Py_ChatGPT_PCA1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criamos um dataset para exemplificar o estudo da PCA\n",
    "### Após a criação/importação do Dataset, precisamos entender o que queremos.\n",
    "### É fundamental entender qual é o objetivo do estudo, qual é a necessidade.\n",
    "### Vamos supor que queremos saber qual o melhor \"custo-benefício\" dentre os automóveis citados - OBJETIVO: CUSTO-BENEFÍCIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPORTANDO AS BIBLIOTECAS\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CRIANDO UM DATASET E ANALISANDO VISUALMENTE (ISSO É FUNDAMENTAL PARA ENTENDERMOS O DATASET)\n",
    "# Valores e dados reais de Janeiro de 2023.\n",
    "auto = {'modelo': ['Strada CS', 'Gol msi', 'Onix hb', 'Ka hb','Hb20', 'Kwid'],\n",
    "              'motor':[1.4, 1.0, 1.0, 1.0, 1.0, 1.0], \n",
    "              'km/litro_gas_cid': [10.8, 13.3, 13.9, 13.4, 12.8, 14.9],\n",
    "              'km/litro_gas_est': [12.3, 14.4, 16.7, 15.5, 14.6, 15.6],\n",
    "              'km/litro_alc_cid': [7.4, 9.1, 9.9, 9.2, 9.1, 10.3],\n",
    "              'km/litro_alc_est': [8.5, 10.1, 11.7, 10.7, 10.1, 10.6],\n",
    "              'peso/kg': [1.151, 1.001, 1.037, 1.037, 1.091, 0.779],\n",
    "              'torque_gas': [13.7, 9.7, 9.6, 10.2, 9.4, 9.4],\n",
    "              'torque_alc': [14.2, 10.4, 10.6, 10.7, 10.2, 9.8],\n",
    "              'cavalos_gas': [85, 75, 78, 80, 75, 66],\n",
    "              'cavalos_alc': [88, 84, 82, 85, 80, 70],\n",
    "              'valor': [93.890, 63.738, 63.430, 49.890, 63.690, 42.690]       \n",
    "       }                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      modelo  motor  km/litro_gas_cid  km/litro_gas_est  km/litro_alc_cid  \\\n",
      "0  Strada CS    1.4              10.8              12.3               7.4   \n",
      "1    Gol msi    1.0              13.3              14.4               9.1   \n",
      "2    Onix hb    1.0              13.9              16.7               9.9   \n",
      "3      Ka hb    1.0              13.4              15.5               9.2   \n",
      "4       Hb20    1.0              12.8              14.6               9.1   \n",
      "5       Kwid    1.0              14.9              15.6              10.3   \n",
      "\n",
      "   km/litro_alc_est  peso/kg  torque_gas  torque_alc  cavalos_gas  \\\n",
      "0               8.5    1.151        13.7        14.2           85   \n",
      "1              10.1    1.001         9.7        10.4           75   \n",
      "2              11.7    1.037         9.6        10.6           78   \n",
      "3              10.7    1.037        10.2        10.7           80   \n",
      "4              10.1    1.091         9.4        10.2           75   \n",
      "5              10.6    0.779         9.4         9.8           66   \n",
      "\n",
      "   cavalos_alc   valor  \n",
      "0           88  93.890  \n",
      "1           84  63.738  \n",
      "2           82  63.430  \n",
      "3           85  49.890  \n",
      "4           80  63.690  \n",
      "5           70  42.690  \n"
     ]
    }
   ],
   "source": [
    "# VISUALIZANDO O DATASET\n",
    "df =pd.DataFrame(auto)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>motor</th>\n",
       "      <th>km/litro_gas_cid</th>\n",
       "      <th>km/litro_gas_est</th>\n",
       "      <th>km/litro_alc_cid</th>\n",
       "      <th>km/litro_alc_est</th>\n",
       "      <th>peso/kg</th>\n",
       "      <th>torque_gas</th>\n",
       "      <th>torque_alc</th>\n",
       "      <th>cavalos_gas</th>\n",
       "      <th>cavalos_alc</th>\n",
       "      <th>valor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.066667</td>\n",
       "      <td>13.183333</td>\n",
       "      <td>14.850000</td>\n",
       "      <td>9.166667</td>\n",
       "      <td>10.283333</td>\n",
       "      <td>1.016000</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>10.983333</td>\n",
       "      <td>76.500000</td>\n",
       "      <td>81.500000</td>\n",
