caracterização.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bibliotecas e Configurações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'import warnings\\nfrom pandas.core.common import SettingWithCopyWarning\\n\\nwarnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ignorar SettingWithCopyWarning\n",
    "'''import warnings\n",
    "from pandas.core.common import SettingWithCopyWarning\n",
    "\n",
    "warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importação dos Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>45201</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>45202</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>45203</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>45204</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>45205</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",
       "45201   48         Controle  Uso Dados                  0   \n",
       "45202   35         Controle  Uso Dados                  7   \n",
       "45203   43         Controle  Uso Dados                 10   \n",
       "45204   42         Controle  Uso Dados                 10   \n",
       "45205   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",
       "45201       Sinal/Cobertura  Detrator            193.0      221.0   \n",
       "45202       Sinal/Cobertura   Passivo           2918.0      426.0   \n",
       "45203  Duracao pacote dados  Promotor             31.0      207.0   \n",
       "45204       Sinal/Cobertura  Promotor              0.0      174.0   \n",
       "45205  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",
       "45201             46.0             160.0  ...         0.0         xiaomi   \n",
       "45202           2336.0              50.0  ...         0.0        samsung   \n",
       "45203             70.0               4.0  ...       100.0       motorola   \n",
       "45204              4.0               0.0  ...         0.0        samsung   \n",
       "45205             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",
       "45201           4G       0.000000      0.000000       0.552183        0   \n",
       "45202           4G       0.000000      0.000000       0.000000        0   \n",
       "45203           5G       0.226444      0.000000       0.000000        0   \n",
       "45204           3G       0.000000      0.000000       0.000000        0   \n",
       "45205           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",
       "45201                   0.0            0.0             0.000000e+00  \n",
       "45202                   0.0            0.0             2.258065e+16  \n",
       "45203                   0.0            0.0             0.000000e+00  \n",
       "45204                   0.0            0.0             0.000000e+00  \n",
       "45205                   0.0            0.0             0.000000e+00  \n",
       "\n",
       "[45206 rows x 47 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('NPS_Complete_UFF_2022-07-18_2022-12-31_pre-tratado.csv', sep=';')\n",
    "\n",
    "df['USERVALUEQ1_LABEL'].value_counts()\n",
    "\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45206 entries, 0 to 45205\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: 16.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() # Não serão feitos mais tratamentos nos dados, o pré-tratamento já foi suficiente"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise Inicial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Percentual Geral das classes NPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Promotor    23515\n",
      "Detrator    13335\n",
      "Passivo      8356\n",
      "Name: NPS_Class, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exibe valores inteiros\n",
    "print(df[\"NPS_Class\"].value_counts())\n",
    "\n",
    "# Gráfico de Pizza\n",
    "\n",
    "labels = df['NPS_Class'].value_counts().index\n",
    "sizes = df[\"NPS_Class\"].value_counts()\n",
    "explode = [0, 0.1, 0]\n",
    "cores = ['#90ee90', '#ff6961', '#add8e6']\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(7, 7))\n",
    "\n",
    "ax1.pie(sizes, labels=labels, explode=explode, autopct='%1.1f%%', shadow=True, startangle=90, colors=cores)\n",
    "\n",
    "ax1.axis('equal')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "ax = sns.countplot(x=\"NPS_Class\", data=df, palette=cores, order=df['NPS_Class'].value_counts().index)\n",
    "ax.set_xlabel('Classe NPS')\n",
    "ax.set_ylabel('Quantidade')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, horizontalalignment='right')\n",
    "\n",
    "# label com percentual\n",
    "total = len(df['NPS_Class'])\n",
    "for p in ax.patches:\n",
    "    percentage = '{:.1f}%'.format(100 * p.get_height()/total)\n",
    "    x = p.get_x() + p.get_width() / 2 - 0.1\n",
    "    y = p.get_y() + p.get_height() + 5\n",
    "    ax.annotate(percentage, (x, y))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Incidencias das respostas da segunda pergunta (justificativa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dicionário Promotores: {'Sinal/Cobertura': 5271, 'Não Informado': 3986, 'Velocidade': 2808, 'Plano/Bonus': 2718, 'Duracao pacote dados': 2359, 'Estabilidade': 2068, 'Outros': 2062, 'Qualidade das ligações': 793, 'Qualidade da Internet': 617, 'Planos e ofertas': 530, 'Atendimento': 199, 'Recarga/Conta': 104}\n",
      "Dicionário Detratores (antes): {'Sinal/Cobertura': 3801, 'Não Informado': 1721, 'Velocidade': 1585, 'Duracao pacote dados': 1441, 'Plano/Bonus': 1249, 'Estabilidade': 1239, 'Outros': 1237, 'Qualidade da Internet': 303, 'Planos e ofertas': 299, 'Qualidade das ligações': 236, 'Atendimento': 119, 'Recarga/Conta': 105}\n",
      "Dicionário Detratores (depois): {'Sinal/Cobertura': 3801, 'Não Informado': 1721, 'Velocidade': 1585, 'Plano/Bonus': 1249, 'Duracao pacote dados': 1441, 'Estabilidade': 1239, 'Outros': 1237, 'Qualidade das ligações': 236, 'Qualidade da Internet': 303, 'Planos e ofertas': 299, 'Atendimento': 119, 'Recarga/Conta': 105}\n"
     ]
    }
   ],
   "source": [
    "# Dataframes apenas com detratores e apenas com promotores\n",
    "df_promotores = df[df['NPS_Class'] == 'Promotor']\n",
    "df_detratores = df[df['NPS_Class'] == 'Detrator']\n",
    "\n",
    "# Dicionário de dados \n",
    "count_pro = df_promotores['USERVALUEQ2_VALUE'].value_counts()\n",
    "count_det = df_detratores['USERVALUEQ2_VALUE'].value_counts()\n",
    "\n",
    "# Convertendo a série resultante em um dicionário \n",
    "dict_pro = count_pro.to_dict()\n",
    "dict_det = count_det.to_dict()\n",
    "\n",
    "# Obtendo a lista ordenada de chaves comuns a ambos os dicionários\n",
    "chaves_ordenadas = list(dict_pro.keys())\n",
    "\n",
    "# Reorganizando o dict_det na mesma ordem das chaves de dict_pro\n",
    "dict_det_ordenado = {chave: dict_det[chave] for chave in chaves_ordenadas}\n",
    "\n",
    "# Exibindo os dicionários originais e o dict_det ordenado\n",
    "print(\"Dicionário Promotores:\", dict_pro)\n",
    "print(\"Dicionário Detratores (antes):\", dict_det)\n",
    "print(\"Dicionário Detratores (depois):\", dict_det_ordenado)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#list(dict_pro.values())\n",
    "qtdx = list(dict_pro.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ajusta tamanho da figura\n",
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "x = np.arange(len(qtdx))  # Use np.arange para criar uma sequência de posições x\n",
    "\n",
    "y1 = list(dict_pro.values())\n",
    "y2 = list(dict_det_ordenado.values())\n",
    "\n",
    "width = 0.3\n",
    "plt.bar(x - 0.3, y1, width, color='blue')\n",
    "plt.bar(x, y2, width, color='red')\n",
    "\n",
    "plt.xticks(x, list(dict_pro.keys()), rotation=45)\n",
    "plt.xlabel(\"Motivo de (in)satisfação\")\n",
    "plt.ylabel(\"Quantidade de clientes\")\n",
    "\n",
    "# Cria legenda e exibe gráfico\n",
    "plt.legend([\"Promotores\", \"Detratores\"])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NPS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por Estado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SP    11966\n",
      "RJ     6882\n",
      "PR     4394\n",
      "MG     4234\n",
      "SC     2432\n",
      "BA     2226\n",
      "PE     1861\n",
      "RS     1449\n",
      "CE     1417\n",
      "DF     1219\n",
      "PA     1195\n",
      "GO      925\n",
      "RN      667\n",
      "PB      612\n",
      "AL      581\n",
      "AM      579\n",
      "MA      510\n",
      "PI      409\n",
      "MS      373\n",
      "MT      344\n",
      "ES      220\n",
      "SE      190\n",
      "AP      149\n",
      "RR      126\n",
      "RO      107\n",
      "TO       98\n",
      "AC       41\n",
      "Name: UF, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Criando um novo Data Frame com as colunas de interesse\n",
    "df_target = df[['ANF', 'NPS_Class']]\n",
    "\n",
    "# Agrupando os DDDs por estado\n",
    "DDD_UF = {\n",
    "    61:\"DF\", 62:\"GO\", 64:\"GO\", 65:\"MT\", 66:\"MT\", 67:\"MS\", 82:\"AL\", 71:\"BA\", 73:\"BA\", 74:\"BA\", 75:\"BA\", 77:\"BA\",\n",
    "    85:\"CE\", 88:\"CE\", 98:\"MA\", 99:\"MA\", 83:\"PB\", 81:\"PE\", 87:\"PE\", 86:\"PI\", 89:\"PI\", 84:\"RN\", 79:\"SE\", 68:\"AC\",\n",
    "    96:\"AP\", 92:\"AM\", 97:\"AM\", 91:\"PA\", 93:\"PA\", 94:\"PA\", 69:\"RO\", 95:\"RR\", 63:\"TO\", 27:\"ES\", 28:\"ES\", 31:\"MG\", \n",
    "    32:\"MG\", 33:\"MG\", 34:\"MG\", 35:\"MG\", 37:\"MG\", 38:\"MG\", 21:\"RJ\", 22:\"RJ\", 24:\"RJ\", 11:\"SP\", 12:\"SP\", 13:\"SP\", \n",
    "    14:\"SP\", 15:\"SP\", 16:\"SP\", 17:\"SP\", 18:\"SP\", 19:\"SP\", 41:\"PR\", 42:\"PR\", 43:\"PR\", 44:\"PR\", 45:\"PR\", 46:\"PR\", \n",
    "    51:\"RS\", 52:\"RS\", 53:\"RS\", 54:\"RS\", 55:\"RS\", 47:\"SC\", 48:\"SC\", 49:\"SC\"\n",
    "}\n",
    "df_target = df_target.assign(ANF = df_target[\"ANF\"].map(DDD_UF))\n",
    "\n",
    "# renomear coluna ANF para UF\n",
    "df_target.rename(columns={'ANF': 'UF'}, inplace=True)\n",
    "\n",
    "# Quantidade de Clientes por Estado\n",
    "print(df_target['UF'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>NPS_Class</th>\n",
       "      <th>Detrator</th>\n",
