{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"gpuClass":"standard"},"cells":[{"cell_type":"markdown","source":["# **Aula 4 - 28/08 - Pandas, Manipulação e Limpeza de Dados, Estatísticas Descritivas.\n","\n"],"metadata":{"id":"_ns7mAL7Sqmm"}},{"cell_type":"markdown","source":["<p align=\"justify\"> O Pandas é uma biblioteca Python amplamente utilizada para análise e manipulação de dados. Ela fornece estruturas de dados e funções que facilitam a manipulação, limpeza, agregação e análise de dados. Pandas é uma ferramenta essencial para qualquer pessoa que trabalhe com dados em Python.\n","\n","<p align=\"justify\"> Para começar a usar o Pandas, você deve importá-lo em seu ambiente Python, geralmente usando o seguinte comando:\n","\n"],"metadata":{"id":"WbPvhjqETV7N"}},{"cell_type":"code","source":["import pandas as pd"],"metadata":{"id":"CX6Y11MuuMq6","executionInfo":{"status":"ok","timestamp":1693243598753,"user_tz":180,"elapsed":377,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}}},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":["## 1. Series\n","\n"],"metadata":{"id":"D0rD_ajPUnll"}},{"cell_type":"markdown","source":["<p align=\"justify\">  Em Pandas, uma Series é uma estrutura de dados unidimensional que se assemelha a uma coluna em uma planilha ou a um vetor em programação. Cada elemento em uma Series possui um rótulo associado, chamado de índice. Essa combinação de dados e índices torna as Series poderosas e flexíveis para trabalhar com dados em Python.\n","\n","Aqui estão alguns pontos-chave sobre as Series em Pandas:\n","\n","\n","* Unidimensional: As Series são estruturas de dados unidimensionais, o que significa que elas contêm apenas uma sequência de dados. Enquanto os DataFrames, outra estrutura de dados Pandas, são bidimensionais, as Series são projetadas para armazenar dados unidimensionais, como uma coluna de valores.\n","\n","* Índices: Cada elemento em uma Series tem um rótulo de índice exclusivo. Esses índices são automaticamente atribuídos às posições dos elementos na Series quando você a cria. Você também pode definir índices personalizados se desejar.\n","\n","* Tipos de Dados: As Series podem conter dados de diversos tipos, como números inteiros, números de ponto flutuante, strings, datas e assim por diante. O Pandas permite que as Series armazenem dados heterogêneos, ou seja, dados de diferentes tipos em uma única Series.\n","\n","* Operações de Vetorização: Uma das vantagens das Series é que elas suportam operações de vetorização. Isso significa que você pode aplicar operações diretamente a uma Series sem precisar de loops explícitos. Isso torna o código mais eficiente e legível.\n","\n","* Acesso aos Dados: Você pode acessar os elementos de uma Series de várias maneiras, incluindo índices numéricos, índices de rótulos, fatiamento e seleção condicional. Isso facilita a recuperação de dados específicos ou a realização de operações neles.\n","\n","* Nome da Série: Você pode atribuir um nome a uma Series, o que pode ser útil para documentação e referência. O nome é armazenado como um atributo da Series.\n","\n"],"metadata":{"id":"sw2sE3Gw6Umh"}},{"cell_type":"markdown","source":["Para criar uma série em pandas, pode se fazer de forma parecida com os numpy arrays:"],"metadata":{"id":"6_rJpIZ3ymjK"}},{"cell_type":"code","source":["import pandas as pd\n","\n","# Criando uma série a partir de uma lista\n","\n","dados = [1,2,3,4,5]\n","\n","serie = pd.Series(dados)\n","\n","print(serie)\n","print(type(serie))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ULdzEgdbymNq","executionInfo":{"status":"ok","timestamp":1693245760886,"user_tz":180,"elapsed":281,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"fd127c01-0069-4141-eb01-7db948cdbe82"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["0    1\n","1    2\n","2    3\n","3    4\n","4    5\n","dtype: int64\n","<class 'pandas.core.series.Series'>\n"]}]},{"cell_type":"markdown","source":["Veja que o valor impresso é diferente de uma lista ou de um numpy array: automaticamente são criados índices para cada elemento da serie. As Series são uma parte fundamental do Pandas e são frequentemente usadas em conjunto com DataFrames para realizar análises de dados e manipulações de dados mais detalhadas. Elas são versáteis e desempenham um papel crucial em muitas operações de análise de dados em Python. Se quiser definir o índice a ser utilizado (ao invés de um indice que começa do 0):\n"],"metadata":{"id":"-nImsdUHy6gi"}},{"cell_type":"code","source":["dados2 = pd.Series([10,7,3,2,-15], # Lista de valores\n","                   index = ['Brasil', 'Japão', 'Coréia', 'México', 'EUA']) # Valores do índice\n","\n","print(dados2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0Nam8DSlzWqO","executionInfo":{"status":"ok","timestamp":1693179234197,"user_tz":180,"elapsed":25,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"ae33c5ae-aea3-43ad-c053-3dfdba871f74"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Brasil    10\n","Japão      7\n","Coréia     3\n","México     2\n","EUA      -15\n","dtype: int64\n"]}]},{"cell_type":"markdown","source":["De forma parecida com arrays do Numpy, você pode usar os labels para selecionar valores:"],"metadata":{"id":"28vPJ0c8zxjV"}},{"cell_type":"code","source":["dados2[\"Brasil\"]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GWJSPcwpz4Nu","executionInfo":{"status":"ok","timestamp":1693179234198,"user_tz":180,"elapsed":23,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"df19bc3a-0a56-4db7-9146-61be71941cc0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["10"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","source":["Ou uma lista de valores:"],"metadata":{"id":"k6PVlOKJz7Uk"}},{"cell_type":"code","source":["dados2[['Brasil', 'México', 'EUA']]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gQ9huagHz_K2","executionInfo":{"status":"ok","timestamp":1693179234198,"user_tz":180,"elapsed":19,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"db4bb48c-89a2-4d4a-d1c9-928ebaeeafab"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Brasil    10\n","México     2\n","EUA      -15\n","dtype: int64"]},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","source":["Você pode filtrar valores utilizando operações Booleanas:"],"metadata":{"id":"zY_0dz8B0JBU"}},{"cell_type":"code","source":["dados2[dados2 > 5]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q8owSzBZ0MSJ","executionInfo":{"status":"ok","timestamp":1693179234198,"user_tz":180,"elapsed":17,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"429e675b-2a53-4c2e-c230-a00c09b97b89"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Brasil    10\n","Japão      7\n","dtype: int64"]},"metadata":{},"execution_count":7}]},{"cell_type":"markdown","source":["Pode fazer operações matemáticas"],"metadata":{"id":"rXnG4yPF0Sgs"}},{"cell_type":"code","source":["dados2 * 2"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HEQT0ex-0Vcx","executionInfo":{"status":"ok","timestamp":1693179234199,"user_tz":180,"elapsed":15,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"f5fa8899-611c-43ec-8951-f93e8d8e8fb9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Brasil    20\n","Japão     14\n","Coréia     6\n","México     4\n","EUA      -30\n","dtype: int64"]},"metadata":{},"execution_count":8}]},{"cell_type":"markdown","source":["E assim por diante. Além disso, como ressalta Mckinney (2022), series de Pandas são parecidas também com dicionários. Você pode verificar se determinada \"chave\" (ou índice nesse contexto) está dentro de uma serie:"],"metadata":{"id":"filPLVhW0ctK"}},{"cell_type":"code","source":["\"Brasil\" in dados2"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MpR_OU8a0zhj","executionInfo":{"status":"ok","timestamp":1693179234199,"user_tz":180,"elapsed":10,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"3144bd71-2e6f-42c9-dc5f-3a7e8a159639"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["\"Argentina\" in dados2"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BaFovLdJ03i0","executionInfo":{"status":"ok","timestamp":1693179234782,"user_tz":180,"elapsed":588,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"7cb32f61-bb9b-49f6-f6a9-1b21d29c0ca7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["False"]},"metadata":{},"execution_count":10}]},{"cell_type":"markdown","source":["Se passar um dicionário para transformar em série, automaticamente o Pandas utilizará as chaves como índice"],"metadata":{"id":"UNDGBnk406Y8"}},{"cell_type":"code","source":["dados_dict = {'Brasil': 10, 'Japão': 7, 'Coréia': 3, 'México':2, 'EUA': -15}\n","\n","dados3 = pd.Series(dados_dict)\n","\n","dados3\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5xoo7Y5O1CMi","executionInfo":{"status":"ok","timestamp":1693179234783,"user_tz":180,"elapsed":43,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"ab4815c7-9e04-4563-c989-903dbeb43d3f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Brasil    10\n","Japão      7\n","Coréia     3\n","México     2\n","EUA      -15\n","dtype: int64"]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","source":["E pode transformar de volta em um dicionário utilizando o método \".to_dict()\"\n"],"metadata":{"id":"g30I3UA71W92"}},{"cell_type":"code","source":["dados_dict2 = dados3.to_dict()\n","\n","print(dados_dict2)\n","print(type(dados_dict2))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XdE6Oe1X1WSf","executionInfo":{"status":"ok","timestamp":1693179234783,"user_tz":180,"elapsed":41,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"51e70eac-7f8f-4fa1-f99b-25517eb5edb1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'Brasil': 10, 'Japão': 7, 'Coréia': 3, 'México': 2, 'EUA': -15}\n","<class 'dict'>\n"]}]},{"cell_type":"markdown","source":["Quando existem valores *NA* (Ou *missing*), você pode usar o método isna() para identificá-los:"],"metadata":{"id":"bAT8vxbF1s5j"}},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","\n","# Criando uma Series com índice de países\n","dados4 = {'Brasil': 210_000_000,\n","          'EUA': 328_000_000,\n","          'China': 1_409_000_000}\n","\n","series2 = pd.Series(dados4)\n","\n","# Definindo um valor ausente para um país\n","series2['Índia'] = np.nan\n","\n","# Imprimindo a série\n","print(series2)\n","\n","print(\" \")\n","print(\"*****Missings******\")\n","\n","\n","# Checando os ausentes\n","print(series2.isna())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"byp9fysg2o09","executionInfo":{"status":"ok","timestamp":1693179234783,"user_tz":180,"elapsed":38,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"ec54e327-c428-47cb-d66e-34a85d8afc24"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Brasil    2.100000e+08\n","EUA       3.280000e+08\n","China     1.409000e+09\n","Índia              NaN\n","dtype: float64\n"," \n","*****Missings******\n","Brasil    False\n","EUA       False\n","China     False\n","Índia      True\n","dtype: bool\n"]}]},{"cell_type":"markdown","source":["Por fim, se quiser pode nomear tanto os índices da série quanto seus valores:"],"metadata":{"id":"DQrG8uJr4JOI"}},{"cell_type":"code","source":["# Nomeando os valores\n","\n","series2.name = 'PIB per capita'\n","\n","# nomeando o índice\n","\n","series2.index.name = 'Países'\n","\n","\n","print(series2)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_D5NcurZ4MzV","executionInfo":{"status":"ok","timestamp":1693179234783,"user_tz":180,"elapsed":36,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"1f1d86e6-83f4-41f9-a232-f64ad4e4a415"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Países\n","Brasil    2.100000e+08\n","EUA       3.280000e+08\n","China     1.409000e+09\n","Índia              NaN\n","Name: PIB per capita, dtype: float64\n"]}]},{"cell_type":"markdown","source":["### Exercícios"],"metadata":{"id":"xS640Eo_B_A_"}},{"cell_type":"markdown","source":["Para os exercícios, sempre é bom consultar a documentação específica do Pandas sobre [Series](https://pandas.pydata.org/docs/reference/series.html)"],"metadata":{"id":"CCt-g6L05n_o"}},{"cell_type":"markdown","source":["1 - Filtragem com Series - Crie uma Series com números inteiros aleatórios. Em seguida, crie uma nova Series que contenha apenas os valores maiores que 50 da primeira Series."],"metadata":{"id":"TIwStOxHUTai"}},{"cell_type":"code","source":["# Criando uma Series com números inteiros aleatórios\n","data = [45, 60, 75, 30, 90, 55, 70]\n","series = pd.Series(data)\n","\n","# Filtrando valores maiores que 50\n","filtrados = series[series > 50]\n","\n","print(\"Série original:\")\n","print(series)\n","print(\"\\nValores maiores que 50:\")\n","print(filtrados)"],"metadata":{"id":"6UVus0k3TOLV","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1693245771460,"user_tz":180,"elapsed":257,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"b8f26b24-532b-485f-fe0c-8aa0c7a00df5"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Série original:\n","0    45\n","1    60\n","2    75\n","3    30\n","4    90\n","5    55\n","6    70\n","dtype: int64\n","\n","Valores maiores que 50:\n","1    60\n","2    75\n","4    90\n","5    55\n","6    70\n","dtype: int64\n"]}]},{"cell_type":"markdown","source":["2 - Operações com Series - Primeiro, vamos criar duas Series com dados numéricos:"],"metadata":{"id":"8MNj4OSyU8Us"}},{"cell_type":"code","source":["# Criando as duas Series\n","serie1 = pd.Series([10, 20, 30, 40, 50])\n","serie2 = pd.Series([5, 15, 25, 35, 45])"],"metadata":{"id":"y_fLjaLvTOEy","executionInfo":{"status":"ok","timestamp":1693245893523,"user_tz":180,"elapsed":283,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":["a) Some as duas Series"],"metadata":{"id":"qUGa4Wiy54-G"}},{"cell_type":"code","source":["soma = serie1 + serie2\n","print(\"Soma das Series:\")\n","print(soma)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mAAx4l0g56km","executionInfo":{"status":"ok","timestamp":1693245896503,"user_tz":180,"elapsed":255,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"d378f685-f8a4-4ff4-cf77-f560a8a1e7b2"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Soma das Series:\n","0    15\n","1    35\n","2    55\n","3    75\n","4    95\n","dtype: int64\n"]}]},{"cell_type":"markdown","source":["b) Multiplique as Series uma pela outra"],"metadata":{"id":"sVVyRh9l568n"}},{"cell_type":"code","source":["mult = serie1 * serie2\n","\n","print(\"Multplicação das Series: \")\n","print(mult)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MRSyVHip5_pg","executionInfo":{"status":"ok","timestamp":1693246038620,"user_tz":180,"elapsed":243,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"add5cac4-9fdb-4025-85f3-bb33c9b4f782"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Multplicação das Series: \n","0      50\n","1     300\n","2     750\n","3    1400\n","4    2250\n","dtype: int64\n"]}]},{"cell_type":"markdown","source":["c) Encontre o valor máximo nas duas Series"],"metadata":{"id":"xiZyx2r36ADR"}},{"cell_type":"code","source":["print(serie1.max())\n","print(serie2.max())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aFKOBlHG6C5u","executionInfo":{"status":"ok","timestamp":1693179234784,"user_tz":180,"elapsed":26,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"8b15243b-f2f6-4834-8617-aaf4e528a0a7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["50\n","45\n"]}]},{"cell_type":"markdown","source":["d) Calcule a média dos valores nas duas séries:"],"metadata":{"id":"qCFBAj0J6Rk_"}},{"cell_type":"code","source":["media_serie1 = serie1.mean()\n","media_serie2 = serie2.mean()\n","\n","print(\"Média de Serie1:\", media_serie1)\n","print(\"Média de Serie2:\", media_serie2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V38BByp46UBk","executionInfo":{"status":"ok","timestamp":1693179234784,"user_tz":180,"elapsed":23,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"b92cf68e-2c67-490e-e80e-4f2c8be79390"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Média de Serie1: 30.0\n","Média de Serie2: 25.0\n"]}]},{"cell_type":"markdown","source":["3 - Crie uma Series com nomes de cidades e seus respectivos números de habitantes (Não precisam ser dados reais). Classifique a Series em ordem decrescente com base no número de habitantes."],"metadata":{"id":"YCLW__joVLop"}},{"cell_type":"code","source":["# Criando uma Series com nomes de cidades e seus respectivos números de habitantes\n","data = {'Cidade A': 750000, 'Cidade B': 610000, 'Cidade C': 850000, 'Cidade D': 420000, 'Cidade E': 320000}\n","series = pd.Series(data)\n","\n","# Classificando a Series em ordem decrescente com base no número de habitantes\n","series_ordenada = series.sort_values(ascending=False)\n","\n","print(\"Série original:\")\n","print(series)\n","print(\"\\nSérie classificada em ordem decrescente:\")\n","print(series_ordenada)"],"metadata":{"id":"TWbms9g7TN-I","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1693179234784,"user_tz":180,"elapsed":20,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"a6477902-a5a2-4e28-9c30-335345d02e4e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Série original:\n","Cidade A    750000\n","Cidade B    610000\n","Cidade C    850000\n","Cidade D    420000\n","Cidade E    320000\n","dtype: int64\n","\n","Série classificada em ordem decrescente:\n","Cidade C    850000\n","Cidade A    750000\n","Cidade B    610000\n","Cidade D    420000\n","Cidade E    320000\n","dtype: int64\n"]}]},{"cell_type":"markdown","source":["4 - Crie duas Series com dados de sua escolha. Concatene as duas Series em uma única Series. Certifique-se de que os índices sejam únicos após a concatenação."],"metadata":{"id":"g04JWLs3DBTt"}},{"cell_type":"code","source":["# Criando duas Series com dados de sua escolha\n","serie1 = pd.Series([1, 2, 3], index=['A', 'B', 'C'])\n","serie2 = pd.Series([4, 5, 6], index=['D', 'E', 'F'])\n","\n","# Concatenando as duas Series em uma única Series\n","serie_concatenada = pd.concat([serie1, serie2])\n","\n","print(\"Primeira Series:\")\n","print(serie1)\n","print(\"\\nSegunda Series:\")\n","print(serie2)\n","print(\"\\nSeries Concatenada:\")\n","print(serie_concatenada)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"o8shTSEQDWzY","executionInfo":{"status":"ok","timestamp":1693179234784,"user_tz":180,"elapsed":18,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"75d6c10c-c896-414c-c90a-4158592f9364"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Primeira Series:\n","A    1\n","B    2\n","C    3\n","dtype: int64\n","\n","Segunda Series:\n","D    4\n","E    5\n","F    6\n","dtype: int64\n","\n","Series Concatenada:\n","A    1\n","B    2\n","C    3\n","D    4\n","E    5\n","F    6\n","dtype: int64\n"]}]},{"cell_type":"markdown","source":["5 - No seguinte dicionário estão duas informações: O nome de políticos e a quantidade de tweets que fizeram sobre as vacinas de Covid-19 no período de 2020 a 2021. Converta esse dicionário em uma série e dê as seguintes informações: Qual a média de tweets, o desvio padrão e quem tweetou mais."],"metadata":{"id":"EVFVGeJ7Vfza"}},{"cell_type":"code","source":["import random\n","\n","# Criando o dicionário com políticos e números de tweets\n","politicos = {\n","    \"Politico1\": random.randint(0, 500),\n","    \"Politico2\": random.randint(0, 500),\n","    \"Politico3\": random.randint(0, 500),\n","    \"Politico4\": random.randint(0, 500),\n","    \"Politico5\": random.randint(0, 500),\n","    \"Politico6\": random.randint(0, 500),\n","    \"Politico7\": random.randint(0, 500),\n","    \"Politico8\": random.randint(0, 500),\n","    \"Politico9\": random.randint(0, 500),\n","    \"Politico10\": random.randint(0, 500),\n","    \"Politico11\": random.randint(0, 500),\n","    \"Politico12\": random.randint(0, 500),\n","    \"Politico13\": random.randint(0, 500),\n","    \"Politico14\": random.randint(0, 500),\n","    \"Politico15\": random.randint(0, 500),\n","    \"Politico16\": random.randint(0, 500),\n","    \"Politico17\": random.randint(0, 500),\n","    \"Politico18\": random.randint(0, 500),\n","    \"Politico19\": random.randint(0, 500),\n","    \"Politico20\": random.randint(0, 500)\n","}\n","\n","# Convertendo o dicionário em uma Series\n","serie_politicos = pd.Series(politicos)\n","\n","# Calculando a média de tweets\n","media_tweets = serie_politicos.mean()\n","\n","# Calculando o desvio padrão dos tweets\n","desvio_padrao_tweets = serie_politicos.std()\n","\n","# Encontrando o político que tweetou mais\n","politico_mais_tweets = serie_politicos.idxmax()\n","\n","print(\"Série de Políticos e Números de Tweets:\")\n","print(serie_politicos)\n","print(\"\\nMédia de Tweets:\", media_tweets)\n","print(\"Desvio Padrão dos Tweets:\", desvio_padrao_tweets)\n","print(\"Político que Tweetou Mais:\", politico_mais_tweets, \"com\", serie_politicos[politico_mais_tweets], \"tweets.\")\n","\n","\n","\n","\n","\n"],"metadata":{"id":"NO3cS6etWG5V","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1693246617405,"user_tz":180,"elapsed":317,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"643a1ebc-238c-466c-a73e-0145d9b2f30a"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Série de Políticos e Números de Tweets:\n","Politico1     304\n","Politico2     468\n","Politico3     451\n","Politico4     166\n","Politico5     328\n","Politico6     270\n","Politico7     431\n","Politico8     271\n","Politico9     182\n","Politico10    477\n","Politico11    500\n","Politico12    212\n","Politico13    327\n","Politico14    149\n","Politico15     29\n","Politico16    376\n","Politico17    158\n","Politico18    446\n","Politico19    281\n","Politico20    397\n","dtype: int64\n","\n","Média de Tweets: 311.15\n","Desvio Padrão dos Tweets: 132.42486449621956\n","Político que Tweetou Mais: Politico11 com 500 tweets.