18 - Pandas II.ipynb

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    "# Introdução à Programação para Ciência de Dados\n",
    "\n",
    "### Aula 18: Pandas II\n",
    "\n",
    "**Professor:** Igor Malheiros"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27973952",
   "metadata": {},
   "source": [
    "## Construindo tabela via .csv\n",
    "\n",
    "A maneira mais comum para processar uma tabela no Pandas é tomando como entrada uma planilha. O Pandas oferece suporte para leitura direta de extensões como `.csv`, `.json`, `.html`. Durante esse curso, focaremos na leitura de planilhas com extensão `.csv`.\n",
    "\n",
    "Os arquivos `.csv` (*comma-separated values*) são arquivos de textos cujas colunas são separadas por vírgulas e cada linha por uma quebra de linha. Quando abrimos um arquivo `.csv` por algum programa de edição de planilhas, o que o software faz é formatar o texto lido e apresentá-lo de forma mais simples para o usuário. O Pandas fornece uma função chamada `read_csv()` que tem o mesmo comportamento, ou seja, vai ler o arquivo de texto e formatá-lo para melhor apresentação e processamento. Essa função retorna uma estrutura de dados do tipo **DataFrame** com as informações do arquivo lido.\n",
    "\n",
    "</br></br>\n",
    "\n",
    "```\n",
    "# Arquivo \"auto.csv\" cru\n",
    "\n",
    "Focus,1198,11.90\n",
    "F-150,2136,5.53\n",
    "Fusion,1492,8.50\n",
    "Taurus,1652,7.65\n",
    "```\n",
    "</br></br>\n",
    "\n",
    "```Python\n",
    "import pandas as pd\n",
    "\n",
    "# Lendo arquivo \"auto.csv\"\n",
    "df = pd.read_csv(\"auto.csv\")\n",
    "```\n",
    "\n",
    "|     |     |       |\n",
    "|:---:|:---:|:-----:|\n",
    "Focus | 1198 | 11.90|\n",
    "F-150 | 2136 | 5.53 |\n",
    "Fusion| 1492 | 8.50 |\n",
    "Taurus| 1652 |7.65  |\n",
    "\n",
    "</br></br>\n",
    "\n",
    "É importante destacar que se o arquivo não estiver no mesmo diretório em que o seu arquivo Python `.py` ou seu notebook `ipynb`, devemos passar seu caminho relativo arquivo `.csv`. Por exemplo, se o arquivo `\"auto.csv\"` estiver dentro de um outro diretório chamado `\"data\"`, teríamos que utilizar `\"data/auto.csv\"`."
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "27721b11",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ee8edd48",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>modelo</th>\n",
       "      <th>massa (kg)</th>\n",
       "      <th>C (km/litro)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Focus</td>\n",
       "      <td>1198</td>\n",
       "      <td>11.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Crown Victoria</td>\n",
       "      <td>1715</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Expedition</td>\n",
       "      <td>2530</td>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Explorer</td>\n",
       "      <td>2014</td>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F-150</td>\n",
       "      <td>2136</td>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fusion</td>\n",
       "      <td>1492</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Taurus</td>\n",
       "      <td>1652</td>\n",
       "      <td>7.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fit</td>\n",
       "      <td>1168</td>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Accord</td>\n",
       "      <td>1492</td>\n",
       "      <td>9.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CR-V</td>\n",
       "      <td>1602</td>\n",
       "      <td>8.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Civic</td>\n",
       "      <td>1192</td>\n",
       "      <td>11.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ridgeline</td>\n",
       "      <td>2045</td>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            modelo  massa (kg)  C (km/litro)\n",
       "0            Focus        1198         11.90\n",
       "1   Crown Victoria        1715          6.80\n",
       "2       Expedition        2530          5.53\n",
       "3         Explorer        2014          6.38\n",
       "4            F-150        2136          5.53\n",
       "5           Fusion        1492          8.50\n",
       "6           Taurus        1652          7.65\n",
       "7              Fit        1168         13.60\n",
       "8           Accord        1492          9.78\n",
       "9             CR-V        1602          8.93\n",
       "10           Civic        1192         11.90\n",
       "11       Ridgeline        2045          6.38"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "# Lendo arquivo csv\n",
    "df = pd.read_csv(\"data/auto.csv\")\n",
    "df"
   ]
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  {
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   "execution_count": 5,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>massa (kg)</th>\n",
       "      <th>C (km/litro)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modelo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Focus</th>\n",
       "      <td>1198</td>\n",
       "      <td>11.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crown Victoria</th>\n",
       "      <td>1715</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expedition</th>\n",
       "      <td>2530</td>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Explorer</th>\n",
       "      <td>2014</td>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F-150</th>\n",
       "      <td>2136</td>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fusion</th>\n",
       "      <td>1492</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taurus</th>\n",
       "      <td>1652</td>\n",
       "      <td>7.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fit</th>\n",
       "      <td>1168</td>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accord</th>\n",
       "      <td>1492</td>\n",
       "      <td>9.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR-V</th>\n",
       "      <td>1602</td>\n",
       "      <td>8.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Civic</th>\n",
       "      <td>1192</td>\n",
       "      <td>11.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridgeline</th>\n",
       "      <td>2045</td>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                massa (kg)  C (km/litro)\n",
       "modelo                                  \n",
       "Focus                 1198         11.90\n",
       "Crown Victoria        1715          6.80\n",
       "Expedition            2530          5.53\n",
       "Explorer              2014          6.38\n",
       "F-150                 2136          5.53\n",
       "Fusion                1492          8.50\n",
       "Taurus                1652          7.65\n",
       "Fit                   1168         13.60\n",
       "Accord                1492          9.78\n",
       "CR-V                  1602          8.93\n",
       "Civic                 1192         11.90\n",
       "Ridgeline             2045          6.38"
      ]
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   "source": [
    "# Para utilizar alguma das colunas como índice,\n",
    "# precisamos utilizar o parametro `index_col=[n]`\n",
    "# Em que `n` é a posição da coluna em que\n",
    "# utilizaremos como nome da linha\n",
    "df = pd.read_csv(\"data/auto.csv\", index_col=[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bbf3361",
   "metadata": {},
   "source": [
    "## Removendo dados do DataFrame\n",
    "\n",
    "Um importante processamento que podemos utilizar nas tabelas é remover informações, sejam elas linhas ou colunas.\n",
    "\n",
    "### Removendo colunas\n",
    "\n",
    "Para remover as colunas de um DataFrame, basta utilizarmos a função `drop()`. Essa função recebe como parâmetro uma lista com os nomes das colunas que desejamos remover e utilizaremos o parâmetro `axis='columns'` para determinar que estamos removendo colunas.\n",
    "\n",
    "### Removendo linhas\n",
    "\n",
    "Para remover as linhas de um DataFrame, também utilizamos a função `drop()`. Passaremos também uma lista com os nomes das linhas que desejamos remover, entretanto, utilizaremos o parâmetro `axis='rows'` para determinar que estamos removendo linhas do DataFrame.\n",
    "\n",
    "É importante destacar que por padrão o `drop()` vai retornar uma nova tabela, deixando a tabela original intacta. Se quisermos realmente modificar a tabela que estamos manipulando, precisamos utilizar o parâmetro `inplace=True`."