       "      <th>Passivo</th>\n",
       "      <th>Promotor</th>\n",
       "      <th>NPS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UF</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AM</th>\n",
       "      <td>0.202073</td>\n",
       "      <td>0.174439</td>\n",
       "      <td>0.623489</td>\n",
       "      <td>0.421416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS</th>\n",
       "      <td>0.233244</td>\n",
       "      <td>0.182306</td>\n",
       "      <td>0.584450</td>\n",
       "      <td>0.351206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>0.245098</td>\n",
       "      <td>0.168627</td>\n",
       "      <td>0.586275</td>\n",
       "      <td>0.341176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PB</th>\n",
       "      <td>0.245098</td>\n",
       "      <td>0.173203</td>\n",
       "      <td>0.581699</td>\n",
       "      <td>0.336601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA</th>\n",
       "      <td>0.241841</td>\n",
       "      <td>0.183264</td>\n",
       "      <td>0.574895</td>\n",
       "      <td>0.333054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CE</th>\n",
       "      <td>0.239238</td>\n",
       "      <td>0.191249</td>\n",
       "      <td>0.569513</td>\n",
       "      <td>0.330275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AP</th>\n",
       "      <td>0.268456</td>\n",
       "      <td>0.134228</td>\n",
       "      <td>0.597315</td>\n",
       "      <td>0.328859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RN</th>\n",
       "      <td>0.242879</td>\n",
       "      <td>0.187406</td>\n",
       "      <td>0.569715</td>\n",
       "      <td>0.326837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PI</th>\n",
       "      <td>0.234719</td>\n",
       "      <td>0.210269</td>\n",
       "      <td>0.555012</td>\n",
       "      <td>0.320293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RR</th>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PE</th>\n",
       "      <td>0.261687</td>\n",
       "      <td>0.197743</td>\n",
       "      <td>0.540570</td>\n",
       "      <td>0.278882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>0.268421</td>\n",
       "      <td>0.194737</td>\n",
       "      <td>0.536842</td>\n",
       "      <td>0.268421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>0.269542</td>\n",
       "      <td>0.194519</td>\n",
       "      <td>0.535939</td>\n",
       "      <td>0.266397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RJ</th>\n",
       "      <td>0.280151</td>\n",
       "      <td>0.175094</td>\n",
       "      <td>0.544754</td>\n",
       "      <td>0.264603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT</th>\n",
       "      <td>0.270349</td>\n",
       "      <td>0.212209</td>\n",
       "      <td>0.517442</td>\n",
       "      <td>0.247093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>0.275387</td>\n",
       "      <td>0.209983</td>\n",
       "      <td>0.514630</td>\n",
       "      <td>0.239243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP</th>\n",
       "      <td>0.302440</td>\n",
       "      <td>0.185526</td>\n",
       "      <td>0.512034</td>\n",
       "      <td>0.209594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>0.290909</td>\n",
       "      <td>0.209091</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.209091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PR</th>\n",
       "      <td>0.309741</td>\n",
       "      <td>0.189804</td>\n",
       "      <td>0.500455</td>\n",
       "      <td>0.190715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC</th>\n",
       "      <td>0.322368</td>\n",
       "      <td>0.199424</td>\n",
       "      <td>0.478207</td>\n",
       "      <td>0.155839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RS</th>\n",
       "      <td>0.340235</td>\n",
       "      <td>0.168392</td>\n",
       "      <td>0.491373</td>\n",
       "      <td>0.151139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MG</th>\n",
       "      <td>0.335616</td>\n",
       "      <td>0.178791</td>\n",
       "      <td>0.485593</td>\n",
       "      <td>0.149976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AC</th>\n",
       "      <td>0.365854</td>\n",
       "      <td>0.121951</td>\n",
       "      <td>0.512195</td>\n",
       "      <td>0.146341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DF</th>\n",
       "      <td>0.337162</td>\n",
       "      <td>0.190320</td>\n",
       "      <td>0.472518</td>\n",
       "      <td>0.135357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RO</th>\n",
       "      <td>0.364486</td>\n",
       "      <td>0.140187</td>\n",
       "      <td>0.495327</td>\n",
       "      <td>0.130841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GO</th>\n",
       "      <td>0.355676</td>\n",
       "      <td>0.177297</td>\n",
       "      <td>0.467027</td>\n",
       "      <td>0.111351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TO</th>\n",
       "      <td>0.397959</td>\n",
       "      <td>0.163265</td>\n",
       "      <td>0.438776</td>\n",
       "      <td>0.040816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NPS_Class  Detrator   Passivo  Promotor       NPS\n",
       "UF                                               \n",
       "AM         0.202073  0.174439  0.623489  0.421416\n",
       "MS         0.233244  0.182306  0.584450  0.351206\n",
       "MA         0.245098  0.168627  0.586275  0.341176\n",
       "PB         0.245098  0.173203  0.581699  0.336601\n",
       "PA         0.241841  0.183264  0.574895  0.333054\n",
       "CE         0.239238  0.191249  0.569513  0.330275\n",
       "AP         0.268456  0.134228  0.597315  0.328859\n",
       "RN         0.242879  0.187406  0.569715  0.326837\n",
       "PI         0.234719  0.210269  0.555012  0.320293\n",
       "RR         0.285714  0.142857  0.571429  0.285714\n",
       "PE         0.261687  0.197743  0.540570  0.278882\n",
       "SE         0.268421  0.194737  0.536842  0.268421\n",
       "BA         0.269542  0.194519  0.535939  0.266397\n",
       "RJ         0.280151  0.175094  0.544754  0.264603\n",
       "MT         0.270349  0.212209  0.517442  0.247093\n",
       "AL         0.275387  0.209983  0.514630  0.239243\n",
       "SP         0.302440  0.185526  0.512034  0.209594\n",
       "ES         0.290909  0.209091  0.500000  0.209091\n",
       "PR         0.309741  0.189804  0.500455  0.190715\n",
       "SC         0.322368  0.199424  0.478207  0.155839\n",
       "RS         0.340235  0.168392  0.491373  0.151139\n",
       "MG         0.335616  0.178791  0.485593  0.149976\n",
       "AC         0.365854  0.121951  0.512195  0.146341\n",
       "DF         0.337162  0.190320  0.472518  0.135357\n",
       "RO         0.364486  0.140187  0.495327  0.130841\n",
       "GO         0.355676  0.177297  0.467027  0.111351\n",
       "TO         0.397959  0.163265  0.438776  0.040816"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cálculo do NPS por Estado\n",
    "nps_state = df_target.groupby('UF')['NPS_Class'].value_counts(normalize=True).unstack()\n",
    "nps_state['NPS'] = nps_state['Promotor'] - nps_state['Detrator']\n",
    "nps_state = nps_state.sort_values(by='NPS', ascending=False)\n",
    "\n",
    "display(nps_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luiza\\AppData\\Local\\Temp\\ipykernel_17088\\1666562585.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_nps['UF'] = df_nps.index\n"
     ]
    },
    {
     "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>NPS_Class</th>\n",
       "      <th>NPS</th>\n",
       "      <th>UF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.421416</td>\n",
       "      <td>AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.351206</td>\n",
       "      <td>MS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.341176</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.336601</td>\n",
       "      <td>PB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.333054</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.330275</td>\n",
       "      <td>CE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.328859</td>\n",
       "      <td>AP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.326837</td>\n",
       "      <td>RN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.320293</td>\n",
       "      <td>PI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.285714</td>\n",
       "      <td>RR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.278882</td>\n",
       "      <td>PE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.268421</td>\n",
       "      <td>SE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.266397</td>\n",
       "      <td>BA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.264603</td>\n",
       "      <td>RJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.247093</td>\n",
       "      <td>MT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.239243</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.209594</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.209091</td>\n",
       "      <td>ES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.190715</td>\n",
       "      <td>PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.155839</td>\n",
       "      <td>SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.151139</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.149976</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.146341</td>\n",
       "      <td>AC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.135357</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.130841</td>\n",
       "      <td>RO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.111351</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.040816</td>\n",
       "      <td>TO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NPS_Class       NPS  UF\n",
       "0          0.421416  AM\n",
       "1          0.351206  MS\n",
       "2          0.341176  MA\n",
       "3          0.336601  PB\n",