       "      <td>62.888000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.163299</td>\n",
       "      <td>1.367358</td>\n",
       "      <td>1.494992</td>\n",
       "      <td>0.995322</td>\n",
       "      <td>1.051507</td>\n",
       "      <td>0.127378</td>\n",
       "      <td>1.675311</td>\n",
       "      <td>1.608001</td>\n",
       "      <td>6.348228</td>\n",
       "      <td>6.252999</td>\n",
       "      <td>17.548166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>0.779000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>42.690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.925000</td>\n",
       "      <td>14.450000</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>9.450000</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>80.500000</td>\n",
       "      <td>53.275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.350000</td>\n",
       "      <td>15.050000</td>\n",
       "      <td>9.150000</td>\n",
       "      <td>10.350000</td>\n",
       "      <td>1.037000</td>\n",
       "      <td>9.650000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>76.500000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>63.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.775000</td>\n",
       "      <td>15.575000</td>\n",
       "      <td>9.725000</td>\n",
       "      <td>10.675000</td>\n",
       "      <td>1.077500</td>\n",
       "      <td>10.075000</td>\n",
       "      <td>10.675000</td>\n",
       "      <td>79.500000</td>\n",
       "      <td>84.750000</td>\n",
       "      <td>63.726000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>1.151000</td>\n",
       "      <td>13.700000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>93.890000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "          motor  km/litro_gas_cid  km/litro_gas_est  km/litro_alc_cid  \\\n",
       "count  6.000000          6.000000          6.000000          6.000000   \n",
       "mean   1.066667         13.183333         14.850000          9.166667   \n",
       "std    0.163299          1.367358          1.494992          0.995322   \n",
       "min    1.000000         10.800000         12.300000          7.400000   \n",
       "25%    1.000000         12.925000         14.450000          9.100000   \n",
       "50%    1.000000         13.350000         15.050000          9.150000   \n",
       "75%    1.000000         13.775000         15.575000          9.725000   \n",
       "max    1.400000         14.900000         16.700000         10.300000   \n",
       "\n",
       "       km/litro_alc_est   peso/kg  torque_gas  torque_alc  cavalos_gas  \\\n",
       "count          6.000000  6.000000    6.000000    6.000000     6.000000   \n",
       "mean          10.283333  1.016000   10.333333   10.983333    76.500000   \n",
       "std            1.051507  0.127378    1.675311    1.608001     6.348228   \n",
       "min            8.500000  0.779000    9.400000    9.800000    66.000000   \n",
       "25%           10.100000  1.010000    9.450000   10.250000    75.000000   \n",
       "50%           10.350000  1.037000    9.650000   10.500000    76.500000   \n",
       "75%           10.675000  1.077500   10.075000   10.675000    79.500000   \n",
       "max           11.700000  1.151000   13.700000   14.200000    85.000000   \n",
       "\n",
       "       cavalos_alc      valor  \n",
       "count     6.000000   6.000000  \n",
       "mean     81.500000  62.888000  \n",
       "std       6.252999  17.548166  \n",
       "min      70.000000  42.690000  \n",
       "25%      80.500000  53.275000  \n",
       "50%      83.000000  63.560000  \n",
       "75%      84.750000  63.726000  \n",
       "max      88.000000  93.890000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BUSCANDO AS INFORMAÇÕES DO DATASET, SEGUINDO O ROTEIRO DO ChatGPT.\n",
    "# O NOSSO DATASET É PEQUENO, MAS VAMOS SEGUIR O PROTOCOLO.\n",
    "df.describe() # A função \"describe()\" nos traz a estatística básica. Podemos destacar a média \"mean\", que \n",