\n"]}]},{"cell_type":"markdown","source":["6 - Faça uma função que recebe um dicionário como argumento e retorne esse dicionário como série, junto de informações descritivas como média, moda, mediana."],"metadata":{"id":"KeELXjCYWZpy"}},{"cell_type":"code","source":["def analisar_dicionario(dic):\n","    # Convertendo o dicionário em uma Series\n","    serie = pd.Series(dic)\n","\n","    # Calculando as estatísticas descritivas\n","    media = serie.mean()\n","    moda = serie.mode().to_list()  # Pode haver mais de uma moda\n","    mediana = serie.median()\n","\n","    # Criando um dicionário com as informações\n","    informacoes = {\n","        \"Serie\": serie,\n","        \"Média\": media,\n","        \"Moda\": moda,\n","        \"Mediana\": mediana\n","    }\n","\n","    return informacoes\n","\n","# Exemplo de uso da função com um dicionário fictício\n","dicionario_exemplo = {\n","    \"A\": 10,\n","    \"B\": 20,\n","    \"C\": 30,\n","    \"D\": 20,\n","    \"E\": 10\n","}\n","\n","resultado = analisar_dicionario(dicionario_exemplo)\n","print(\"Informações Descritivas:\")\n","for chave, valor in resultado.items():\n","    if chave == \"Serie\":\n","        print(f\"{chave}:\\n{valor}\\n\")\n","    else:\n","        print(f\"{chave}: {valor}\")"],"metadata":{"id":"Sr0mYe09TN3y"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## 2. Dataframes"],"metadata":{"id":"FpvzYRmGtMfz"}},{"cell_type":"markdown","source":["Um DataFrame é uma estrutura de dados bidimensional, tabular e flexível amplamente usada na biblioteca Pandas do Python para armazenar e manipular dados de forma semelhante a uma planilha ou tabela em um banco de dados. Ele é composto por linhas e colunas, onde cada coluna pode conter dados de diferentes tipos, como números inteiros, números de ponto flutuante, strings, datas, entre outros.\n","\n","Aqui estão algumas características e conceitos importantes relacionados a DataFrames:\n","\n","* Bidimensional: DataFrames são bidimensionais, o que significa que eles têm duas dimensões: linhas e colunas. Cada linha representa um registro ou entrada de dados, enquanto cada coluna representa uma variável ou atributo específico.\n","\n","* Estrutura Tabular: Os dados em um DataFrame são organizados em uma estrutura tabular semelhante a uma planilha. As colunas são nomeadas e as linhas são numeradas, permitindo fácil indexação e identificação dos dados.\n","\n","* Tipos de Dados Heterogêneos: Diferentemente das estruturas de arrays NumPy, em que todos os elementos devem ser do mesmo tipo de dados, DataFrames do Pandas permitem que cada coluna contenha dados de tipos diferentes. Isso é particularmente útil para lidar com conjuntos de dados do mundo real, onde os tipos de dados variam.\n","\n","* Rótulos de Linha e Coluna: Os DataFrames têm rótulos de linha (índices) e rótulos de coluna. Os rótulos de linha são usados para identificar as entradas de dados, enquanto os rótulos de coluna são usados para nomear as variáveis ou atributos.\n","\n","* Operações de Dados: Os DataFrames oferecem uma ampla gama de funcionalidades para realizar operações de manipulação de dados, como filtragem, seleção, ordenação, agregação e cálculos estatísticos.\n","\n","* Integração com Pandas: Os DataFrames são a estrutura de dados central na biblioteca Pandas, o que os torna altamente compatíveis com outras funcionalidades do Pandas, como leitura e escrita de dados em diferentes formatos, manipulação de datas e horas, e visualização de dados.\n","\n","* Origem de Dados Diversificada: DataFrames podem ser criados a partir de várias fontes de dados, como arquivos CSV, bancos de dados SQL, dicionários Python e muito mais. Eles também podem ser exportados para diversos formatos, tornando-os ideais para análise de dados e ciência de dados.\n","\n","Em resumo, um DataFrame é uma estrutura de dados extremamente versátil e poderosa no Pandas, projetada para lidar com uma ampla variedade de tarefas de manipulação e análise de dados. É uma escolha popular para qualquer pessoa que trabalhe com dados em Python devido à sua flexibilidade e eficiência."],"metadata":{"id":"9hNLrbJTllgz"}},{"cell_type":"markdown","source":["Para criar um dataframe, você pode utilizar um dicionário com listas aninhadas:"],"metadata":{"id":"7b8cnFhp30Ur"}},{"cell_type":"code","source":["# Criando uma lista de dicionários com dados fictícios\n","dados = [\n","    {'Nome': 'Alice', 'Idade': 25, 'Cidade': 'São Paulo'},\n","    {'Nome': 'João', 'Idade': 30, 'Cidade': 'Rio de Janeiro'},\n","    {'Nome': 'Carol', 'Idade': 22, 'Cidade': 'Belo Horizonte'},\n","    {'Nome': 'David', 'Idade': 35, 'Cidade': 'São Paulo'},\n","]\n","\n","# Criando um DataFrame a partir da lista de dicionários\n","df = pd.DataFrame(dados)\n","\n","# Exibindo o DataFrame\n","print(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"R1RFzZ1s36OD","executionInfo":{"status":"ok","timestamp":1693179234785,"user_tz":180,"elapsed":12,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"94b79918-5f5e-4ff6-b5ee-8e2cba5e6fba"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Nome  Idade          Cidade\n","0  Alice     25       São Paulo\n","1   João     30  Rio de Janeiro\n","2  Carol     22  Belo Horizonte\n","3  David     35       São Paulo\n"]}]},{"cell_type":"markdown","source":["Nem sempre você vai querer imprimir todo o banco. Digamos que você queira só entender mais ou menos o formato do banco. No exemplo a seguir temos um dataframe com 20 observações:"],"metadata":{"id":"hq-z0bdm4Va4"}},{"cell_type":"code","source":["# Criando dados fictícios para o DataFrame\n","dados = {\n","    \"Nome\": [\"Pessoa\" + str(i) for i in range(1, 21)], # usando list comprehension para gerar um índice de 1 a 21 e somar à \"Pessoa\"\n","    \"Idade\": [random.randint(18, 65) for _ in range(20)], # usando list comprehension para gerar números aleatórios no intervalo de 18 a 65 para 20 observações\n","    \"Cidade\": [\"Cidade\" + str(random.randint(1, 5)) for _ in range(20)]  # usando list comprehension para gerar um índice de 1 a 5 e somar à \"Cidade\"\n","}\n","\n","# Criando o DataFrame\n","df = pd.DataFrame(dados)\n","\n","# Exibindo o DataFrame\n","print(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5B6ugB9M4c4l","executionInfo":{"status":"ok","timestamp":1693179235382,"user_tz":180,"elapsed":606,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"75f61bb5-9c59-4ad7-f966-c1ad1daef8fe"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["        Nome  Idade   Cidade\n","0    Pessoa1     18  Cidade3\n","1    Pessoa2     55  Cidade3\n","2    Pessoa3     61  Cidade4\n","3    Pessoa4     19  Cidade4\n","4    Pessoa5     20  Cidade2\n","5    Pessoa6     64  Cidade3\n","6    Pessoa7     38  Cidade2\n","7    Pessoa8     49  Cidade4\n","8    Pessoa9     58  Cidade3\n","9   Pessoa10     53  Cidade4\n","10  Pessoa11     43  Cidade4\n","11  Pessoa12     44  Cidade5\n","12  Pessoa13     34  Cidade1\n","13  Pessoa14     48  Cidade3\n","14  Pessoa15     19  Cidade1\n","15  Pessoa16     64  Cidade3\n","16  Pessoa17     44  Cidade4\n","17  Pessoa18     38  Cidade2\n","18  Pessoa19     29  Cidade3\n","19  Pessoa20     42  Cidade2\n"]}]},{"cell_type":"markdown","source":["Se quiser, pode imprimir só os 5 primeiros valores utilizando \"head\", mostrando a \"cabeça\" do dataframe"],"metadata":{"id":"UThHsHXB40ea"}},{"cell_type":"code","source":["df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"Zh_ISLTW44TF","executionInfo":{"status":"ok","timestamp":1693179235382,"user_tz":180,"elapsed":98,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"42558476-81a0-4c0d-a445-146cd7345650"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Nome  Idade   Cidade\n","0  Pessoa1     18  Cidade3\n","1  Pessoa2     55  Cidade3\n","2  Pessoa3     61  Cidade4\n","3  Pessoa4     19  Cidade4\n","4  Pessoa5     20  Cidade2"],"text/html":["\n","  <div id=\"df-717b1370-8ca8-408b-998a-9c4a183f6290\" class=\"colab-df-container\">\n","    <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","   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Se eu quiser ver apenas os dois primeiros valores"],"metadata":{"id":"9nn8eOP6NHGT"}},{"cell_type":"code","source":["df.head(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"3GrTjELDNMBz","executionInfo":{"status":"ok","timestamp":1693179235383,"user_tz":180,"elapsed":98,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"6266195f-934c-48f0-9db1-581406a41a55"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Nome  Idade   Cidade\n","0  Pessoa1     18  Cidade3\n","1  Pessoa2     55  Cidade3"],"text/html":["\n","  <div id=\"df-fdf92dce-7502-4b7f-8c99-315ebe5a8054\" class=\"colab-df-container\">\n","    <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>Nome</th>\n","      <th>Idade</th>\n","      <th>Cidade</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Pessoa1</td>\n","      <td>18</td>\n","      <td>Cidade3</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Pessoa2</td>\n","      <td>55</td>\n","      <td>Cidade3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fdf92dce-7502-4b7f-8c99-315ebe5a8054')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-fdf92dce-7502-4b7f-8c99-315ebe5a8054 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":28}]},{"cell_type":"markdown","source":["De forma inversa, pode também imprimir os últimos 5 valores utilizando \"tail\", o \"rabo\" do dataframe:"],"metadata":{"id":"ZynAMqd15AN-"}},{"cell_type":"code","source":["df.tail()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"3QBSqx875K2-","executionInfo":{"status":"ok","timestamp":1693179235383,"user_tz":180,"elapsed":97,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"f698b5b2-80fc-4501-daac-bf8279bce5a4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        Nome  Idade   Cidade\n","15  Pessoa16     64  Cidade3\n","16  Pessoa17     44  Cidade4\n","17  Pessoa18     38  Cidade2\n","18  Pessoa19     29  Cidade3\n","19  Pessoa20     42  Cidade2"],"text/html":["\n","  <div id=\"df-20787459-da58-4df1-ba0d-d760f72e002d\" class=\"colab-df-container\">\n","    <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>Nome</th>\n","      <th>Idade</th>\n","      <th>Cidade</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>15</th>\n","      <td>Pessoa16</td>\n","      <td>64</td>\n","      <td>Cidade3</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Pessoa17</td>\n","      <td>44</td>\n","      <td>Cidade4</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Pessoa18</td>\n","      <td>38</td>\n","      <td>Cidade2</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Pessoa19</td>\n","      <td>29</td>\n","      <td>Cidade3</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>Pessoa20</td>\n","      <td>42</td>\n","      <td>Cidade2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-20787459-da58-4df1-ba0d-d760f72e002d')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-20787459-da58-4df1-ba0d-d760f72e002d button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":29}]},{"cell_type":"code","source":["# 2 últimos valores\n","\n","df.tail(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"dZp5fMLTNUKo","executionInfo":{"status":"ok","timestamp":1693179235383,"user_tz":180,"elapsed":97,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"fca95138-4a44-4c7c-e0da-6c44662b2c23"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        Nome  Idade   Cidade\n","18  Pessoa19     29  Cidade3\n","19  Pessoa20     42  Cidade2"],"text/html":["\n","  <div id=\"df-d0722860-892e-4216-9317-c80fa07bc680\" class=\"colab-df-container\">\n","    <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>Nome</th>\n","      <th>Idade</th>\n","      <th>Cidade</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>18</th>\n","      <td>Pessoa19</td>\n","      <td>29</td>\n","      <td>Cidade3</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>Pessoa20</td>\n","      <td>42</td>\n","      <td>Cidade2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0722860-892e-4216-9317-c80fa07bc680')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-d0722860-892e-4216-9317-c80fa07bc680 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":30}]},{"cell_type":"markdown","source":["Pode mudar a ordem das colunas utilizando a opção \"columns\" na hora de criar um dataframe:"],"metadata":{"id":"Rkhj7Jzd5OFa"}},{"cell_type":"code","source":["df = pd.DataFrame(dados, columns=['Cidade', 'Nome','Idade'])\n","\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":676},"id":"r61fLMKm5W8Z","executionInfo":{"status":"ok","timestamp":1693179235383,"user_tz":180,"elapsed":96,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"a911561d-8af8-4ebc-dbe2-f38683739c84"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Cidade      Nome  Idade\n","0   Cidade3   Pessoa1     18\n","1   Cidade3   Pessoa2     55\n","2   Cidade4   Pessoa3     61\n","3   Cidade4   Pessoa4     19\n","4   Cidade2   Pessoa5     20\n","5   Cidade3   Pessoa6     64\n","6   Cidade2   Pessoa7     38\n","7   Cidade4   Pessoa8     49\n","8   Cidade3   Pessoa9     58\n","9   Cidade4  Pessoa10     53\n","10  Cidade4  Pessoa11     43\n","11  Cidade5  Pessoa12     44\n","12  Cidade1  Pessoa13     34\n","13  Cidade3  Pessoa14     48\n","14  Cidade1  Pessoa15     19\n","15  Cidade3  Pessoa16     64\n","16  Cidade4  Pessoa17     44\n","17  Cidade2  Pessoa18     38\n","18  Cidade3  Pessoa19     29\n","19  Cidade2  Pessoa20     42"],"text/html":["\n","  <div id=\"df-e0dae98a-1cf9-48bb-b7a0-336c8dcd8bad\" class=\"colab-df-container\">\n","    <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>Cidade</th>\n","      <th>Nome</th>\n","      <th>Idade</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Cidade3</td>\n","      <td>Pessoa1</td>\n","      <td>18</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Cidade3</td>\n","      <td>Pessoa2</td>\n","      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<td>53</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Cidade4</td>\n","      <td>Pessoa11</td>\n","      <td>43</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Cidade5</td>\n","      <td>Pessoa12</td>\n","      <td>44</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Cidade1</td>\n","      <td>Pessoa13</td>\n","      <td>34</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Cidade3</td>\n","      <td>Pessoa14</td>\n","      <td>48</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Cidade1</td>\n","      <td>Pessoa15</td>\n","      <td>19</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Cidade3</td>\n","      <td>Pessoa16</td>\n","      <td>64</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Cidade4</td>\n","      <td>Pessoa17</td>\n","      <td>44</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Cidade2</td>\n","      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  61    NaN\n","3   Cidade4   Pessoa4     19    NaN\n","4   Cidade2   Pessoa5     20    NaN\n","5   Cidade3   Pessoa6     64    NaN\n","6   Cidade2   Pessoa7     38    NaN\n","7   Cidade4   Pessoa8     49    NaN\n","8   Cidade3   Pessoa9     58    NaN\n","9   Cidade4  Pessoa10     53    NaN\n","10  Cidade4  Pessoa11     43    NaN\n","11  Cidade5  Pessoa12     44    NaN\n","12  Cidade1  Pessoa13     34    NaN\n","13  Cidade3  Pessoa14     48    NaN\n","14  Cidade1  Pessoa15     19    NaN\n","15  Cidade3  Pessoa16     64    NaN\n","16  Cidade4  Pessoa17     44    NaN\n","17  Cidade2  Pessoa18     38    NaN\n","18  Cidade3  Pessoa19     29    NaN\n","19  Cidade2  Pessoa20     42    NaN"],"text/html":["\n","  <div id=\"df-2eb11374-8dd2-4850-bcc9-a7bee8bf692c\" class=\"colab-df-container\">\n","    <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","    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<td>38</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Cidade3</td>\n","      <td>Pessoa19</td>\n","      <td>29</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>Cidade2</td>\n","      <td>Pessoa20</td>\n","      <td>42</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2eb11374-8dd2-4850-bcc9-a7bee8bf692c')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":32}]},{"cell_type":"markdown","source":["Por fim, toda coluna de um dataframe pode ser extraída na forma de uma Series:"],"metadata":{"id":"-EGMQAPy5tat"}},{"cell_type":"code","source":["df['Cidade']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ECw7ilCm5xAI","executionInfo":{"status":"ok","timestamp":1693179235384,"user_tz":180,"elapsed":96,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"482895a3-f285-44fe-bd99-ed2f1cdcd120"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0     Cidade3\n","1     Cidade3\n","2     Cidade4\n","3     Cidade4\n","4     Cidade2\n","5     Cidade3\n","6     Cidade2\n","7     Cidade4\n","8     Cidade3\n","9     Cidade4\n","10    Cidade4\n","11    Cidade5\n","12    Cidade1\n","13    Cidade3\n","14    Cidade1\n","15    Cidade3\n","16    Cidade4\n","17    Cidade2\n","18    Cidade3\n","19    Cidade2\n","Name: Cidade, dtype: object"]},"metadata":{},"execution_count":33}]},{"cell_type":"code","source":["print(type(df['Cidade']))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"s3jGM2Ve50o0","executionInfo":{"status":"ok","timestamp":1693179235384,"user_tz":180,"elapsed":93,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"9b03fbb5-6c1f-4d94-d3b7-3138cb5dec7c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.series.Series'>\n"]}]},{"cell_type":"markdown","source":["Forma alternativa de extrair uma coluna:"],"metadata":{"id":"F2afpgvD55OK"}},{"cell_type":"code","source":["df.Cidade"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y7xDzrf254qp","executionInfo":{"status":"ok","timestamp":1693179235384,"user_tz":180,"elapsed":90,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"3c13945b-29ff-4fb8-99cf-9750df0d9c9f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0     Cidade3\n","1     Cidade3\n","2     Cidade4\n","3     Cidade4\n","4     Cidade2\n","5     Cidade3\n","6     Cidade2\n","7     Cidade4\n","8     Cidade3\n","9     Cidade4\n","10    Cidade4\n","11    Cidade5\n","12    Cidade1\n","13    Cidade3\n","14    Cidade1\n","15    Cidade3\n","16    Cidade4\n","17    Cidade2\n","18    Cidade3\n","19    Cidade2\n","Name: Cidade, dtype: object"]},"metadata":{},"execution_count":35}]},{"cell_type":"markdown","source":["Também pode imprimir as colunas presentes em um dataframe utilizando o atributo \"columns\":"],"metadata":{"id":"f_ZFI5KXFc6W"}},{"cell_type":"code","source":["df.columns"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"v3EVhatiFi1F","executionInfo":{"status":"ok","timestamp":1693179235384,"user_tz":180,"elapsed":87,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"107175a4-d3f7-4ced-943c-0c1c3b804342"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['Cidade', 'Nome', 'Idade', 'Gênero'], dtype='object')"]},"metadata":{},"execution_count":36}]},{"cell_type":"markdown","source":["O mesmo com os índices"],"metadata":{"id":"1nnL2a1VFm1U"}},{"cell_type":"code","source":["df.index"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BvvQGWGkFoIq","executionInfo":{"status":"ok","timestamp":1693179235384,"user_tz":180,"elapsed":85,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"39d854f2-548d-452e-ec7d-6e20c2389068"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["RangeIndex(start=0, stop=20, step=1)"]},"metadata":{},"execution_count":37}]},{"cell_type":"markdown","source":["#### Indexação, Seleção e Filtragem"],"metadata":{"id":"iSifZl2bHFVy"}},{"cell_type":"markdown","source":["A indexação, seleção e filtragem de dados são operações fundamentais ao trabalhar com Pandas para acessar informações específicas em DataFrames. Essas operações permitem que você extraia dados relevantes para análise e manipulação.\n"],"metadata":{"id":"OSwf3yi6HheQ"}},{"cell_type":"markdown","source":["Você pode selecionar colunas e valores em dataframes de forma parecida com o que já vimos em listas, utilizandos índices (i.e. [indice] ou ['coluna'])."],"metadata":{"id":"1DA6xmmvI_wK"}},{"cell_type":"code","source":["df = pd.DataFrame(dados, columns=['Cidade', 'Nome','Idade'])\n","\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":676},"id":"mk-hEmaHJK45","executionInfo":{"status":"ok","timestamp":1693179235385,"user_tz":180,"elapsed":83,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"eb082ec3-ae3f-4b00-d6ce-dd9469dc3df5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Cidade      Nome  Idade\n","0   Cidade3   Pessoa1     18\n","1   Cidade3   Pessoa2     55\n","2   Cidade4   Pessoa3     61\n","3   Cidade4   Pessoa4     19\n","4   Cidade2   Pessoa5     20\n","5   Cidade3   Pessoa6     64\n","6   Cidade2   Pessoa7     38\n","7   Cidade4   Pessoa8     49\n","8   Cidade3   Pessoa9     58\n","9   Cidade4  Pessoa10     53\n","10  Cidade4  Pessoa11     43\n","11  Cidade5  Pessoa12     44\n","12  Cidade1  Pessoa13     34\n","13  Cidade3  Pessoa14     48\n","14  Cidade1  Pessoa15     19\n","15  Cidade3  Pessoa16     64\n","16  Cidade4  Pessoa17     44\n","17  Cidade2  Pessoa18     38\n","18  Cidade3  Pessoa19     29\n","19  Cidade2  Pessoa20     42"],"text/html":["\n","  <div id=\"df-ab1a5d5c-ab62-4c55-9897-6d060b7696c3\" class=\"colab-df-container\">\n","    <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\" 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   <td>Cidade1</td>\n","      <td>Pessoa15</td>\n","      <td>19</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Cidade3</td>\n","      <td>Pessoa16</td>\n","      <td>64</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Cidade4</td>\n","      <td>Pessoa17</td>\n","      <td>44</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Cidade2</td>\n","      <td>Pessoa18</td>\n","      <td>38</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Cidade3</td>\n","      <td>Pessoa19</td>\n","      <td>29</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>Cidade2</td>\n","      <td>Pessoa20</td>\n","      <td>42</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab1a5d5c-ab62-4c55-9897-6d060b7696c3')\"\n","            title=\"Convert this 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 Pessoa18\n","18    Pessoa19\n","19    Pessoa20\n","Name: Nome, dtype: object"]},"metadata":{},"execution_count":39}]},{"cell_type":"markdown","source":["Selecionando nome e idade:"],"metadata":{"id":"O0L5RtjDJd1d"}},{"cell_type":"code","source":["df[['Nome', 'Idade']]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":676},"id":"Uma5OIa5JfPS","executionInfo":{"status":"ok","timestamp":1693179236088,"user_tz":180,"elapsed":783,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"6fd46578-ccb5-43a8-daa5-aff002a27e67"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        Nome  Idade\n","0    Pessoa1     18\n","1    Pessoa2     55\n","2    Pessoa3     61\n","3    Pessoa4     19\n","4    Pessoa5     20\n","5    Pessoa6     64\n","6    Pessoa7     38\n","7    Pessoa8     49\n","8    Pessoa9     58\n","9   Pessoa10     53\n","10  Pessoa11     43\n","11  Pessoa12     44\n","12  Pessoa13     34\n","13  Pessoa14     48\n","14  Pessoa15     19\n","15  Pessoa16     64\n","16  Pessoa17     44\n","17  Pessoa18     38\n","18  Pessoa19     29\n","19  Pessoa20     42"],"text/html":["\n","  <div