       "4          0.333054  PA\n",
       "5          0.330275  CE\n",
       "6          0.328859  AP\n",
       "7          0.326837  RN\n",
       "8          0.320293  PI\n",
       "9          0.285714  RR\n",
       "10         0.278882  PE\n",
       "11         0.268421  SE\n",
       "12         0.266397  BA\n",
       "13         0.264603  RJ\n",
       "14         0.247093  MT\n",
       "15         0.239243  AL\n",
       "16         0.209594  SP\n",
       "17         0.209091  ES\n",
       "18         0.190715  PR\n",
       "19         0.155839  SC\n",
       "20         0.151139  RS\n",
       "21         0.149976  MG\n",
       "22         0.146341  AC\n",
       "23         0.135357  DF\n",
       "24         0.130841  RO\n",
       "25         0.111351  GO\n",
       "26         0.040816  TO"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_nps = nps_state[['NPS']]\n",
    "df_nps['UF'] = df_nps.index\n",
    "\n",
    "# tirar o index\n",
    "df_nps.reset_index(drop=True, inplace=True)\n",
    "\n",
    "display(df_nps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por Região Político-Administrativa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sudeste         23302\n",
      "Nordeste         8473\n",
      "Sul              8275\n",
      "Centro-Oeste     2861\n",
      "Norte            2295\n",
      "Name: UF, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Agrupando as UF por Região\n",
    "UF_REG = {\n",
    "  'AM': 'Norte', 'AP': 'Norte', 'AC': 'Norte', 'PA': 'Norte', 'RO': 'Norte', 'RR': 'Norte', 'TO': 'Norte', \n",
    "  'MA': 'Nordeste', 'PI': 'Nordeste', 'CE': 'Nordeste', 'RN': 'Nordeste', 'PB': 'Nordeste', 'PE': 'Nordeste', 'AL': 'Nordeste', 'SE': 'Nordeste', 'BA': 'Nordeste', \n",
    "  'MG': 'Sudeste', 'ES': 'Sudeste', 'RJ': 'Sudeste', 'SP': 'Sudeste',\n",
    "  'PR': 'Sul', 'SC': 'Sul', 'RS': 'Sul',\n",
    "  'DF': 'Centro-Oeste', 'GO': 'Centro-Oeste', 'MT': 'Centro-Oeste', 'MS': 'Centro-Oeste' \n",
    "}\n",
    "\n",
    "df_target = df_target.assign(UF = df_target[\"UF\"].map(UF_REG))\n",
    "\n",
    "# display(df_target)\n",
    "\n",
    "# Quantidade de Clientes por Região\n",
    "print(df_target['UF'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>NPS_Class</th>\n",
       "      <th>Detrator</th>\n",
       "      <th>Passivo</th>\n",
       "      <th>Promotor</th>\n",
       "      <th>NPS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UF</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Norte</th>\n",
       "      <td>0.250545</td>\n",
       "      <td>0.171678</td>\n",
       "      <td>0.577778</td>\n",
       "      <td>0.327233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nordeste</th>\n",
       "      <td>0.256108</td>\n",
       "      <td>0.192848</td>\n",
       "      <td>0.551044</td>\n",
       "      <td>0.294937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudeste</th>\n",
       "      <td>0.301777</td>\n",
       "      <td>0.181444</td>\n",
       "      <td>0.516780</td>\n",
       "      <td>0.215003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sul</th>\n",
       "      <td>0.318792</td>\n",
       "      <td>0.188882</td>\n",
       "      <td>0.492326</td>\n",
       "      <td>0.173535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Centro-Oeste</th>\n",
       "      <td>0.321566</td>\n",
       "      <td>0.187697</td>\n",
       "      <td>0.490738</td>\n",
       "      <td>0.169172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NPS_Class     Detrator   Passivo  Promotor       NPS\n",
       "UF                                                  \n",
       "Norte         0.250545  0.171678  0.577778  0.327233\n",
       "Nordeste      0.256108  0.192848  0.551044  0.294937\n",
       "Sudeste       0.301777  0.181444  0.516780  0.215003\n",
       "Sul           0.318792  0.188882  0.492326  0.173535\n",
       "Centro-Oeste  0.321566  0.187697  0.490738  0.169172"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cálculo do NPS por Região\n",
    "nps_region = df_target.groupby('UF')['NPS_Class'].value_counts(normalize=True).unstack()\n",
    "nps_region['NPS'] = nps_region['Promotor'] - nps_region['Detrator']\n",
    "nps_region = nps_region.sort_values(by='NPS', ascending=False)\n",
    "\n",
    "display(nps_region)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HIPÓTESE:\n",
    "- Um maior IDH está intimamente relacionado com um menor NPS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por Estilo/Segmento de Contrato"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pré-pago    15752\n",
      "Pós-pago    14863\n",
      "Controle    14591\n",
      "Name: CUSTOMER_SEGMENT, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Data Frame com as colunas de interesse\n",
    "df_target = df[['CUSTOMER_SEGMENT', 'NPS_Class']]\n",
    "\n",
    "# Quantidade de Clientes por Segmento\n",
    "print(df['CUSTOMER_SEGMENT'].value_counts()) # bastante equilibrado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>NPS_Class</th>\n",
       "      <th>Detrator</th>\n",
       "      <th>Passivo</th>\n",
       "      <th>Promotor</th>\n",
       "      <th>NPS</th>\n",
       "      <th>Percentual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUSTOMER_SEGMENT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pré-pago</th>\n",
       "      <td>0.257682</td>\n",
       "      <td>0.155472</td>\n",
       "      <td>0.586846</td>\n",
       "      <td>0.329165</td>\n",
       "      <td>0.348449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pós-pago</th>\n",
       "      <td>0.302429</td>\n",
       "      <td>0.207966</td>\n",
       "      <td>0.489605</td>\n",
       "      <td>0.187176</td>\n",
       "      <td>0.328784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Controle</th>\n",
       "      <td>0.327668</td>\n",
       "      <td>0.192996</td>\n",
       "      <td>0.479337</td>\n",
       "      <td>0.151669</td>\n",
       "      <td>0.322767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NPS_Class         Detrator   Passivo  Promotor       NPS  Percentual\n",
       "CUSTOMER_SEGMENT                                                    \n",
       "Pré-pago          0.257682  0.155472  0.586846  0.329165    0.348449\n",
       "Pós-pago          0.302429  0.207966  0.489605  0.187176    0.328784\n",
       "Controle          0.327668  0.192996  0.479337  0.151669    0.322767"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cálculo do NPS por Segmento\n",
    "nps_segment = df_target.groupby('CUSTOMER_SEGMENT')['NPS_Class'].value_counts(normalize=True).unstack()\n",
    "nps_segment['NPS'] = nps_segment['Promotor'] - nps_segment['Detrator']\n",
    "nps_segment = nps_segment.sort_values(by='NPS', ascending=False)\n",
    "\n",
    "# adiconar coluna que mostra o percentual de clientes por categoria\n",
    "nps_segment['Percentual'] = df['CUSTOMER_SEGMENT'].value_counts(normalize=True)\n",
    "\n",
    "display(nps_segment)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PERGUNTA: Que problemas podem estar vindo a ocorrer com os planos 'Pós-Pago' e 'Controle'?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n1. Pós-pago: Nos planos pós-pagos, os clientes pagam uma taxa mensal fixa com base em um pacote de serviços contratado. Os problemas comuns enfrentados pelos clientes pós-pagos podem incluir:\\n- Cobranças excessivas: Pode haver situações em que os clientes são cobrados incorretamente por serviços não utilizados ou por taxas adicionais não mencionadas claramente no contrato. (LADO DA EMPRESA)\\n- Uso excessivo: Se um cliente ultrapassar os limites do pacote contratado, podem ocorrer cobranças adicionais significativas. (LADO DO CLIENTE)\\n\\n2. Controle: Os planos de controle geralmente combinam características dos planos pré-pagos e pós-pagos. Os problemas comuns enfrentados pelos clientes de planos de controle podem incluir:\\n- Limites de uso: Os clientes podem ter limites de minutos, mensagens de texto ou dados estabelecidos em seu pacote de controle. Se esses limites forem excedidos, o cliente pode enfrentar restrições ou cobranças adicionais. (LADO DO CLIENTE)\\n- Comunicação de uso: Alguns planos de controle podem não oferecer atualizações em tempo real sobre o uso atual, o que pode tornar difícil para o cliente controlar seu consumo. (LADO DA EMPRESA)\\n'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "1. Pós-pago: Nos planos pós-pagos, os clientes pagam uma taxa mensal fixa com base em um pacote de serviços contratado. Os problemas comuns enfrentados pelos clientes pós-pagos podem incluir:\n",
    "- Cobranças excessivas: Pode haver situações em que os clientes são cobrados incorretamente por serviços não utilizados ou por taxas adicionais não mencionadas claramente no contrato. (LADO DA EMPRESA)\n",
    "- Uso excessivo: Se um cliente ultrapassar os limites do pacote contratado, podem ocorrer cobranças adicionais significativas. (LADO DO CLIENTE)\n",
    "\n",
    "2. Controle: Os planos de controle geralmente combinam características dos planos pré-pagos e pós-pagos. Os problemas comuns enfrentados pelos clientes de planos de controle podem incluir:\n",
    "- Limites de uso: Os clientes podem ter limites de minutos, mensagens de texto ou dados estabelecidos em seu pacote de controle. Se esses limites forem excedidos, o cliente pode enfrentar restrições ou cobranças adicionais. (LADO DO CLIENTE)\n",
    "- Comunicação de uso: Alguns planos de controle podem não oferecer atualizações em tempo real sobre o uso atual, o que pode tornar difícil para o cliente controlar seu consumo. (LADO DA EMPRESA)\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por Volume de Dados gasto nas Redes Sociais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Frame com as colunas de interesse\n",
    "df_target = df[['vol_Rede_Social', 'NPS_Class']]\n",
    "\n",
    "# Como está a distribuição de clientes por volume de uso de rede social? \n",
    "#sns.displot(df_target, x=\"vol_Rede_Social\", kind=\"kde\", height=5, aspect=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dar um zoom no plot acima na região de 0 a 8000 com step de 250\n",