   ]
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       "      <th>C (km/litro)</th>\n",
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       "    <tr>\n",
       "      <th>modelo</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Focus</th>\n",
       "      <td>11.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crown Victoria</th>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expedition</th>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Explorer</th>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F-150</th>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fusion</th>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taurus</th>\n",
       "      <td>7.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fit</th>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accord</th>\n",
       "      <td>9.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR-V</th>\n",
       "      <td>8.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Civic</th>\n",
       "      <td>11.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridgeline</th>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                C (km/litro)\n",
       "modelo                      \n",
       "Focus                  11.90\n",
       "Crown Victoria          6.80\n",
       "Expedition              5.53\n",
       "Explorer                6.38\n",
       "F-150                   5.53\n",
       "Fusion                  8.50\n",
       "Taurus                  7.65\n",
       "Fit                    13.60\n",
       "Accord                  9.78\n",
       "CR-V                    8.93\n",
       "Civic                  11.90\n",
       "Ridgeline               6.38"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removendo coluna\n",
    "dff = df.drop(['massa (kg)'], axis='columns')\n",
    "dff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3fadb3f5",
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       "    <tr>\n",
       "      <th>modelo</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Focus</th>\n",
       "      <td>11.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crown Victoria</th>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expedition</th>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Explorer</th>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F-150</th>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fusion</th>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taurus</th>\n",
       "      <td>7.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fit</th>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accord</th>\n",
       "      <td>9.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR-V</th>\n",
       "      <td>8.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Civic</th>\n",
       "      <td>11.90</td>\n",
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       "    <tr>\n",
       "      <th>Ridgeline</th>\n",
       "      <td>6.38</td>\n",
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      "text/plain": [
       "                C (km/litro)\n",
       "modelo                      \n",
       "Focus                  11.90\n",
       "Crown Victoria          6.80\n",
       "Expedition              5.53\n",
       "Explorer                6.38\n",
       "F-150                   5.53\n",
       "Fusion                  8.50\n",
       "Taurus                  7.65\n",
       "Fit                    13.60\n",
       "Accord                  9.78\n",
       "CR-V                    8.93\n",
       "Civic                  11.90\n",
       "Ridgeline               6.38"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removendo coluna in place\n",
    "df.drop([\"massa (kg)\"], axis='columns', inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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       "      <th>Crown Victoria</th>\n",
       "      <td>6.80</td>\n",
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       "    <tr>\n",
       "      <th>Expedition</th>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Explorer</th>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F-150</th>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fusion</th>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taurus</th>\n",
       "      <td>7.65</td>\n",
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       "      <th>Accord</th>\n",
       "      <td>9.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR-V</th>\n",
       "      <td>8.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Civic</th>\n",
       "      <td>11.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridgeline</th>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                C (km/litro)\n",
       "modelo                      \n",
       "Crown Victoria          6.80\n",
       "Expedition              5.53\n",
       "Explorer                6.38\n",
       "F-150                   5.53\n",
       "Fusion                  8.50\n",
       "Taurus                  7.65\n",
       "Accord                  9.78\n",
       "CR-V                    8.93\n",
       "Civic                  11.90\n",
       "Ridgeline               6.38"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removendo linha\n",
    "dff = df.drop(['Fit', 'Focus'], axis='rows')\n",
    "dff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bc964d0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>C (km/litro)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modelo</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Crown Victoria</th>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expedition</th>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Explorer</th>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F-150</th>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fusion</th>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taurus</th>\n",
       "      <td>7.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accord</th>\n",
       "      <td>9.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR-V</th>\n",
       "      <td>8.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Civic</th>\n",
       "      <td>11.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridgeline</th>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                C (km/litro)\n",