    "# nos trás dados interessante que nos ajudam a entender melhor o que queremos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6 entries, 0 to 5\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   modelo            6 non-null      object \n",
      " 1   motor             6 non-null      float64\n",
      " 2   km/litro_gas_cid  6 non-null      float64\n",
      " 3   km/litro_gas_est  6 non-null      float64\n",
      " 4   km/litro_alc_cid  6 non-null      float64\n",
      " 5   km/litro_alc_est  6 non-null      float64\n",
      " 6   peso/kg           6 non-null      float64\n",
      " 7   torque_gas        6 non-null      float64\n",
      " 8   torque_alc        6 non-null      float64\n",
      " 9   cavalos_gas       6 non-null      int64  \n",
      " 10  cavalos_alc       6 non-null      int64  \n",
      " 11  valor             6 non-null      float64\n",
      "dtypes: float64(9), int64(2), object(1)\n",
      "memory usage: 704.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info() # A função \"info()\" nos dá um panorama dos tipo de dados e a sua condição.\n",
    "          # Veja que não temos dados nulos (Null) e os tipos estão descritos corretamente, onde precisa\n",
    "          # ser texto está \"object(string)\" ou ainda,onde precisa ser número está \"Float/Int (Float64/int64)\" \n",
    "          # É importante lembrar que nem sempre é assim. É muito comum termo valores nulos e descritos de \n",
    "          # de forma equivocada. Ex: A coluna \"motor\" poderia está como \"object\" e seria necessário\n",
    "          # o tratamento adequado de TRANSFORMAÇÃO DOS DADOS para transformá-los em numéricos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>motor</th>\n",
       "      <th>km/litro_gas_cid</th>\n",
       "      <th>km/litro_gas_est</th>\n",
       "      <th>km/litro_alc_cid</th>\n",
       "      <th>km/litro_alc_est</th>\n",
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       "      <th>torque_alc</th>\n",
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       "      <th>cavalos_alc</th>\n",
       "      <th>valor</th>\n",
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       "    <tr>\n",
       "      <th>motor</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>-0.830857</td>\n",
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       "      <td>0.979999</td>\n",
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       "      <td>0.865493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>km/litro_gas_cid</th>\n",
       "      <td>-0.853903</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.868315</td>\n",
       "      <td>0.985578</td>\n",
       "      <td>0.827430</td>\n",
       "      <td>-0.849163</td>\n",
       "      <td>-0.864055</td>\n",
       "      <td>-0.891583</td>\n",
       "      <td>-0.830617</td>\n",
       "      <td>-0.777770</td>\n",
       "      <td>-0.926853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>km/litro_gas_est</th>\n",
       "      <td>-0.835617</td>\n",
       "      <td>0.868315</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.911291</td>\n",
       "      <td>0.993006</td>\n",
       "      <td>-0.505699</td>\n",
       "      <td>-0.815308</td>\n",
       "      <td>-0.792446</td>\n",
       "      <td>-0.494176</td>\n",
       "      <td>-0.486726</td>\n",
       "      <td>-0.779379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>km/litro_alc_cid</th>\n",
       "      <td>-0.869554</td>\n",
       "      <td>0.985578</td>\n",
       "      <td>0.911291</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.876499</td>\n",
       "      <td>-0.780866</td>\n",
       "      <td>-0.892767</td>\n",
       "      <td>-0.902646</td>\n",
       "      <td>-0.807149</td>\n",
       "      <td>-0.774453</td>\n",
       "      <td>-0.890952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>km/litro_alc_est</th>\n",
       "      <td>-0.830857</td>\n",
       "      <td>0.827430</td>\n",
       "      <td>0.993006</td>\n",
       "      <td>0.876499</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.430494</td>\n",
       "      <td>-0.806842</td>\n",
