runtime_minutes != 'NaN'\n",
    "            GROUP BY genres\n",
    "            ''' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resultado\n",
    "resultado7 = pd.read_sql_query(consulta7, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "id": "0rzN-DAm5mwA"
   },
   "outputs": [],
   "source": [
    "# Retorna gêneros únicos\n",
    "generos_unicos = retorna_generos(resultado7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['action',\n",
       " 'adult',\n",
       " 'adventure',\n",
       " 'animation',\n",
       " 'biography',\n",
       " 'comedy',\n",
       " 'crime',\n",
       " 'documentary',\n",
       " 'drama',\n",
       " 'family',\n",
       " 'fantasy',\n",
       " 'film-noir',\n",
       " 'game-show',\n",
       " 'history',\n",
       " 'horror',\n",
       " 'music',\n",
       " 'musical',\n",
       " 'mystery',\n",
       " 'news',\n",
       " 'reality-tv',\n",
       " 'romance',\n",
       " 'sci-fi',\n",
       " 'short',\n",
       " 'sport',\n",
       " 'talk-show',\n",
       " 'thriller',\n",
       " 'war',\n",
       " 'western']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualiza\n",
    "generos_unicos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "id": "8f79NjxwEdA4"
   },
   "outputs": [],
   "source": [
    "# Calcula duração por gênero\n",
    "genero_runtime = []\n",
    "for item in generos_unicos:\n",
    "    consulta = 'SELECT runtime_minutes Runtime FROM  titles  WHERE genres LIKE '+ '\\''+'%'+item+'%'+'\\' AND type=\\'movie\\' AND Runtime!=\\'NaN\\''\n",
    "    resultado = pd.read_sql_query(consulta, conn)\n",
    "    genero_runtime.append(np.median(resultado['Runtime']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "id": "bW5jRTqyG5s1"
   },
   "outputs": [],
   "source": [
    "# Prepara o dataframe\n",
    "df_genero_runtime = pd.DataFrame()\n",
    "df_genero_runtime['genre'] = generos_unicos\n",
    "df_genero_runtime['runtime'] = genero_runtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "q03CMhQwHTXs"
   },
   "outputs": [],
   "source": [
    "# Remove índice 18 (news)\n",
    "df_genero_runtime = df_genero_runtime.drop(index = 18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "id": "xa6O1pozHfEH"
   },
   "outputs": [],
   "source": [
    "# Ordena os dados\n",
    "df_genero_runtime = df_genero_runtime.sort_values(by = 'runtime', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "executionInfo": {
     "elapsed": 902,
     "status": "ok",
     "timestamp": 1622096440987,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "AKKU1iTHH19k",
    "outputId": "115ea1e7-7e87-4332-a92a-8851c8081c28"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "\n",
    "# Tamanho da figura\n",
    "plt.figure(figsize = (16,8))\n",
    "\n",
    "# Barplot\n",
    "sns.barplot(y = df_genero_runtime.genre, x = df_genero_runtime.runtime, orient = \"h\")\n",
    "\n",
    "# Loop\n",
    "for i in range(len(df_genero_runtime.index)):\n",
    "    plt.text(df_genero_runtime.runtime[df_genero_runtime.index[i]],\n",
    "             i + 0.25,\n",
    "             round(df_genero_runtime[\"runtime\"][df_genero_runtime.index[i]], 2))\n",
    "\n",
    "plt.ylabel('Gênero')             \n",
    "plt.xlabel('\\nMediana de Tempo de Duração (Minutos)')\n",
    "plt.title('\\nRelação Entre Duração e Gênero\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t6ScDbBNzblj"
   },
   "source": [
    "## 8- Qual o Número de Filmes Produzidos Por País?\n",
    "\n",
    "Número de filmes produzidos por país."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "executionInfo": {
     "elapsed": 29930,
     "status": "ok",
     "timestamp": 1621952554276,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "xit2KSBiLOdf",
    "outputId": "8d660d13-8a78-400e-d827-944ca8ec9802"
   },
   "outputs": [],
   "source": [
    "# Consulta SQL\n",
    "consulta8 = '''\n",
    "            SELECT region, COUNT(*) Number_of_movies FROM \n",
    "            akas JOIN titles ON \n",
    "            akas.title_id = titles.title_id\n",
    "            WHERE region != 'None'\n",
    "            AND type = \\'movie\\'\n",
    "            GROUP BY region\n",
    "            ''' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resultado\n",
    "resultado8 = pd.read_sql_query(consulta8, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>region</th>\n",
       "      <th>Number_of_movies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AD</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AE</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AF</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AG</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL</td>\n",
       "      <td>1230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>YUCS</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>ZA</td>\n",
       "      <td>2930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>ZM</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>ZRCD</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>ZW</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>232 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    region  Number_of_movies\n",
       "0       AD                22\n",
       "1       AE               321\n",
       "2       AF               108\n",
       "3       AG                12\n",
       "4       AL              1230\n",
       "..     ...               ...\n",
       "227   YUCS               146\n",