       "modelo                      \n",
       "Crown Victoria          6.80\n",
       "Expedition              5.53\n",
       "Explorer                6.38\n",
       "F-150                   5.53\n",
       "Fusion                  8.50\n",
       "Taurus                  7.65\n",
       "Accord                  9.78\n",
       "CR-V                    8.93\n",
       "Civic                  11.90\n",
       "Ridgeline               6.38"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removendo linhas inplace\n",
    "df.drop(['Fit', 'Focus'], axis='rows', inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9c1bf3e9",
   "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>massa (kg)</th>\n",
       "      <th>C (km/litro)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modelo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Focus</th>\n",
       "      <td>1198</td>\n",
       "      <td>11.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crown Victoria</th>\n",
       "      <td>1715</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expedition</th>\n",
       "      <td>2530</td>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Explorer</th>\n",
       "      <td>2014</td>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F-150</th>\n",
       "      <td>2136</td>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fusion</th>\n",
       "      <td>1492</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taurus</th>\n",
       "      <td>1652</td>\n",
       "      <td>7.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fit</th>\n",
       "      <td>1168</td>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accord</th>\n",
       "      <td>1492</td>\n",
       "      <td>9.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR-V</th>\n",
       "      <td>1602</td>\n",
       "      <td>8.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Civic</th>\n",
       "      <td>1192</td>\n",
       "      <td>11.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridgeline</th>\n",
       "      <td>2045</td>\n",
       "      <td>6.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                massa (kg)  C (km/litro)\n",
       "modelo                                  \n",
       "Focus                 1198         11.90\n",
       "Crown Victoria        1715          6.80\n",
       "Expedition            2530          5.53\n",
       "Explorer              2014          6.38\n",
       "F-150                 2136          5.53\n",
       "Fusion                1492          8.50\n",
       "Taurus                1652          7.65\n",
       "Fit                   1168         13.60\n",
       "Accord                1492          9.78\n",
       "CR-V                  1602          8.93\n",
       "Civic                 1192         11.90\n",
       "Ridgeline             2045          6.38"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carregando o DataFrame mais uma vez\n",
    "df = pd.read_csv(\"data/auto.csv\", index_col=[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05f72445",
   "metadata": {},
   "source": [
    "## Adicionando dados ao DataFrame\n",
    "\n",
    "O Pandas também fornece funções para adicionarmos linhas e colunas ao nosso DataFrame.\n",
    "\n",
    "### Adicionando colunas\n",
    "\n",
    "Para adicionarmos uma nova coluna ao DataFrame, basta criamos uma nova Serie, pois cada coluna em um DataFrame é uma Serie. Em seguida, precisamos acessar uma nova coluna que estamos criando no DataFrame e atribuir o valor da Serie criada, similar ao que acontece com os dicionários em Python.\n",
    "\n",
    "</br></br>\n",
    "\n",
    "```Python\n",
    "serie_marca = pd.Series(\n",
    "    ['Ford', 'Ford', 'Ford', 'Ford', 'Ford', 'Ford', 'Ford', 'Honda', 'Honda', 'Honda', 'Honda', 'Honda'],\n",
    "    index=['Focus', 'Crown Victoria', 'Expedition', 'Explorer', 'F-150', 'Fusion', 'Taurus', 'Fit', 'Accord', 'CR-V', 'Civic', 'Ridgeline'],\n",
    "    name='marcas'\n",
    ")\n",
    "df['marca'] = serie_marca\n",
    "```\n",
    "\n",
    "</br></br>\n",
    "\n",
    "Poderíamos utilizar o operador de broadcast para fazer uma atribuição de mesmo valor em uma nova coluna criada.\n",
    "\n",
    "</br></br>\n",
    "\n",
    "```Python\n",
    "df['numero de rodas'] = 4\n",
    "```\n",
    "\n",
    "</br></br>\n",
    "\n",
    "### Adicionando linhas\n",
    "\n",
    "Para criarmos uma nova linha no DataFrame, nós também vamos criar primeiro uma Serie. Porém, essa Serie será criada como um dicionário, onde cada chave é o nome de uma das colunas e o valor é o dado inserido naquela coluna. Em seguida, atribuímos via `loc[]` a nova Serie que criamos.\n",
    "\n",
    "</br></br>\n",
    "\n",
    "```Python\n",
    "serie_fox = pd.Series({\n",
    "    'massa (kg)': 1.009,\n",
    "    'C (km/litro)': 7.8,\n",
    "    'marca': 'volkswagen',\n",
    "    'numero de rodas': 4\n",
    "}, name='Fox')\n",
    "\n",
    "df.loc['Fox'] = serie_fox\n",
    "```\n",
    "\n",
    "</br></br>\n",
    "\n",
    "## Modificações\n",
    "\n",
    "### Modificando colunas/linhas\n",
    "\n",
    "Podemos utilizar a mesma sintaxe de adicionar uma coluna/linha para modificar uma coluna/linha já existente. Isso significa que para modificar uma coluna/linha devemos criar uma nova Serie e atribuí-la à coluna/linha já existente, similar aos dicionários de Python.\n",
    "\n",
    "</br></br>\n",
    "\n",
    "```Python\n",
    "serie_fox = pd.Series({\n",
    "    'massa (kg)': 1.100,\n",
    "    'C (km/litro)': 8.5,\n",
    "    'marca': 'volkswagen',\n",
    "    'numero de rodas': 4\n",
    "}, name='Fox')\n",
    "\n",
    "df.loc['Fox'] = serie_fox\n",
    "```\n",
    "\n",
    "</br></br>\n",
    "\n",
    "\n",
    "### Modificando Nomes das Colunas/Linhas\n",
    "\n",
    "É possível renomear os nomes das colunas e das linhas com a função `rename()`. Essa função recebe como parâmetro `columns=dic_c`, onde `dic_c` é um dicionário em que as chaves são os nomes antigos das colunas e os valores associados são os novos nomes das colunas, e `index={dic_i}`, onde `dic_i` é um dicionário em que as chaves são os nomes antigos das linhas e os valores associados são os novos nomes das linhas.\n",
    "\n",