id=\"df-204e300e-0bd7-46a0-9ce3-882051fed3f0\" class=\"colab-df-container\">\n","    <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>Nome</th>\n","      <th>Idade</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Pessoa1</td>\n","      <td>18</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Pessoa2</td>\n","      <td>55</td>\n","    </tr>\n","    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<th>12</th>\n","      <td>Pessoa13</td>\n","      <td>34</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Pessoa14</td>\n","      <td>48</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Pessoa15</td>\n","      <td>19</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Pessoa16</td>\n","      <td>64</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Pessoa17</td>\n","      <td>44</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Pessoa18</td>\n","      <td>38</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Pessoa19</td>\n","      <td>29</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>Pessoa20</td>\n","      <td>42</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" 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25]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":551},"id":"ITYP8dlkJhn0","executionInfo":{"status":"ok","timestamp":1693179236089,"user_tz":180,"elapsed":198,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"ee060935-96fa-4c7a-c97d-801ed0df6750"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Cidade      Nome  Idade\n","1   Cidade3   Pessoa2     55\n","2   Cidade4   Pessoa3     61\n","5   Cidade3   Pessoa6     64\n","6   Cidade2   Pessoa7     38\n","7   Cidade4   Pessoa8     49\n","8   Cidade3   Pessoa9     58\n","9   Cidade4  Pessoa10     53\n","10  Cidade4  Pessoa11     43\n","11  Cidade5  Pessoa12     44\n","12  Cidade1  Pessoa13     34\n","13  Cidade3  Pessoa14     48\n","15  Cidade3  Pessoa16     64\n","16  Cidade4  Pessoa17     44\n","17  Cidade2  Pessoa18     38\n","18  Cidade3  Pessoa19     29\n","19  Cidade2  Pessoa20     42"],"text/html":["\n","  <div 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     <th>6</th>\n","      <td>Cidade2</td>\n","      <td>Pessoa7</td>\n","      <td>38</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Cidade4</td>\n","      <td>Pessoa8</td>\n","      <td>49</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Cidade3</td>\n","      <td>Pessoa9</td>\n","      <td>58</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Cidade4</td>\n","      <td>Pessoa10</td>\n","      <td>53</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Cidade4</td>\n","      <td>Pessoa11</td>\n","      <td>43</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Cidade5</td>\n","      <td>Pessoa12</td>\n","      <td>44</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Cidade1</td>\n","      <td>Pessoa13</td>\n","      <td>34</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Cidade3</td>\n","      <td>Pessoa14</td>\n","      <td>48</td>\n","    </tr>\n","    <tr>\n"," 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False\n","7    False  False  False\n","8    False  False  False\n","9    False  False  False\n","10   False  False  False\n","11   False  False  False\n","12   False  False  False\n","13   False  False  False\n","14   False  False  False\n","15   False  False  False\n","16   False  False  False\n","17   False  False  False\n","18   False  False  False\n","19   False  False  False"],"text/html":["\n","  <div id=\"df-0f500142-a54e-4488-8d97-fe87ba6202a6\" class=\"colab-df-container\">\n","    <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>Cidade</th>\n","      <th>Nome</th>\n","      <th>Idade</th>\n","    </tr>\n","  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Vamos explorar cada um deles em detalhes."],"metadata":{"id":"SMmcseCK6NP0"}},{"cell_type":"markdown","source":["###### loc (localização):\n","\n","* loc é usado para acessar dados com base em rótulos ou rótulos de índices, em vez de posições numéricas.\n","* Ele permite que você selecione dados com base em nomes de colunas ou rótulos de índices, usando uma sintaxe semelhante à da notação de dicionário.\n","* O primeiro argumento de loc é o rótulo das linhas que você deseja selecionar, e o segundo argumento é o rótulo das colunas (opcional).\n","* Pode ser usado para seleções condicionais e fatiamento baseado em rótulos."],"metadata":{"id":"egwA_c_g6Qvm"}},{"cell_type":"code","source":["# Exemplo da documentação do loc\n","\n","df = pd.DataFrame({\n","    \"Animal\": ['cobra', 'viper', 'sidewinder'],\n","    \"Velocidade\": [1,4,7],\n","    \"Escudo\": [2,5,8]\n","})\n","\n","df\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"y0SzNKsv6g7z","executionInfo":{"status":"ok","timestamp":1693179236090,"user_tz":180,"elapsed":197,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"8ad24f7f-a5dc-4701-a5fb-4a58cf5bcba6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       Animal  Velocidade  Escudo\n","0       cobra           1       2\n","1       viper           4       5\n","2  sidewinder           7       8"],"text/html":["\n","  <div id=\"df-c3cf78d1-f9c5-451d-8b79-88efbe655e98\" class=\"colab-df-container\">\n","    <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>Animal</th>\n","      <th>Velocidade</th>\n","      <th>Escudo</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>cobra</td>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>viper</td>\n","      <td>4</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>sidewinder</td>\n","      <td>7</td>\n","      <td>8</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3cf78d1-f9c5-451d-8b79-88efbe655e98')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-c3cf78d1-f9c5-451d-8b79-88efbe655e98 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":44}]},{"cell_type":"markdown","source":["Para pegar um label só"],"metadata":{"id":"WaYJ2Ft-7Ft_"}},{"cell_type":"code","source":["# Pegando a primeira observação\n","\n","df.loc[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"r04_gEn_7FVH","executionInfo":{"status":"ok","timestamp":1693179236090,"user_tz":180,"elapsed":196,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"c2ac4beb-5307-4584-a5ee-10aa22f3a209"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Animal        cobra\n","Velocidade        1\n","Escudo            2\n","Name: 0, dtype: object"]},"metadata":{},"execution_count":45}]},{"cell_type":"markdown","source":["Se fornecer uma lista de índices, o retorno da função será um dataframe:"],"metadata":{"id":"nR4toIXU6vKT"}},{"cell_type":"code","source":["df.loc[[0, 1]]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"Wf3YCpuM6znM","executionInfo":{"status":"ok","timestamp":1693179236090,"user_tz":180,"elapsed":193,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"a8f1d7a5-2bf2-427c-c6c2-60cbb1a2f40b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  Animal  Velocidade  Escudo\n","0  cobra           1       2\n","1  viper           4       5"],"text/html":["\n","  <div id=\"df-a5954241-5206-4a25-b7d5-710a96401e2b\" class=\"colab-df-container\">\n","    <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>Animal</th>\n","      <th>Velocidade</th>\n","      <th>Escudo</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>cobra</td>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>viper</td>\n","      <td>4</td>\n","      <td>5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5954241-5206-4a25-b7d5-710a96401e2b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-a5954241-5206-4a25-b7d5-710a96401e2b button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":46}]},{"cell_type":"markdown","source":["Se o índice do dataframe fosse o nome dos animais, você poderia extrair diretamente utilizando o *label*:"],"metadata":{"id":"x7CwC-8a8nO_"}},{"cell_type":"code","source":["df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],\n","     index=['cobra', 'viper', 'sidewinder'],\n","     columns=['max_speed', 'shield'])\n","\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"9QzKTRJe8xIh","executionInfo":{"status":"ok","timestamp":1693179236090,"user_tz":180,"elapsed":192,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"d0aeef3e-1391-421f-b550-6b8708311323"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            max_speed  shield\n","cobra               1       2\n","viper               4       5\n","sidewinder          7       8"],"text/html":["\n","  <div id=\"df-0eded326-a3d2-4456-946e-8cbc25f2b47b\" class=\"colab-df-container\">\n","    <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>max_speed</th>\n","      <th>shield</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>cobra</th>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>viper</th>\n","      <td>4</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>sidewinder</th>\n","      <td>7</td>\n","      <td>8</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0eded326-a3d2-4456-946e-8cbc25f2b47b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-0eded326-a3d2-4456-946e-8cbc25f2b47b button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":49}]},{"cell_type":"markdown","source":["E assim por diante..."],"metadata":{"id":"mRXN9Q1-85hR"}},{"cell_type":"markdown","source":["###### Iloc (indexação da localização)"],"metadata":{"id":"q4Sy6Ddq6gRc"}},{"cell_type":"markdown","source":["* iloc é usado para acessar dados com base em posições numéricas de linhas e colunas, semelhante ao uso de índices em listas.\n","* Ele não considera rótulos, apenas índices inteiros.\n","* O primeiro argumento de iloc é o índice da linha que você deseja selecionar e  o segundo argumento é o índice da coluna (opcional).\n","* Pode ser usado para seleções condicionais e fatiamento baseado em índices inteiros."],"metadata":{"id":"S1tGMYRb9FKB"}},{"cell_type":"code","source":["dados = {\n","    \"Nome\": [\"Alice\", \"Bob\", \"Carol\", \"David\", \"Eva\"],\n","    \"Idade\": [25, 30, 22, 35, 28],\n","    \"Cidade\": [\"São Paulo\", \"Rio de Janeiro\", \"Belo Horizonte\", \"São Paulo\", \"Rio de Janeiro\"]\n","}\n","\n","df = pd.DataFrame(dados)\n","\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"d5Q6vXST9Tz6","executionInfo":{"status":"ok","timestamp":1693179236091,"user_tz":180,"elapsed":179,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"6202a278-f136-44b0-f782-3ab184c0fb5b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Nome  Idade          Cidade\n","0  Alice     25       São Paulo\n","1    Bob     30  Rio de Janeiro\n","2  Carol     22  Belo Horizonte\n","3  David     35       São Paulo\n","4    Eva     28  Rio de Janeiro"],"text/html":["\n","  <div id=\"df-f2e18c45-98e8-4770-96a7-521283d2587d\" class=\"colab-df-container\">\n","    <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>Nome</th>\n","      <th>Idade</th>\n","      <th>Cidade</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Alice</td>\n","      <td>25</td>\n","      <td>São Paulo</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Bob</td>\n","      <td>30</td>\n","      <td>Rio de Janeiro</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Carol</td>\n","      <td>22</td>\n","      <td>Belo Horizonte</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>David</td>\n","      <td>35</td>\n","      <td>São Paulo</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Eva</td>\n","      <td>28</td>\n","      <td>Rio de Janeiro</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2e18c45-98e8-4770-96a7-521283d2587d')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":50}]},{"cell_type":"code","source":["# Selecionando a primeira linha\n","df.iloc[0]\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JZrqq7Vv9V1j","executionInfo":{"status":"ok","timestamp":1693179236091,"user_tz":180,"elapsed":178,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"c30de8a1-3393-4ebe-b830-d16ea331a380"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Nome          Alice\n","Idade            25\n","Cidade    São Paulo\n","Name: 0, dtype: object"]},"metadata":{},"execution_count":51}]},{"cell_type":"code","source":["\n","# Selecionando a segunda célula (1,2)\n","df.iloc[0, 1]\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7yElCehL9ZJm","executionInfo":{"status":"ok","timestamp":1693179236091,"user_tz":180,"elapsed":174,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"495f6501-c3a1-4a13-f7a8-863f9a01c441"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["25"]},"metadata":{},"execution_count":52}]},{"cell_type":"code","source":["# Selecionando as três primeiras linhas e todas as colunas\n","df.iloc[:3, :]\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"kMxBIKoy9aV7","executionInfo":{"status":"ok","timestamp":1693179236092,"user_tz":180,"elapsed":171,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"3d05a813-3b9d-4fad-cd95-79825139c17b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Nome  Idade          Cidade\n","0  Alice     25       São Paulo\n","1    Bob     30  Rio de Janeiro\n","2  Carol     22  Belo Horizonte"],"text/html":["\n","  <div id=\"df-73a2ab5f-8314-4981-9bea-76ac0dc051ab\" class=\"colab-df-container\">\n","    <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>Nome</th>\n","      <th>Idade</th>\n","      <th>Cidade</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Alice</td>\n","      <td>25</td>\n","      <td>São Paulo</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Bob</td>\n","      <td>30</td>\n","      <td>Rio de Janeiro</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Carol</td>\n","      <td>22</td>\n","      <td>Belo Horizonte</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73a2ab5f-8314-4981-9bea-76ac0dc051ab')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-73a2ab5f-8314-4981-9bea-76ac0dc051ab button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-73a2ab5f-8314-4981-9bea-76ac0dc051ab');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":53}]},{"cell_type":"markdown","source":["##### Resumo"],"metadata":{"id":"DfhibGyqKVBO"}},{"cell_type":"markdown","source":["A tabela abaixo resume as principais opções de indexação e seleção com dataframes:"],"metadata":{"id":"EGIFFH8LKOPc"}},{"cell_type":"markdown","source":["![image.png](data:image/png;base64,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Operações em Colunas"],"metadata":{"id":"7aGQzvj0_XhE"}},{"cell_type":"markdown","source":["Você pode modificar manualmente os valores de coluna atribuindo um valor para ela, como mostra o seguinte exemplo"],"metadata":{"id":"bk1Hj2dB_h-o"}},{"cell_type":"code","source":["df = pd.DataFrame(dados, columns=['Cidade', 'Nome','Idade','Salário'])\n","\n","df[\"Salário\"] = 1000\n","\n","df\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"T0pxCPDJ_pOc","executionInfo":{"status":"ok","timestamp":1693179236092,"user_tz":180,"elapsed":170,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"83d9373c-acc7-408d-a483-28fdc87f0543"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           Cidade   Nome  Idade  Salário\n","0       São Paulo  Alice     25     1000\n","1  Rio de Janeiro    Bob     30     1000\n","2  Belo Horizonte  Carol     22     1000\n","3       São Paulo  David     35     1000\n","4  Rio de Janeiro    Eva     28     1000"],"text/html":["\n","  <div id=\"df-cab7e316-df60-46c8-8b44-06f642b9ae19\" class=\"colab-df-container\">\n","    <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>Cidade</th>\n","      <th>Nome</th>\n","      <th>Idade</th>\n","      <th>Salário</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>São Paulo</td>\n","      <td>Alice</td>\n","      <td>25</td>\n","      <td>1000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Rio de Janeiro</td>\n","      <td>Bob</td>\n","      <td>30</td>\n","   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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":56}]},{"cell_type":"markdown","source":["Pode criar uma coluna por meio de operações em outras colunas:"],"metadata":{"id":"m7jN6JB9FEPJ"}},{"cell_type":"code","source":["# Calculando um aumento de 20% do salário\n","\n","df['salário_+20%'] = df['Salário'] * 1.2\n","\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"udZkHgE7FH3R","executionInfo":{"status":"ok","timestamp":1693179237389,"user_tz":180,"elapsed":1465,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"5b9077a8-c672-401e-b8c0-b9e0472c1579"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           Cidade   Nome  Idade  Salário  Educação  salário_+20%\n","0       São Paulo  Alice     25     1964       NaN        2356.8\n","1  Rio de Janeiro    Bob     30     2305       NaN        2766.0\n","2  Belo Horizonte  Carol     22     1814       NaN        2176.8\n","3       São Paulo  David     35     2986       NaN        3583.2\n","4  Rio de Janeiro    Eva     28     1112       NaN        1334.4"],"text/html":["\n","  <div id=\"df-1478413d-ba0f-4f29-abad-72f9c9f3b109\" class=\"colab-df-container\">\n","    <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>Cidade</th>\n","      <th>Nome</th>\n","      <th>Idade</th>\n","      <th>Salário</th>\n","      <th>Educação</th>\n","      <th>salário_+20%</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>São Paulo</td>\n","      <td>Alice</td>\n","      <td>25</td>\n","      <td>1964</td>\n","      <td>NaN</td>\n","      <td>2356.8</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Rio de Janeiro</td>\n","      <td>Bob</td>\n","      <td>30</td>\n","      <td>2305</td>\n","      <td>NaN</td>\n","      <td>2766.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Belo Horizonte</td>\n","      <td>Carol</td>\n","      <td>22</td>\n","      <td>1814</td>\n","      <td>NaN</td>\n","      <td>2176.8</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>São Paulo</td>\n","      <td>David</td>\n","      <td>35</td>\n","      <td>2986</td>\n","      <td>NaN</td>\n","      <td>3583.2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Rio de Janeiro</td>\n","      <td>Eva</td>\n","      <td>28</td>\n","      <td>1112</td>\n","      <td>NaN</td>\n","      <td>1334.4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1478413d-ba0f-4f29-abad-72f9c9f3b109')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-1478413d-ba0f-4f29-abad-72f9c9f3b109 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-fcf76d61-2f2a-4703-8783-f736ea7931f8\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fcf76d61-2f2a-4703-8783-f736ea7931f8')\"\n","            title=\"Suggest charts.\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","    background-color: #E8F0FE;\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: #1967D2;\n","    height: 32px;\n","    padding: 0 0 0 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: #E2EBFA;\n","    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: #174EA6;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart:hover {\n","    background-color: #434B5C;\n","    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","    fill: #FFFFFF;\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const charts = await google.colab.kernel.invokeFunction(\n","          'suggestCharts', [key], {});\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-fcf76d61-2f2a-4703-8783-f736ea7931f8 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":57}]},{"cell_type":"markdown","source":["Para deletar uma coluna, pode utilizar o método \"del\":"],"metadata":{"id":"jMOhd508Dt_d"}},{"cell_type":"code","source":["del df['Educação']\n","\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"tUqOCBmWDw1A","executionInfo":{"status":"ok","timestamp":1693179237389,"user_tz":180,"elapsed":298,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"4575078f-7faa-4fb2-b43e-68ca62baaf3c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           Cidade   Nome  Idade  Salário  salário_+20%\n","0       São Paulo  Alice     25     1964        2356.8\n","1  Rio de Janeiro    Bob     30     2305        2766.0\n","2  Belo Horizonte  Carol     22     1814        2176.8\n","3       São Paulo  David     35     2986        3583.2\n","4  Rio de Janeiro    Eva     28     1112        1334.4"],"text/html":["\n","  <div id=\"df-0fbf51a8-b907-4198-b80f-3d0a2d0e6617\" class=\"colab-df-container\">\n","    <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>Cidade</th>\n","      <th>Nome</th>\n","      <th>Idade</th>\n","      <th>Salário</th>\n","      <th>salário_+20%</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>São Paulo</td>\n","      <td>Alice</td>\n","      <td>25</td>\n","      <td>1964</td>\n","      <td>2356.8</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Rio de Janeiro</td>\n","      <td>Bob</td>\n","      <td>30</td>\n","      <td>2305</td>\n","      <td>2766.