       "228     ZA              2930\n",
       "229     ZM                11\n",
       "230   ZRCD                 2\n",
       "231     ZW                46\n",
       "\n",
       "[232 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(resultado8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(232, 2)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape\n",
    "resultado8.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "232"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Número de linhas\n",
    "resultado8.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Listas auxiliares\n",
    "nomes_paises = []\n",
    "contagem = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "id": "y0EiQBfgNEgb"
   },
   "outputs": [],
   "source": [
    "# Loop para obter o país de acordo com a região\n",
    "for i in range(resultado8.shape[0]):\n",
    "    try:\n",
    "        coun = resultado8['region'].values[i]\n",
    "        nomes_paises.append(pycountry.countries.get(alpha_2 = coun).name)\n",
    "        contagem.append(resultado8['Number_of_movies'].values[i])\n",
    "    except: \n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "id": "Ke69xq96Oeeu"
   },
   "outputs": [],
   "source": [
    "# Prepara o dataframe\n",
    "df_filmes_paises = pd.DataFrame()\n",
    "df_filmes_paises['country'] = nomes_paises\n",
    "df_filmes_paises['Movie_Count'] = contagem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "id": "RyaO-hmNOxv-"
   },
   "outputs": [],
   "source": [
    "# Ordena o resultado\n",
    "df_filmes_paises = df_filmes_paises.sort_values(by = 'Movie_Count', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Movie_Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>United States</td>\n",
       "      <td>255987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>113225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Japan</td>\n",
       "      <td>87815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>France</td>\n",
       "      <td>80993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>India</td>\n",
       "      <td>70743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Canada</td>\n",
       "      <td>67483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Germany</td>\n",
       "      <td>65316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Italy</td>\n",
       "      <td>63060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>61825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Spain</td>\n",
       "      <td>61704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            country  Movie_Count\n",
       "198   United States       255987\n",
       "65   United Kingdom       113225\n",
       "96            Japan        87815\n",
       "63           France        80993\n",
       "89            India        70743\n",
       "32           Canada        67483\n",
       "47          Germany        65316\n",
       "93            Italy        63060\n",
       "26           Brazil        61825\n",
       "58            Spain        61704"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualiza\n",
    "df_filmes_paises.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "executionInfo": {
     "elapsed": 1725,
     "status": "ok",
     "timestamp": 1621952785166,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "9vLWE-JaPB8K",
    "outputId": "f738f6a3-4128-469f-c772-bbd2614d8caf"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "\n",
    "# Figura\n",
    "plt.figure(figsize = (20,8))\n",
    "\n",
    "# Barplot\n",
    "sns.barplot(y = df_filmes_paises[:20].country, x = df_filmes_paises[:20].Movie_Count, orient = \"h\")\n",
    "\n",
    "# Loop\n",
    "for i in range(0,20):\n",
    "    plt.text(df_filmes_paises.Movie_Count[df_filmes_paises.index[i]]-1,\n",
    "             i + 0.30,\n",
    "             round(df_filmes_paises[\"Movie_Count\"][df_filmes_paises.index[i]],2))\n",
    "\n",
    "plt.ylabel('País')             \n",
    "plt.xlabel('\\nNúmero de Filmes')\n",
    "plt.title('\\nNúmero de Filmes Produzidos Por País\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "szAHC3FEyoiv"
   },
   "source": [
    "## 9- Quais São os Top 10 Melhores Filmes?\n",
    "\n",
    "Top 10 filmes com melhor avaliação e mais de 25 mil votos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 343
    },
    "executionInfo": {
     "elapsed": 1249,
     "status": "ok",
     "timestamp": 1621952795722,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "b4IB3sTJiStg",
    "outputId": "b8f85ad1-c741-4883-81ad-6e56aa690ba0"
   },
   "outputs": [],
   "source": [
    "# Consulta SQL\n",
    "consulta9 = '''\n",
    "            SELECT primary_title AS Movie_Name, genres, rating\n",
    "            FROM \n",
    "            titles JOIN ratings\n",
    "            ON  titles.title_id = ratings.title_id\n",
    "            WHERE titles.type = 'movie' AND ratings.votes >= 25000\n",
    "            ORDER BY rating DESC\n",
    "            LIMIT 10          \n",
    "            ''' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resultado\n",
    "top10_melhores_filmes = pd.read_sql_query(consulta9, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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_Name</th>\n",