    "A função `rename()` não modifica o DataFrame por padrão. Se quisermos modificá-lo, precisamos utilizar o parâmetro `inplace=True`.\n",
    "\n",
    "</br></br>\n",
    "\n",
    "```Python\n",
    "df.rename(\n",
    "    columns={\n",
    "        'massa (kg)': 'Massa',\n",
    "        'C (km/litro)': 'Km/L'\n",
    "    },\n",
    "    index={\n",
    "        'Fox': 'FOX'\n",
    "    }\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a2f59f66",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>massa (kg)</th>\n",
       "      <th>C (km/litro)</th>\n",
       "      <th>marcas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modelo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Focus</th>\n",
       "      <td>1198</td>\n",
       "      <td>11.90</td>\n",
       "      <td>Ford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crown Victoria</th>\n",
       "      <td>1715</td>\n",
       "      <td>6.80</td>\n",
       "      <td>Ford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expedition</th>\n",
       "      <td>2530</td>\n",
       "      <td>5.53</td>\n",
       "      <td>Ford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Explorer</th>\n",
       "      <td>2014</td>\n",
       "      <td>6.38</td>\n",
       "      <td>Ford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F-150</th>\n",
       "      <td>2136</td>\n",
       "      <td>5.53</td>\n",
       "      <td>Ford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fusion</th>\n",
       "      <td>1492</td>\n",
       "      <td>8.50</td>\n",
       "      <td>Ford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taurus</th>\n",
       "      <td>1652</td>\n",
       "      <td>7.65</td>\n",
       "      <td>Ford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fit</th>\n",
       "      <td>1168</td>\n",
       "      <td>13.60</td>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accord</th>\n",
       "      <td>1492</td>\n",
       "      <td>9.78</td>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR-V</th>\n",
       "      <td>1602</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Civic</th>\n",
       "      <td>1192</td>\n",
       "      <td>11.90</td>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridgeline</th>\n",
       "      <td>2045</td>\n",
       "      <td>6.38</td>\n",
       "      <td>Honda</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                massa (kg)  C (km/litro) marcas\n",
       "modelo                                         \n",
       "Focus                 1198         11.90   Ford\n",
       "Crown Victoria        1715          6.80   Ford\n",
       "Expedition            2530          5.53   Ford\n",
       "Explorer              2014          6.38   Ford\n",
       "F-150                 2136          5.53   Ford\n",
       "Fusion                1492          8.50   Ford\n",
       "Taurus                1652          7.65   Ford\n",
       "Fit                   1168         13.60  Honda\n",
       "Accord                1492          9.78  Honda\n",
       "CR-V                  1602          8.93  Honda\n",
       "Civic                 1192         11.90  Honda\n",
       "Ridgeline             2045          6.38  Honda"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adicionando uma nova coluna por uma Serie\n",
    "serie_marca = pd.Series(\n",
    "    ['Ford', 'Ford', 'Ford', 'Ford', 'Ford', 'Ford', 'Ford', 'Honda', 'Honda', 'Honda', 'Honda', 'Honda'],\n",
    "    index=['Focus', 'Crown Victoria', 'Expedition', 'Explorer', 'F-150', 'Fusion', 'Taurus', 'Fit', 'Accord', 'CR-V', 'Civic', 'Ridgeline'],\n",
    "    name='marcas'\n",
    ")\n",
    "df['marcas'] = serie_marca\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7ba46637",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>massa (kg)</th>\n",
       "      <th>C (km/litro)</th>\n",
       "      <th>marcas</th>\n",
       "      <th>Número de rodas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modelo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Focus</th>\n",
       "      <td>1198</td>\n",
       "      <td>11.90</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crown Victoria</th>\n",
       "      <td>1715</td>\n",
       "      <td>6.80</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expedition</th>\n",
       "      <td>2530</td>\n",
       "      <td>5.53</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Explorer</th>\n",
       "      <td>2014</td>\n",
       "      <td>6.38</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F-150</th>\n",
       "      <td>2136</td>\n",
       "      <td>5.53</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fusion</th>\n",
       "      <td>1492</td>\n",
       "      <td>8.50</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taurus</th>\n",
       "      <td>1652</td>\n",
       "      <td>7.65</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fit</th>\n",
       "      <td>1168</td>\n",
       "      <td>13.60</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accord</th>\n",
       "      <td>1492</td>\n",
       "      <td>9.78</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR-V</th>\n",
       "      <td>1602</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Civic</th>\n",
       "      <td>1192</td>\n",
       "      <td>11.90</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridgeline</th>\n",
       "      <td>2045</td>\n",
       "      <td>6.38</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                massa (kg)  C (km/litro) marcas  Número de rodas\n",
       "modelo                                                          \n",
       "Focus                 1198         11.90   Ford                4\n",
       "Crown Victoria        1715          6.80   Ford                4\n",
       "Expedition            2530          5.53   Ford                4\n",
       "Explorer              2014          6.38   Ford                4\n",
       "F-150                 2136          5.53   Ford                4\n",
       "Fusion                1492          8.50   Ford                4\n",
       "Taurus                1652          7.65   Ford                4\n",
       "Fit                   1168         13.60  Honda                4\n",