    "#sns.displot(df_target, x=\"vol_Rede_Social\", kind=\"kde\", height=5, aspect=3).set(xlim=(0, 8000), xticks=np.arange(0, 8000+1, 250));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Médio Consumo      16156\n",
      "Baixo Consumo      15294\n",
      "Extremo Consumo     7492\n",
      "Alto Consumo        6264\n",
      "Name: vol_Rede_Social, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Classificando o volume gasto == MARK MANSON \"a maioria absoluta da humanidade é, no máximo, medíocre em quase tudo\"\n",
    "for index, row in df_target.iterrows():\n",
    "    if row['vol_Rede_Social'] <= 200.0: # até 200 MB \n",
    "        df_target.loc[index, 'vol_Rede_Social'] = 'Baixo Consumo'\n",
    "    elif row['vol_Rede_Social'] > 200.0 and row['vol_Rede_Social'] <= 1800.0: # entre 200 MB e 1.8 GB \n",
    "        df_target.loc[index, 'vol_Rede_Social'] = 'Médio Consumo'\n",
    "    elif row['vol_Rede_Social'] > 1800.0 and row['vol_Rede_Social'] <= 4000.0: # entre 1.8 GB e 4 GB\n",
    "        df_target.loc[index, 'vol_Rede_Social'] = 'Alto Consumo'\n",
    "    else:\n",
    "        df_target.loc[index, 'vol_Rede_Social'] = 'Extremo Consumo'\n",
    "\n",
    "# Quantidade de Clientes por Consumo de Rede Social\n",
    "print(df_target['vol_Rede_Social'].value_counts())"
   ]
  },
  {
   "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>NPS_Class</th>\n",
       "      <th>Detrator</th>\n",
       "      <th>Passivo</th>\n",
       "      <th>Promotor</th>\n",
       "      <th>NPS</th>\n",
       "      <th>Percentual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol_Rede_Social</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baixo Consumo</th>\n",
       "      <td>0.268994</td>\n",
       "      <td>0.190925</td>\n",
       "      <td>0.540081</td>\n",
       "      <td>0.271087</td>\n",
       "      <td>0.338318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Médio Consumo</th>\n",
       "      <td>0.296237</td>\n",
       "      <td>0.184142</td>\n",
       "      <td>0.519621</td>\n",
       "      <td>0.223385</td>\n",
       "      <td>0.357386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alto Consumo</th>\n",
       "      <td>0.311782</td>\n",
       "      <td>0.185185</td>\n",
       "      <td>0.503033</td>\n",
       "      <td>0.191252</td>\n",
       "      <td>0.138566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extremo Consumo</th>\n",
       "      <td>0.331287</td>\n",
       "      <td>0.173652</td>\n",
       "      <td>0.495061</td>\n",
       "      <td>0.163775</td>\n",
       "      <td>0.165730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NPS_Class        Detrator   Passivo  Promotor       NPS  Percentual\n",
       "vol_Rede_Social                                                    \n",
       "Baixo Consumo    0.268994  0.190925  0.540081  0.271087    0.338318\n",
       "Médio Consumo    0.296237  0.184142  0.519621  0.223385    0.357386\n",
       "Alto Consumo     0.311782  0.185185  0.503033  0.191252    0.138566\n",
       "Extremo Consumo  0.331287  0.173652  0.495061  0.163775    0.165730"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NPS por Consumo de Rede Social\n",
    "nps_vol_redes_soc = df_target.groupby('vol_Rede_Social')['NPS_Class'].value_counts(normalize=True).unstack()\n",
    "nps_vol_redes_soc['NPS'] = nps_vol_redes_soc['Promotor'] - nps_vol_redes_soc['Detrator']\n",
    "nps_vol_redes_soc = nps_vol_redes_soc.sort_values(by='NPS', ascending=False)\n",
    "\n",
    "# adiconar coluna que mostra o percentual de clientes por categoria\n",
    "nps_vol_redes_soc['Percentual'] = df_target['vol_Rede_Social'].value_counts(normalize=True)\n",
    "\n",
    "display(nps_vol_redes_soc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HIPÓTESES:\n",
    "- Quanto maior o uso, ou a dependência do uso (no caso de pessoas que trabalham com rede social) maiores as exigências.\n",
    "- Imediatismo atrelado ao vício em redes sociais (qualquer perda de sinal tira o viciado em rede social do sério)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por volume de dados gasto em navegação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Frame com as colunas de interesse\n",
    "df_target = df[['vol_Navegacao', 'NPS_Class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baixo Consumo      35641\n",
      "Médio Consumo       4027\n",
      "Extremo Consumo     3345\n",
      "Alto Consumo        2193\n",
      "Name: vol_Navegacao, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for index, row in df_target.iterrows():\n",
    "    if row['vol_Navegacao'] <= 5.0: \n",
    "        df_target.loc[index, 'vol_Navegacao'] = 'Baixo Consumo'\n",
    "    elif row['vol_Navegacao'] > 5.0 and row['vol_Navegacao'] <= 15.0:\n",
    "        df_target.loc[index, 'vol_Navegacao'] = 'Médio Consumo'\n",
    "    elif row['vol_Navegacao'] > 15.0 and row['vol_Navegacao'] <= 30.0: \n",
    "        df_target.loc[index, 'vol_Navegacao'] = 'Alto Consumo'\n",
    "    else:\n",
    "        df_target.loc[index, 'vol_Navegacao'] = 'Extremo Consumo'\n",
    "\n",
    "# Quantidade de Clientes por Consumo de Navegacao\n",
    "print(df_target['vol_Navegacao'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>NPS_Class</th>\n",
       "      <th>Detrator</th>\n",
       "      <th>Passivo</th>\n",
       "      <th>Promotor</th>\n",
       "      <th>NPS</th>\n",
       "      <th>Percentual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol_Navegacao</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baixo Consumo</th>\n",
       "      <td>0.277910</td>\n",
       "      <td>0.181224</td>\n",
       "      <td>0.540866</td>\n",
       "      <td>0.262956</td>\n",
       "      <td>0.788413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Médio Consumo</th>\n",
       "      <td>0.343928</td>\n",
       "      <td>0.199404</td>\n",
       "      <td>0.456667</td>\n",
       "      <td>0.112739</td>\n",
       "      <td>0.089081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alto Consumo</th>\n",
       "      <td>0.350205</td>\n",
       "      <td>0.206566</td>\n",
       "      <td>0.443228</td>\n",
       "      <td>0.093023</td>\n",
       "      <td>0.048511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extremo Consumo</th>\n",
       "      <td>0.381764</td>\n",
       "      <td>0.191629</td>\n",
       "      <td>0.426607</td>\n",
       "      <td>0.044843</td>\n",
       "      <td>0.073995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NPS_Class        Detrator   Passivo  Promotor       NPS  Percentual\n",
       "vol_Navegacao                                                      \n",
       "Baixo Consumo    0.277910  0.181224  0.540866  0.262956    0.788413\n",
       "Médio Consumo    0.343928  0.199404  0.456667  0.112739    0.089081\n",
       "Alto Consumo     0.350205  0.206566  0.443228  0.093023    0.048511\n",
       "Extremo Consumo  0.381764  0.191629  0.426607  0.044843    0.073995"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NPS por Consumo de Navegacao\n",
    "nps_vol_navegacao = df_target.groupby('vol_Navegacao')['NPS_Class'].value_counts(normalize=True).unstack()\n",
    "nps_vol_navegacao['NPS'] = nps_vol_navegacao['Promotor'] - nps_vol_navegacao['Detrator']\n",
    "nps_vol_navegacao = nps_vol_navegacao.sort_values(by='NPS', ascending=False)\n",
    "\n",
    "# adiconar coluna que mostra o percentual de clientes por categoria\n",
    "nps_vol_navegacao['Percentual'] = df_target['vol_Navegacao'].value_counts(normalize=True)\n",
    "\n",
    "display(nps_vol_navegacao)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por volume de dados gasto em música"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Frame com as colunas de interesse\n",
    "df_target = df[['vol_Musica', 'NPS_Class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baixo Consumo      35639\n",
      "Extremo Consumo     6854\n",
      "Médio Consumo       1514\n",
      "Alto Consumo        1199\n",
      "Name: vol_Musica, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for index, row in df_target.iterrows():\n",
    "    if row['vol_Musica'] <= 5.0: \n",
    "        df_target.loc[index, 'vol_Musica'] = 'Baixo Consumo'\n",
    "    elif row['vol_Musica'] > 5.0 and row['vol_Musica'] <= 15.0:\n",
    "        df_target.loc[index, 'vol_Musica'] = 'Médio Consumo'\n",
    "    elif row['vol_Musica'] > 15.0 and row['vol_Musica'] <= 30.0: \n",
    "        df_target.loc[index, 'vol_Musica'] = 'Alto Consumo'\n",
    "    else:\n",
    "        df_target.loc[index, 'vol_Musica'] = 'Extremo Consumo'\n",
    "\n",
    "# Quantidade de Clientes por Consumo\n",
    "print(df_target['vol_Musica'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>NPS_Class</th>\n",
       "      <th>Detrator</th>\n",
       "      <th>Passivo</th>\n",
       "      <th>Promotor</th>\n",
       "      <th>NPS</th>\n",
       "      <th>Percentual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol_Musica</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baixo Consumo</th>\n",
       "      <td>0.281966</td>\n",
       "      <td>0.179831</td>\n",
       "      <td>0.538203</td>\n",
       "      <td>0.256236</td>\n",
       "      <td>0.788369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alto Consumo</th>\n",
       "      <td>0.321101</td>\n",
       "      <td>0.211009</td>\n",
       "      <td>0.467890</td>\n",
       "      <td>0.146789</td>\n",
       "      <td>0.026523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Médio Consumo</th>\n",
       "      <td>0.326948</td>\n",
       "      <td>0.202114</td>\n",
       "      <td>0.470938</td>\n",
       "      <td>0.143989</td>\n",
       "      <td>0.033491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extremo Consumo</th>\n",
       "      <td>0.351036</td>\n",
       "      <td>0.202509</td>\n",
       "      <td>0.446455</td>\n",
       "      <td>0.095419</td>\n",
       "      <td>0.151617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NPS_Class        Detrator   Passivo  Promotor       NPS  Percentual\n",