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Belo Horizonte</td>\n","      <td>Carol</td>\n","      <td>22</td>\n","      <td>1814</td>\n","      <td>2176.8</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>São Paulo</td>\n","      <td>David</td>\n","      <td>35</td>\n","      <td>2986</td>\n","      <td>3583.2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Rio de Janeiro</td>\n","      <td>Eva</td>\n","      <td>28</td>\n","      <td>1112</td>\n","      <td>1334.4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0fbf51a8-b907-4198-b80f-3d0a2d0e6617')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-0fbf51a8-b907-4198-b80f-3d0a2d0e6617 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":60}]},{"cell_type":"markdown","source":["Também pode tirar uma lista de valores:"],"metadata":{"id":"9GJhxRIwGVRa"}},{"cell_type":"code","source":["df3 = df2.drop([0,1])\n","\n","df3"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"6DJkksVsGYrX","executionInfo":{"status":"ok","timestamp":1693179237390,"user_tz":180,"elapsed":285,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"4fe316b0-4dfc-4e06-f9f0-c8e53e159906"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           Cidade   Nome  Idade  Salário  salário_+20%\n","2  Belo Horizonte  Carol     22     1814        2176.8\n","3       São Paulo  David     35     2986        3583.2"],"text/html":["\n","  <div id=\"df-3c958f43-60f5-47ad-a19c-80daab6c8fe4\" class=\"colab-df-container\">\n","    <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>Cidade</th>\n","      <th>Nome</th>\n","      <th>Idade</th>\n","      <th>Salário</th>\n","      <th>salário_+20%</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2</th>\n","      <td>Belo Horizonte</td>\n","      <td>Carol</td>\n","      <td>22</td>\n","      <td>1814</td>\n","      <td>2176.8</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>São Paulo</td>\n","      <td>David</td>\n","      <td>35</td>\n","      <td>2986</td>\n","      <td>3583.2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c958f43-60f5-47ad-a19c-80daab6c8fe4')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-3c958f43-60f5-47ad-a19c-80daab6c8fe4 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-3c958f43-60f5-47ad-a19c-80daab6c8fe4');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":61}]},{"cell_type":"markdown","source":["#### Aplicando funções e utilizando o MAP"],"metadata":{"id":"cc8_aMTcGRXs"}},{"cell_type":"markdown","source":["Você pode aplicar funções em dataframes da mesma forma que o Numpy. Você pode aplicar tanto no dataframe inteiro quanto em uma coluna específica."],"metadata":{"id":"9bb-S1_x6Kxv"}},{"cell_type":"code","source":["# Criando um dataframe só de números\n","\n","dados = pd.DataFrame(np.random.standard_normal((4,3)),\n","                     columns = list(\"bde\"),\n","                     index = [\"São Paulo\", \"Rio de Janeiro\", \"Bahia\", \"Amazonas\"])\n","\n","dados"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":174},"id":"9CFQ91Cj6TOV","executionInfo":{"status":"ok","timestamp":1693179237390,"user_tz":180,"elapsed":284,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"73924837-c33b-4530-9667-aaedde2d4bd4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                       b         d         e\n","São Paulo      -0.383808 -1.239598  0.499179\n","Rio de Janeiro -1.088417 -0.299615 -0.316137\n","Bahia          -2.734952  0.262000  0.248584\n","Amazonas        1.615267 -0.030235 -0.820150"],"text/html":["\n","  <div id=\"df-18a00db3-4efa-403b-8a0f-675a9f9d12a1\" class=\"colab-df-container\">\n","    <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>b</th>\n","      <th>d</th>\n","      <th>e</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>São Paulo</th>\n","      <td>-0.383808</td>\n","      <td>-1.239598</td>\n","      <td>0.499179</td>\n","    </tr>\n","    <tr>\n","      <th>Rio de Janeiro</th>\n","      <td>-1.088417</td>\n","      <td>-0.299615</td>\n","      <td>-0.316137</td>\n","    </tr>\n","    <tr>\n","      <th>Bahia</th>\n","      <td>-2.734952</td>\n","      <td>0.262000</td>\n","      <td>0.248584</td>\n","    </tr>\n","    <tr>\n","      <th>Amazonas</th>\n","      <td>1.615267</td>\n","      <td>-0.030235</td>\n","      <td>-0.820150</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-18a00db3-4efa-403b-8a0f-675a9f9d12a1')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-18a00db3-4efa-403b-8a0f-675a9f9d12a1 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-18a00db3-4efa-403b-8a0f-675a9f9d12a1');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":62}]},{"cell_type":"code","source":["# Usando o método abs() do numpy\n","\n","np.abs(dados)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":174},"id":"BSC2NWwr6upv","executionInfo":{"status":"ok","timestamp":1693179237390,"user_tz":180,"elapsed":283,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"a0fc00a5-e0a6-4396-cfd2-9f1718742586"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                       b         d         e\n","São Paulo       0.383808  1.239598  0.499179\n","Rio de Janeiro  1.088417  0.299615  0.316137\n","Bahia           2.734952  0.262000  0.248584\n","Amazonas        1.615267  0.030235  0.820150"],"text/html":["\n","  <div id=\"df-e2b5d946-a74f-4ccc-af4b-d0258b4aa2cd\" class=\"colab-df-container\">\n","    <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>b</th>\n","      <th>d</th>\n","      <th>e</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>São Paulo</th>\n","      <td>0.383808</td>\n","      <td>1.239598</td>\n","      <td>0.499179</td>\n","    </tr>\n","    <tr>\n","      <th>Rio de Janeiro</th>\n","      <td>1.088417</td>\n","      <td>0.299615</td>\n","      <td>0.316137</td>\n","    </tr>\n","    <tr>\n","      <th>Bahia</th>\n","      <td>2.734952</td>\n","      <td>0.262000</td>\n","      <td>0.248584</td>\n","    </tr>\n","    <tr>\n","      <th>Amazonas</th>\n","      <td>1.615267</td>\n","      <td>0.030235</td>\n","      <td>0.820150</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e2b5d946-a74f-4ccc-af4b-d0258b4aa2cd')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-e2b5d946-a74f-4ccc-af4b-d0258b4aa2cd button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-e2b5d946-a74f-4ccc-af4b-d0258b4aa2cd');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":63}]},{"cell_type":"code","source":["# Criando uma função e aplicando ela no dataframe\n","\n","def quadrado(x):\n","  return x ** 2\n","\n","dados.apply(quadrado)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":174},"id":"pcnaWJtE68NM","executionInfo":{"status":"ok","timestamp":1693179237390,"user_tz":180,"elapsed":283,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"31f2a08e-9529-4927-e7df-f36a556e7206"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                       b         d         e\n","São Paulo       0.147309  1.536603  0.249180\n","Rio de Janeiro  1.184651  0.089769  0.099943\n","Bahia           7.479961  0.068644  0.061794\n","Amazonas        2.609089  0.000914  0.672646"],"text/html":["\n","  <div id=\"df-5ee65379-42bb-4fa5-b250-8f343bd15643\" class=\"colab-df-container\">\n","    <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>b</th>\n","      <th>d</th>\n","      <th>e</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>São Paulo</th>\n","      <td>0.147309</td>\n","      <td>1.536603</td>\n","      <td>0.249180</td>\n","    </tr>\n","    <tr>\n","      <th>Rio de Janeiro</th>\n","      <td>1.184651</td>\n","      <td>0.089769</td>\n","      <td>0.099943</td>\n","    </tr>\n","    <tr>\n","      <th>Bahia</th>\n","      <td>7.479961</td>\n","      <td>0.068644</td>\n","      <td>0.061794</td>\n","    </tr>\n","    <tr>\n","      <th>Amazonas</th>\n","      <td>2.609089</td>\n","      <td>0.000914</td>\n","      <td>0.672646</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ee65379-42bb-4fa5-b250-8f343bd15643')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-5ee65379-42bb-4fa5-b250-8f343bd15643 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-5ee65379-42bb-4fa5-b250-8f343bd15643');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":64}]},{"cell_type":"code","source":["# Aplicando em uma só coluna\n","\n","dados['b'].apply(quadrado)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I4wOarQC7KM5","executionInfo":{"status":"ok","timestamp":1693179237390,"user_tz":180,"elapsed":282,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"777ef800-f936-4212-d71b-1212850b92ba"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["São Paulo         0.147309\n","Rio de Janeiro    1.184651\n","Bahia             7.479961\n","Amazonas          2.609089\n","Name: b, dtype: float64"]},"metadata":{},"execution_count":65}]},{"cell_type":"markdown","source":["#### Ordenando valores"],"metadata":{"id":"MogS7uz97ZLX"}},{"cell_type":"markdown","source":["Você pode ordenar valores no Pandas pelo Índice"],"metadata":{"id":"z94fPXK_7f3G"}},{"cell_type":"code","source":["dados.sort_index() # Neste caso, vai ordernar por ordem alfabética"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":174},"id":"NYwDTiYU7kwe","executionInfo":{"status":"ok","timestamp":1693179237391,"user_tz":180,"elapsed":194,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"a93b9bf9-dad4-42e3-bf72-ee672365806f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                       b         d         e\n","Amazonas        1.615267 -0.030235 -0.820150\n","Bahia          -2.734952  0.262000  0.248584\n","Rio de Janeiro -1.088417 -0.299615 -0.316137\n","São Paulo      -0.383808 -1.239598  0.499179"],"text/html":["\n","  <div id=\"df-c5adc67e-94ff-4475-91b1-2a1eaa3fa5c0\" class=\"colab-df-container\">\n","    <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>b</th>\n","      <th>d</th>\n","      <th>e</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Amazonas</th>\n","      <td>1.615267</td>\n","      <td>-0.030235</td>\n","      <td>-0.820150</td>\n","    </tr>\n","    <tr>\n","      <th>Bahia</th>\n","      <td>-2.734952</td>\n","      <td>0.262000</td>\n","      <td>0.248584</td>\n","    </tr>\n","    <tr>\n","      <th>Rio de Janeiro</th>\n","      <td>-1.088417</td>\n","      <td>-0.299615</td>\n","      <td>-0.316137</td>\n","    </tr>\n","    <tr>\n","      <th>São Paulo</th>\n","      <td>-0.383808</td>\n","      <td>-1.239598</td>\n","      <td>0.499179</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5adc67e-94ff-4475-91b1-2a1eaa3fa5c0')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-c5adc67e-94ff-4475-91b1-2a1eaa3fa5c0 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":66}]},{"cell_type":"markdown","source":["Ou pelas colunas"],"metadata":{"id":"NpWVBhoT7q-S"}},{"cell_type":"code","source":["# Novo banco\n","\n","dados = pd.DataFrame(np.random.standard_normal((4,9)),\n","                     columns = list(\"fghabcxyz\"),\n","                     index = [\"São Paulo\", \"Rio de Janeiro\", \"Bahia\", \"Amazonas\"])\n","\n","dados"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":212},"id":"mQIhZJ-X7wgz","executionInfo":{"status":"ok","timestamp":1693179237391,"user_tz":180,"elapsed":193,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"3c02282d-9587-45d8-97a5-bce37b2d98f7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                       f         g         h         a         b         c  \\\n","São Paulo       1.022577  1.492797  0.071414  0.029688  0.331256  0.443853   \n","Rio de Janeiro  1.439898 -1.129786  0.235635  0.217949  0.005071 -0.327253   \n","Bahia          -0.045757 -0.960246 -0.654577  0.270228  0.164398 -3.077879   \n","Amazonas       -0.758025 -0.241740  0.725681 -0.401317 -0.102967  1.705606   \n","\n","                       x         y         z  \n","São Paulo       3.153975  0.301381  0.206233  \n","Rio de Janeiro -1.554360  0.326408 -0.149114  \n","Bahia           1.223405 -1.096877 -0.121402  \n","Amazonas        1.174856  0.138757 -0.346709  "],"text/html":["\n","  <div id=\"df-54007080-8c1f-44d1-bf37-42ad91d96aeb\" class=\"colab-df-container\">\n","    <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>f</th>\n","      <th>g</th>\n","      <th>h</th>\n","      <th>a</th>\n","      <th>b</th>\n","      <th>c</th>\n","      <th>x</th>\n","      <th>y</th>\n","      <th>z</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>São Paulo</th>\n","      <td>1.022577</td>\n","      <td>1.492797</td>\n","      <td>0.071414</td>\n","      <td>0.029688</td>\n","      <td>0.331256</td>\n","      <td>0.443853</td>\n","      <td>3.153975</td>\n","      <td>0.301381</td>\n","      <td>0.206233</td>\n","    </tr>\n","    <tr>\n","      <th>Rio de Janeiro</th>\n","      <td>1.439898</td>\n","      <td>-1.129786</td>\n","      <td>0.235635</td>\n","      <td>0.217949</td>\n","      <td>0.005071</td>\n","      <td>-0.327253</td>\n","      <td>-1.554360</td>\n","      <td>0.326408</td>\n","      <td>-0.149114</td>\n","    </tr>\n","    <tr>\n","      <th>Bahia</th>\n","      <td>-0.045757</td>\n","      <td>-0.960246</td>\n","      <td>-0.654577</td>\n","      <td>0.270228</td>\n","      <td>0.164398</td>\n","      <td>-3.077879</td>\n","      <td>1.223405</td>\n","      <td>-1.096877</td>\n","      <td>-0.121402</td>\n","    </tr>\n","    <tr>\n","      <th>Amazonas</th>\n","      <td>-0.758025</td>\n","      <td>-0.241740</td>\n","      <td>0.725681</td>\n","      <td>-0.401317</td>\n","      <td>-0.102967</td>\n","      <td>1.705606</td>\n","      <td>1.174856</td>\n","      <td>0.138757</td>\n","      <td>-0.346709</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-54007080-8c1f-44d1-bf37-42ad91d96aeb')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":67}]},{"cell_type":"code","source":["# Ordenando pelas colunas\n","\n","dados.sort_index(axis=\"columns\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":212},"id":"eW-THtk3751B","executionInfo":{"status":"ok","timestamp":1693179237391,"user_tz":180,"elapsed":193,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"b217bcbd-2319-442a-9bf9-a4124f8b26fe"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                       a         b         c         f         g         h  \\\n","São Paulo       0.029688  0.331256  0.443853  1.022577  1.492797  0.071414   \n","Rio de Janeiro  0.217949  0.005071 -0.327253  1.439898 -1.129786  0.235635   \n","Bahia           0.270228  0.164398 -3.077879 -0.045757 -0.960246 -0.654577   \n","Amazonas       -0.401317 -0.102967  1.705606 -0.758025 -0.241740  0.725681   \n","\n","                       x         y         z  \n","São Paulo       3.153975  0.301381  0.206233  \n","Rio de Janeiro -1.554360  0.326408 -0.149114  \n","Bahia           1.223405 -1.096877 -0.121402  \n","Amazonas        1.174856  0.138757 -0.346709  "],"text/html":["\n","  <div id=\"df-826c5768-2773-4e12-b529-13f869b05efa\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    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<td>0.005071</td>\n","      <td>-0.327253</td>\n","      <td>1.439898</td>\n","      <td>-1.129786</td>\n","      <td>0.235635</td>\n","      <td>-1.554360</td>\n","      <td>0.326408</td>\n","      <td>-0.149114</td>\n","    </tr>\n","    <tr>\n","      <th>Bahia</th>\n","      <td>0.270228</td>\n","      <td>0.164398</td>\n","      <td>-3.077879</td>\n","      <td>-0.045757</td>\n","      <td>-0.960246</td>\n","      <td>-0.654577</td>\n","      <td>1.223405</td>\n","      <td>-1.096877</td>\n","      <td>-0.121402</td>\n","    </tr>\n","    <tr>\n","      <th>Amazonas</th>\n","      <td>-0.401317</td>\n","      <td>-0.102967</td>\n","      <td>1.705606</td>\n","      <td>-0.758025</td>\n","      <td>-0.241740</td>\n","      <td>0.725681</td>\n","      <td>1.174856</td>\n","      <td>0.138757</td>\n","      <td>-0.346709</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":68}]},{"cell_type":"markdown","source":["Você pode ordenar pelos valores de uma coluna:"],"metadata":{"id":"LG-AXT7D8ELe"}},{"cell_type":"code","source":["dados['a'].sort_values()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yeFhNoLT8HWu","executionInfo":{"status":"ok","timestamp":1693179237391,"user_tz":180,"elapsed":192,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"f39ff7c7-4640-4a0e-ae23-ee9d37538025"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Amazonas         -0.401317\n","São Paulo         0.029688\n","Rio de Janeiro    0.217949\n","Bahia             0.270228\n","Name: a, dtype: float64"]},"metadata":{},"execution_count":69}]},{"cell_type":"code","source":["# Alternativamente\n","\n","dados.sort_values('a')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":212},"id":"M7ietQf38Wl0","executionInfo":{"status":"ok","timestamp":1693179237391,"user_tz":180,"elapsed":189,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"3cd5967f-d4cc-4b04-d207-479614b895ac"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                       f         g         h         a         b         c  \\\n","Amazonas       -0.758025 -0.241740  0.725681 -0.401317 -0.102967  1.705606   \n","São Paulo       1.022577  1.492797  0.071414  0.029688  0.331256  0.443853   \n","Rio de Janeiro  1.439898 -1.129786  0.235635  0.217949  0.005071 -0.327253   \n","Bahia          -0.045757 -0.960246 -0.654577  0.270228  0.164398 -3.077879   \n","\n","                       x         y         z  \n","Amazonas        1.174856  0.138757 -0.346709  \n","São Paulo       3.153975  0.301381  0.206233  \n","Rio de Janeiro -1.554360  0.326408 -0.149114  \n","Bahia           1.223405 -1.096877 -0.121402  "],"text/html":["\n","  <div id=\"df-9dff0a55-47e2-44ba-a2be-8e53a857e62f\" class=\"colab-df-container\">\n","    <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>f</th>\n","      <th>g</th>\n","      <th>h</th>\n","      <th>a</th>\n","      <th>b</th>\n","      <th>c</th>\n","      <th>x</th>\n","      <th>y</th>\n","      <th>z</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Amazonas</th>\n","      <td>-0.758025</td>\n","      <td>-0.241740</td>\n","      <td>0.725681</td>\n","      <td>-0.401317</td>\n","      <td>-0.102967</td>\n","      <td>1.705606</td>\n","      <td>1.174856</td>\n","      <td>0.138757</td>\n","      <td>-0.346709</td>\n","    </tr>\n","    <tr>\n","      <th>São Paulo</th>\n","      <td>1.022577</td>\n","      <td>1.492797</td>\n","      <td>0.071414</td>\n","      <td>0.029688</td>\n","      <td>0.331256</td>\n","      <td>0.443853</td>\n","      <td>3.153975</td>\n","      <td>0.301381</td>\n","      <td>0.206233</td>\n","    </tr>\n","    <tr>\n","      <th>Rio de Janeiro</th>\n","      <td>1.439898</td>\n","      <td>-1.129786</td>\n","      <td>0.235635</td>\n","      <td>0.217949</td>\n","      <td>0.005071</td>\n","      <td>-0.327253</td>\n","      <td>-1.554360</td>\n","      <td>0.326408</td>\n","      <td>-0.149114</td>\n","    </tr>\n","    <tr>\n","      <th>Bahia</th>\n","      <td>-0.045757</td>\n","      <td>-0.960246</td>\n","      <td>-0.654577</td>\n","      <td>0.270228</td>\n","      <td>0.164398</td>\n","      <td>-3.077879</td>\n","      <td>1.223405</td>\n","      <td>-1.096877</td>\n","      <td>-0.121402</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9dff0a55-47e2-44ba-a2be-8e53a857e62f')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" 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'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-9dff0a55-47e2-44ba-a2be-8e53a857e62f');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":70}]},{"cell_type":"markdown","source":["#### Estatísticas Descritivas e de Resumo"],"metadata":{"id":"-8iVS7_P9H7l"}},{"cell_type":"markdown","source":["Calculando a média de uma coluna"],"metadata":{"id":"nWN7E_cp9LSv"}},{"cell_type":"code","source":["dados.f.mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eKeNRlrA9RTc","executionInfo":{"status":"ok","timestamp":1693179237391,"user_tz":180,"elapsed":188,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"f1bfaeb7-dc2c-49e4-d359-f81fc1698218"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.4146730834180107"]},"metadata":{},"execution_count":71}]},{"cell_type":"markdown","source":["Alternativamente"],"metadata":{"id":"AckEVYTv9iRe"}},{"cell_type":"code","source":["dados['f'].mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nBpjUBSS9jJh","executionInfo":{"status":"ok","timestamp":1693179237392,"user_tz":180,"elapsed":187,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"70ca6c7c-f6be-404f-a63b-5350b187ebf9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.4146730834180107"]},"metadata":{},"execution_count":72}]},{"cell_type":"markdown","source":["Obs: mean() exige pelo menos um valor que não seja *missing* na coluna."],"metadata":{"id":"yIXPYaCa9nFo"}},{"cell_type":"markdown","source":["Podemos também achar o maior valor de uma coluna (por meio do índice):"],"metadata":{"id":"GApzlLTC9wBl"}},{"cell_type":"code","source":["dados.f.idxmax()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"kM0s1TDq93Ef","executionInfo":{"status":"ok","timestamp":1693179238604,"user_tz":180,"elapsed":416,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"4a5a94c0-7a6c-40ef-d88e-2f831a219f7d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Rio de Janeiro'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":73}]},{"cell_type":"code","source":["# Alternativamente\n","\n","dados['f'].idxmax()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"k06QjjaP97mZ","executionInfo":{"status":"ok","timestamp":1693179238605,"user_tz":180,"elapsed":415,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"46cc8e04-5bfb-4d10-aacb-54c4592a098a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Rio de Janeiro'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":74}]},{"cell_type":"markdown","source":["Ou o valor mínimo"],"metadata":{"id":"9XD4qi9h-AgA"}},{"cell_type":"code","source":["dados.f.idxmin()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"QfJrFDPC-Bo3","executionInfo":{"status":"ok","timestamp":1693179238605,"user_tz":180,"elapsed":414,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"db1cf97e-f9fd-4dc4-eefb-247ea098da69"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Amazonas'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":75}]},{"cell_type":"markdown","source":["O método describe é um dos mais utilizados, pois permite resumir boa parte das estatísticas descritivas do dataframe com um simples comando:"],"metadata":{"id":"7-XIWVsi-H-W"}},{"cell_type":"code","source":["dados.describe()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":320},"id":"xOm3PiWY-ODm","executionInfo":{"status":"ok","timestamp":1693179238605,"user_tz":180,"elapsed":413,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"610a22fe-4270-481e-e0c5-e5afcd2ce960"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["              f         g         h         a         b         c         x  \\\n","count  4.000000  4.000000  4.000000  4.000000  4.000000  4.000000  4.000000   \n","mean   0.414673 -0.209744  0.094538  0.029137  0.099440 -0.313918  0.999469   \n","std    1.001308  1.198527  0.571535  0.304995  0.189589  2.024216  1.936048   \n","min   -0.758025 -1.129786 -0.654577 -0.401317 -0.102967 -3.077879 -1.554360   \n","25%   -0.223824 -1.002631 -0.110084 -0.078063 -0.021938 -1.014909  0.492552   \n","50%    0.488410 -0.600993  0.153525  0.123819  0.084735  0.058300  1.199131   \n","75%    1.126907  0.191894  0.358146  0.231019  0.206112  0.759292  1.706048   \n","max    1.439898  1.492797  0.725681  0.270228  0.331256  1.705606  3.153975   \n","\n","              y         z  \n","count  4.000000  4.000000  \n","mean  -0.082583 -0.102748  \n","std    0.681294  0.229117  \n","min   -1.096877 -0.346709  \n","25%   -0.170152 -0.198513  \n","50%    0.220069 -0.135258  \n","75%    0.307638 -0.039493  \n","max    0.326408  0.206233  "],"text/html":["\n","  <div id=\"df-97fd078b-c34f-41ea-bf42-7fab835585ae\" class=\"colab-df-container\">\n","    <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>f</th>\n","      <th>g</th>\n","      <th>h</th>\n","      <th>a</th>\n","      <th>b</th>\n","      <th>c</th>\n","      <th>x</th>\n","      <th>y</th>\n","      <th>z</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>4.000000</td>\n","      <td>4.000000</td>\n","      <td>4.000000</td>\n","      <td>4.000000</td>\n","      <td>4.000000</td>\n","      <td>4.000000</td>\n","      <td>4.000000</td>\n","      <td>4.000000</td>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.414673</td>\n","      <td>-0.209744</td>\n","      <td>0.094538</td>\n","      <td>0.029137</td>\n","      <td>0.099440</td>\n","      <td>-0.313918</td>\n","      <td>0.999469</td>\n","      <td>-0.082583</td>\n","      <td>-0.102748</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>1.001308</td>\n","      <td>1.198527</td>\n","      <td>0.571535</td>\n","      <td>0.304995</td>\n","      <td>0.189589</td>\n","      <td>2.024216</td>\n","      <td>1.936048</td>\n","      <td>0.681294</td>\n","      <td>0.229117</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>-0.758025</td>\n","      <td>-1.129786</td>\n","      <td>-0.654577</td>\n","      <td>-0.401317</td>\n","      <td>-0.102967</td>\n","      <td>-3.077879</td>\n","      <td>-1.554360</td>\n","      <td>-1.096877</td>\n","      <td>-0.346709</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>-0.223824</td>\n","      <td>-1.002631</td>\n","      <td>-0.110084</td>\n","      <td>-0.078063</td>\n","      <td>-0.021938</td>\n","      <td>-1.014909</td>\n","      <td>0.492552</td>\n","      <td>-0.170152</td>\n","      <td>-0.198513</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.488410</td>\n","      <td>-0.600993</td>\n","      <td>0.153525</td>\n","      <td>0.123819</td>\n","      <td>0.084735</td>\n","      <td>0.058300</td>\n","      <td>1.199131</td>\n","      <td>0.220069</td>\n","      <td>-0.135258</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>1.126907</td>\n","      <td>0.191894</td>\n","      <td>0.358146</td>\n","      <td>0.231019</td>\n","      <td>0.206112</td>\n","      <td>0.759292</td>\n","      <td>1.706048</td>\n","      <td>0.307638</td>\n","      <td>-0.039493</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>1.439898</td>\n","      <td>1.492797</td>\n","      <td>0.725681</td>\n","      <td>0.270228</td>\n","      <td>0.331256</td>\n","      <td>1.705606</td>\n","      <td>3.153975</td>\n","      <td>0.326408</td>\n","      <td>0.206233</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" 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)"],"metadata":{"id":"TpGvK1qi-idl"}},{"cell_type":"markdown","source":["#### Correlação e Covariância"],"metadata":{"id":"jo0TBvIk-ssw"}},{"cell_type":"markdown","source":["Temos alguns métodos que permitem avaliar correlação e covariância entre colunas de forma direta:"],"metadata":{"id":"TKxc2Xxm_DdK"}},{"cell_type":"code","source":["# Correlação da coluna 'a' com a coluna 'b'\n","\n","dados['a'].corr(dados['b'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LB7ZW7jd_JSs","executionInfo":{"status":"ok","timestamp":1693179238605,"user_tz":180,"elapsed":412,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"b9a15479-0c09-45b8-ead1-50c99139117f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.49055502889540636"]},"metadata":{},"execution_count":77}]},{"cell_type":"markdown","source":["Podemos imprimir também a matriz de correlação e covariância de todo o banco de dados, se quisermos:"],"metadata":{"id":"9KinlBXt_WFL"}},{"cell_type":"code","source":["# Matriz de correlação\n","\n","dados.corr()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":351},"id":"xP_rTsvp_aY5","executionInfo":{"status":"ok","timestamp":1693179238605,"user_tz":180,"elapsed":405,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"e6c2b0b6-99a5-4bb2-f613-2923522aeb5f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["          f         g         h         a         b         c         x  \\\n","f  1.000000  0.131880 -0.154135  0.641464  0.441828 -0.106684 -0.278090   \n","g  0.131880  1.000000  0.181428 -0.309988  0.644324  0.455076  0.839521   \n","h -0.154135  0.181428  1.000000 -0.813955 -0.600128  0.958221 -0.140747   \n","a  0.641464 -0.309988 -0.813955  1.000000  0.490555 -0.830277 -0.283674   \n","b  0.441828  0.644324 -0.600128  0.490555  1.000000 -0.357319  0.653399   \n","c -0.106684  0.455076  0.958221 -0.830277 -0.357319  1.000000  0.119242   \n","x -0.278090  0.839521 -0.140747 -0.283674  0.653399  0.119242  1.000000   \n","y  0.420300  0.421111  0.811837 -0.420902 -0.155547  0.854665 -0.102489   \n","z  0.632004  0.716813 -0.391215  0.438528  0.952849 -0.148325  0.553936   \n","\n","          y         z  \n","f  0.420300  0.632004  \n","g  0.421111  0.716813  \n","h  0.811837 -0.391215  \n","a -0.420902  0.438528  \n","b -0.155547  0.952849  \n","c  0.854665 -0.148325  \n","x -0.102489  0.553936  \n","y  1.000000  0.137942  \n","z  0.137942  1.000000  "],"text/html":["\n","  <div id=\"df-3c841c2d-c1e6-4754-9fab-c23b953bd327\" class=\"colab-df-container\">\n","    <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","    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Se estivesse trabalhando com um banco de dados em painel (*cross-sectional time-series*), você poderia se deparar com a seguinte situação:"],"metadata":{"id":"Hy_zo0ahAtaX"}},{"cell_type":"code","source":["# Exemplo de dataframe com observações repetidas\n","\n","dados = pd.DataFrame(np.random.standard_normal((16,9)),\n","                     columns = list(\"fghabcxyz\"),\n","                     index = [\"São Paulo\", \"São Paulo\", \"São Paulo\", \"São Paulo\",\n","                              \"Rio de Janeiro\", \"Rio de Janeiro\", \"Rio de Janeiro\", \"Rio de Janeiro\",\n","                              \"Bahia\", \"Bahia\", \"Bahia\",\"Bahia\",\n","                              \"Amazonas\", \"Amazonas\", \"Amazonas\", \"Amazonas\"])\n","\n","dados"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":640},"id":"q89ZEHevA2SL","executionInfo":{"status":"ok","timestamp":1693179238606,"user_tz":180,"elapsed":403,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"a7ad44e6-01b2-46ce-8ec8-e51f9449a35c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                       f         g         h         a         b         c  \\\n","São Paulo       0.664323  1.184861  0.383780  0.143867  0.812195  0.103040   \n","São Paulo       0.214523 -0.402695 -1.770324 -0.886441  0.209151  0.866039   \n","São Paulo       0.716182  1.930404  0.823714  1.237475 -1.663921  0.847546   \n","São Paulo      -1.437160  0.201392 -1.519896 -0.447850 -0.175983 -0.055107   \n","Rio de Janeiro  1.705115 -0.689666  0.134545  0.681008 -0.374179  0.600981   \n","Rio de Janeiro  0.187134  2.018913 -0.844864  0.556782 -1.067094 -0.228595   \n","Rio de Janeiro -0.376557  0.021239 -0.372819 -0.938130 -0.069948  0.677145   \n","Rio de Janeiro -0.083162 -0.105895  1.164889 -1.547337 -0.953965 -0.205647   \n","Bahia          -0.967530  0.604208 -0.347085 -0.499036  0.573012  0.197807   \n","Bahia           1.053381  0.033392 -2.225249  0.352641  1.729083  0.419244   \n","Bahia          -0.321728 -0.171935  0.563197 -1.747815  0.912835 -0.091437   \n","Bahia          -0.054135  0.912634 -1.173538  0.205134  1.997942 -1.795110   \n","Amazonas        1.490718  0.102630 -0.250795 -0.376549  0.389303 -0.780141   \n","Amazonas       -1.310531  0.525307  1.430346  0.548366 -0.458478 -0.575812   \n","Amazonas        0.723731  0.032805 -0.223663 -0.525158  1.562698 -0.357892   \n","Amazonas        0.766400  1.578308  0.324688  0.726015 -1.560864 -0.387407   \n","\n","                       x         y         z  \n","São Paulo       0.356247  0.970838  0.117627  \n","São Paulo       0.967529 -0.424679  0.041379  \n","São Paulo       0.028738  0.305845  0.151194  \n","São Paulo       2.275731  1.550338 -1.336130  \n","Rio de Janeiro -1.552967  1.936072  0.162693  \n","Rio de Janeiro -0.985260  0.510822 -0.833265  \n","Rio de Janeiro  0.123835  0.857263  0.058431  \n","Rio de Janeiro -0.374214 -1.161383  2.082187  \n","Bahia          -0.849232  0.692951  0.001714  \n","Bahia          -1.369206  1.052892  0.914143  \n","Bahia           0.383071  0.580436 -0.149406  \n","Bahia           1.905910  0.851747 -0.239839  \n","Amazonas        0.429009 -0.255121  0.402060  \n","Amazonas        0.038672  1.295246 -1.238116  \n","Amazonas       -0.307470  0.484074  0.676844  \n","Amazonas        0.746945 -1.505453  0.174705  "],"text/html":["\n","  <div id=\"df-bb4db8bf-a540-41b9-b091-135a1d59c036\" class=\"colab-df-container\">\n","    <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>f</th>\n","      <th>g</th>\n","      <th>h</th>\n","      <th>a</th>\n","      <th>b</th>\n","      <th>c</th>\n","      <th>x</th>\n","      <th>y</th>\n","      <th>z</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>São Paulo</th>\n","      <td>0.664323</td>\n","      <td>1.184861</td>\n","      <td>0.383780</td>\n","      <td>0.143867</td>\n","      <td>0.812195</td>\n","      <td>0.103040</td>\n","      <td>0.356247</td>\n","      <td>0.970838</td>\n","      <td>0.117627</td>\n","    </tr>\n","    <tr>\n","      <th>São Paulo</th>\n","      <td>0.214523</td>\n","      <td>-0.402695</td>\n","      <td>-1.770324</td>\n","      <td>-0.886441</td>\n","      <td>0.209151</td>\n","      <td>0.866039</td>\n","      <td>0.967529</td>\n","      <td>-0.424679</td>\n","      <td>0.041379</td>\n","    </tr>\n","    <tr>\n","      <th>São Paulo</th>\n","      <td>0.716182</td>\n","      <td>1.930404</td>\n","      <td>0.823714</td>\n","      <td>1.237475</td>\n","      <td>-1.663921</td>\n","      <td>0.847546</td>\n","      <td>0.028738</td>\n","      <td>0.305845</td>\n","      <td>0.151194</td>\n","    </tr>\n","    <tr>\n","      <th>São Paulo</th>\n","      <td>-1.437160</td>\n","      <td>0.201392</td>\n","      <td>-1.519896</td>\n","      <td>-0.447850</td>\n","      <td>-0.175983</td>\n","      <td>-0.055107</td>\n","      <td>2.275731</td>\n","      <td>1.550338</td>\n","      <td>-1.336130</td>\n","    </tr>\n","    <tr>\n","      <th>Rio de Janeiro</th>\n","      <td>1.705115</td>\n","      <td>-0.689666</td>\n","      <td>0.134545</td>\n","      <td>0.681008</td>\n","      <td>-0.374179</td>\n","      <td>0.600981</td>\n","      <td>-1.552967</td>\n","      <td>1.936072</td>\n","      <td>0.162693</td>\n","    </tr>\n","    <tr>\n","      <th>Rio de Janeiro</th>\n","      <td>0.187134</td>\n","      <td>2.018913</td>\n","      <td>-0.844864</td>\n","      <td>0.556782</td>\n","      <td>-1.067094</td>\n","      <td>-0.228595</td>\n","      <td>-0.985260</td>\n","      <td>0.510822</td>\n","      <td>-0.833265</td>\n","    </tr>\n","    <tr>\n","      <th>Rio de Janeiro</th>\n","      <td>-0.376557</td>\n","      <td>0.021239</td>\n","      <td>-0.372819</td>\n","      <td>-0.938130</td>\n","      <td>-0.069948</td>\n","      <td>0.677145</td>\n","      <td>0.123835</td>\n","      <td>0.857263</td>\n","      <td>0.058431</td>\n","    </tr>\n","    <tr>\n","      <th>Rio de Janeiro</th>\n","      <td>-0.083162</td>\n","      <td>-0.105895</td>\n","      <td>1.164889</td>\n","      <td>-1.547337</td>\n","      <td>-0.953965</td>\n","      <td>-0.205647</td>\n","      <td>-0.374214</td>\n","      <td>-1.161383</td>\n","      <td>2.082187</td>\n","    </tr>\n","    <tr>\n","      <th>Bahia</th>\n","      <td>-0.967530</td>\n","      <td>0.604208</td>\n","      <td>-0.347085</td>\n","      <td>-0.499036</td>\n","      <td>0.573012</td>\n","      <td>0.197807</td>\n","      <td>-0.849232</td>\n","      <td>0.692951</td>\n","      <td>0.001714</td>\n","    </tr>\n","    <tr>\n","      <th>Bahia</th>\n","      <td>1.053381</td>\n","      <td>0.033392</td>\n","      <td>-2.225249</td>\n","      <td>0.352641</td>\n","      <td>1.729083</td>\n","      <td>0.419244</td>\n","      <td>-1.369206</td>\n","      <td>1.052892</td>\n","      <td>0.914143</td>\n","    </tr>\n","    <tr>\n","      <th>Bahia</th>\n","      <td>-0.321728</td>\n","      <td>-0.171935</td>\n","      <td>0.563197</td>\n","      <td>-1.747815</td>\n","      <td>0.912835</td>\n","      <td>-0.091437</td>\n","      <td>0.383071</td>\n","      <td>0.580436</td>\n","      <td>-0.149406</td>\n","    </tr>\n","    <tr>\n","      <th>Bahia</th>\n","      <td>-0.054135</td>\n","      <td>0.912634</td>\n","      <td>-1.173538</td>\n","      <td>0.205134</td>\n","      <td>1.997942</td>\n","      <td>-1.795110</td>\n","      <td>1.905910</td>\n","      <td>0.851747</td>\n","      <td>-0.239839</td>\n","    </tr>\n","    <tr>\n","      <th>Amazonas</th>\n","      <td>1.490718</td>\n","      <td>0.102630</td>\n","      <td>-0.250795</td>\n","      <td>-0.376549</td>\n","      <td>0.389303</td>\n","      <td>-0.780141</td>\n","      <td>0.429009</td>\n","      <td>-0.255121</td>\n","      <td>0.402060</td>\n","    </tr>\n","    <tr>\n","      <th>Amazonas</th>\n","      <td>-1.310531</td>\n","      <td>0.525307</td>\n","      <td>1.430346</td>\n","      <td>0.548366</td>\n","      <td>-0.458478</td>\n","      <td>-0.575812</td>\n","      <td>0.038672</td>\n","      <td>1.295246</td>\n","      <td>-1.238116</td>\n","    </tr>\n","    <tr>\n","      <th>Amazonas</th>\n","      <td>0.723731</td>\n","      <td>0.032805</td>\n","      <td>-0.223663</td>\n","      <td>-0.525158</td>\n","      <td>1.562698</td>\n","      <td>-0.357892</td>\n","      <td>-0.307470</td>\n","      <td>0.484074</td>\n","      <td>0.676844</td>\n","    </tr>\n","    <tr>\n","      <th>Amazonas</th>\n","      <td>0.766400</td>\n","      <td>1.578308</td>\n","      <td>0.324688</td>\n","      <td>0.726015</td>\n","      <td>-1.560864</td>\n","      <td>-0.387407</td>\n","      <td>0.746945</td>\n","      <td>-1.505453</td>\n","      <td>0.174705</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb4db8bf-a540-41b9-b091-135a1d59c036')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","     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Mas também pode ser utilizado nas colunas"],"metadata":{"id":"8SWQRaxiBgs3"}},{"cell_type":"code","source":["dados['f'].unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1FMoMHj2BgVy","executionInfo":{"status":"ok","timestamp":1693179238606,"user_tz":180,"elapsed":399,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"377ff3c2-157c-4808-da8f-0cded9819817"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 0.6643233 ,  0.21452288,  0.71618168, -1.43716002,  1.70511515,\n","        0.18713378, -0.37655687, -0.08316233, -0.96752981,  1.05338131,\n","       -0.32172834, -0.05413454,  1.49071821, -1.31053141,  0.72373087,\n","        0.76640031])"]},"metadata":{},"execution_count":82}]},{"cell_type":"markdown","source":["Como as variáveis foram extraídas de uma distribuição normal aleatória, temos muitos valores únicos. Esta função é útil quando queremos descobrir quantas unidades de análise realmente temos. Por exemplo, no caso de 'tweets', poderíamos desejar identificar a quantidade de usuários que realmente temos."],"metadata":{"id":"fSoLX_l2BqaW"}},{"cell_type":"markdown","source":["Outra função útil é o value counts, que ajuda quando queremos saber a frequência de cada grupo ou unidade de análise:"],"metadata":{"id":"-Q7PhUBbB4Nm"}},{"cell_type":"code","source":["series = pd.Series(['José', 'José','Ana','Maria','José','João'])\n","\n","series.value_counts()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GvZ6l6DwCHm5","executionInfo":{"status":"ok","timestamp":1693179238606,"user_tz":180,"elapsed":395,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"f940216f-8fdf-4f05-986b-07fe0314b201"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["José     3\n","Ana      1\n","Maria    1\n","João     1\n","dtype: int64"]},"metadata":{},"execution_count":83}]},{"cell_type":"markdown","source":["Podemos observar que temos 3 'Josés' no banco. Essa função vai ser de grande utilidade na próxima aula, quando fizermos visualizações com os bancos de dados."],"metadata":{"id":"lk74k6fCCSxa"}},{"cell_type":"markdown","source":["### Exercícios"],"metadata":{"id":"pB76DHQgnI5g"}},{"cell_type":"markdown","source":["Antes de ir aos exercícios, explore a documentação própria do Pandas ([Link](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html)) sobre Dataframes"],"metadata":{"id":"np3urlXCTVgw"}},{"cell_type":"markdown","source":["1 - Considere o seguinte DataFrame:"],"metadata":{"id":"leaLDwwHp1lA"}},{"cell_type":"code","source":["dados = {\n","    'Nome': ['Alice', 'Bob', 'Carol', 'David', 'Eva'],\n","    'Idade': [25, 30, 22, 35, 28],\n","    'Cidade': ['São Paulo', 'Rio de Janeiro', 'Belo Horizonte', 'São Paulo', 'Rio de Janeiro']\n","}\n","\n","df = pd.DataFrame(dados)\n","\n","df"],"metadata":{"id":"zRAbyoOmp1T8"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["a) Selecione a coluna 'Nome'.\n"],"metadata":{"id":"JAibmZjgCw0g"}},{"cell_type":"code","source":["nomes = df['Nome']\n","print(nomes)"],"metadata":{"id":"j8kq98oKC4YW"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["b) Selecione a primeira linha do DataFrame.\n"],"metadata":{"id":"K5FgPXJGC2CS"}},{"cell_type":"code","source":["primeira_linha = df.iloc[0]\n","print(primeira_linha)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"luk3KTFpC7tZ","executionInfo":{"status":"ok","timestamp":1693179238607,"user_tz":180,"elapsed":386,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"47707ced-da91-4632-bfab-9445fdb3a6ab"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Nome          Alice\n","Idade            25\n","Cidade    São Paulo\n","Name: 0, dtype: object\n"]}]},{"cell_type":"markdown","source":["c) Selecione o valor 'Bob' da coluna 'Nome'.\n"],"metadata":{"id":"pFr6uY-kC20P"}},{"cell_type":"code","source":["nome_bob = df.loc[df['Nome'] == 'Bob', 'Nome'].values[0]\n","print(nome_bob)"],"metadata":{"id":"JHWUfNXlC9A2"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["d) Selecione as linhas onde a idade é maior que 28."],"metadata":{"id":"m7Y4_qLYC31m"}},{"cell_type":"code","source":["idade_maior_que_28 = df[df['Idade'] > 28]\n","print(idade_maior_que_28)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9bjtUhgxDBry","executionInfo":{"status":"ok","timestamp":1693179238607,"user_tz":180,"elapsed":380,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"072449ac-567d-4cfd-d998-fbd5ccba729f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Nome  Idade          Cidade\n","1    Bob     30  Rio de Janeiro\n","3  David     35       São Paulo\n"]}]},{"cell_type":"markdown","source":["e) Selecione as linhas onde a idade está entre 25 e 30 anos (inclusive) e a cidade é 'São Paulo':"],"metadata":{"id":"04210DpTD54d"}},{"cell_type":"code","source":["filtro = (df['Idade'] >= 25) & (df['Idade'] <= 30) & (df['Cidade'] == 'São Paulo')\n","resultados = df[filtro]\n","print(resultados)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2fUkQuAQEATj","executionInfo":{"status":"ok","timestamp":1693179238607,"user_tz":180,"elapsed":375,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"d48014f5-60f7-45c3-8a01-9d60403eaba9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Nome  Idade     Cidade\n","0  Alice     25  São Paulo\n"]}]},{"cell_type":"markdown","source":["f) Selecione as linhas onde o nome começa com a letra 'A' ou a idade é menor que 25:\n","\n","Dica: Você vai precisar do método de strings 'startswith()' ([Link](https://docs.python.org/3/library/stdtypes.html#str.startswith))"],"metadata":{"id":"rej3TTUyEDY_"}},{"cell_type":"code","source":["filtro = (df['Nome'].str.startswith('A')) | (df['Idade'] < 25)\n","resultados = df[filtro]\n","print(resultados)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8MB6BAPnEKmL","executionInfo":{"status":"ok","timestamp":1693179238607,"user_tz":180,"elapsed":372,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"5dd780bc-07ef-4460-dd35-4fbf2b24acbe"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Nome  Idade          Cidade\n","0  Alice     25       São Paulo\n","2  Carol     22  Belo Horizonte\n"]}]},{"cell_type":"markdown","source":["2 - Agora vamos exercitar o uso do loc. Considere o seguinte dataframe:"],"metadata":{"id":"R273PMnp2-fE"}},{"cell_type":"code","source":["dados = {\n","    'Nome': ['Alice', 'Bob', 'Carol', 'David', 'Eva'],\n","    'Idade': [25, 30, 22, 35, 28],\n","    'Cidade': ['São Paulo', 'Rio de Janeiro', 'Belo Horizonte', 'São Paulo', 'Rio de Janeiro']\n","}\n","\n","df = pd.DataFrame(dados)"],"metadata":{"id":"0vbthugl3rbT"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["a) Use 'loc' para selecionar todas as linhas, mas só as colunas 'nome' e 'idade':"],"metadata":{"id":"Q16lS8o73s3r"}},{"cell_type":"code","source":["selecao_a = df.loc[:, ['Nome', 'Idade']]\n","print(selecao_a)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8JVsU_-d3zug","executionInfo":{"status":"ok","timestamp":1693179238608,"user_tz":180,"elapsed":369,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"48a81a84-efa8-44a5-d1f0-0c658b6d9564"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Nome  Idade\n","0  Alice     25\n","1    Bob     30\n","2  Carol     22\n","3  David     35\n","4    Eva     28\n"]}]},{"cell_type":"markdown","source":["b) Use 'loc' para selecionar a linha com o nome 'David', mas com todas as colunas:"],"metadata":{"id":"kcQTF1Ti31vl"}},{"cell_type":"code","source":["selecao_b = df.loc[df['Nome'] == 'David', :]\n","print(selecao_b)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OSrNE-t936lr","executionInfo":{"status":"ok","timestamp":1693179238608,"user_tz":180,"elapsed":366,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"3740151a-8116-4198-a94e-0bb9f6842992"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Nome  Idade     Cidade\n","3  David     35  São Paulo\n"]}]},{"cell_type":"markdown","source":["c) Por fim, use 'loc' para selecionar as linhas onde a idade é maior que 28 e todas as colunas:"],"metadata":{"id":"1i6zzqgR3_dZ"}},{"cell_type":"code","source":["selecao_c = df.loc[df['Idade'] > 28, :]\n","print(selecao_c)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"X5ouWLv14DgT","executionInfo":{"status":"ok","timestamp":1693179238608,"user_tz":180,"elapsed":362,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"4b6aaf51-c401-402b-a02d-e13b5c2f290f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Nome  Idade          Cidade\n","1    Bob     30  Rio de Janeiro\n","3  David     35       São Paulo\n"]}]},{"cell_type":"markdown","source":["3 - Vamos trabalhar um pouco o iloc. Considere o mesmo dataframe do exercício 2"],"metadata":{"id":"V5Lw9rxu4IVb"}},{"cell_type":"markdown","source":["a)  Use iloc para selecionar