pre-tratamento.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importação dos Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('NPS_Complete_UFF_2022-07-18_2022-12-31.csv', sep=';', thousands = '.', decimal = ',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pré-Tratamento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Informações Irrelevantes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['ref_month', 'CONTACT_DT'], axis=1) # Datas (irrelevante)\n",
    "df = df.drop(['treatment_nm'], axis=1) # Muitas categorias possíveis que não sabemos o que significa ao certo\n",
    "df = df.drop(['term_class'], axis=1) # Muitas categorias possíveis que estão mal divididas "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Valores Nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "MIN_NON_NULL = len(df) * 0.8 # Definindo um limite mínimo de valores não nulos para cada coluna como 80% do total (de linhas)\n",
    "df = df.dropna(axis=1, thresh=MIN_NON_NULL) # Excluindo colunas que excedem o máximo de valores nulos\n",
    "\n",
    "# ======== Tratando restantes =========\n",
    "\n",
    "# FLOAT\n",
    "\n",
    "# Nas colunas do tipo float, substituir valores nulos pela mediana (mais robustez contra outliers do que a média) \n",
    "for col in df.columns:\n",
    "    if df[col].dtype == 'float64':\n",
    "        df[col] = df[col].fillna(df[col].median())\n",
    "\n",
    "# CATEGÓRICAS (casos especiais)\n",
    "\n",
    "# USERVALUEQ2_VALUE\n",
    "df['USERVALUEQ2_VALUE'] = df['USERVALUEQ2_VALUE'].str.replace(r'\\d+\\. ', '', regex=True) # Ajuste de ortografia\n",
    "df['USERVALUEQ2_VALUE'] = df['USERVALUEQ2_VALUE'].str.replace(r'\\d+\\.', '', regex=True) # Ajuste de ortografia\n",
    "df['USERVALUEQ2_VALUE'] = df['USERVALUEQ2_VALUE'].fillna('Não Informado') # Substitui nulos por 'Não Informado'\n",
    "\n",
    "# term_fabr\n",
    "df['term_fabr'] = df['term_fabr'].fillna('não informado') # Substitui nulos por 'não informado'\n",
    "marcas = df['term_fabr'].value_counts()[df['term_fabr'].value_counts() < 200].index # Marcas com menos de 200 ocorrências\n",
    "df['term_fabr'] = df['term_fabr'].replace(marcas, 'outros') # Substitui marcas com menos de 200 ocorrências por 'outros'\n",
    "\n",
    "# device_tech\n",
    "df = df.dropna(subset=['device_tech']) # Exclui linhas nulas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Valores Únicos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Existem colunas totalmente preenchidas pelo mesmo valor (independentemente de qual valor este seja)? Se sim, excluir\n",
    "for col in df.columns:\n",
    "    if len(df[col].unique()) == 1:\n",
    "        df = df.drop(col, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers [atenção aqui]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['vol_Rede_Social', 'vol_Video', 'vol_Comunicacao', 'vol_Loja_de_Apps', 'vol_Musica', 'vol_Google', 'vol_Navegacao', 'vol_InternetBank', 'vol_Transporte', 'vol_Jogos', 'vol_MarketPlace', 'vol_TIM', 'vol_Alimentacao', 'vol_Ecommerce', 'vol_Viagem', 'tot_duracao_chamada', 'originada_perc', 'eficiencia_Voz', 'tot_volume', 'vol_gratuito_perc', 'vol_4g_perc', 'vol_ul_perc', 'eficiencia_pdp', 'qtde_municipio', 'VoLTE_perc', 'DISP_STATUS_p', 'ACD_STATUS_p', 'OCUP_STATUS_p', 'dias_franquia_zerada', 'dias_traf_red', 'dias_consumo_bonus_ctrl']\n"
     ]
    }
   ],
   "source": [
    "# Visualizando outliers para todas as colunas de tipo numérico usando o .describe()\n",
    "'''for col in df.columns:\n",
    "    if df[col].dtype == 'float64':\n",
    "        display(df[col].describe())'''\n",
    "\n",
    "# nomes das colunas que são float\n",
    "float_cols = [col for col in df.columns if df[col].dtype == 'float64']\n",