       "vol_Musica                                                         \n",
       "Baixo Consumo    0.281966  0.179831  0.538203  0.256236    0.788369\n",
       "Alto Consumo     0.321101  0.211009  0.467890  0.146789    0.026523\n",
       "Médio Consumo    0.326948  0.202114  0.470938  0.143989    0.033491\n",
       "Extremo Consumo  0.351036  0.202509  0.446455  0.095419    0.151617"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NPS por Consumo de Musica\n",
    "nps_vol_musica = df_target.groupby('vol_Musica')['NPS_Class'].value_counts(normalize=True).unstack()\n",
    "nps_vol_musica['NPS'] = nps_vol_musica['Promotor'] - nps_vol_musica['Detrator']\n",
    "nps_vol_musica = nps_vol_musica.sort_values(by='NPS', ascending=False)\n",
    "\n",
    "# adiconar coluna que mostra o percentual de clientes por categoria\n",
    "nps_vol_musica['Percentual'] = df_target['vol_Musica'].value_counts(normalize=True)\n",
    "\n",
    "display(nps_vol_musica)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por volume de dados gasto na loja de apps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Frame com as colunas de interesse\n",
    "df_target = df[['vol_MarketPlace', 'NPS_Class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baixo Consumo      39257\n",
      "Médio Consumo       2918\n",
      "Extremo Consumo     1650\n",
      "Alto Consumo        1381\n",
      "Name: vol_MarketPlace, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for index, row in df_target.iterrows():\n",
    "    if row['vol_MarketPlace'] <= 5.0: \n",
    "        df_target.loc[index, 'vol_MarketPlace'] = 'Baixo Consumo'\n",
    "    elif row['vol_MarketPlace'] > 5.0 and row['vol_MarketPlace'] <= 15.0:\n",
    "        df_target.loc[index, 'vol_MarketPlace'] = 'Médio Consumo'\n",
    "    elif row['vol_MarketPlace'] > 15.0 and row['vol_MarketPlace'] <= 30.0: \n",
    "        df_target.loc[index, 'vol_MarketPlace'] = 'Alto Consumo'\n",
    "    else:\n",
    "        df_target.loc[index, 'vol_MarketPlace'] = 'Extremo Consumo'\n",
    "\n",
    "# Quantidade de Clientes por Consumo \n",
    "print(df_target['vol_MarketPlace'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>NPS_Class</th>\n",
       "      <th>Detrator</th>\n",
       "      <th>Passivo</th>\n",
       "      <th>Promotor</th>\n",
       "      <th>NPS</th>\n",
       "      <th>Percentual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol_MarketPlace</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baixo Consumo</th>\n",
       "      <td>0.283847</td>\n",
       "      <td>0.184884</td>\n",
       "      <td>0.531268</td>\n",
       "      <td>0.247421</td>\n",
       "      <td>0.868402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Médio Consumo</th>\n",
       "      <td>0.346813</td>\n",
       "      <td>0.189513</td>\n",
       "      <td>0.463674</td>\n",
       "      <td>0.116861</td>\n",
       "      <td>0.064549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alto Consumo</th>\n",
       "      <td>0.377263</td>\n",
       "      <td>0.183201</td>\n",
       "      <td>0.439537</td>\n",
       "      <td>0.062274</td>\n",
       "      <td>0.030549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extremo Consumo</th>\n",
       "      <td>0.399394</td>\n",
       "      <td>0.176970</td>\n",
       "      <td>0.423636</td>\n",
       "      <td>0.024242</td>\n",
       "      <td>0.036500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NPS_Class        Detrator   Passivo  Promotor       NPS  Percentual\n",
       "vol_MarketPlace                                                    \n",
       "Baixo Consumo    0.283847  0.184884  0.531268  0.247421    0.868402\n",
       "Médio Consumo    0.346813  0.189513  0.463674  0.116861    0.064549\n",
       "Alto Consumo     0.377263  0.183201  0.439537  0.062274    0.030549\n",
       "Extremo Consumo  0.399394  0.176970  0.423636  0.024242    0.036500"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NPS por Consumo de Musica\n",
    "nps_vol_marketplace = df_target.groupby('vol_MarketPlace')['NPS_Class'].value_counts(normalize=True).unstack()\n",
    "nps_vol_marketplace['NPS'] = nps_vol_marketplace['Promotor'] - nps_vol_marketplace['Detrator']\n",
    "nps_vol_marketplace = nps_vol_marketplace.sort_values(by='NPS', ascending=False)\n",
    "\n",
    "# adiconar coluna que mostra o percentual de clientes por categoria\n",
    "nps_vol_marketplace['Percentual'] = df_target['vol_MarketPlace'].value_counts(normalize=True)\n",
    "\n",
    "display(nps_vol_marketplace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por Mobilidade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Frame com as colunas de interesse\n",
    "df_target = df[['qtde_celula', 'NPS_Class']]\n",
    "\n",
    "# displot kde\n",
    "# sns.displot(df_target, x=\"qtde_celula\", kind=\"kde\", height=5, aspect=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.displot(df_target, x=\"qtde_celula\", kind=\"kde\", height=5, aspect=3).set(xlim=(0, 2000), xticks=np.arange(0, 2000+1, 100));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Média Mobilidade      17975\n",
      "Baixa Mobilidade      12794\n",
      "Alta Mobilidade        9872\n",
      "Extrema Mobilidade     4565\n",
      "Name: qtde_celula, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Classificando a mobilidade baseado na quantidade de células(antenas) por cliente == MARK MANSON\n",
    "for index, row in df_target.iterrows():\n",
    "    if row['qtde_celula'] <= 100:\n",
    "        df_target.loc[index, 'qtde_celula'] = 'Baixa Mobilidade'\n",
    "    elif row['qtde_celula'] > 100 and row['qtde_celula'] <= 300:\n",
    "        df_target.loc[index, 'qtde_celula'] = 'Média Mobilidade' \n",
    "    elif row['qtde_celula'] > 300 and row['qtde_celula'] <= 600:\n",
    "        df_target.loc[index, 'qtde_celula'] = 'Alta Mobilidade'\n",
    "    else:\n",
    "        df_target.loc[index, 'qtde_celula'] = 'Extrema Mobilidade'\n",
    "\n",
    "# Quantidade de Clientes por Mobilidade\n",
    "print(df_target['qtde_celula'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>NPS_Class</th>\n",
       "      <th>Detrator</th>\n",
       "      <th>Passivo</th>\n",
       "      <th>Promotor</th>\n",
       "      <th>NPS</th>\n",
       "      <th>Percentual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qtde_celula</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baixa Mobilidade</th>\n",
       "      <td>0.252071</td>\n",
       "      <td>0.167813</td>\n",
       "      <td>0.580116</td>\n",
       "      <td>0.328044</td>\n",
       "      <td>0.283016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Média Mobilidade</th>\n",
       "      <td>0.285563</td>\n",
       "      <td>0.185257</td>\n",
       "      <td>0.529179</td>\n",
       "      <td>0.243616</td>\n",
       "      <td>0.397624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alta Mobilidade</th>\n",
       "      <td>0.327694</td>\n",
       "      <td>0.203201</td>\n",
       "      <td>0.469105</td>\n",
       "      <td>0.141410</td>\n",
       "      <td>0.218378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extrema Mobilidade</th>\n",
       "      <td>0.381599</td>\n",
       "      <td>0.191238</td>\n",
       "      <td>0.427163</td>\n",
       "      <td>0.045564</td>\n",
       "      <td>0.100982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NPS_Class           Detrator   Passivo  Promotor       NPS  Percentual\n",
       "qtde_celula                                                           \n",
       "Baixa Mobilidade    0.252071  0.167813  0.580116  0.328044    0.283016\n",
       "Média Mobilidade    0.285563  0.185257  0.529179  0.243616    0.397624\n",
       "Alta Mobilidade     0.327694  0.203201  0.469105  0.141410    0.218378\n",
       "Extrema Mobilidade  0.381599  0.191238  0.427163  0.045564    0.100982"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cálculo do NPS por Mobilidade\n",
    "nps_mobility = df_target.groupby('qtde_celula')['NPS_Class'].value_counts(normalize=True).unstack()\n",
    "nps_mobility['NPS'] = nps_mobility['Promotor'] - nps_mobility['Detrator']\n",
    "nps_mobility = nps_mobility.sort_values(by='NPS', ascending=False)\n",
    "\n",
    "# adiconar coluna que mostra o percentual de clientes por categoria\n",
    "nps_mobility['Percentual'] = df_target['qtde_celula'].value_counts(normalize=True)\n",
    "\n",
    "display(nps_mobility)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HIPÓTESE:\n",
    "- Quanto mais alta a mobilidade, maior a chance do cliente ter uma experiência ruim com a rede devido a troca constante de antenas e algumas não funcionarem como o esperado."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por Consumo de Chamadas Telefônicas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Frame com as colunas de interesse\n",
    "df_target = df[['tot_chamada', 'NPS_Class']]\n",
    "\n",
    "# displot kde\n",
    "# sns.displot(df_target, x=\"tot_chamada\", kind=\"kde\", height=5, aspect=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.displot(df_target, x=\"tot_chamada\", kind=\"kde\", height=5, aspect=3).set(xlim=(0, 1000), xticks=np.arange(0, 1000+1, 50));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Médio Consumo      19693\n",
      "Baixo Consumo      11582\n",
      "Alto Consumo        7544\n",
      "Extremo Consumo     6387\n",
      "Name: tot_chamada, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Classificando os consumos de chamadas por cliente == MARK MANSON\n",
    "for index, row in df_target.iterrows():\n",
    "    if row['tot_chamada'] <= 45:\n",
    "        df_target.loc[index, 'tot_chamada'] = 'Baixo Consumo'\n",
    "    elif row['tot_chamada'] > 45 and row['tot_chamada'] <= 175:\n",
    "        df_target.loc[index, 'tot_chamada'] = 'Médio Consumo' \n",
    "    elif row['tot_chamada'] > 175 and row['tot_chamada'] <= 300:\n",
    "        df_target.loc[index, 'tot_chamada'] = 'Alto Consumo'\n",
    "    else:\n",
    "        df_target.loc[index, 'tot_chamada'] = 'Extremo Consumo'\n",