a primeira linha e todas as colunas"],"metadata":{"id":"vksjcYby4Nz7"}},{"cell_type":"code","source":["selecao_a = df.iloc[0, :]\n","print(selecao_a)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"riawGH3m4P21","executionInfo":{"status":"ok","timestamp":1693179238608,"user_tz":180,"elapsed":359,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"dc457b39-f98d-47df-ca1a-2a934d14e233"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Nome          Alice\n","Idade            25\n","Cidade    São Paulo\n","Name: 0, dtype: object\n"]}]},{"cell_type":"markdown","source":["b)  Use iloc para selecionar a última linha e todas as colunas:"],"metadata":{"id":"CXhU-6-z4SNm"}},{"cell_type":"code","source":["selecao_b = df.iloc[-1, :]\n","print(selecao_b)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"24BUWV294TyP","executionInfo":{"status":"ok","timestamp":1693179238608,"user_tz":180,"elapsed":356,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"087a5e52-1bfd-4794-911f-6b02733254fa"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Nome                 Eva\n","Idade                 28\n","Cidade    Rio de Janeiro\n","Name: 4, dtype: object\n"]}]},{"cell_type":"markdown","source":["c) Use iloc para selecionar as três primeiras linhas e todas as colunas:"],"metadata":{"id":"_lLcJ1ho4VgQ"}},{"cell_type":"code","source":["selecao_c = df.iloc[:3, :]\n","print(selecao_c)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AA7ZDaoS4XSs","executionInfo":{"status":"ok","timestamp":1693179238608,"user_tz":180,"elapsed":353,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"9e9e7ca0-16f5-4df4-d02a-2d2b0e59e4b5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Nome  Idade          Cidade\n","0  Alice     25       São Paulo\n","1    Bob     30  Rio de Janeiro\n","2  Carol     22  Belo Horizonte\n"]}]},{"cell_type":"markdown","source":["4) Vamos criar o banco de novo para garantir que ele não foi modificado."],"metadata":{"id":"cK4CWWll4fO2"}},{"cell_type":"code","source":["dados = {\n","    'Nome': ['Alice', 'Bob', 'Carol', 'David', 'Eva'],\n","    'Idade': [25, 30, 22, 35, 28],\n","    'Cidade': ['São Paulo', 'Rio de Janeiro', 'Belo Horizonte', 'São Paulo', 'Rio de Janeiro']\n","}\n","\n","df = pd.DataFrame(dados)\n","\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"JRaRKR8I4kLv","executionInfo":{"status":"ok","timestamp":1693179238609,"user_tz":180,"elapsed":350,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"d1501f02-b805-4486-ea5c-399b8bdc1c85"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Nome  Idade          Cidade\n","0  Alice     25       São Paulo\n","1    Bob     30  Rio de Janeiro\n","2  Carol     22  Belo Horizonte\n","3  David     35       São Paulo\n","4    Eva     28  Rio de Janeiro"],"text/html":["\n","  <div id=\"df-9a3676de-6bdb-479c-822f-9e886427c29d\" class=\"colab-df-container\">\n","    <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>Nome</th>\n","      <th>Idade</th>\n","      <th>Cidade</th>\n","    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Ela tem de ser aleatoriamente gerada"],"metadata":{"id":"zIgNcXAj4mZt"}},{"cell_type":"code","source":["df['Transporte'] = [random.randint(15, 90) for _ in range(5)]\n","\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"25tlXAPk4780","executionInfo":{"status":"ok","timestamp":1693179238609,"user_tz":180,"elapsed":349,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"ebb9287c-e132-4599-f7d2-3d36e24a24bc"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Nome  Idade          Cidade  Transporte\n","0  Alice     25       São Paulo          32\n","1    Bob     30  Rio de Janeiro          17\n","2  Carol     22  Belo Horizonte          19\n","3  David     35       São Paulo          87\n","4    Eva     28  Rio de Janeiro          47"],"text/html":["\n","  <div id=\"df-2a00f391-b489-4ca3-936b-96cb7ff968a9\" class=\"colab-df-container\">\n","    <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>Nome</th>\n","      <th>Idade</th>\n","      <th>Cidade</th>\n","      <th>Transporte</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Alice</td>\n","      <td>25</td>\n","      <td>São Paulo</td>\n","      <td>32</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Bob</td>\n","      <td>30</td>\n","      <td>Rio de Janeiro</td>\n","      <td>17</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Carol</td>\n","      <td>22</td>\n","      <td>Belo Horizonte</td>\n","      <td>19</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>David</td>\n","      <td>35</td>\n","      <td>São Paulo</td>\n","      <td>87</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Eva</td>\n","      <td>28</td>\n","      <td>Rio de Janeiro</td>\n","      <td>47</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a00f391-b489-4ca3-936b-96cb7ff968a9')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-2a00f391-b489-4ca3-936b-96cb7ff968a9 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Agora, crie uma nova coluna que mostre a razão de 'salário' e 'transporte'."],"metadata":{"id":"F4tt15Ju5nqn"}},{"cell_type":"code","source":["df['Salário'] = [random.randint(1000,3000) for _ in range(len(df))]\n","\n","df['salario/transporte'] = df['Salário'] / df['Transporte']\n","\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":313},"id":"meqQBmdj5y12","executionInfo":{"status":"ok","timestamp":1693179238609,"user_tz":180,"elapsed":346,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"a0f32f3f-7212-4232-a38b-aadc9db71922"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Nome  Idade          Cidade  Transporte     Genero  Salário  \\\n","0  Alice     25       São Paulo          32   feminino     1518   \n","1    Bob     30  Rio de Janeiro          17  masculino     1796   \n","2  Carol     22  Belo Horizonte          19  masculino     2941   \n","3  David     35       São Paulo          87  masculino     2078   \n","4    Eva     28  Rio de Janeiro          47  masculino     2872   \n","\n","   salario/transporte  \n","0           47.437500  \n","1          105.647059  \n","2          154.789474  \n","3           23.885057  \n","4           61.106383  "],"text/html":["\n","  <div id=\"df-6f163858-925d-4ff3-814b-1dbe1ccab9e2\" class=\"colab-df-container\">\n","    <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>Nome</th>\n","      <th>Idade</th>\n","      <th>Cidade</th>\n","      <th>Transporte</th>\n","      <th>Genero</th>\n","      <th>Salário</th>\n","      <th>salario/transporte</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Alice</td>\n","      <td>25</td>\n","      <td>São Paulo</td>\n","      <td>32</td>\n","      <td>feminino</td>\n","      <td>1518</td>\n","      <td>47.437500</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Bob</td>\n","      <td>30</td>\n","      <td>Rio de Janeiro</td>\n","      <td>17</td>\n","      <td>masculino</td>\n","      <td>1796</td>\n","      <td>105.647059</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Carol</td>\n","      <td>22</td>\n","      <td>Belo Horizonte</td>\n","      <td>19</td>\n","      <td>masculino</td>\n","      <td>2941</td>\n","      <td>154.789474</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>David</td>\n","      <td>35</td>\n","      <td>São Paulo</td>\n","      <td>87</td>\n","      <td>masculino</td>\n","      <td>2078</td>\n","      <td>23.885057</td>\n","    </tr>\n","    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Considere o banco de dados:"],"metadata":{"id":"k_zXGADdAYrG"}},{"cell_type":"code","source":["dados = {\n","    'Nome': ['Alice', 'Bob', 'Carol', 'David', 'Eva'],\n","    'Salario': [50000, 60000, 75000, 48000, 80000],\n","}\n","\n","df = pd.DataFrame(dados)\n","\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"OoUL_SvbAgMp","executionInfo":{"status":"ok","timestamp":1693179238609,"user_tz":180,"elapsed":344,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"42fbcb80-aaf4-48d8-b298-13acf40a2225"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Nome  Salario\n","0  Alice    50000\n","1    Bob    60000\n","2  Carol    75000\n","3  David    48000\n","4    Eva    80000"],"text/html":["\n","  <div id=\"df-970bc899-0d58-4d69-997c-c21c0b16d391\" class=\"colab-df-container\">\n","    <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>Nome</th>\n","      <th>Salario</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Alice</td>\n","      <td>50000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Bob</td>\n","      <td>60000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Carol</td>\n","      <td>75000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>David</td>\n","      <td>48000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Eva</td>\n","      <td>80000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-970bc899-0d58-4d69-997c-c21c0b16d391')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-970bc899-0d58-4d69-997c-c21c0b16d391 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":103}]},{"cell_type":"markdown","source":["a) Faça uma função chamada \"aumento_salarial\" que recebe o salário como entrada e retorna um aumento de 15%"],"metadata":{"id":"MjczFypNAoa9"}},{"cell_type":"code","source":["def aumento_salarial(x):\n","  return x * 1.15\n","\n"],"metadata":{"id":"nJEhTIipBE6L"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["b) Aplique a função na coluna \"Salario\" utilizando \"apply\""],"metadata":{"id":"CN9C4ux9AzFV"}},{"cell_type":"code","source":["df['Salario'].apply(aumento_salarial)\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ccdfmu5BBPQP","executionInfo":{"status":"ok","timestamp":1693179238610,"user_tz":180,"elapsed":344,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"9e814184-a566-43c3-dff3-7873a2c8e82f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    57500.0\n","1    69000.0\n","2    86250.0\n","3    55200.0\n","4    92000.0\n","Name: Salario, dtype: float64"]},"metadata":{},"execution_count":105}]},{"cell_type":"markdown","source":["c) Aqui está um dicionário com um mapeamento de categorias com base nos salários. Crie uma função que retorne uma categoria de acordo com o salário da pessoa. Dica -> Mckinney, 2022, P.211"],"metadata":{"id":"59LTe6CNBpbW"}},{"cell_type":"code","source":["\n","# Dicionário de Mapeamento\n","\n","mapeamento_categorias = {\n","    (0, 50000): 'Baixo',\n","    (50001, 70000): 'Médio',\n","    (70001, 80000): 'Alto'\n","}\n","\n","def mapear_categoria_salario(salario):\n","    for intervalo, categoria in mapeamento_categorias.items():\n","        if intervalo[0] <= salario <= intervalo[1]:\n","            return categoria"],"metadata":{"id":"nMVuhq-VBvY1"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["d) Aplique essa função no dataframe, criando uma nova coluna chamada \"categoria_salario\". Imprima o banco de dados"],"metadata":{"id":"8dJlhlTWAt9A"}},{"cell_type":"code","source":["df['categoria_salario'] = df[\"Salario\"].apply(mapear_categoria_salario)\n","\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"5-glsfgCCfQv","executionInfo":{"status":"ok","timestamp":1693179238610,"user_tz":180,"elapsed":340,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"3ef1a780-4583-47b9-a4b5-e4c32dbecba9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Nome  Salario categoria_salario\n","0  Alice    50000             Baixo\n","1    Bob    60000             Médio\n","2  Carol    75000              Alto\n","3  David    48000             Baixo\n","4    Eva    80000              Alto"],"text/html":["\n","  <div id=\"df-25a4f855-0b37-4491-9041-515d5a19aea9\" class=\"colab-df-container\">\n","    <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>Nome</th>\n","      <th>Salario</th>\n","      <th>categoria_salario</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Alice</td>\n","      <td>50000</td>\n","      <td>Baixo</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Bob</td>\n","      <td>60000</td>\n","      <td>Médio</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Carol</td>\n","      <td>75000</td>\n","      <td>Alto</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>David</td>\n","      <td>48000</td>\n","      <td>Baixo</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Eva</td>\n","      <td>80000</td>\n","      <td>Alto</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25a4f855-0b37-4491-9041-515d5a19aea9')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-25a4f855-0b37-4491-9041-515d5a19aea9 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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DataFrame\n","print(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xl4VF_EaDV0W","executionInfo":{"status":"ok","timestamp":1693179238610,"user_tz":180,"elapsed":339,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"e2917421-7812-4b77-c10f-74137b589001"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["        Nome  Idade          Cidade  Salario\n","0      Pedro     56  Rio de Janeiro     5562\n","1     Sandra     22        Salvador     5029\n","2    Ricardo     32        Brasília     1745\n","3    Mariana     48       São Paulo     3785\n","4   Fernanda     60        Brasília     6650\n","5    Gustavo     26  Belo Horizonte     1915\n","6       João     43  Belo Horizonte     1942\n","7    Mariana     20        Brasília     3806\n","8    Gustavo     53  Belo Horizonte     6869\n","9    Isabela     59  Rio de Janeiro     3353\n","10      José     48  Rio de Janeiro     5329\n","11   Ricardo     28        Brasília     1595\n","12      João     60        Brasília     1748\n","13  Fernanda     43        Salvador     6462\n","14   Isabela     39  Belo Horizonte     4726\n","15    Felipe     22        Salvador     5106\n","16     Maria     30        Salvador     2710\n","17    Camila     39        Salvador     6113\n","18      José     49        Salvador     2016\n","19    Camila     23  Belo Horizonte     5326\n","20     Luiza     34  Rio de Janeiro     2007\n","21   Juliana     34        Salvador     2269\n","22      José     45        Brasília     1768\n","23   Juliana     46        Salvador     3995\n","24     Luiza     39        Brasília     2996\n","25      João     40       São Paulo     4475\n","26    Felipe     60  Belo Horizonte     3092\n","27     Pedro     36        Brasília     3995\n","28      José     56       São Paulo     2376\n","29    Camila     33        Salvador     5074\n"]}]},{"cell_type":"markdown","source":["a) Calcule a idade média e imprima o resultado como um valor inteiro"],"metadata":{"id":"jG5aH_8_DdwF"}},{"cell_type":"code","source":["media = df.Idade.mean()\n","\n","print(int(media))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BmopFoOyDf7O","executionInfo":{"status":"ok","timestamp":1693179238610,"user_tz":180,"elapsed":336,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"4e0379dc-864b-4325-fee7-5b692296f3b5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["40\n"]}]},{"cell_type":"markdown","source":["b) Calcule a mediana dos salários"],"metadata":{"id":"VsfKpfwmDrFv"}},{"cell_type":"code","source":["df.Salario.median()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wJoRTT2ND-AB","executionInfo":{"status":"ok","timestamp":1693179238610,"user_tz":180,"elapsed":332,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"9866f7e0-1bdd-40a3-8aa6-88fc30fcf9b1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["3795.5"]},"metadata":{},"execution_count":110}]},{"cell_type":"markdown","source":["c) Calcule o desvio padrão do salário e das idades e imprima de forma adequada usando f strings."],"metadata":{"id":"T8VNPD92EFu0"}},{"cell_type":"code","source":["desv_sal = df.Salario.std()\n","desv_idade = df.Idade.std()\n","\n","\n","print(f'O Desvio Padrão do salário é de {round(desv_sal,2)} e o da Idade é {round(desv_idade,2)}')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cjikRPg6EJsq","executionInfo":{"status":"ok","timestamp":1693179238611,"user_tz":180,"elapsed":329,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"8383e368-1b98-43bd-aafa-c7821a7c38f1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["O Desvio Padrão do salário é de 1678.58 e o da Idade é 12.45\n"]}]},{"cell_type":"markdown","source":["d) Calcule a soma dos salários:"],"metadata":{"id":"O2Vlu83yEezq"}},{"cell_type":"code","source":["df.Salario.sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"R44fcdn_EgRL","executionInfo":{"status":"ok","timestamp":1693179238611,"user_tz":180,"elapsed":325,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"735e70d0-06c3-441d-a471-19e970614240"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["113834"]},"metadata":{},"execution_count":112}]},{"cell_type":"markdown","source":["e) Encontre o indice do maior valor da idade e imprima a linha"],"metadata":{"id":"A0_ThrDREjqA"}},{"cell_type":"code","source":["indice = df.Idade.idxmax()\n","\n","print(df.iloc[indice])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aa53Rih7ElzK","executionInfo":{"status":"ok","timestamp":1693179241161,"user_tz":180,"elapsed":5,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"e289a202-763e-4309-cd71-7477ab32f3a0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Nome       Fernanda\n","Idade            60\n","Cidade     Brasília\n","Salario        6650\n","Name: 4, dtype: object\n"]}]},{"cell_type":"markdown","source":["f) Utilize describe para obter estatísticas descritivas de toda a tabela"],"metadata":{"id":"flP_TezfD9JS"}},{"cell_type":"code","source":["df.describe()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"kTNamaVtFFRQ","executionInfo":{"status":"ok","timestamp":1693057398517,"user_tz":180,"elapsed":226,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"c8ebfafd-5878-4440-fe50-b4da401f1476"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           Idade      Salario\n","count  30.000000    30.000000\n","mean   41.200000  3952.800000\n","std     9.263946  1775.988047\n","min    26.000000  1122.000000\n","25%    33.250000  2175.000000\n","50%    41.500000  4289.000000\n","75%    48.250000  5318.750000\n","max    57.000000  6966.000000"],"text/html":["\n","  <div id=\"df-4e23495c-4394-4e98-b305-dfb6b9e377f2\" class=\"colab-df-container\">\n","    <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>Idade</th>\n","      <th>Salario</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>30.000000</td>\n","      <td>30.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>41.200000</td>\n","      <td>3952.800000</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>9.263946</td>\n","      <td>1775.988047</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>26.000000</td>\n","      <td>1122.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>33.250000</td>\n","      <td>2175.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>41.500000</td>\n","      <td>4289.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>48.250000</td>\n","      <td>5318.750000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>57.000000</td>\n","      <td>6966.000000</td>\n","    </tr>\n","  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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":141}]},{"cell_type":"markdown","source":["g) O describe também gera um dataframe como resultado. Atribua o resultado do describe à um objeto e selecione as linhas de '25%', '50%' e '75%'"],"metadata":{"id":"fZajY6euFcIe"}},{"cell_type":"code","source":["dados2 = df.describe()\n","\n","dados2.iloc[4:7, :]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"6nFGcmBBFnz1","executionInfo":{"status":"ok","timestamp":1693057489296,"user_tz":180,"elapsed":212,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"65c9712c-646a-4dfb-a697-eb10db8ad59e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Idade  Salario\n","25%  33.25  2175.00\n","50%  41.50  4289.00\n","75%  48.25  5318.75"],"text/html":["\n","  <div id=\"df-4ed7091d-3db6-4797-9063-f59563f3571e\" class=\"colab-df-container\">\n","    <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>Idade</th>\n","      <th>Salario</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>25%</th>\n","      <td>33.25</td>\n","      <td>2175.00</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>41.50</td>\n","      <td>4289.00</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>48.25</td>\n","      <td>5318.75</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ed7091d-3db6-4797-9063-f59563f3571e')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-4ed7091d-3db6-4797-9063-f59563f3571e button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-4ed7091d-3db6-4797-9063-f59563f3571e');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":143}]},{"cell_type":"markdown","source":["e) Qual a correlação entre idade e salário?"],"metadata":{"id":"oJ1Gq2juF1tn"}},{"cell_type":"code","source":["df['Idade'].corr(df['Salario'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"liEOBc5RF5OK","executionInfo":{"status":"ok","timestamp":1693057565806,"user_tz":180,"elapsed":2,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"5303ade7-7968-405a-a4b9-c5fd0d8510dc"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.22319883260726447"]},"metadata":{},"execution_count":145}]},{"cell_type":"markdown","source":["f) e a Covariância destes dois?"],"metadata":{"id":"WF2nptR-GE-q"}},{"cell_type":"code","source":["df['Idade'].cov(df['Salario'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hocm73S1GHbz","executionInfo":{"status":"ok","timestamp":1693057586616,"user_tz":180,"elapsed":207,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"f15f76db-995a-4f96-d789-890e20422afb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["3672.213793103449"]},"metadata":{},"execution_count":146}]},{"cell_type":"markdown","source":["h) Quantos nomes únicos temos no dataframe?"],"metadata":{"id":"8P6kRxdYGM-P"}},{"cell_type":"code","source":["# Resposta mais simples\n","\n","df.Nome.unique()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uFzsTPYaGPTG","executionInfo":{"status":"ok","timestamp":1693057646573,"user_tz":180,"elapsed":336,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"40725101-0509-4ddf-df9a-8fbcac85968d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['Isabela', 'Ana', 'Gustavo', 'Carolina', 'Marcos', 'Juliana',\n","       'Rafael', 'Lucas', 'Pedro', 'Sandra', 'Luiza', 'José', 'Camila',\n","       'Maria', 'João'], dtype=object)"]},"metadata":{},"execution_count":152}]},{"cell_type":"code","source":["# Resposta mais elaborada\n","\n","array_nomes = df.Nome.unique()\n","\n","series_nomes = pd.Series(array_nomes)\n","\n","series_nomes.value_counts().