    "print(float_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"# calculando o limite superior para cada coluna\\nfor col in df.columns:\\n    if df[col].dtype == 'float64':\\n        q1 = df[col].quantile(0.25)\\n        q3 = df[col].quantile(0.75)\\n        iqr = q3 - q1\\n        upper_limit = q3 + 12 * iqr # limite já foi muito elevado\\n        # exibindo quantidade de outliers em cada coluna\\n        print(f'Coluna {col} possui {len(df[df[col] > upper_limit])} outliers.')\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# não há outliers aparentes no limite inferior \n",
    "# há outliers aparentes no limite superior\n",
    "\n",
    "'''# calculando o limite superior para cada coluna\n",
    "for col in df.columns:\n",
    "    if df[col].dtype == 'float64':\n",
    "        q1 = df[col].quantile(0.25)\n",
    "        q3 = df[col].quantile(0.75)\n",
    "        iqr = q3 - q1\n",
    "        upper_limit = q3 + 12 * iqr # limite já foi muito elevado\n",
    "        # exibindo quantidade de outliers em cada coluna\n",
    "        print(f'Coluna {col} possui {len(df[df[col] > upper_limit])} outliers.')'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Muitos outliers apontados... o que vai contra a ideia de outlier\n",
    "\n",
    "Além disso, não temos unidades de medida, portanto, não podemos afirmar que valores são erros de medida. \n",
    "\n",
    "Dessa forma, vamos aplicar uma transformação logarítmica como forma de reduzir a influência dos outliers - caso existam.\n",
    "\n",
    "Entretanto, a transformação logarítmica mitigaria a análise exploratória desses dados. Sendo assim, não será feita aqui. Será abordada em um tratamento extra antes da etapa de predição."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Valores infinitos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    45206\n",
       "Name: vol_Rede_Social, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifica se há colunas com valores infinitos na coluna vol_Rede_Social\n",
    "isinf = df['vol_Rede_Social'].apply(np.isinf)\n",
    "isinf.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminar linhas com valores infinitos\n",
    "#df = df[~isinf]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exportar df Final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>ANF</th>\n",
       "      <th>CUSTOMER_SEGMENT</th>\n",
       "      <th>SURVEY</th>\n",
       "      <th>USERVALUEQ1_LABEL</th>\n",
       "      <th>USERVALUEQ2_VALUE</th>\n",
       "      <th>NPS_Class</th>\n",
       "      <th>vol_Rede_Social</th>\n",
       "      <th>vol_Video</th>\n",
       "      <th>vol_Comunicacao</th>\n",
       "      <th>vol_Loja_de_Apps</th>\n",
       "      <th>...</th>\n",
       "      <th>VoLTE_perc</th>\n",
       "      <th>term_fabr</th>\n",
       "      <th>device_tech</th>\n",
       "      <th>DISP_STATUS_p</th>\n",
       "      <th>ACD_STATUS_p</th>\n",
       "      <th>OCUP_STATUS_p</th>\n",
       "      <th>Qtd_CRM</th>\n",
       "      <th>dias_franquia_zerada</th>\n",
       "      <th>dias_traf_red</th>\n",
       "      <th>dias_consumo_bonus_ctrl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>Pós-pago</td>\n",
       "      <td>Uso Dados</td>\n",
       "      <td>8</td>\n",
       "      <td>Não Informado</td>\n",
       "      <td>Passivo</td>\n",
       "      <td>3331.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>...</td>\n",
       "      <td>86.0</td>\n",
       "      <td>lg</td>\n",
       "      <td>4G</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>Pré-pago</td>\n",
       "      <td>Uso Dados</td>\n",
       "      <td>9</td>\n",
       "      <td>Plano/Bonus</td>\n",
       "      <td>Promotor</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>91.0</td>\n",
       "      <td>samsung</td>\n",
       "      <td>4G</td>\n",
       "      <td>0.164670</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.056800</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Pré-pago</td>\n",
       "      <td>Uso Dados</td>\n",
       "      <td>10</td>\n",
       "      <td>Velocidade</td>\n",
       "      <td>Promotor</td>\n",
       "      <td>3276.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>798.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>motorola</td>\n",