    "\n",
    "# Quantidade de Clientes por Quantidade de Chamadas\n",
    "print(df_target['tot_chamada'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>NPS_Class</th>\n",
       "      <th>Detrator</th>\n",
       "      <th>Passivo</th>\n",
       "      <th>Promotor</th>\n",
       "      <th>NPS</th>\n",
       "      <th>Percentual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tot_chamada</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baixo Consumo</th>\n",
       "      <td>0.267829</td>\n",
       "      <td>0.178208</td>\n",
       "      <td>0.553963</td>\n",
       "      <td>0.286134</td>\n",
       "      <td>0.256205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Médio Consumo</th>\n",
       "      <td>0.299243</td>\n",
       "      <td>0.192860</td>\n",
       "      <td>0.507896</td>\n",
       "      <td>0.208653</td>\n",
       "      <td>0.435628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alto Consumo</th>\n",
       "      <td>0.307662</td>\n",
       "      <td>0.184783</td>\n",
       "      <td>0.507556</td>\n",
       "      <td>0.199894</td>\n",
       "      <td>0.166881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extremo Consumo</th>\n",
       "      <td>0.316111</td>\n",
       "      <td>0.172225</td>\n",
       "      <td>0.511664</td>\n",
       "      <td>0.195553</td>\n",
       "      <td>0.141287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NPS_Class        Detrator   Passivo  Promotor       NPS  Percentual\n",
       "tot_chamada                                                        \n",
       "Baixo Consumo    0.267829  0.178208  0.553963  0.286134    0.256205\n",
       "Médio Consumo    0.299243  0.192860  0.507896  0.208653    0.435628\n",
       "Alto Consumo     0.307662  0.184783  0.507556  0.199894    0.166881\n",
       "Extremo Consumo  0.316111  0.172225  0.511664  0.195553    0.141287"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cálculo do NPS por Consumo de Chamadas\n",
    "nps_calls = df_target.groupby('tot_chamada')['NPS_Class'].value_counts(normalize=True).unstack()\n",
    "nps_calls['NPS'] = nps_calls['Promotor'] - nps_calls['Detrator']\n",
    "nps_calls = nps_calls.sort_values(by='NPS', ascending=False)\n",
    "\n",
    "# adiconar coluna que mostra o percentual de clientes por categoria\n",
    "nps_calls['Percentual'] = df_target['tot_chamada'].value_counts(normalize=True)\n",
    "\n",
    "display(nps_calls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por percentual de chamadas originadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Frame com as colunas de interesse\n",
    "df_target = df[['originada_perc', 'NPS_Class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mais originadas    26214\n",
      "Mais recebidas     13318\n",
      "Equilibrado         5674\n",
      "Name: originada_perc, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Classificando \n",
    "for index, row in df_target.iterrows():\n",
    "    if row['originada_perc'] <= 45:\n",
    "        df_target.loc[index, 'originada_perc'] = 'Mais originadas'\n",
    "    elif row['originada_perc'] > 45 and row['originada_perc'] <= 55:\n",
    "        df_target.loc[index, 'originada_perc'] = 'Equilibrado' \n",
    "    else:\n",
    "        df_target.loc[index, 'originada_perc'] = 'Mais recebidas'\n",
    "\n",
    "# Quantidade de Clientes por Quantidade de Chamadas\n",
    "print(df_target['originada_perc'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>NPS_Class</th>\n",
       "      <th>Detrator</th>\n",
       "      <th>Passivo</th>\n",
       "      <th>Promotor</th>\n",
       "      <th>NPS</th>\n",
       "      <th>Percentual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>originada_perc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mais recebidas</th>\n",
       "      <td>0.282775</td>\n",
       "      <td>0.172849</td>\n",
       "      <td>0.544376</td>\n",
       "      <td>0.261601</td>\n",
       "      <td>0.294607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mais originadas</th>\n",
       "      <td>0.298505</td>\n",
       "      <td>0.189937</td>\n",
       "      <td>0.511559</td>\n",
       "      <td>0.213054</td>\n",
       "      <td>0.579879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equilibrado</th>\n",
       "      <td>0.307367</td>\n",
       "      <td>0.189461</td>\n",
       "      <td>0.503172</td>\n",
       "      <td>0.195805</td>\n",
       "      <td>0.125514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NPS_Class        Detrator   Passivo  Promotor       NPS  Percentual\n",
       "originada_perc                                                     \n",
       "Mais recebidas   0.282775  0.172849  0.544376  0.261601    0.294607\n",
       "Mais originadas  0.298505  0.189937  0.511559  0.213054    0.579879\n",
       "Equilibrado      0.307367  0.189461  0.503172  0.195805    0.125514"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cálculo do NPS por Consumo de Chamadas\n",
    "nps_originada_perc = df_target.groupby('originada_perc')['NPS_Class'].value_counts(normalize=True).unstack()\n",
    "nps_originada_perc['NPS'] = nps_originada_perc['Promotor'] - nps_originada_perc['Detrator']\n",
    "nps_originada_perc = nps_originada_perc.sort_values(by='NPS', ascending=False)\n",
    "\n",
    "# adiconar coluna que mostra o percentual de clientes por categoria\n",
    "nps_originada_perc['Percentual'] = df_target['originada_perc'].value_counts(normalize=True)\n",
    "\n",
    "display(nps_originada_perc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por Marca Fabricante do Dispositivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "term_fabr\n",
       "samsung          21267\n",
       "motorola         12678\n",
       "lg                3429\n",
       "não informado     2213\n",
       "apple             1972\n",
       "xiaomi            1966\n",
       "outros            1107\n",
       "asus               574\n",
       "dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Frame com as colunas de interesse\n",
    "df_target = df[['term_fabr', 'NPS_Class']]\n",
    "\n",
    "# Quantidade de Clientes por Marca\n",
    "df_target.value_counts('term_fabr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>NPS_Class</th>\n",
       "      <th>Detrator</th>\n",
       "      <th>Passivo</th>\n",
       "      <th>Promotor</th>\n",
       "      <th>NPS</th>\n",
       "      <th>Percentual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>term_fabr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lg</th>\n",
       "      <td>0.253135</td>\n",
       "      <td>0.171770</td>\n",
       "      <td>0.575095</td>\n",
       "      <td>0.321960</td>\n",
       "      <td>0.075853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outros</th>\n",
       "      <td>0.245709</td>\n",
       "      <td>0.204155</td>\n",
       "      <td>0.550136</td>\n",
       "      <td>0.304426</td>\n",
       "      <td>0.024488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>não informado</th>\n",
       "      <td>0.279711</td>\n",
       "      <td>0.178491</td>\n",
       "      <td>0.541798</td>\n",
       "      <td>0.262088</td>\n",
       "      <td>0.048954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>motorola</th>\n",
       "      <td>0.292791</td>\n",
       "      <td>0.185360</td>\n",
       "      <td>0.521849</td>\n",
       "      <td>0.229058</td>\n",
       "      <td>0.280449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samsung</th>\n",
       "      <td>0.296751</td>\n",
       "      <td>0.185828</td>\n",
       "      <td>0.517421</td>\n",
       "      <td>0.220671</td>\n",
       "      <td>0.470446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asus</th>\n",
       "      <td>0.329268</td>\n",
       "      <td>0.195122</td>\n",
       "      <td>0.475610</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>0.012697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xiaomi</th>\n",
       "      <td>0.341811</td>\n",
       "      <td>0.172431</td>\n",
       "      <td>0.485758</td>\n",
       "      <td>0.143947</td>\n",
       "      <td>0.043490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>0.350913</td>\n",
       "      <td>0.199290</td>\n",
       "      <td>0.449797</td>\n",
       "      <td>0.098884</td>\n",
       "      <td>0.043623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NPS_Class      Detrator   Passivo  Promotor       NPS  Percentual\n",
       "term_fabr                                                        \n",
       "lg             0.253135  0.171770  0.575095  0.321960    0.075853\n",
       "outros         0.245709  0.204155  0.550136  0.304426    0.024488\n",
       "não informado  0.279711  0.178491  0.541798  0.262088    0.048954\n",
       "motorola       0.292791  0.185360  0.521849  0.229058    0.280449\n",
       "samsung        0.296751  0.185828  0.517421  0.220671    0.470446\n",
       "asus           0.329268  0.195122  0.475610  0.146341    0.012697\n",
       "xiaomi         0.341811  0.172431  0.485758  0.143947    0.043490\n",
       "apple          0.350913  0.199290  0.449797  0.098884    0.043623"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cálculo do NPS por Marca\n",
    "nps_brand = df_target.groupby('term_fabr')['NPS_Class'].value_counts(normalize=True).unstack()\n",
    "nps_brand['NPS'] = nps_brand['Promotor'] - nps_brand['Detrator']\n",
    "nps_brand = nps_brand.sort_values(by='NPS', ascending=False)\n",
    "\n",
    "# adiconar coluna que mostra o percentual de clientes por categoria\n",
    "nps_brand['Percentual'] = df_target['term_fabr'].value_counts(normalize=True)\n",
    "\n",
    "display(nps_brand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HIPÓTESE:\n",
    "- Existe alguma incompatibilidade tecnológica entre os aparelhos da Apple e a rede da operadora (muito pouco provável)\n",
    "- Reforça a hipótese da maior exigência para um melhor idh. Nesse caso, os usuários de iPhone possuem, na maioria das vezes, melhor poder aquisitivo (mais provável)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por Interações com CRM (dúvidas, reclamações, etc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     42354\n",