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"l2fxRn2AGb5n","executionInfo":{"status":"ok","timestamp":1693057730656,"user_tz":180,"elapsed":220,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"35512841-ee98-43a4-c4c8-91200f8f8203"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["15"]},"metadata":{},"execution_count":155}]},{"cell_type":"markdown","source":["## Importando Arquivos"],"metadata":{"id":"rxPguBLJHvxi"}},{"cell_type":"markdown","source":["O Pandas é uma das bibliotecas Python mais amplamente usadas para a manipulação de dados. Uma das tarefas mais comuns ao trabalhar com dados é importar dados de diferentes fontes, como arquivos CSV, Excel, SQL e muitos outros formatos. O Pandas facilita a importação desses dados em DataFrames, que são similares à planilhas. Agora, finalmente podemos trabalhar com dados reais.\n"],"metadata":{"id":"--SkJDtGJIF_"}},{"cell_type":"markdown","source":["### 1 - Instalar e importar o Pandas\n","\n","O primeiro passo é instalar e importar o Pandas. Como estamos trabalhando no colab, o Pandas já vem instalado. E para importar, fazemos do mesmo modo de sempre."],"metadata":{"id":"HyrAt_1BJxYw"}},{"cell_type":"code","source":["import pandas as pd"],"metadata":{"id":"_XmOIZ8xJ54x"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### 2 - Escolher o método de importação\n","\n","O Pandas oferece diversos métodos de importação, cada um adequado a diferentes tipos de fontes de dados. Alguns dos métodos de importação mais comuns incluem:\n","\n","* pd.read_csv(): Para importar dados de arquivos CSV.\n","* pd.read_excel(): Para importar dados de arquivos Excel (XLSX).\n","* pd.read_stata(): Para importar dados de arquivos no formato do Stata;\n","* pd.read_sql(): Para importar dados de bancos de dados SQL.\n","* pd.read_json(): Para importar dados de arquivos JSON.\n","* pd.read_html(): Para importar dados de páginas da web HTML (usando o Pandas HTML reader)."],"metadata":{"id":"dm5zSlNgJ7au"}},{"cell_type":"markdown","source":["### 3 - Especificar o caminho do arquivo e realizar a importação\n","\n","Se você estivesse trabalhando direto do seu computador (com VScode, Spyder) , poderia fazer do seguinte modo:"],"metadata":{"id":"bJyYi6ooKPx9"}},{"cell_type":"code","source":["caminho_arquivo = 'caminho/do/seu/arquivo.csv'\n","\n","df = pd.read_csv(caminho_arquivo)"],"metadata":{"id":"E9eFuByiKV5C"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["No entanto, estamos no Colab. Aqui há duas maneiras:\n","\n","a) Inserir manualmente o arquivo dentro do Colab.\n","\n","No lado esquerdo da interface do Colab, há uma aba chamada \"Arquivos\", com o formato de uma pasta. Ao clicar nela há algumas opções imediatamente abaixo de \"arquivos\". Uma delas é a de \"Fazer upload para o armazenamento da sessão\". A partir dela poderia inserir diretamente um arquivo do seu computador dentro do colab, que poderia ser chamado do mesmo modo que especificado acima. No entanto, o Colab também nos dá algumas amostras que podemos importar sem baixar nada. Na mesma seção de \"Arquivos\" há uma subpasta chamada \"sample_data\", que podemos utilizar diretamente."],"metadata":{"id":"ukCkihAEKcIT"}},{"cell_type":"code","source":["caminho_arquivo = 'sample_data/mnist_train_small.csv'\n","\n","df = pd.read_csv(caminho_arquivo)\n","\n","df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":236},"id":"KNbK3BtkLJ3B","executionInfo":{"status":"ok","timestamp":1693059049710,"user_tz":180,"elapsed":2587,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"ce2b98dd-2336-4e8e-8203-cddd99d9f561"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   6  0  0.1  0.2  0.3  0.4  0.5  0.6  0.7  0.8  ...  0.581  0.582  0.583  \\\n","0  5  0    0    0    0    0    0    0    0    0  ...      0      0      0   \n","1  7  0    0    0    0    0    0    0    0    0  ...      0      0      0   \n","2  9  0    0    0    0    0    0    0    0    0  ...    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1283.0   \n","1    -114.47     34.40                19.0       7650.0          1901.0   \n","2    -114.56     33.69                17.0        720.0           174.0   \n","3    -114.57     33.64                14.0       1501.0           337.0   \n","4    -114.57     33.57                20.0       1454.0           326.0   \n","\n","   population  households  median_income  median_house_value  \n","0      1015.0       472.0         1.4936             66900.0  \n","1      1129.0       463.0         1.8200             80100.0  \n","2       333.0       117.0         1.6509             85700.0  \n","3       515.0       226.0         3.1917             73400.0  \n","4       624.0       262.0         1.9250             65500.0  "],"text/html":["\n","  <div id=\"df-e590ff0c-00b1-4e9a-b4f3-141888952fa1\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th 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  \n","std        2.005166      2.137340           12.586937   2179.947071   \n","min     -124.350000     32.540000            1.000000      2.000000   \n","25%     -121.790000     33.930000           18.000000   1462.000000   \n","50%     -118.490000     34.250000           29.000000   2127.000000   \n","75%     -118.000000     37.720000           37.000000   3151.250000   \n","max     -114.310000     41.950000           52.000000  37937.000000   \n","\n","       total_bedrooms    population    households  median_income  \\\n","count    17000.000000  17000.000000  17000.000000   17000.000000   \n","mean       539.410824   1429.573941    501.221941       3.883578   \n","std        421.499452   1147.852959    384.520841       1.908157   \n","min          1.000000      3.000000      1.000000       0.499900   \n","25%        297.000000    790.000000    282.000000       2.566375   \n","50%        434.000000   1167.000000    409.000000       3.544600   \n","75%        648.250000   1721.000000    605.250000       4.767000   \n","max       6445.000000  35682.000000   6082.000000      15.000100   \n","\n","       median_house_value  \n","count        17000.000000  \n","mean        207300.912353  \n","std         115983.764387  \n","min          14999.000000  \n","25%         119400.000000  \n","50%         180400.000000  \n","75%         265000.000000  \n","max         500001.000000  "],"text/html":["\n","  <div id=\"df-47075724-c03b-4544-b756-046f3c448668\" class=\"colab-df-container\">\n","    <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>longitude</th>\n","      <th>latitude</th>\n","      <th>housing_median_age</th>\n","      <th>total_rooms</th>\n","      <th>total_bedrooms</th>\n","      <th>population</th>\n","      <th>households</th>\n","      <th>median_income</th>\n","      <th>median_house_value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>-119.562108</td>\n","      <td>35.625225</td>\n","      <td>28.589353</td>\n","      <td>2643.664412</td>\n","      <td>539.410824</td>\n","      <td>1429.573941</td>\n","      <td>501.221941</td>\n","      <td>3.883578</td>\n","      <td>207300.912353</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>2.005166</td>\n","      <td>2.137340</td>\n","      <td>12.586937</td>\n","      <td>2179.947071</td>\n","      <td>421.499452</td>\n","      <td>1147.852959</td>\n","      <td>384.520841</td>\n","      <td>1.908157</td>\n","      <td>115983.764387</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>-124.350000</td>\n","      <td>32.540000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>1.000000</td>\n","      <td>0.499900</td>\n","      <td>14999.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>-121.790000</td>\n","      <td>33.930000</td>\n","      <td>18.000000</td>\n","      <td>1462.000000</td>\n","      <td>297.000000</td>\n","      <td>790.000000</td>\n","      <td>282.000000</td>\n","      <td>2.566375</td>\n","      <td>119400.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>-118.490000</td>\n","      <td>34.250000</td>\n","      <td>29.000000</td>\n","      <td>2127.000000</td>\n","      <td>434.000000</td>\n","      <td>1167.000000</td>\n","      <td>409.000000</td>\n","      <td>3.544600</td>\n","      <td>180400.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>-118.000000</td>\n","      <td>37.720000</td>\n","      <td>37.000000</td>\n","      <td>3151.250000</td>\n","      <td>648.250000</td>\n","      <td>1721.000000</td>\n","      <td>605.250000</td>\n","      <td>4.767000</td>\n","      <td>265000.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>-114.310000</td>\n","      <td>41.950000</td>\n","      <td>52.000000</td>\n","      <td>37937.000000</td>\n","      <td>6445.000000</td>\n","      <td>35682.000000</td>\n","      <td>6082.000000</td>\n","      <td>15.000100</td>\n","      <td>500001.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    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Ao rodar o seguinte código, ele vai te pedir permissão para acessar seu drive."],"metadata":{"id":"tyYE9oxDMB2s"}},{"cell_type":"code","source":["from google.colab import drive\n","\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_K3-XFcHML86","executionInfo":{"status":"ok","timestamp":1693059586480,"user_tz":180,"elapsed":17470,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"1dc6671e-fa78-413d-a6e9-0cbd94c45d53"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"markdown","source":["Por exemplo, digamos que seus dados estivessem na pasta 'projeto\" dentro da pasta \"meu drive\"."],"metadata":{"id":"GDtbZM-pMcRH"}},{"cell_type":"code","source":["caminho = 'content/drive/MyDrive/projeto/dados.csv'\n","\n","df = pd.read_csv(caminho)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":373},"id":"Fy0WNVW1Mj4a","executionInfo":{"status":"error","timestamp":1693059586480,"user_tz":180,"elapsed":4,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"c7ddd5e0-7ad7-4136-9b40-3af3878b659c"},"execution_count":null,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-164-b63a85f4fc5f>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcaminho\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'content/drive/MyDrive/projeto/dados.csv'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcaminho\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1736\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    854\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    857\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'content/drive/MyDrive/projeto/dados.csv'"]}]},{"cell_type":"markdown","source":["Aqui o código vai dar erro, por que nem a pasta nem os dados existem. Agora, vamos tentar acessar o .csv que deixei na pasta da semana 4. Note que não é preciso atribuir o caminho, sendo possível colocá-lo diretamente dentro do 'read_csv'."],"metadata":{"id":"pbUo0Ze5Mzx_"}},{"cell_type":"code","source":["# Vespertino\n","\n","df = pd.read_csv('/content/drive/MyDrive/FLS6513 FLP0422 - Alunos (Vespertino)/Semana 4 (28-08)/wti-monthly.csv')\n","\n","# Noturno\n","\n","# df = pd.read_csv('/content/drive/MyDrive/FLS6513 FLP0422 - Alunos (Noturno)/Semana 4 (28-08)/wti-monthly.csv')\n","\n","df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"nDIbDPh9NQUF","executionInfo":{"status":"ok","timestamp":1693059657639,"user_tz":180,"elapsed":215,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"096669d2-5ffc-4294-f8b6-b6ed057b1503"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         Date  Price\n","0  1986-01-15  22.93\n","1  1986-02-15  15.46\n","2  1986-03-15  12.61\n","3  1986-04-15  12.84\n","4  1986-05-15  15.38"],"text/html":["\n","  <div id=\"df-fa8fab95-8a22-4f2a-bcee-dc6b494ac72e\" class=\"colab-df-container\">\n","    <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>Date</th>\n","      <th>Price</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1986-01-15</td>\n","      <td>22.93</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1986-02-15</td>\n","      <td>15.46</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1986-03-15</td>\n","      <td>12.61</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1986-04-15</td>\n","      <td>12.84</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1986-05-15</td>\n","      <td>15.38</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fa8fab95-8a22-4f2a-bcee-dc6b494ac72e')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-fa8fab95-8a22-4f2a-bcee-dc6b494ac72e button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Por fim, copie o url dele e o use para carregar bancos de dados diretamente do github."],"metadata":{"id":"PS4k-v1oOi3z"}},{"cell_type":"code","source":["caminho = 'https://raw.githubusercontent.com/datasets/oil-prices/main/data/wti-daily.csv'\n","\n","df = pd.read_csv(caminho, index_col=0)\n","\n","df.head()\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"IqM5vvW4Ozuy","executionInfo":{"status":"ok","timestamp":1693060088771,"user_tz":180,"elapsed":553,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"4ea07c0f-19b3-4b49-a067-d7ce095b728f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            Price\n","Date             \n","1986-01-02  25.56\n","1986-01-03  26.00\n","1986-01-06  26.53\n","1986-01-07  25.85\n","1986-01-08  25.87"],"text/html":["\n","  <div id=\"df-d03ff4d8-5684-4c94-a2da-41eedd5690ab\" class=\"colab-df-container\">\n","    <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>Price</th>\n","    </tr>\n","    <tr>\n","      <th>Date</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1986-01-02</th>\n","      <td>25.56</td>\n","    </tr>\n","    <tr>\n","      <th>1986-01-03</th>\n","      <td>26.00</td>\n","    </tr>\n","    <tr>\n","      <th>1986-01-06</th>\n","      <td>26.53</td>\n","    </tr>\n","    <tr>\n","      <th>1986-01-07</th>\n","      <td>25.85</td>\n","    </tr>\n","    <tr>\n","      <th>1986-01-08</th>\n","      <td>25.87</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d03ff4d8-5684-4c94-a2da-41eedd5690ab')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-d03ff4d8-5684-4c94-a2da-41eedd5690ab button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":168}]},{"cell_type":"markdown","source":["No capítulo 6 do livro texto obrigatório, o autor discorre sobre várias formas de importação com diversos tipos de arquivos, introduzindo um pouco até o webscraping. Como esse não é o intuito da aula, pararemos por aqui. Ele também mostra alguns arquivos com problemas comuns e como solucionar eles. Insistimos que leiam sobre os principais erros que podem encontrar pois podem ser soluções utéis."],"metadata":{"id":"NRZImg9UQD45"}},{"cell_type":"markdown","source":["### Exercícios"],"metadata":{"id":"Q7mAUlWATFvF"}},{"cell_type":"markdown","source":["1 - Baixe o seguinte banco de dados do [Kaggle](https://www.kaggle.com/datasets/zynicide/wine-reviews?resource=download) e o importe no Colab utilizando o primeiro modo ensinado. Isto é, ir em arquivos e inseri-lo manualmente. Dica: Depois de inseri-lo, pode copiar o caminho clicando com o botão direito em cima do .csv no colab"],"metadata":{"id":"dkWjgZhWTHub"}},{"cell_type":"code","source":["caminho_arquivo = '/content/winemag-data-130k-v2.csv'\n","\n","df = pd.read_csv(caminho_arquivo)\n","\n","df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":660},"id":"72g8jGdzQlPv","executionInfo":{"status":"ok","timestamp":1693251699597,"user_tz":180,"elapsed":1424,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"d2d6a947-2a04-4fe2-8a25-ecbb4a39484b"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Unnamed: 0   country                                        description  \\\n","0           0     Italy  Aromas include tropical fruit, broom, brimston...   \n","1           1  Portugal  This is ripe and fruity, a wine that is smooth...   \n","2           2        US  Tart and snappy, the flavors of lime flesh and...   \n","3           3        US  Pineapple rind, lemon pith and orange blossom ...   \n","4           4        US  Much like the regular bottling from 2012, this...   \n","\n","                          designation  points  price           province  \\\n","0                        Vulkà Bianco      87    NaN  Sicily & Sardinia   \n","1                            Avidagos      87   15.0              Douro   \n","2                                 NaN      87   14.0             Oregon   \n","3                Reserve Late Harvest      87   13.0           Michigan   \n","4  Vintner's Reserve Wild Child Block      87   65.0             Oregon   \n","\n","              region_1           region_2         taster_name  \\\n","0                 Etna                NaN       Kerin O’Keefe   \n","1                  NaN                NaN          Roger Voss   \n","2    Willamette Valley  Willamette Valley        Paul Gregutt   \n","3  Lake Michigan Shore                NaN  Alexander Peartree   \n","4    Willamette Valley  Willamette Valley        Paul Gregutt   \n","\n","  taster_twitter_handle                                              title  \\\n","0          @kerinokeefe                  Nicosia 2013 Vulkà Bianco  (Etna)   \n","1            @vossroger      Quinta dos Avidagos 2011 Avidagos Red (Douro)   \n","2           @paulgwine       Rainstorm 2013 Pinot Gris (Willamette Valley)   \n","3                   NaN  St. Julian 2013 Reserve Late Harvest Riesling ...   \n","4           @paulgwine   Sweet Cheeks 2012 Vintner's Reserve Wild Child...   \n","\n","          variety               winery  \n","0     White Blend              Nicosia  \n","1  Portuguese Red  Quinta dos Avidagos  \n","2      Pinot Gris            Rainstorm  \n","3        Riesling           St. Julian  \n","4      Pinot Noir         Sweet Cheeks  "],"text/html":["\n","  <div id=\"df-f4219d4d-5072-47b5-9217-1a819ca315dc\" class=\"colab-df-container\">\n","    <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>Unnamed: 0</th>\n","      <th>country</th>\n","      <th>description</th>\n","      <th>designation</th>\n","      <th>points</th>\n","      <th>price</th>\n","      <th>province</th>\n","      <th>region_1</th>\n","      <th>region_2</th>\n","      <th>taster_name</th>\n","      <th>taster_twitter_handle</th>\n","      <th>title</th>\n","      <th>variety</th>\n","      <th>winery</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>Italy</td>\n","      <td>Aromas include tropical fruit, broom, brimston...</td>\n","      <td>Vulkà Bianco</td>\n","      <td>87</td>\n","      <td>NaN</td>\n","      <td>Sicily &amp; Sardinia</td>\n","      <td>Etna</td>\n","      <td>NaN</td>\n","      <td>Kerin O’Keefe</td>\n","      <td>@kerinokeefe</td>\n","      <td>Nicosia 2013 Vulkà Bianco  (Etna)</td>\n","      <td>White Blend</td>\n","      <td>Nicosia</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>Portugal</td>\n","      <td>This is ripe and fruity, a wine that is smooth...</td>\n","      <td>Avidagos</td>\n","      <td>87</td>\n","      <td>15.0</td>\n","      <td>Douro</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Roger Voss</td>\n","      <td>@vossroger</td>\n","      <td>Quinta dos Avidagos 2011 Avidagos Red (Douro)</td>\n","      <td>Portuguese Red</td>\n","      <td>Quinta dos Avidagos</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>US</td>\n","      <td>Tart and snappy, the flavors of lime flesh and...</td>\n","      <td>NaN</td>\n","      <td>87</td>\n","      <td>14.0</td>\n","      <td>Oregon</td>\n","      <td>Willamette Valley</td>\n","      <td>Willamette Valley</td>\n","      <td>Paul Gregutt</td>\n","      <td>@paulgwine</td>\n","      <td>Rainstorm 2013 Pinot Gris (Willamette Valley)</td>\n","      <td>Pinot Gris</td>\n","      <td>Rainstorm</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>US</td>\n","      <td>Pineapple rind, lemon pith and orange blossom ...</td>\n","      <td>Reserve Late Harvest</td>\n","      <td>87</td>\n","      <td>13.0</td>\n","      <td>Michigan</td>\n","      <td>Lake Michigan Shore</td>\n","      <td>NaN</td>\n","      <td>Alexander Peartree</td>\n","      <td>NaN</td>\n","      <td>St. Julian 2013 Reserve Late Harvest Riesling ...</td>\n","      <td>Riesling</td>\n","      <td>St. Julian</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>US</td>\n","      <td>Much like the regular bottling from 2012, this...</td>\n","      <td>Vintner's Reserve Wild Child Block</td>\n","      <td>87</td>\n","      <td>65.0</td>\n","      <td>Oregon</td>\n","      <td>Willamette Valley</td>\n","      <td>Willamette Valley</td>\n","      <td>Paul Gregutt</td>\n","      <td>@paulgwine</td>\n","      <td>Sweet Cheeks 2012 Vintner's Reserve Wild Child...