       "      <td>-0.771419</td>\n",
       "      <td>-0.429949</td>\n",
       "      <td>-0.403037</td>\n",
       "      <td>-0.722239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peso/kg</th>\n",
       "      <td>0.519212</td>\n",
       "      <td>-0.849163</td>\n",
       "      <td>-0.505699</td>\n",
       "      <td>-0.780866</td>\n",
       "      <td>-0.430494</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.559518</td>\n",
       "      <td>0.635374</td>\n",
       "      <td>0.903014</td>\n",
       "      <td>0.888140</td>\n",
       "      <td>0.783627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>torque_gas</th>\n",
       "      <td>0.984488</td>\n",
       "      <td>-0.864055</td>\n",
       "      <td>-0.815308</td>\n",
       "      <td>-0.892767</td>\n",
       "      <td>-0.806842</td>\n",
       "      <td>0.559518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991375</td>\n",
       "      <td>0.737171</td>\n",
       "      <td>0.603300</td>\n",
       "      <td>0.838635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>torque_alc</th>\n",
       "      <td>0.979999</td>\n",
       "      <td>-0.891583</td>\n",
       "      <td>-0.792446</td>\n",
       "      <td>-0.902646</td>\n",
       "      <td>-0.771419</td>\n",
       "      <td>0.635374</td>\n",
       "      <td>0.991375</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.788601</td>\n",
       "      <td>0.657396</td>\n",
       "      <td>0.892887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cavalos_gas</th>\n",
       "      <td>0.655952</td>\n",
       "      <td>-0.830617</td>\n",
       "      <td>-0.494176</td>\n",
       "      <td>-0.807149</td>\n",
       "      <td>-0.429949</td>\n",
       "      <td>0.903014</td>\n",
       "      <td>0.737171</td>\n",
       "      <td>0.788601</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.944692</td>\n",
       "      <td>0.769190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cavalos_alc</th>\n",
       "      <td>0.509249</td>\n",
       "      <td>-0.777770</td>\n",
       "      <td>-0.486726</td>\n",
       "      <td>-0.774453</td>\n",
       "      <td>-0.403037</td>\n",
       "      <td>0.888140</td>\n",
       "      <td>0.603300</td>\n",
       "      <td>0.657396</td>\n",
       "      <td>0.944692</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.709915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valor</th>\n",
       "      <td>0.865493</td>\n",
       "      <td>-0.926853</td>\n",
       "      <td>-0.779379</td>\n",
       "      <td>-0.890952</td>\n",
       "      <td>-0.722239</td>\n",
       "      <td>0.783627</td>\n",
       "      <td>0.838635</td>\n",
       "      <td>0.892887</td>\n",
       "      <td>0.769190</td>\n",
       "      <td>0.709915</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     motor  km/litro_gas_cid  km/litro_gas_est  \\\n",
       "motor             1.000000         -0.853903         -0.835617   \n",
       "km/litro_gas_cid -0.853903          1.000000          0.868315   \n",
       "km/litro_gas_est -0.835617          0.868315          1.000000   \n",
       "km/litro_alc_cid -0.869554          0.985578          0.911291   \n",
       "km/litro_alc_est -0.830857          0.827430          0.993006   \n",
       "peso/kg           0.519212         -0.849163         -0.505699   \n",
       "torque_gas        0.984488         -0.864055         -0.815308   \n",
       "torque_alc        0.979999         -0.891583         -0.792446   \n",
       "cavalos_gas       0.655952         -0.830617         -0.494176   \n",
       "cavalos_alc       0.509249         -0.777770         -0.486726   \n",
       "valor             0.865493         -0.926853         -0.779379   \n",
       "\n",
       "                  km/litro_alc_cid  km/litro_alc_est   peso/kg  torque_gas  \\\n",
       "motor                    -0.869554         -0.830857  0.519212    0.984488   \n",
       "km/litro_gas_cid          0.985578          0.827430 -0.849163   -0.864055   \n",
       "km/litro_gas_est          0.911291          0.993006 -0.505699   -0.815308   \n",