      "1      1927\n",
      "2       537\n",
      "3       198\n",
      "4        92\n",
      "5        49\n",
      "7        17\n",
      "6        16\n",
      "9         4\n",
      "10        4\n",
      "8         3\n",
      "14        1\n",
      "20        1\n",
      "13        1\n",
      "19        1\n",
      "15        1\n",
      "Name: Qtd_CRM, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Data Frame com as colunas de interesse\n",
    "df_target = df[['Qtd_CRM', 'NPS_Class']]\n",
    "\n",
    "# Quantidade de Clientes por Quantidade de Interacoes\n",
    "print(df_target['Qtd_CRM'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nunca abriu chamado              42354\n",
       "Já abriu pelo menos 1 chamado     2852\n",
       "Name: Qtd_CRM, dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Substituir números por categoria \n",
    "for idx in df_target.index:\n",
    "    if df_target.loc[idx, 'Qtd_CRM'] == 0:\n",
    "        df_target.loc[idx, 'Qtd_CRM'] = 'Nunca abriu chamado'\n",
    "    else:\n",
    "        df_target.loc[idx, 'Qtd_CRM'] = 'Já abriu pelo menos 1 chamado'\n",
    "\n",
    "df_target['Qtd_CRM'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>NPS_Class</th>\n",
       "      <th>Detrator</th>\n",
       "      <th>Passivo</th>\n",
       "      <th>Promotor</th>\n",
       "      <th>NPS</th>\n",
       "      <th>Percentual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qtd_CRM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nunca abriu chamado</th>\n",
       "      <td>0.286797</td>\n",
       "      <td>0.185697</td>\n",
       "      <td>0.527506</td>\n",
       "      <td>0.240709</td>\n",
       "      <td>0.936911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Já abriu pelo menos 1 chamado</th>\n",
       "      <td>0.416550</td>\n",
       "      <td>0.172160</td>\n",
       "      <td>0.411290</td>\n",
       "      <td>-0.005259</td>\n",
       "      <td>0.063089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NPS_Class                      Detrator   Passivo  Promotor       NPS  \\\n",
       "Qtd_CRM                                                                 \n",
       "Nunca abriu chamado            0.286797  0.185697  0.527506  0.240709   \n",
       "Já abriu pelo menos 1 chamado  0.416550  0.172160  0.411290 -0.005259   \n",
       "\n",
       "NPS_Class                      Percentual  \n",
       "Qtd_CRM                                    \n",
       "Nunca abriu chamado              0.936911  \n",
       "Já abriu pelo menos 1 chamado    0.063089  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NPS por abertura de chamados\n",
    "nps_chamados = df_target.groupby('Qtd_CRM')['NPS_Class'].value_counts(normalize=True).unstack()\n",
    "nps_chamados['NPS'] = nps_chamados['Promotor'] - nps_chamados['Detrator']\n",
    "nps_chamados = nps_chamados.sort_values(by='NPS', ascending=False)\n",
    "\n",
    "# adiconar coluna que mostra o percentual de clientes por categoria\n",
    "nps_chamados['Percentual'] = df_target['Qtd_CRM'].value_counts(normalize=True)\n",
    "\n",
    "display(nps_chamados)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Por Eficiência dos Dados [???]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataframe com as colunas de interesse\n",
    "df_target = df[['eficiencia_pdp', 'NPS_Class']]\n",
    "\n",
    "# displot kde\n",
    "# sns.displot(df_target, x=\"eficiencia_pdp\", kind=\"kde\", height=5, aspect=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95_100    19947\n",
      "81_95     19311\n",
      "61_80      4823\n",
      "0_60       1125\n",
      "Name: eficiencia_pdp, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Classificando a eficiência de dados por cliente \n",
    "for index, row in df_target.iterrows():\n",
    "    if row['eficiencia_pdp'] <= 60:\n",
    "        df_target.loc[index, 'eficiencia_pdp'] = '0_60'\n",
    "    elif row['eficiencia_pdp'] > 60 and row['eficiencia_pdp'] <= 80:\n",
    "        df_target.loc[index, 'eficiencia_pdp'] = '61_80' \n",
    "    elif row['eficiencia_pdp'] > 80 and row['eficiencia_pdp'] <= 95:\n",
    "        df_target.loc[index, 'eficiencia_pdp'] = '81_95'\n",
    "    else:\n",
    "        df_target.loc[index, 'eficiencia_pdp'] = '95_100'\n",
    "\n",
    "# Quantidade de Clientes por Eficiência de Dados\n",
    "print(df_target['eficiencia_pdp'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>NPS_Class</th>\n",
       "      <th>Detrator</th>\n",
       "      <th>Passivo</th>\n",
       "      <th>Promotor</th>\n",
       "      <th>NPS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eficiencia_pdp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0_60</th>\n",
       "      <td>0.232889</td>\n",
       "      <td>0.220444</td>\n",
       "      <td>0.546667</td>\n",
       "      <td>0.313778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61_80</th>\n",
       "      <td>0.256687</td>\n",
       "      <td>0.205059</td>\n",
       "      <td>0.538254</td>\n",
       "      <td>0.281567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81_95</th>\n",
       "      <td>0.292010</td>\n",
       "      <td>0.187613</td>\n",
       "      <td>0.520377</td>\n",
       "      <td>0.228367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95_100</th>\n",
       "      <td>0.310623</td>\n",
       "      <td>0.175264</td>\n",
       "      <td>0.514112</td>\n",
       "      <td>0.203489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NPS_Class       Detrator   Passivo  Promotor       NPS\n",
       "eficiencia_pdp                                        \n",
       "0_60            0.232889  0.220444  0.546667  0.313778\n",
       "61_80           0.256687  0.205059  0.538254  0.281567\n",
       "81_95           0.292010  0.187613  0.520377  0.228367\n",
       "95_100          0.310623  0.175264  0.514112  0.203489"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NPS por Eficiência de Dados\n",
    "nps_efficiency = df_target.groupby('eficiencia_pdp')['NPS_Class'].value_counts(normalize=True).unstack()\n",
    "nps_efficiency['NPS'] = nps_efficiency['Promotor'] - nps_efficiency['Detrator']\n",
    "nps_efficiency = nps_efficiency.sort_values(by='NPS', ascending=False)\n",
    "\n",
    "display(nps_efficiency)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlação Linear entre IDH e NPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 31 entries, 0 to 30\n",
      "Data columns (total 2 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   Territorialidades  31 non-null     object\n",
      " 1   IDHM 2021          29 non-null     object\n",
      "dtypes: object(2)\n",
      "memory usage: 624.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df_idh = pd.read_excel('idh_estados_IPEA_2021.xlsx')\n",
    "#display(idh)\n",
    "df_idh.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>Territorialidades</th>\n",
       "      <th>IDHM 2021</th>\n",
       "      <th>UF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acre</td>\n",
       "      <td>0.71</td>\n",
       "      <td>AC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alagoas</td>\n",
       "      <td>0.684</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amapá</td>\n",
       "      <td>0.688</td>\n",
       "      <td>AP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amazonas</td>\n",
       "      <td>0.7</td>\n",
       "      <td>AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>0.691</td>\n",
       "      <td>BA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ceará</td>\n",
       "      <td>0.734</td>\n",
       "      <td>CE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>0.814</td>\n",
       "      <td>DF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Espírito Santo</td>\n",
       "      <td>0.771</td>\n",
       "      <td>ES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Goiás</td>\n",
       "      <td>0.737</td>\n",
       "      <td>GO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Maranhão</td>\n",
       "      <td>0.676</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mato Grosso do Sul</td>\n",
       "      <td>0.742</td>\n",
       "      <td>MS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>0.736</td>\n",
       "      <td>MT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Minas Gerais</td>\n",
       "      <td>0.774</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Paraíba</td>\n",
       "      <td>0.698</td>\n",
       "      <td>PB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Paraná</td>\n",
       "      <td>0.769</td>\n",
       "      <td>PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Pará</td>\n",
       "      <td>0.69</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Pernambuco</td>\n",
       "      <td>0.719</td>\n",
       "      <td>PE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Piauí</td>\n",
       "      <td>0.69</td>\n",
       "      <td>PI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>0.762</td>\n",
       "      <td>RJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Rio Grande do Norte</td>\n",
       "      <td>0.728</td>\n",
       "      <td>RN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Rio Grande do Sul</td>\n",
       "      <td>0.771</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>0.7</td>\n",
       "      <td>RO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Roraima</td>\n",
       "      <td>0.699</td>\n",
       "      <td>RR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Santa Catarina</td>\n",
       "      <td>0.792</td>\n",
       "      <td>SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>0.806</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Sergipe</td>\n",
       "      <td>0.702</td>\n",
       "      <td>SE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Tocantins</td>\n",
       "      <td>0.731</td>\n",
       "      <td>TO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Territorialidades IDHM 2021  UF\n",