       "      <td>4G</td>\n",
       "      <td>0.799740</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.327936</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>81</td>\n",
       "      <td>Pré-pago</td>\n",
       "      <td>Uso Dados</td>\n",
       "      <td>10</td>\n",
       "      <td>Outros</td>\n",
       "      <td>Promotor</td>\n",
       "      <td>134.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>outros</td>\n",
       "      <td>3G</td>\n",
       "      <td>0.387239</td>\n",
       "      <td>0.040754</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85</td>\n",
       "      <td>Pré-pago</td>\n",
       "      <td>Uso Dados</td>\n",
       "      <td>10</td>\n",
       "      <td>Sinal/Cobertura</td>\n",
       "      <td>Promotor</td>\n",
       "      <td>44.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>não informado</td>\n",
       "      <td>4G</td>\n",
       "      <td>0.227265</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46635</th>\n",
       "      <td>48</td>\n",
       "      <td>Controle</td>\n",
       "      <td>Uso Dados</td>\n",
       "      <td>0</td>\n",
       "      <td>Sinal/Cobertura</td>\n",
       "      <td>Detrator</td>\n",
       "      <td>193.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>xiaomi</td>\n",
       "      <td>4G</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.552183</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46636</th>\n",
       "      <td>35</td>\n",
       "      <td>Controle</td>\n",
       "      <td>Uso Dados</td>\n",
       "      <td>7</td>\n",
       "      <td>Sinal/Cobertura</td>\n",
       "      <td>Passivo</td>\n",
       "      <td>2918.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>2336.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>samsung</td>\n",
       "      <td>4G</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.258065e+16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46637</th>\n",
       "      <td>43</td>\n",
       "      <td>Controle</td>\n",
       "      <td>Uso Dados</td>\n",
       "      <td>10</td>\n",
       "      <td>Duracao pacote dados</td>\n",
       "      <td>Promotor</td>\n",
       "      <td>31.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>motorola</td>\n",
       "      <td>5G</td>\n",
       "      <td>0.226444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46638</th>\n",
       "      <td>42</td>\n",
       "      <td>Controle</td>\n",
       "      <td>Uso Dados</td>\n",
       "      <td>10</td>\n",
       "      <td>Sinal/Cobertura</td>\n",
       "      <td>Promotor</td>\n",
       "      <td>0.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>samsung</td>\n",
       "      <td>3G</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46639</th>\n",
       "      <td>22</td>\n",
       "      <td>Controle</td>\n",
       "      <td>Uso Dados</td>\n",
       "      <td>9</td>\n",
       "      <td>Duracao pacote dados</td>\n",
       "      <td>Promotor</td>\n",
       "      <td>14.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>87.0</td>\n",
       "      <td>motorola</td>\n",
       "      <td>4G</td>\n",
       "      <td>0.154201</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019412</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45206 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ANF CUSTOMER_SEGMENT     SURVEY  USERVALUEQ1_LABEL  \\\n",
       "0       51         Pós-pago  Uso Dados                  8   \n",
       "1       11         Pré-pago  Uso Dados                  9   \n",
       "2       37         Pré-pago  Uso Dados                 10   \n",
       "3       81         Pré-pago  Uso Dados                 10   \n",
       "4       85         Pré-pago  Uso Dados                 10   \n",
       "...    ...              ...        ...                ...   \n",
       "46635   48         Controle  Uso Dados                  0   \n",