       "Accord                1492          9.78  Honda                4\n",
       "CR-V                  1602          8.93  Honda                4\n",
       "Civic                 1192         11.90  Honda                4\n",
       "Ridgeline             2045          6.38  Honda                4"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adicionando uma nova coluna com um valor padrão\n",
    "df['Número de rodas'] = 4\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b774a008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>massa (kg)</th>\n",
       "      <th>C (km/litro)</th>\n",
       "      <th>marcas</th>\n",
       "      <th>Número de rodas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modelo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Focus</th>\n",
       "      <td>1198.000</td>\n",
       "      <td>11.90</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crown Victoria</th>\n",
       "      <td>1715.000</td>\n",
       "      <td>6.80</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expedition</th>\n",
       "      <td>2530.000</td>\n",
       "      <td>5.53</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Explorer</th>\n",
       "      <td>2014.000</td>\n",
       "      <td>6.38</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F-150</th>\n",
       "      <td>2136.000</td>\n",
       "      <td>5.53</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fusion</th>\n",
       "      <td>1492.000</td>\n",
       "      <td>8.50</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taurus</th>\n",
       "      <td>1652.000</td>\n",
       "      <td>7.65</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fit</th>\n",
       "      <td>1168.000</td>\n",
       "      <td>13.60</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accord</th>\n",
       "      <td>1492.000</td>\n",
       "      <td>9.78</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR-V</th>\n",
       "      <td>1602.000</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Civic</th>\n",
       "      <td>1192.000</td>\n",
       "      <td>11.90</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridgeline</th>\n",
       "      <td>2045.000</td>\n",
       "      <td>6.38</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>1.009</td>\n",
       "      <td>7.80</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                massa (kg)  C (km/litro)      marcas  Número de rodas\n",
       "modelo                                                               \n",
       "Focus             1198.000         11.90        Ford              4.0\n",
       "Crown Victoria    1715.000          6.80        Ford              4.0\n",
       "Expedition        2530.000          5.53        Ford              4.0\n",
       "Explorer          2014.000          6.38        Ford              4.0\n",
       "F-150             2136.000          5.53        Ford              4.0\n",
       "Fusion            1492.000          8.50        Ford              4.0\n",
       "Taurus            1652.000          7.65        Ford              4.0\n",
       "Fit               1168.000         13.60       Honda              4.0\n",
       "Accord            1492.000          9.78       Honda              4.0\n",
       "CR-V              1602.000          8.93       Honda              4.0\n",
       "Civic             1192.000         11.90       Honda              4.0\n",
       "Ridgeline         2045.000          6.38       Honda              4.0\n",
       "Fox                  1.009          7.80  volkswagen              4.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adicionando uma nova linha\n",
    "serie_fox = pd.Series({\n",
    "    'massa (kg)': 1.009,\n",
    "    'C (km/litro)': 7.8,\n",
    "    'marcas': 'volkswagen',\n",
    "    'Número de rodas': 4\n",
    "}, name='Fox')\n",
    "\n",
    "df.loc['Fox'] = serie_fox\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "48aa9c5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>massa (kg)</th>\n",
       "      <th>C (km/litro)</th>\n",
       "      <th>marcas</th>\n",
       "      <th>Número de rodas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modelo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Focus</th>\n",
       "      <td>1198.0</td>\n",
       "      <td>11.90</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crown Victoria</th>\n",
       "      <td>1715.0</td>\n",
       "      <td>6.80</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expedition</th>\n",
       "      <td>2530.0</td>\n",
       "      <td>5.53</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Explorer</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>6.38</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F-150</th>\n",
       "      <td>2136.0</td>\n",
       "      <td>5.53</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fusion</th>\n",
       "      <td>1492.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taurus</th>\n",
       "      <td>1652.0</td>\n",
       "      <td>7.65</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fit</th>\n",
       "      <td>1168.0</td>\n",
       "      <td>13.60</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accord</th>\n",
       "      <td>1492.0</td>\n",
       "      <td>9.78</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR-V</th>\n",
       "      <td>1602.0</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Civic</th>\n",
       "      <td>1192.0</td>\n",
       "      <td>11.90</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridgeline</th>\n",
       "      <td>2045.0</td>\n",
       "      <td>6.38</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>1100.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                massa (kg)  C (km/litro)      marcas  Número de rodas\n",
       "modelo                                                               \n",
       "Focus               1198.0         11.90        Ford              4.0\n",
       "Crown Victoria      1715.0          6.80        Ford              4.0\n",
       "Expedition          2530.0          5.53        Ford              4.0\n",
       "Explorer            2014.0          6.38        Ford              4.0\n",
       "F-150               2136.0          5.53        Ford              4.0\n",