       "1                  Acre      0.71  AC\n",
       "2               Alagoas     0.684  AL\n",
       "3                 Amapá     0.688  AP\n",
       "4              Amazonas       0.7  AM\n",
       "5                 Bahia     0.691  BA\n",
       "6                 Ceará     0.734  CE\n",
       "7      Distrito Federal     0.814  DF\n",
       "8        Espírito Santo     0.771  ES\n",
       "9                 Goiás     0.737  GO\n",
       "10             Maranhão     0.676  MA\n",
       "11   Mato Grosso do Sul     0.742  MS\n",
       "12          Mato Grosso     0.736  MT\n",
       "13         Minas Gerais     0.774  MG\n",
       "14              Paraíba     0.698  PB\n",
       "15               Paraná     0.769  PR\n",
       "16                 Pará      0.69  PA\n",
       "17           Pernambuco     0.719  PE\n",
       "18                Piauí      0.69  PI\n",
       "19       Rio de Janeiro     0.762  RJ\n",
       "20  Rio Grande do Norte     0.728  RN\n",
       "21    Rio Grande do Sul     0.771  RS\n",
       "22             Rondônia       0.7  RO\n",
       "23              Roraima     0.699  RR\n",
       "24       Santa Catarina     0.792  SC\n",
       "25            São Paulo     0.806  SP\n",
       "26              Sergipe     0.702  SE\n",
       "27            Tocantins     0.731  TO"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# eliminar linhas com nulos \n",
    "df_idh.dropna(inplace=True, axis=0)\n",
    "# eliminar primeira linha (Brasil)\n",
    "df_idh.drop(0, inplace=True, axis=0)\n",
    "# eliminar ultima linha (Nan)\n",
    "df_idh.drop(df_idh.tail(1).index,inplace=True)\n",
    "\n",
    "# As siglas UF serão usadas para o merge entre os dois data frames\n",
    "Estados_UF = {\n",
    "    'Acre': 'AC', 'Alagoas': 'AL', 'Amapá': 'AP', 'Amazonas': 'AM', 'Bahia': 'BA', 'Ceará': 'CE', \n",
    "    'Distrito Federal': 'DF', 'Espírito Santo': 'ES', 'Goiás': 'GO', 'Maranhão': 'MA', 'Mato Grosso': 'MT', \n",
    "    'Mato Grosso do Sul': 'MS', 'Minas Gerais': 'MG', 'Pará': 'PA', 'Paraíba': 'PB', 'Paraná': 'PR', \n",
    "    'Pernambuco': 'PE', 'Piauí': 'PI', 'Rio de Janeiro': 'RJ', 'Rio Grande do Norte': 'RN', \n",
    "    'Rio Grande do Sul': 'RS', 'Rondônia': 'RO', 'Roraima': 'RR', 'Santa Catarina': 'SC', 'São Paulo': 'SP', \n",
    "    'Sergipe': 'SE', 'Tocantins': 'TO'\n",
    "}\n",
    "\n",
    "# adicionar coluna com UF\n",
    "df_idh = df_idh.assign(UF = df_idh[\"Territorialidades\"].map(Estados_UF))\n",
    "\n",
    "display(df_idh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Territorialidades</th>\n",
       "      <th>IDHM 2021</th>\n",
       "      <th>UF</th>\n",
       "      <th>NPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acre</td>\n",
       "      <td>0.71</td>\n",
       "      <td>AC</td>\n",
       "      <td>0.146341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alagoas</td>\n",
       "      <td>0.684</td>\n",
       "      <td>AL</td>\n",
       "      <td>0.239243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amapá</td>\n",
       "      <td>0.688</td>\n",
       "      <td>AP</td>\n",
       "      <td>0.328859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amazonas</td>\n",
       "      <td>0.7</td>\n",
       "      <td>AM</td>\n",
       "      <td>0.421416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>0.691</td>\n",
       "      <td>BA</td>\n",
       "      <td>0.266397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ceará</td>\n",
       "      <td>0.734</td>\n",
       "      <td>CE</td>\n",
       "      <td>0.330275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>0.814</td>\n",
       "      <td>DF</td>\n",
       "      <td>0.135357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Espírito Santo</td>\n",
       "      <td>0.771</td>\n",
       "      <td>ES</td>\n",
       "      <td>0.209091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Goiás</td>\n",
       "      <td>0.737</td>\n",
       "      <td>GO</td>\n",
       "      <td>0.111351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Maranhão</td>\n",
       "      <td>0.676</td>\n",
       "      <td>MA</td>\n",
       "      <td>0.341176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mato Grosso do Sul</td>\n",
       "      <td>0.742</td>\n",
       "      <td>MS</td>\n",
       "      <td>0.351206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>0.736</td>\n",
       "      <td>MT</td>\n",
       "      <td>0.247093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Minas Gerais</td>\n",
       "      <td>0.774</td>\n",
       "      <td>MG</td>\n",
       "      <td>0.149976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Paraíba</td>\n",
       "      <td>0.698</td>\n",
       "      <td>PB</td>\n",
       "      <td>0.336601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Paraná</td>\n",
       "      <td>0.769</td>\n",
       "      <td>PR</td>\n",
       "      <td>0.190715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pará</td>\n",
       "      <td>0.69</td>\n",
       "      <td>PA</td>\n",
       "      <td>0.333054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Pernambuco</td>\n",
       "      <td>0.719</td>\n",
       "      <td>PE</td>\n",
       "      <td>0.278882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Piauí</td>\n",
       "      <td>0.69</td>\n",
       "      <td>PI</td>\n",
       "      <td>0.320293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>0.762</td>\n",
       "      <td>RJ</td>\n",
       "      <td>0.264603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Rio Grande do Norte</td>\n",
       "      <td>0.728</td>\n",
       "      <td>RN</td>\n",
       "      <td>0.326837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Rio Grande do Sul</td>\n",
       "      <td>0.771</td>\n",
       "      <td>RS</td>\n",
       "      <td>0.151139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>0.7</td>\n",
       "      <td>RO</td>\n",
       "      <td>0.130841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Roraima</td>\n",
       "      <td>0.699</td>\n",
       "      <td>RR</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Santa Catarina</td>\n",
       "      <td>0.792</td>\n",
       "      <td>SC</td>\n",
       "      <td>0.155839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>0.806</td>\n",
       "      <td>SP</td>\n",
       "      <td>0.209594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Sergipe</td>\n",
       "      <td>0.702</td>\n",
       "      <td>SE</td>\n",
       "      <td>0.268421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Tocantins</td>\n",
       "      <td>0.731</td>\n",
       "      <td>TO</td>\n",
       "      <td>0.040816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Territorialidades IDHM 2021  UF       NPS\n",
       "0                  Acre      0.71  AC  0.146341\n",
       "1               Alagoas     0.684  AL  0.239243\n",
       "2                 Amapá     0.688  AP  0.328859\n",
       "3              Amazonas       0.7  AM  0.421416\n",
       "4                 Bahia     0.691  BA  0.266397\n",
       "5                 Ceará     0.734  CE  0.330275\n",
       "6      Distrito Federal     0.814  DF  0.135357\n",
       "7        Espírito Santo     0.771  ES  0.209091\n",
       "8                 Goiás     0.737  GO  0.111351\n",
       "9              Maranhão     0.676  MA  0.341176\n",
       "10   Mato Grosso do Sul     0.742  MS  0.351206\n",
       "11          Mato Grosso     0.736  MT  0.247093\n",
       "12         Minas Gerais     0.774  MG  0.149976\n",
       "13              Paraíba     0.698  PB  0.336601\n",
       "14               Paraná     0.769  PR  0.190715\n",
       "15                 Pará      0.69  PA  0.333054\n",
       "16           Pernambuco     0.719  PE  0.278882\n",
       "17                Piauí      0.69  PI  0.320293\n",
       "18       Rio de Janeiro     0.762  RJ  0.264603\n",
       "19  Rio Grande do Norte     0.728  RN  0.326837\n",
       "20    Rio Grande do Sul     0.771  RS  0.151139\n",
       "21             Rondônia       0.7  RO  0.130841\n",
       "22              Roraima     0.699  RR  0.285714\n",
       "23       Santa Catarina     0.792  SC  0.155839\n",
       "24            São Paulo     0.806  SP  0.209594\n",
       "25              Sergipe     0.702  SE  0.268421\n",
       "26            Tocantins     0.731  TO  0.040816"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# MERGE dos DataFrames\n",
    "df_merge = pd.merge(df_idh, df_nps, on='UF', how='left')\n",
    "\n",
    "display(df_merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 27 entries, 0 to 26\n",
      "Data columns (total 4 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Territorialidades  27 non-null     object \n",
      " 1   IDHM 2021          27 non-null     object \n",
      " 2   UF                 27 non-null     object \n",
      " 3   NPS                27 non-null     float64\n",
      "dtypes: float64(1), object(3)\n",
      "memory usage: 1.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_merge.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 27 entries, 0 to 26\n",
      "Data columns (total 4 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Territorialidades  27 non-null     object \n",
      " 1   IDHM 2021          27 non-null     object \n",
      " 2   UF                 27 non-null     object \n",
      " 3   NPS                27 non-null     float64\n",
      "dtypes: float64(1), object(3)\n",
      "memory usage: 1.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_merge.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transformar valores pertencentes a coluna IDHM em floats\n",
    "df_merge['IDHM 2021'] = df_merge['IDHM 2021'].astype(float)\n",
    "\n",
    "# Excluir o que não importa\n",
    "df_merge.drop(['Territorialidades', 'UF'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlação entre NPS e IDHM - mapa de calor\n",
    "sns.heatmap(df_merge.corr(), annot=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}