       "      <th>genres</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>Drama</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Chaos Class</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>Crime,Drama</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CM101MMXI Fundamentals</td>\n",
       "      <td>Comedy,Documentary</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Soorarai Pottru</td>\n",
       "      <td>Drama</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>Crime,Drama</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>Crime,Drama</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Dara of Jasenovac</td>\n",
       "      <td>Drama,War</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>David Attenborough: A Life on Our Planet</td>\n",
       "      <td>Biography,Documentary</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Movie_Name                 genres  rating\n",
       "0                  The Shawshank Redemption                  Drama     9.3\n",
       "1                           The Chaos Class           Comedy,Drama     9.3\n",
       "2                             The Godfather            Crime,Drama     9.2\n",
       "3                    CM101MMXI Fundamentals     Comedy,Documentary     9.2\n",
       "4                           Soorarai Pottru                  Drama     9.1\n",
       "5                              12 Angry Men            Crime,Drama     9.0\n",
       "6                    The Godfather: Part II            Crime,Drama     9.0\n",
       "7                           The Dark Knight     Action,Crime,Drama     9.0\n",
       "8                         Dara of Jasenovac              Drama,War     9.0\n",
       "9  David Attenborough: A Life on Our Planet  Biography,Documentary     9.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(top10_melhores_filmes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10- Quais São os Top 10 Piores Filmes?\n",
    "\n",
    "Top 10 filmes com pior avaliação e mais de 25 mil votos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 343
    },
    "executionInfo": {
     "elapsed": 613,
     "status": "ok",
     "timestamp": 1621952796329,
     "user": {
      "displayName": "Utkarsh Sinha",
      "photoUrl": "",
      "userId": "11911393678467085382"
     },
     "user_tz": -330
    },
    "id": "xNkQXdifjWEF",
    "outputId": "16e506a2-6cfd-49e6-f67d-b0b3894e4ff4"
   },
   "outputs": [],
   "source": [
    "# Consulta SQL\n",
    "consulta10 = '''\n",
    "            SELECT primary_title AS Movie_Name, genres, rating\n",
    "            FROM \n",
    "            titles JOIN ratings\n",
    "            ON  titles.title_id = ratings.title_id\n",
    "            WHERE titles.type = 'movie' AND ratings.votes >= 25000\n",
    "            ORDER BY rating ASC\n",
    "            LIMIT 10\n",
    "            ''' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "id": "naOoI-IXfKSI"
   },
   "outputs": [],
   "source": [
    "# Resultado\n",
    "top10_piores_filmes = pd.read_sql_query(consulta10, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>Movie_Name</th>\n",
       "      <th>genres</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cumali Ceber</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smolensk</td>\n",
       "      <td>Drama,Thriller</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sadak 2</td>\n",
       "      <td>Action,Drama</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Reis</td>\n",
       "      <td>Biography,Drama</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Justin Bieber: Never Say Never</td>\n",
       "      <td>Documentary,Music</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Manos: the Hands of Fate</td>\n",
       "      <td>Drama,Horror</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Radhe</td>\n",
       "      <td>Action,Crime,Thriller</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Superbabies: Baby Geniuses 2</td>\n",
       "      <td>Comedy,Family,Sci-Fi</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>The Hottie &amp; the Nottie</td>\n",
       "      <td>Comedy,Romance</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Disaster Movie</td>\n",
       "      <td>Comedy,Sci-Fi</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Movie_Name                 genres  rating\n",
       "0                    Cumali Ceber                 Comedy     1.0\n",
       "1                        Smolensk         Drama,Thriller     1.1\n",
       "2                         Sadak 2           Action,Drama     1.1\n",
       "3                            Reis        Biography,Drama     1.4\n",
       "4  Justin Bieber: Never Say Never      Documentary,Music     1.6\n",
       "5        Manos: the Hands of Fate           Drama,Horror     1.8\n",
       "6                           Radhe  Action,Crime,Thriller     1.8\n",
       "7    Superbabies: Baby Geniuses 2   Comedy,Family,Sci-Fi     1.9\n",
       "8         The Hottie & the Nottie         Comedy,Romance     1.9\n",
       "9                  Disaster Movie          Comedy,Sci-Fi     1.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(top10_piores_filmes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obrigado\n",
    "\n",
    "### Visite o Blog da Data Science Academy - <a href=\"http://blog.dsacademy.com.br\">Blog DSA</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parabéns se você chegou até aqui. Use o voucher PYTHONDSA9642 para comprar qualquer curso ou Formação da DSA com 5% de desconto."
   ]
  }
 ],
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