       "46636   35         Controle  Uso Dados                  7   \n",
       "46637   43         Controle  Uso Dados                 10   \n",
       "46638   42         Controle  Uso Dados                 10   \n",
       "46639   22         Controle  Uso Dados                  9   \n",
       "\n",
       "          USERVALUEQ2_VALUE NPS_Class  vol_Rede_Social  vol_Video  \\\n",
       "0             Não Informado   Passivo           3331.0      535.0   \n",
       "1               Plano/Bonus  Promotor           1314.0      173.0   \n",
       "2                Velocidade  Promotor           3276.0      346.0   \n",
       "3                    Outros  Promotor            134.0      321.0   \n",
       "4           Sinal/Cobertura  Promotor             44.0       31.0   \n",
       "...                     ...       ...              ...        ...   \n",
       "46635       Sinal/Cobertura  Detrator            193.0      221.0   \n",
       "46636       Sinal/Cobertura   Passivo           2918.0      426.0   \n",
       "46637  Duracao pacote dados  Promotor             31.0      207.0   \n",
       "46638       Sinal/Cobertura  Promotor              0.0      174.0   \n",
       "46639  Duracao pacote dados  Promotor             14.0       38.0   \n",
       "\n",
       "       vol_Comunicacao  vol_Loja_de_Apps  ...  VoLTE_perc      term_fabr  \\\n",
       "0                363.0             210.0  ...        86.0             lg   \n",
       "1                197.0               7.0  ...        91.0        samsung   \n",
       "2                798.0              18.0  ...        63.0       motorola   \n",
       "3                  4.0               0.0  ...         0.0         outros   \n",
       "4                372.0               6.0  ...         0.0  não informado   \n",
       "...                ...               ...  ...         ...            ...   \n",
       "46635             46.0             160.0  ...         0.0         xiaomi   \n",
       "46636           2336.0              50.0  ...         0.0        samsung   \n",
       "46637             70.0               4.0  ...       100.0       motorola   \n",
       "46638              4.0               0.0  ...         0.0        samsung   \n",
       "46639             18.0               0.0  ...        87.0       motorola   \n",
       "\n",
       "       device_tech  DISP_STATUS_p  ACD_STATUS_p  OCUP_STATUS_p  Qtd_CRM  \\\n",
       "0               4G       0.000000      0.000000       0.000000        0   \n",
       "1               4G       0.164670      0.000000       0.056800        0   \n",
       "2               4G       0.799740      0.000000       1.327936        0   \n",
       "3               3G       0.387239      0.040754       0.000000        0   \n",
       "4               4G       0.227265      0.000000       0.000000        0   \n",
       "...            ...            ...           ...            ...      ...   \n",
       "46635           4G       0.000000      0.000000       0.552183        0   \n",
       "46636           4G       0.000000      0.000000       0.000000        0   \n",
       "46637           5G       0.226444      0.000000       0.000000        0   \n",
       "46638           3G       0.000000      0.000000       0.000000        0   \n",
       "46639           4G       0.154201      0.000000       0.019412        0   \n",
       "\n",
       "       dias_franquia_zerada  dias_traf_red  dias_consumo_bonus_ctrl  \n",
       "0                       0.0            0.0             0.000000e+00  \n",
       "1                       0.0            0.0             0.000000e+00  \n",
       "2                       0.0            0.0             0.000000e+00  \n",
       "3                       0.0            0.0             0.000000e+00  \n",
       "4                       0.0            0.0             0.000000e+00  \n",
       "...                     ...            ...                      ...  \n",