       "km/litro_alc_cid          1.000000          0.876499 -0.780866   -0.892767   \n",
       "km/litro_alc_est          0.876499          1.000000 -0.430494   -0.806842   \n",
       "peso/kg                  -0.780866         -0.430494  1.000000    0.559518   \n",
       "torque_gas               -0.892767         -0.806842  0.559518    1.000000   \n",
       "torque_alc               -0.902646         -0.771419  0.635374    0.991375   \n",
       "cavalos_gas              -0.807149         -0.429949  0.903014    0.737171   \n",
       "cavalos_alc              -0.774453         -0.403037  0.888140    0.603300   \n",
       "valor                    -0.890952         -0.722239  0.783627    0.838635   \n",
       "\n",
       "                  torque_alc  cavalos_gas  cavalos_alc     valor  \n",
       "motor               0.979999     0.655952     0.509249  0.865493  \n",
       "km/litro_gas_cid   -0.891583    -0.830617    -0.777770 -0.926853  \n",
       "km/litro_gas_est   -0.792446    -0.494176    -0.486726 -0.779379  \n",
       "km/litro_alc_cid   -0.902646    -0.807149    -0.774453 -0.890952  \n",
       "km/litro_alc_est   -0.771419    -0.429949    -0.403037 -0.722239  \n",
       "peso/kg             0.635374     0.903014     0.888140  0.783627  \n",
       "torque_gas          0.991375     0.737171     0.603300  0.838635  \n",
       "torque_alc          1.000000     0.788601     0.657396  0.892887  \n",
       "cavalos_gas         0.788601     1.000000     0.944692  0.769190  \n",
       "cavalos_alc         0.657396     0.944692     1.000000  0.709915  \n",
       "valor               0.892887     0.769190     0.709915  1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A função corr() é usada para calcular a correlação entre duas variáveis. A correlação pode ser medida como \n",
    "# Pearson, Spearman ou Kendall(falaremos de ccada uma em outros arquivos), que medem a relação linear, \n",
    "# monotônica ou de ranking entre as variáveis. A saída será a matriz de correlação entre as colunas de df, \n",
    "# mostrando a relação linear entre as variáveis. Valores próximos a 1 indicam uma forte correlação positiva, \n",
    "# valores próximos a -1 indicam uma forte correlação negativa, e valores próximos a 0 indicam ausência de \n",
    "# correlação respectivamente.A correlação positiva indica que duas variáveis aumentam juntas. \n",
    "# Quando uma das variáveis aumenta, a outra também aumenta, e vice-versa. Por exemplo, se a quantidade de \n",
    "# horas de estudo aumenta, a probabilidade de uma pessoa ter uma boa nota também aumenta.\n",
    "# Já a correlação negativa indica que duas variáveis se movem em direções opostas. Quando uma das variáveis\n",
    "# aumenta, a outra diminui, e vice-versa. Por exemplo, se a temperatura aumenta, a demanda por energia para\n",
    "# ar condicionado diminui.\n",
    "# Em ambos os casos, quanto mais próximo de 1 ou -1, mais forte é a correlação entre as variáveis. \n",
    "# Valores próximos a zero indicam ausência de correlação.\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A finção plo.scatter() nos dá uma visão mais ampla do que vimos na correlação. Repare que, apesar de muito\n",
    "# simples, fica muito mais fácil entender uma \"correlação linear\" entre o \"valor x km/litro_gas_cid\".\n",
    "# a função corrobora com o que vimos na função \"df.corr\".\n",
    "df.plot.scatter((['valor','km/litro_gas_cid']),(['valor','km/litro_alc_cid']));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A função gráfica \"df.plot.bar\" nos dá um panorama interessante, veja que existe uma certa linearidade\n",
    "# entre o \"valor x cavalos_\", logicamente, que precisa ser analisada.\n",
    "df.plot.bar(x = 'modelo');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vamos salvar este dataset.\n",
    "df.to_csv('cb_br_2022.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continua no \"2\""
   ]
  }
 ],
 "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": "3.8.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}