       "Fusion              1492.0          8.50        Ford              4.0\n",
       "Taurus              1652.0          7.65        Ford              4.0\n",
       "Fit                 1168.0         13.60       Honda              4.0\n",
       "Accord              1492.0          9.78       Honda              4.0\n",
       "CR-V                1602.0          8.93       Honda              4.0\n",
       "Civic               1192.0         11.90       Honda              4.0\n",
       "Ridgeline           2045.0          6.38       Honda              4.0\n",
       "Fox                 1100.0          8.50  volkswagen              4.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modificando uma linha existente\n",
    "serie2_fox = pd.Series({\n",
    "    'massa (kg)': 1100,\n",
    "    'C (km/litro)': 8.5,\n",
    "    'marcas': 'volkswagen',\n",
    "    'Número de rodas': 4\n",
    "}, name='Fox')\n",
    "\n",
    "df.loc['Fox'] = serie2_fox\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d622ea44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Massa</th>\n",
       "      <th>Consumo</th>\n",
       "      <th>Marcas</th>\n",
       "      <th>Número de rodas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modelo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Focus</th>\n",
       "      <td>1198.0</td>\n",
       "      <td>11.90</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crown Victoria</th>\n",
       "      <td>1715.0</td>\n",
       "      <td>6.80</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Expedition</th>\n",
       "      <td>2530.0</td>\n",
       "      <td>5.53</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Explorer</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>6.38</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F-150</th>\n",
       "      <td>2136.0</td>\n",
       "      <td>5.53</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fusion</th>\n",
       "      <td>1492.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taurus</th>\n",
       "      <td>1652.0</td>\n",
       "      <td>7.65</td>\n",
       "      <td>Ford</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fit</th>\n",
       "      <td>1168.0</td>\n",
       "      <td>13.60</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accord</th>\n",
       "      <td>1492.0</td>\n",
       "      <td>9.78</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CR-V</th>\n",
       "      <td>1602.0</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Civic</th>\n",
       "      <td>1192.0</td>\n",
       "      <td>11.90</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridgeline</th>\n",
       "      <td>2045.0</td>\n",
       "      <td>6.38</td>\n",
       "      <td>Honda</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOX</th>\n",
       "      <td>1100.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Massa  Consumo      Marcas  Número de rodas\n",
       "modelo                                                      \n",
       "Focus           1198.0    11.90        Ford              4.0\n",
       "Crown Victoria  1715.0     6.80        Ford              4.0\n",
       "Expedition      2530.0     5.53        Ford              4.0\n",
       "Explorer        2014.0     6.38        Ford              4.0\n",
       "F-150           2136.0     5.53        Ford              4.0\n",
       "Fusion          1492.0     8.50        Ford              4.0\n",
       "Taurus          1652.0     7.65        Ford              4.0\n",
       "Fit             1168.0    13.60       Honda              4.0\n",
       "Accord          1492.0     9.78       Honda              4.0\n",
       "CR-V            1602.0     8.93       Honda              4.0\n",
       "Civic           1192.0    11.90       Honda              4.0\n",
       "Ridgeline       2045.0     6.38       Honda              4.0\n",
       "FOX             1100.0     8.50  volkswagen              4.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modificando os nomes das linhas e colunas\n",
    "df.rename(\n",
    "    columns={\n",
    "        'massa (kg)': 'Massa',\n",
    "        'C (km/litro)': 'Consumo',\n",
    "        'marcas': 'Marcas'\n",
    "    },index={\n",
    "        'Fox':'FOX'\n",
    "    }, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09cea208",
   "metadata": {},
   "source": [
    "## Estatísticas\n",
    "\n",
    "Dado uma Serie, é possível extrair algumas informações estatísticas com funções disponibilizadas pelo Pandas. Isso pode ser muito útil para analisarmos os dados de uma coluna inteira.\n",
    "\n",
    "- `df['c'].min()` - Retorna o valor mínimo para coluna `c`\n",
    "\n",
    "- `df['c'].argmin()` - Retorna a posição da linha com valor mínimo para coluna `c`\n",
    "\n",
    "- `df['c'].max()` - Retorna o valor máximo para coluna `c`\n",
    "\n",
    "- `df['c'].argmax()` - Retorna a posição da linha com valor máximo para coluna `c`\n",
    "\n",
    "- `df['c'].sum()` - Retorna a soma dos valores para coluna `c`\n",
    "\n",
    "- `df['c'].mean()` - Retorna a média dos valores para coluna `c`\n",
    "\n",
    "- `df['c'].std()` - Retorna o desvio padrão dos valores para coluna `c`\n",
    "\n",
    "- `df['c'].median()` - Retorna a mediana dos valores para coluna `c`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4b4d2a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1100.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Valor mínimo\n",
    "df['Massa'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e9d8ec8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Posição da linha de valor mínimo\n",
    "df['Massa'].argmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6571f8c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2530.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Valor máximo\n",
    "df['Massa'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8b5404ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Posição da linha de valor máximo\n",
    "df['Massa'].argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a664e4a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21336.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Soma dos valores\n",
