sessao_da_tarde/cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>date</th>\n",
       "      <th>title</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-01-02</td>\n",
       "      <td>Não Houve Exibição</td>\n",
       "      <td>velório de Pelé, intitulada Rei Pelé: Cobertura</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-01-03</td>\n",
       "      <td>A Nova Cinderela</td>\n",
       "      <td>exibição às 15h55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-04</td>\n",
       "      <td>Agente 86</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date                title  \\\n",
       "0  2023-01-02  Não Houve Exibição    \n",
       "1  2023-01-03    A Nova Cinderela    \n",
       "2  2023-01-04           Agente 86    \n",
       "\n",
       "                                          comments  \n",
       "0  velório de Pelé, intitulada Rei Pelé: Cobertura  \n",
       "1                                exibição às 15h55  \n",
       "2                                             2008  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('datasets/whole_century.csv')\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Limpeza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>title</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-01-02</td>\n",
       "      <td>Não Houve Exibição</td>\n",
       "      <td>velório de Pelé, intitulada Rei Pelé: Cobertura</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2023-02-07</td>\n",
       "      <td>Não Houve Exibição - Mundial de Clubes: Flamen...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2023-02-09</td>\n",
       "      <td>Não Houve Exibição - Supercopa Feminina de Fut...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date                                              title  \\\n",
       "0   2023-01-02                                Não Houve Exibição    \n",
       "26  2023-02-07  Não Houve Exibição - Mundial de Clubes: Flamen...   \n",
       "28  2023-02-09  Não Houve Exibição - Supercopa Feminina de Fut...   \n",
       "\n",
       "                                           comments  \n",
       "0   velório de Pelé, intitulada Rei Pelé: Cobertura  \n",
       "26                                              NaN  \n",
       "28                                              NaN  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_example = df.dropna(subset='title')\n",
    "df_example[df_example['title'].str.contains('Não Houve', regex= False, case= False)].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pode-se observar que:\n",
    "- Houveram dias que não houveram exibição de filmes na sessão da tarde\n",
    "- Existem dias que não há informação sobre o filme passado (título nulo)\n",
    "\n",
    "Retiraremos esses dados do dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(subset=['title'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows_to_delete = df.loc[df['title'].str.contains('Não Houve', case=False, regex=False)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(rows_to_delete, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecionando apenas o primeiro título (para dias com múltiplas exibições para diferentes estados)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>date</th>\n",
       "      <th>title</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>2021-04-12</td>\n",
       "      <td>Intocáveis | Meu Pai, Meu Herói</td>\n",
       "      <td>somente para o ES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>2020-04-20</td>\n",
       "      <td>Homem-Aranha  | Tainá 2: A Aventura Continua</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>2019-02-04</td>\n",
       "      <td>Soul Surfer: Coragem de Viver | Kung Fu Panda 2</td>\n",
       "      <td>somente para as afiliadas da Globo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date                                             title  \\\n",
       "482  2021-04-12                  Intocáveis | Meu Pai, Meu Herói    \n",
       "750  2020-04-20     Homem-Aranha  | Tainá 2: A Aventura Continua    \n",
       "958  2019-02-04  Soul Surfer: Coragem de Viver | Kung Fu Panda 2    \n",
       "\n",
       "                               comments  \n",
       "482                   somente para o ES  \n",
       "750                                2002  \n",
       "958  somente para as afiliadas da Globo  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['title'].str.contains('|', case=False, regex=False)].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['title'] = df['title'].str.split('|').str[0].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('datasets/cleanedcentury.csv', index = False)"
   ]
  }
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