       "46635                   0.0            0.0             0.000000e+00  \n",
       "46636                   0.0            0.0             2.258065e+16  \n",
       "46637                   0.0            0.0             0.000000e+00  \n",
       "46638                   0.0            0.0             0.000000e+00  \n",
       "46639                   0.0            0.0             0.000000e+00  \n",
       "\n",
       "[45206 rows x 47 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 45206 entries, 0 to 46639\n",
      "Data columns (total 47 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   ANF                      45206 non-null  int64  \n",
      " 1   CUSTOMER_SEGMENT         45206 non-null  object \n",
      " 2   SURVEY                   45206 non-null  object \n",
      " 3   USERVALUEQ1_LABEL        45206 non-null  int64  \n",
      " 4   USERVALUEQ2_VALUE        45206 non-null  object \n",
      " 5   NPS_Class                45206 non-null  object \n",
      " 6   vol_Rede_Social          45206 non-null  float64\n",
      " 7   vol_Video                45206 non-null  float64\n",
      " 8   vol_Comunicacao          45206 non-null  float64\n",
      " 9   vol_Loja_de_Apps         45206 non-null  float64\n",
      " 10  vol_Musica               45206 non-null  float64\n",
      " 11  vol_Google               45206 non-null  float64\n",
      " 12  vol_Navegacao            45206 non-null  float64\n",
      " 13  vol_InternetBank         45206 non-null  float64\n",
      " 14  vol_Transporte           45206 non-null  float64\n",
      " 15  vol_Jogos                45206 non-null  float64\n",
      " 16  vol_MarketPlace          45206 non-null  float64\n",
      " 17  vol_TIM                  45206 non-null  float64\n",
      " 18  vol_Alimentacao          45206 non-null  float64\n",
      " 19  vol_Ecommerce            45206 non-null  float64\n",
      " 20  vol_Viagem               45206 non-null  float64\n",
      " 21  FLAG_EXISTENCIA_R        45206 non-null  int64  \n",
      " 22  FLAG_PARAMETRO_R         45206 non-null  int64  \n",
      " 23  FLAG_ELEGIBILIDADE_R     45206 non-null  int64  \n",
      " 24  imsi_LTE                 45206 non-null  int64  \n",
      " 25  qtde_celula              45206 non-null  int64  \n",
      " 26  tot_chamada              45206 non-null  int64  \n",
      " 27  tot_duracao_chamada      45206 non-null  float64\n",
      " 28  tot_sms_env_receb        45206 non-null  int64  \n",
      " 29  originada_perc           45206 non-null  float64\n",
      " 30  eficiencia_Voz           45206 non-null  float64\n",
      " 31  tot_volume               45206 non-null  float64\n",
      " 32  vol_gratuito_perc        45206 non-null  float64\n",
      " 33  vol_4g_perc              45206 non-null  float64\n",
      " 34  vol_ul_perc              45206 non-null  float64\n",
      " 35  eficiencia_pdp           45206 non-null  float64\n",
      " 36  qtde_municipio           45206 non-null  float64\n",
      " 37  VoLTE_perc               45206 non-null  float64\n",
      " 38  term_fabr                45206 non-null  object \n",
      " 39  device_tech              45206 non-null  object \n",
      " 40  DISP_STATUS_p            45206 non-null  float64\n",
      " 41  ACD_STATUS_p             45206 non-null  float64\n",
      " 42  OCUP_STATUS_p            45206 non-null  float64\n",
      " 43  Qtd_CRM                  45206 non-null  int64  \n",
      " 44  dias_franquia_zerada     45206 non-null  float64\n",
      " 45  dias_traf_red            45206 non-null  float64\n",
      " 46  dias_consumo_bonus_ctrl  45206 non-null  float64\n",
      "dtypes: float64(31), int64(10), object(6)\n",
      "memory usage: 16.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Salvar df em arquivo csv - Lembrando: sem transformação logarítmica - para a caracterização\n",
    "df.to_csv('NPS_Complete_UFF_2022-07-18_2022-12-31_pre-tratado.csv', sep=';', index=False)"
   ]
  }
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