    "df['Massa'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "568d2527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1641.2307692307693"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Média dos valores\n",
    "df['Massa'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "35afefb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "438.1276742849725"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Desvio padrão\n",
    "df['Massa'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3c73eb4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1602.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mediana\n",
    "df['Massa'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "055a70ab",
   "metadata": {},
   "source": [
    "## Matplotlib\n",
    "\n",
    "O Matplotlib é mais uma biblioteca de Python muito utilizada pelos cientistas de dados. Essa biblioteca fornece ferramentas voltadas para **visualização de dados**.\n",
    "\n",
    "### Plot\n",
    "\n",
    "O gráfico mais simples da Matplotlib são os gráficos Plot que vão traçar em um plano cartesiano associações entre duas listas (ou arrays Numpy, ou valores numéricos das Series), onde a primeira lista será o eixo $x$ e a segundo o eixo $y$.\n",
    "\n",
    "A função que utilizaremos é a `plt.plot()`, deveremos passar duas listas(arrays Numpy, ou Series) de mesmo tamanho. Além disso, podemos modificar a cor da linha traçada, seu desenho, adicionar títulos e legendas.\n",
    "\n",
    "Por último, podemos traçar mais de uma linha por gráfico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "eae33480",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "43167fec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9ec9a67e20>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Traçando uma reta (y = x)\n",
    "x = [i for i in range(10)]\n",
    "print(x)\n",
    "y = x\n",
    "print(y)\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3ecaa959",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9ec91c6a60>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Traçando uma curva (y = x^2)\n",
    "# plt.plot(x, y)\n",
    "x2 = [i for i in range(10)]\n",
    "print(x2)\n",
    "y2 = [i**2 for i in x2]\n",
    "print(y2)\n",
    "plt.plot(x2, y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dd407a9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9ec913aa90>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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09BvMmHE3ZhpVTSRR/dcysynAh4H7T9i9GFgRebwCuGpIk4lIzPT1HWPLlo9w8OATzJx5D6WlX9Nc3gko2p+V7gFuB3JO2Ffs7o0A7t5oZqe81sjMlgJLAUpKSgaeVESGRE9PC1u2fJjW1j8yZ85PmDjxb4OOJAMUzar0VwBN7v7KQA7g7svdvdLdK4uKigbyJURkiHR3h9i48f20tb3MvHmPq7wTXDRn4BcCV5rZ5UAGkGtmDwMhM5sYOfueCDTFMqiIDE5nZ31kOtgGFix4moKCS4KOJIN02jNwd/+Ku09x91LgBuC37n4TsAZYEnnZEuCpmKUUkUHp6NjGhg3vobu7ifLyX6u8R4jBXC90J/CYmd0C1APXDU0kERlKbW0b2by5v7ArKl4gJ6ci2EAyZM6owN39BeCFyONDwKKhjyQiQ+Xo0d+zefPlpKTkUl7+HFlZc4KOJENIF32KjFCHDz/Hpk0Xk5Y2PjIdrMp7pFGBi4xAzc1PsGXLFWRmzmLhwt+RkaFLeEciFbjICNPY+FOqq68jJ+c8KiqeJy2tOOhIEiMqcJERwt2pr7+LbdtuJj9/EeXlz5GaqimKRjLNWiMyAvT1HWPbtk/T1LSKoqLrmDt3JUlJ6UHHkhhTgYskuOPHd1FVdTXHjm1h2rR/paRkmeY1GSVU4CIJ7NChZ6mp+WsAFix4hsLCywJOJMNJY+AiCcjdqav7V7ZsuZz09Kmcd956lfcopDNwkQTT29tGbe0SDh58kvHjb2TOnB9r+bNRSgUukkA6OrZRVXU1HR3bmTHjbqZMuU3j3aOYClwkQRw8uIaamo+TlJRGeflz5Od/IOhIEjCNgYvEOfcwu3d/jaqqxWRlzea8815ReQugM3CRuNbT00JNzd9w+PAzTJhwM7Nm3UdyckbQsSROqMBF4lR7exXV1VfT2VnHrFn3MWnSZzTeLSdRgYvEoaamx6it/SQpKTlUVDzP2LEXBh1J4pAKXCSOhMO97N79Vfbu/Xdyc9/NvHmPk54+KehYEqdOW+BmlgG8CKRHXr/a3b9uZgXAo0ApsAe43t2PxC6qyMjW3X2QmpobOXLkN0ya9PfMnHkPSUlpQceSOBbNVShdwAfdvRyoAC4zswuAZcBad58FrI08F5EBaGvbwCuvVNLS8iJz5jzA7Nn3qbzltKJZ1NjdvT3yNDXyx4HFwIrI/hXAVbEIKDLSHTiwkg0b3g30sXDhS0yc+MmgI0mCiOo6cDNLNrONQBPwnLuvA4rdvREgsh3/Fp+71MzWm9n65ubmIYotkvjC4R527PgCtbWfICfnfM477xVyc98RdCxJIFEVuLv3uXsFMAV4p5nNj/YA7r7c3SvdvbKoqGiAMUVGlu7uEJs2XURDw/eZMuU2ysufIy3tlOdAIm/pTFelbzGzF4DLgJCZTXT3RjObSP/ZuYicRmvrOqqqPkpv72Hmzn2E4uK/DjqSJKjTnoGbWZGZ5UUeZwIXAbXAGmBJ5GVLgKdilFFkxNi//342bHgvSUlpLFz4e5W3DEo0Z+ATgRVmlkx/4T/m7k+b2R+Ax8zsFqAeuC6GOUUSWjjcxY4dt9LYuJz8/EsoK1tFampB0LEkwZ22wN19M7DwFPsPAYtiEUpkJOnqaqC6+lpaW/9ISckypk37Nv3nQyKDozsxRWKopeV3VFdfR19fO/Pmraao6KNBR5IRRAUuEgPuTkPDD3n11S+SkTGNiorfkp1dFnQsGWFU4CJDrK/vONu3f4ZQ6CEKCz/C3LkrSUkZG3QsGYFU4CJDqLOzjqqqa2hv/zOlpXdw1ln/gpnWTZHYUIGLDJEjR9ZSXf0x3HuYP/+XjBt3RdCRZIRTgYsMUjjcTV3dv1JX9y2yss5m/vxfkJU1K+hYMgqowEUGobX1T9TWfpKOjmqKi29i1qz7SEnJCTqWjBIqcJEB6OvrYPfuf2HfvntIT5/EggVPU1j44aBjySijAhc5Q0eOPM+2bZ+is3MXkyZ9hunT/42UlNygY8kopAIXiVJv71FeffUfaWz8MZmZM6moeIG8vPcFHUtGMRW4SBQOHlzD9u1/T3f3AaZO/UdKS+8gOTkr6FgyyqnARd5Gd3cTO3bcSnPzo2RnL2D+/KfIza0MOpYIoAIXOSV3p6npZ+zY8QX6+tooLf0WJSW3a51KiSsqcJE36Ozcy/btf8/hw78iN/cC5sx5QPOYSFxSgYtEuIfZv385u3bdjnsfM2few+TJn9PUrxK3TlvgZjYVeAiYAISB5e5+r5kVAI8CpcAe4Hp3PxK7qCKx09Gxg23bPs3Ro/+PvLxFzJmznMzM6UHHEnlb0cyy0wt82d3nAhcAnzWzMmAZsNbdZwFrI89FEko43Et9/V2sX38O7e0bmTPnAcrLn1N5S0KIZkWeRqAx8rjNzGqAycBi4P2Rl60AXgD+KSYpRWKgvX0TtbW30N7+CuPGXcWsWT8kPX1S0LFEonZGY+BmVkr/8mrrgOJIuRNZmX78W3zOUmApQElJyaDCigyFcLiLurpvU19/JykpBZSVPUZR0bWYWdDRRM5I1AVuZmOAnwO3uXtrtH/Z3X05sBygsrLSBxJSZKgcPfoHtm27hY6OGoqLP8HMmXeTmloYdCyRAYmqwM0slf7yfsTdn4jsDpnZxMjZ90SgKVYhRQarr+8Yu3Z9lYaG75OePoUFC56hsPBDQccSGZRorkIx4AGgxt3vPuFDa4AlwJ2R7VMxSSgySIcP/4bt2z9NZ+ceJk36LNOnf1dTvsqIEM0Z+IXAx4EtZrYxsu+f6S/ux8zsFqAeuC4mCUUGqKfnCK+++n84cOBBMjNnU1HxInl5fxV0LJEhE81VKC8BbzXgvWho44gMjebmJ9mx4x/o7m6mpGQZZ531dZKTM4KOJTKkdCemjCjd3SF27Pg8zc2PM2ZMBQsW/IqcnHODjiUSEypwGRHcnVBoJTt33kZf3zGmTfsOU6f+I0lJqUFHE4kZFbgkvM7OerZv/zsOH36W3Nx3RyafOjvoWCIxpwKXhNXT08K+ffewb9/3cHdmzvw+kyd/FrNoZogQSXwqcEk4vb2t7Nt3L/v23U1vbwvjxl3NjBl3k5lZGnQ0kWGlApeE0dvbRkPD99m793v09h6hsHAxpaV3kJNTEXQ0kUCowCXu9fa209DwA/bu/Q96ew9RWHhFpLjPCzqaSKBU4BK3+vqO0dDwQ/buvYuenoMUFFxOaekd5Oa+I+hoInFBBS5xp6+vg/37f0R9/b/T09NEfv6llJbewdixFwQdTSSuqMAlbvT1HWf//v9Lff2d9PSEyM+/iNLSbzB27LuDjiYSl1TgEri+vk4aG39Mff136e5uJC/vg5SWriYv7z1BRxOJaypwCUw43EVj4/3U1X2X7u4Gxo59H2Vlq8jLe1/Q0UQSggpchl043E1j44PU13+Hrq59jB37HubOXUl+/geCjiaSUFTgMmzC4W4OHPgpdXXfoaurPnLb+0/Iz1+k5cxEBkAFLjEXDvcQCj1EXd236ezcQ07O+cyZs5z8/EtU3CKDEM2KPA8CVwBN7j4/sq8AeBQoBfYA17v7kdjFlEQUDvcSCq2MFPcucnLewaxZ91FQcJmKW2QIRDPrz0+By96wbxmw1t1nAWsjz0WA/uI+cOAhXn55Ltu2fZKUlDzmz/8l5567jsLCD6m8RYZINCvyvGhmpW/YvRh4f+TxCuAF4J+GMpgkHvc+mpr+mz17vsnx49sZM6aC+fOforDwIyptkRgY6Bh4sbs3AkRWpR8/hJkkwfQX92PU1X2Tjo5asrPPYd68Jxg3brGmdhWJoZj/EtPMlgJLAUpKSmJ9OBlG7mGam1ezZ8836OjYSlbWPMrKHqeo6BoVt8gwGGiBh8xsYuTseyLQ9FYvdPflwHKAyspKH+DxJI50dTXS1LSKxsYHIsVdRlnZoxQVXaviFhlGAy3wNcAS4M7I9qkhSyRxqbe3nYMHnyQUepgjR34DhBkz5jzmzv0Z48dfj1ly0BFFRp1oLiNcRf8vLMeZ2T7g6/QX92NmdgtQD1wXy5ASjHC4l5aWtRw4sJKDB58kHO4gPf0sSkq+QnHx35CdPTfoiCKjWjRXodz4Fh9aNMRZJA64O+3tGwiFVhIKraKnJ0RKSh7FxTdRXHwTY8deqGESkTihOzEFgM7OOkKhnxEKraSjowazVAoLr6C4+CYKCi4nOTkj6Igi8gYq8FGsp6eF5ubVhEIrOXr0RQDGjn0Ps2f/F0VF15GaWhBwQhF5OyrwUSYc7ubw4f/hwIGVHDr0NO5dZGbOprT0WxQX/w2ZmdOCjigiUVKBjwLuTmvrHwiFHqap6VF6ew+TmlrEpEl/R3HxTeTkVOpOSZEEpAIfwTo6dhAKPUwo9DCdnbtISspk3LirKC6+ifz8i0lKSg06oogMggp8hOnubqap6VFCoYdpa1sHGPn5iygt/Rrjxl1DSkpO0BFFZIiowEeAvr7jHDq0hlDoYQ4ffhb3XrKzy5k+/S6Ki28kPX1y0BFFJAZU4AnKPUxLywuEQg/T3Lyavr420tImM2XKlyguvokxYxYEHVFEYkwFniDcna6uvbS2rqO19fc0N6+mq2sfyck5FBVdS3Hxx8nLe69uaRcZRVTgcaq3t522tvW0tv6RtrZ1tLauo7u7EQCzdAoKLmbGjO9RWPgRkpMzA04rIkFQgccB9zAdHTWRs+s/0tq6jmPHqoAwAJmZs8jPX0Ru7gXk5JzPmDHnkJSUFmxoEQmcCjwA3d1NJ5V1W9uf6OtrAyAlJZ+cnHcybtzV5OaeT27uO0lNLQw4sYjEIxV4jPX1ddLevvGEoZA/0tm5BwCzFLKzz6G4+OORsj6fzMxZmixKRKKiAh9C7k5n567Xz6xbW/9Ie/tG3HsASE+fSm7uBUye/Dlycs4nJ+dckpOzAk4tIolKBT4IPT0ttLX96fWybmv7Ez09BwFISsomJ6eSKVO+9PrZdXr6pIATi8hIogI/jXC4i56eQ/T0HKa39xDHjtW8PhzS0VEbeZWRlTWXwsIrI2V9AVlZZSQl6e0VkdgZVMOY2WXAvUAycL+73zkkqWIgHO6ht/cwPT2H6ek5FHn8l+1rBf3Gj4fDHW/6WqmpReTmXkBx8U2RK0MqSUkZG8B3JSKj2YAL3PrvGPkhcDGwD3jZzNa4+9ahCncq4XAvvb0tpyzbtyvj167yOPX3kkJKSgGpqQWkpBSSkVFCaurCyL7C17epqQVkZEwnI6NUs/eJSOAGcwb+TmCnu+8CMLP/BhYDQ17ge/Z8iwMHfkpv72F6e1ve5pVJpKTkv162aWkTyc6ed1IJv1bUJ+5LTs5RIYtIwhlMgU8G9p7wfB9w/htfZGZLgaUAJSUlAzpQWtpEcnMveNPZcEpK4UllnJIyVpfgicioMZgCP9Upq79ph/tyYDlAZWXlmz4ejUmTPsWkSZ8ayKeKiIxYgzld3QdMPeH5FGD/4OKIiEi0BlPgLwOzzGyamaUBNwBrhiaWiIiczoCHUNy918w+B/wv/ZcRPuju1UOWTERE3tagrgN392eAZ4Yoi4iInAFdsiEikqBU4CIiCUoFLiKSoFTgIiIJytwHdG/NwA5m1gzUDfDTxwEHhzBOotP78Rd6L06m9+NkI+H9OMvdi964c1gLfDDMbL27VwadI17o/fgLvRcn0/txspH8fmgIRUQkQanARUQSVCIV+PKgA8QZvR9/offiZHo/TjZi34+EGQMXEZGTJdIZuIiInEAFLiKSoBKiwM3sMjPbZmY7zWxZ0HmCYmZTzex5M6sxs2oz+0LQmeKBmSWb2QYzezroLEEzszwzW21mtZG/J+8KOlNQzOyLkX8nVWa2yswygs401OK+wE9YPPlDQBlwo5mVBZsqML3Al919LnAB8NlR/F6c6AtATdAh4sS9wLPufjZQzih9X8xsMnArUOnu8+mf8vqGYFMNvbgvcE5YPNndu4HXFk8eddy90d3/HHncRv8/zsnBpgqWmU0BPgzcH3SWoJlZLvBe4AEAd+9295ZAQwUrBcg0sxQgixG4YlgiFPipFk8e1aUFYGalwEJgXcBRgnYPcDsQDjhHPJgONAM/iQwp3W9m2UGHCoK7NwD/AdQDjcBRd/91sKmGXiIUeFSLJ48mZjYG+Dlwm7u3Bp0nKGZ2BdDk7q8EnSVOpADnAj9y94XAMWBU/s7IzPLp/0l9GjAJyDazm4JNNfQSocC1ePIJzCyV/vJ+xN2fCDpPwC4ErjSzPfQPrX3QzB4ONlKg9gH73P21n8pW01/oo9FFwG53b3b3HuAJ4N0BZxpyiVDgWjw5wsyM/vHNGne/O+g8QXP3r7j7FHcvpf/vxW/dfcSdZUXL3Q8Ae81sTmTXImBrgJGCVA9cYGZZkX83ixiBv9Ad1JqYw0GLJ5/kQuDjwBYz2xjZ98+RtUlFAD4PPBI52dkF3BxwnkC4+zozWw38mf6rtzYwAm+p1630IiIJKhGGUERE5BRU4CIiCUoFLiKSoFTgIiIJSgUuIpKgVOAiIglKBS4ikqD+P8hUAE6Mx5PhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Alterando a cor\n",
    "# r -> red\n",
    "# b -> blue\n",
    "# g -> green\n",
    "# y -> yellow\n",
    "plt.plot(x2, y2, 'y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d02b433e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9ec90a2cd0>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Alterando o estilo da linha - -. :\n",
    "plt.plot(x2, y2, 'r:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d35aa6ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9ec901e100>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Múltiplos gráficos\n",
    "plt.plot(x, y, 'b')\n",
    "plt.plot(x2, y2, 'r:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "73d891c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9ec900a670>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Título plt.title()\n",
    "plt.title(\"Comparações entre funções\")\n",
    "plt.plot(x, y, 'b')\n",
    "plt.plot(x2, y2, 'r:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5abeac33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9ec8f6ebb0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Legenda interna - label = '', plt.legend()\n",
    "plt.title(\"Comparações entre funções\")\n",
    "plt.plot(x, y, 'b', label='y = x')\n",
    "plt.plot(x2, y2, 'r:', label='y = x^2')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66d5bde6",
   "metadata": {},
   "source": [
    "### Histogramas\n",
    "\n",
    "Outro tipo de gráfico muito importante são os histogramas. Esses gráficos expressam a frequência em que determinado valor aconteceu em uma lista. Para criarmos esse tipo de gráfico, utilizaremos a função `plt.hist()` que vai receber apenas uma lista (ou array Numpy, ou Serie).\n",
    "\n",
    "Podemos modificar sua cor e adicionar títulos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "92867a0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histograma')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Exemplo histograma\n",
    "data = [1, 2, 1, 4, 6, 6, 4, 1]\n",
    "plt.hist(data, color='b')\n",
    "plt.title(\"Histograma\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "869c4b12",
   "metadata": {},
   "source": [
    "## Exercício 1\n",
    "\n",
    "- Qual modelo de carro com menor massa?\n",
    "- Qual modelo de carro mais econômico?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "68993e98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOX\n",
      "Fit\n"
     ]
    }
   ],
   "source": [
    "linha_menor_massa = df['Massa'].argmin()\n",
    "print(df.iloc[linha_menor_massa].name)\n",
    "\n",
    "linha_maior_consumo = df['Consumo'].argmax()\n",
    "print(df.iloc[linha_maior_consumo].name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e93f586",
   "metadata": {},
   "source": [
    "## Exercício 2\n",
    "\n",
    "Construa uma subtabela apenas com veículos de massa menor do que `2000`. Qual a média de consumos desses veículos?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3728c96c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.72888888888889"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff = df[df['Massa'] < 2000]\n",
    "dff['Consumo'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c9d0434",
   "metadata": {},
   "source": [
    "## Exercício 3\n",
    "\n",
    "Construa um histograma da marca dos veículos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3deac70e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histograma Marcas')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.hist(df['Marcas'], color='b')\n",
    "plt.title(\"Histograma Marcas\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dec60f8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}