</td>\n","      <td>Pinot Noir</td>\n","      <td>Sweet Cheeks</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4219d4d-5072-47b5-9217-1a819ca315dc')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-f4219d4d-5072-47b5-9217-1a819ca315dc button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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(Top 5)"],"metadata":{"id":"7XL0RZWkUda3"}},{"cell_type":"code","source":["df.country.value_counts()[:5]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7IGM3lb-Ug3W","executionInfo":{"status":"ok","timestamp":1693061377197,"user_tz":180,"elapsed":2,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"e5fa3fe6-4ecf-45af-be91-9ed63b235d83"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["US          19216\n","France       7937\n","Italy        7163\n","Spain        2363\n","Portugal     2074\n","Name: country, dtype: int64"]},"metadata":{},"execution_count":178}]},{"cell_type":"markdown","source":["c) Qual o preço médio?"],"metadata":{"id":"FHz83CJbQh2a"}},{"cell_type":"code","source":["df.price.mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LyyfxBtlU39a","executionInfo":{"status":"ok","timestamp":1693061454838,"user_tz":180,"elapsed":2,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"5e547a86-32e4-4ced-a74d-172b18d9f46e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["35.33328695006841"]},"metadata":{},"execution_count":179}]},{"cell_type":"markdown","source":["d) Quem é o sommelier que mais aparece no banco?"],"metadata":{"id":"GhqqSKtGVDYS"}},{"cell_type":"code","source":["df.taster_name.value_counts()[:1]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lB-cYlnyVGPR","executionInfo":{"status":"ok","timestamp":1693061526972,"user_tz":180,"elapsed":225,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"77d5f719-a114-4b1d-f15f-c1025d328ae3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Roger Voss    9213\n","Name: taster_name, dtype: int64"]},"metadata":{},"execution_count":180}]},{"cell_type":"markdown","source":["2) Baixe os arquivos com os resultados das [eleições](https://sig.tse.jus.br/ords/dwapr/r/seai/sig-eleicao-resultados/resultado-da-eleição?p0_abrangencia=UF&clear=RP&session=6209947300226) de 2022 por UF e importe no Pandas da mesma forma que o 1. Dicas: Problemas no [delimiter](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html) e no [encoding](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html) (encoding = 'latin-1')"],"metadata":{"id":"Xou5HshPVPgt"}},{"cell_type":"code","source":["caminho_arquivo = '/content/votacao_candidato-uf_2022.csv'\n","\n","df = pd.read_csv(caminho_arquivo,\n","                 delimiter = ';',\n","                 encoding = 'latin-1')\n","\n","df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":400},"id":"fOqU-DvfWDS6","executionInfo":{"status":"ok","timestamp":1693063996279,"user_tz":180,"elapsed":703,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"bcdfc014-d8d2-43b1-e872-fd9134dc6d33"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  sg_uf  cd_cargo    ds_cargo  nr_candidato               nm_candidato  \\\n","0    AC         1  Presidente            22     JAIR MESSIAS BOLSONARO   \n","1    AC         1  Presidente            13  LUIZ INÁCIO LULA DA SILVA   \n","2    AC         1  Presidente            15        SIMONE NASSAR TEBET   \n","3    AC         1  Presidente            12        CIRO FERREIRA GOMES   \n","4    AC         1  Presidente            44    SORAYA VIEIRA THRONICKE   \n","\n","  nm_urna_candidato sg_partido  \\\n","0    JAIR BOLSONARO         PL   \n","1              LULA         PT   \n","2      SIMONE TEBET        MDB   \n","3        CIRO GOMES        PDT   \n","4  SORAYA THRONICKE      UNIÃO   \n","\n","                             ds_composicao_coligacao  nr_turno  \\\n","0                             PP / REPUBLICANOS / PL         1   \n","1  (PT/PC do B/PV) / SOLIDARIEDADE / (PSOL/REDE) ...         1   \n","2                      MDB / (PSDB/CIDADANIA) / PODE         1   \n","3                                                PDT         1   \n","4                                              UNIÃO      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Presidente            15   \n","3        AC         1  Presidente            12   \n","4        AC         1  Presidente            44   \n","...     ...       ...         ...           ...   \n","26537    ZZ         1  Presidente            21   \n","26538    ZZ         1  Presidente            16   \n","26539    ZZ         1  Presidente            14   \n","26540    ZZ         1  Presidente            80   \n","26541    ZZ         1  Presidente            27   \n","\n","                              nm_candidato    nm_urna_candidato sg_partido  \\\n","0                   JAIR MESSIAS BOLSONARO       JAIR BOLSONARO         PL   \n","1                LUIZ INÁCIO LULA DA SILVA                 LULA         PT   \n","2                      SIMONE NASSAR TEBET         SIMONE TEBET        MDB   \n","3                      CIRO FERREIRA GOMES           CIRO GOMES        PDT   \n","4                  SORAYA VIEIRA THRONICKE     SORAYA THRONICKE      UNIÃO   \n","...                                    ...                  ...        ...   \n","26537                  SOFIA PADUA MANZANO        SOFIA MANZANO        PCB   \n","26538  VERA LUCIA PEREIRA DA SILVA SALGADO                 VERA       PSTU   \n","26539           KELMON LUIS DA SILVA SOUZA         PADRE KELMON        PTB   \n","26540       LEONARDO PÉRICLES VIEIRA ROQUE         LÉO PÉRICLES         UP   \n","26541                    JOSE MARIA EYMAEL  CONSTITUINTE EYMAEL         DC   \n","\n","                                 ds_composicao_coligacao  nr_turno  \\\n","0                                 PP / REPUBLICANOS / PL         1   \n","1      (PT/PC do B/PV) / SOLIDARIEDADE / (PSOL/REDE) ...         1   \n","2                          MDB / (PSDB/CIDADANIA) / PODE         1   \n","3                                                    PDT         1   \n","4                                                  UNIÃO         1   \n","...                                                  ...       ...   \n","26537                                                PCB         1   \n","26538                                               PSTU         1   \n","26539                                                PTB         1   \n","26540                                                 UP         1   \n","26541                                                 DC         1   \n","\n","      ds_sit_totalizacao   dt_ult_totalizacao sg_ue  sq_candidato  \\\n","0          Segundo turno  2022-10-04 12:06:36    BR  280001618036   \n","1          Segundo turno  2022-10-04 12:06:36    BR  280001607829   \n","2             Não Eleito  2022-10-04 12:06:36    BR  280001607833   \n","3             Não Eleito  2022-10-04 12:06:36    BR  280001612393   \n","4             Não Eleito  2022-10-04 12:06:36    BR  280001644128   \n","...                  ...                  ...   ...           ...   \n","26537         Não Eleito  2022-10-04 12:06:36    BR  280001600167   \n","26538         Não Eleito  2022-10-04 12:06:36    BR  280001607831   \n","26539         Não Eleito  2022-10-04 12:06:36    BR  280001734029   \n","26540         Não Eleito  2022-10-04 12:06:36    BR  280001602702   \n","26541         Não Eleito  2022-10-04 12:06:36    BR  280001677435   \n","\n","      nm_tipo_destinacao_votos  sq_eleicao_divulga pc_votos_validos  \\\n","0                       Válido          2040602022            0,625   \n","1                       Válido          2040602022           0,2926   \n","2                       Válido          2040602022           0,0456   \n","3                       Válido          2040602022           0,0279   \n","4                       Válido          2040602022           0,0055   \n","...                        ...                 ...              ...   \n","26537                   Válido          2040602022           0,0021   \n","26538                   Válido          2040602022           0,0016   \n","26539                   Válido          2040602022           0,0012   \n","26540 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 <td>PDT</td>\n","      <td>PDT</td>\n","      <td>1</td>\n","      <td>Não Eleito</td>\n","      <td>2022-10-04 12:06:36</td>\n","      <td>BR</td>\n","      <td>280001612393</td>\n","      <td>Válido</td>\n","      <td>2040602022</td>\n","      <td>0,0279</td>\n","      <td>12314</td>\n","      <td>440917</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>AC</td>\n","      <td>1</td>\n","      <td>Presidente</td>\n","      <td>44</td>\n","      <td>SORAYA VIEIRA THRONICKE</td>\n","      <td>SORAYA THRONICKE</td>\n","      <td>UNIÃO</td>\n","      <td>UNIÃO</td>\n","      <td>1</td>\n","      <td>Não Eleito</td>\n","      <td>2022-10-04 12:06:36</td>\n","      <td>BR</td>\n","      <td>280001644128</td>\n","      <td>Válido</td>\n","      <td>2040602022</td>\n","      <td>0,0055</td>\n","      <td>2444</td>\n","      <td>440917</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      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<td>Presidente</td>\n","      <td>16</td>\n","      <td>VERA LUCIA PEREIRA DA SILVA SALGADO</td>\n","      <td>VERA</td>\n","      <td>PSTU</td>\n","      <td>PSTU</td>\n","      <td>1</td>\n","      <td>Não Eleito</td>\n","      <td>2022-10-04 12:06:36</td>\n","      <td>BR</td>\n","      <td>280001607831</td>\n","      <td>Válido</td>\n","      <td>2040602022</td>\n","      <td>0,0016</td>\n","      <td>464</td>\n","      <td>294525</td>\n","    </tr>\n","    <tr>\n","      <th>26539</th>\n","      <td>ZZ</td>\n","      <td>1</td>\n","      <td>Presidente</td>\n","      <td>14</td>\n","      <td>KELMON LUIS DA SILVA SOUZA</td>\n","      <td>PADRE KELMON</td>\n","      <td>PTB</td>\n","      <td>PTB</td>\n","      <td>1</td>\n","      <td>Não Eleito</td>\n","      <td>2022-10-04 12:06:36</td>\n","      <td>BR</td>\n","      <td>280001734029</td>\n","      <td>Válido</td>\n","      <td>2040602022</td>\n","      <td>0,0012</td>\n","      <td>344</td>\n","      <td>294525</td>\n","    </tr>\n","    <tr>\n","      <th>26540</th>\n","      <td>ZZ</td>\n","      <td>1</td>\n","      <td>Presidente</td>\n","      <td>80</td>\n","      <td>LEONARDO PÉRICLES VIEIRA ROQUE</td>\n","      <td>LÉO PÉRICLES</td>\n","      <td>UP</td>\n","      <td>UP</td>\n","      <td>1</td>\n","      <td>Não Eleito</td>\n","      <td>2022-10-04 12:06:36</td>\n","      <td>BR</td>\n","      <td>280001602702</td>\n","      <td>Válido</td>\n","      <td>2040602022</td>\n","      <td>0,0011</td>\n","      <td>319</td>\n","      <td>294525</td>\n","    </tr>\n","    <tr>\n","      <th>26541</th>\n","      <td>ZZ</td>\n","      <td>1</td>\n","      <td>Presidente</td>\n","      <td>27</td>\n","      <td>JOSE MARIA EYMAEL</td>\n","      <td>CONSTITUINTE EYMAEL</td>\n","      <td>DC</td>\n","      <td>DC</td>\n","      <td>1</td>\n","      <td>Não Eleito</td>\n","      <td>2022-10-04 12:06:36</td>\n","      <td>BR</td>\n","      <td>280001677435</td>\n","      <td>Válido</td>\n","      <td>2040602022</td>\n","      <td>0,0009</td>\n","      <td>258</td>\n","      <td>294525</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>308 rows × 18 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a6e11dd7-8a4f-43d9-a8f4-63af3afbcb6c')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      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Use 'reset_index()' para reconfigurar o índice."],"metadata":{"id":"eOXAKdXkbg46"}},{"cell_type":"code","source":["df2 = df[['sg_uf', 'nm_urna_candidato', 'pc_votos_validos', 'qt_votos_nom_validos']].reset_index()\n","\n","df2"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":442},"id":"mWuYRi6kbt9H","executionInfo":{"status":"error","timestamp":1693251742631,"user_tz":180,"elapsed":222,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"0cc05631-6fa4-4f90-9b01-0bb333be5301"},"execution_count":14,"outputs":[{"output_type":"error","ename":"KeyError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m<ipython-input-14-644a9d9fb72f>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sg_uf'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'nm_urna_candidato'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'pc_votos_validos'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'qt_votos_nom_validos'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3811\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3812\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3813\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_indexer_strict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3814\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3815\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m   6068\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6069\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6070\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_if_missing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6071\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6072\u001b[0m         \u001b[0mkeyarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m   6128\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6129\u001b[0m                     \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6130\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6132\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['sg_uf', 'nm_urna_candidato', 'pc_votos_validos',\\n       'qt_votos_nom_validos'],\\n      dtype='object')] are in the [columns]\""]}]},{"cell_type":"markdown","source":["c) Mostre o maior valor de qt_votos_nom_validos e a linha em que ocorre"],"metadata":{"id":"fKhfHl67dGlI"}},{"cell_type":"code","source":["maior = df2.qt_votos_nom_validos.max()\n","linha = df2.qt_votos_nom_validos.idxmax()\n","\n","print(f'O maior valor é de {maior} e ele ocorre na linha {linha}')\n","print(df2.iloc[275])\n","\n","\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":245},"id":"Wo_rgQO6dKbs","executionInfo":{"status":"error","timestamp":1693251738141,"user_tz":180,"elapsed":272,"user":{"displayName":"Rebeca Carvalho","userId":"01975075342439777451"}},"outputId":"a99a4a72-cb47-4788-b191-80dffe321cba"},"execution_count":13,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-13-4e8adc162f68>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmaior\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqt_votos_nom_validos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mlinha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqt_votos_nom_validos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midxmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'O maior valor é de {maior} e ele ocorre na linha {linha}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m275\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'df2' is not defined"]}]},{"cell_type":"markdown","source":["3) Importe \"tweets_annotated.csv\" do seguinte [repositório](https://github.com/PedroSchmalz/covid19-tweets-brazilian-mayoral-candidates)."],"metadata":{"id":"5M-5KCzme8u9"}},{"cell_type":"code","source":["df = pd.read_csv('https://raw.githubusercontent.com/PedroSchmalz/covid19-tweets-brazilian-mayoral-candidates/main/tweets_annotated.csv')\n","\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"k4FmyG79fTJ0","executionInfo":{"status":"ok","timestamp":1693064203702,"user_tz":180,"elapsed":376,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"d12e1f6f-86d9-46bf-d4b3-9da546372fda"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       date_post             tweet_id  \\\n","0     2020-03-19  1240808239224090112   \n","1     2020-03-20  1241121140988410112   \n","2     2020-03-22  1241825454539120128   \n","3     2020-09-01  1300918558067770112   \n","4     2020-10-21  1319042054463290112   \n","...          ...                  ...   \n","7051  2021-06-28  1409592686840066048   \n","7052  2021-04-30  1388202351996150016   \n","7053  2021-03-19  1372931201871281920   \n","7054  2021-10-09  1446834442031575040   \n","7055  2021-08-02  1422321283350639104   \n","\n","                                                content  post_vaccine  \\\n","0     Tenho duas amigas doutorandas na USP. Não estã...             1   \n","1     Senado: Promulgado artigo que protege IBGE, Em...             0   \n","2     ATENÇÃO: programem-se para a vacinação contra ...             0   \n","3     Chega a ser inacreditável que o governo esteja...             1   \n","4     Como prefeito, vamos garantir através da Prefe...             1   \n","...                                                 ...           ...   \n","7051  Com os depoimentos dos irmãos Miranda foi dado...             1   \n","7052  Muitas pessoas estão questionando por que segu...             1   \n","7053  Gen Heleno de MÁSCARA, tomando VACINA. Cloroqu...             1   \n","7054  As UBS Hélio Macedo, Aygara Motta, Dalmo Feito...             1   \n","7055  Acompanhei a vacinação contra covid-19 das equ...             1   \n","\n","      positions_vac  \n","0               1.0  \n","1               NaN  \n","2               NaN  \n","3               1.0  \n","4               1.0  \n","...             ...  \n","7051            2.0  \n","7052            2.0  \n","7053            1.0  \n","7054            1.0  \n","7055            1.0  \n","\n","[7056 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-328da4b1-13c3-4a70-8762-a99e6dd0497e\" class=\"colab-df-container\">\n","    <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>date_post</th>\n","      <th>tweet_id</th>\n","      <th>content</th>\n","      <th>post_vaccine</th>\n","      <th>positions_vac</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2020-03-19</td>\n","      <td>1240808239224090112</td>\n","      <td>Tenho duas amigas doutorandas na USP. Não estã...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2020-03-20</td>\n","      <td>1241121140988410112</td>\n","      <td>Senado: Promulgado artigo que protege IBGE, Em...</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2020-03-22</td>\n","      <td>1241825454539120128</td>\n","      <td>ATENÇÃO: programem-se para a vacinação contra ...</td>\n","      <td>0</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2020-09-01</td>\n","      <td>1300918558067770112</td>\n","      <td>Chega a ser inacreditável que o governo esteja...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2020-10-21</td>\n","      <td>1319042054463290112</td>\n","      <td>Como prefeito, vamos garantir através da Prefe...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>7051</th>\n","      <td>2021-06-28</td>\n","      <td>1409592686840066048</td>\n","      <td>Com os depoimentos dos irmãos Miranda foi dado...</td>\n","      <td>1</td>\n","      <td>2.0</td>\n","    </tr>\n","    <tr>\n","      <th>7052</th>\n","      <td>2021-04-30</td>\n","      <td>1388202351996150016</td>\n","      <td>Muitas pessoas estão questionando por que segu...</td>\n","      <td>1</td>\n","      <td>2.0</td>\n","    </tr>\n","    <tr>\n","      <th>7053</th>\n","      <td>2021-03-19</td>\n","      <td>1372931201871281920</td>\n","      <td>Gen Heleno de MÁSCARA, tomando VACINA. Cloroqu...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>7054</th>\n","      <td>2021-10-09</td>\n","      <td>1446834442031575040</td>\n","      <td>As UBS Hélio Macedo, Aygara Motta, Dalmo Feito...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>7055</th>\n","      <td>2021-08-02</td>\n","      <td>1422321283350639104</td>\n","      <td>Acompanhei a vacinação contra covid-19 das equ...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>7056 rows × 5 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-328da4b1-13c3-4a70-8762-a99e6dd0497e')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-328da4b1-13c3-4a70-8762-a99e6dd0497e button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Não estã...             1   \n","3     Chega a ser inacreditável que o governo esteja...             1   \n","4     Como prefeito, vamos garantir através da Prefe...             1   \n","5     E reafirmando o compromisso de, como Prefeito,...             1   \n","6     Égua já pensaste?! Não sei se dá pra ser assim...             1   \n","...                                                 ...           ...   \n","7051  Com os depoimentos dos irmãos Miranda foi dado...             1   \n","7052  Muitas pessoas estão questionando por que segu...             1   \n","7053  Gen Heleno de MÁSCARA, tomando VACINA. Cloroqu...             1   \n","7054  As UBS Hélio Macedo, Aygara Motta, Dalmo Feito...             1   \n","7055  Acompanhei a vacinação contra covid-19 das equ...             1   \n","\n","      positions_vac  \n","0               1.0  \n","3               1.0  \n","4               1.0  \n","5               1.0  \n","6               1.0  \n","...             ...  \n","7051            2.0  \n","7052            2.0  \n","7053            1.0  \n","7054            1.0  \n","7055            1.0  \n","\n","[6420 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-06421311-1c83-494f-a900-a4fd2aa97ebd\" class=\"colab-df-container\">\n","    <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>date_post</th>\n","      <th>tweet_id</th>\n","      <th>content</th>\n","      <th>post_vaccine</th>\n","      <th>positions_vac</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2020-03-19</td>\n","      <td>1240808239224090112</td>\n","      <td>Tenho duas amigas doutorandas na USP. Não estã...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2020-09-01</td>\n","      <td>1300918558067770112</td>\n","      <td>Chega a ser inacreditável que o governo esteja...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2020-10-21</td>\n","      <td>1319042054463290112</td>\n","      <td>Como prefeito, vamos garantir através da Prefe...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>2020-11-17</td>\n","      <td>1328772384585860096</td>\n","      <td>E reafirmando o compromisso de, como Prefeito,...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>2020-11-19</td>\n","      <td>1329419704847490048</td>\n","      <td>Égua já pensaste?! Não sei se dá pra ser assim...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>7051</th>\n","      <td>2021-06-28</td>\n","      <td>1409592686840066048</td>\n","      <td>Com os depoimentos dos irmãos Miranda foi dado...</td>\n","      <td>1</td>\n","      <td>2.0</td>\n","    </tr>\n","    <tr>\n","      <th>7052</th>\n","      <td>2021-04-30</td>\n","      <td>1388202351996150016</td>\n","      <td>Muitas pessoas estão questionando por que segu...</td>\n","      <td>1</td>\n","      <td>2.0</td>\n","    </tr>\n","    <tr>\n","      <th>7053</th>\n","      <td>2021-03-19</td>\n","      <td>1372931201871281920</td>\n","      <td>Gen Heleno de MÁSCARA, tomando VACINA. Cloroqu...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>7054</th>\n","      <td>2021-10-09</td>\n","      <td>1446834442031575040</td>\n","      <td>As UBS Hélio Macedo, Aygara Motta, Dalmo Feito...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>7055</th>\n","      <td>2021-08-02</td>\n","      <td>1422321283350639104</td>\n","      <td>Acompanhei a vacinação contra covid-19 das equ...</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>6420 rows × 5 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-06421311-1c83-494f-a900-a4fd2aa97ebd')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-06421311-1c83-494f-a900-a4fd2aa97ebd button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Essas tarefas podem tomar até 80% do tempo de um analista' (Mckinney, 2022, p.203). A limpeza e a preparação de dados são etapas essenciais no processo de análise de dados. Elas envolvem a transformação e o aprimoramento dos dados brutos em um formato adequado e limpo para análise. Essas etapas são cruciais porque os dados nem sempre estão prontos para uso imediato e podem conter inconsistências, erros ou valores ausentes.\n","\n","A limpeza e a preparação de dados podem consumir uma parte significativa do tempo de um projeto de análise de dados, mas são fundamentais para garantir que os resultados sejam confiáveis e úteis. Uma vez que os dados estejam preparados adequadamente, você estará pronto para realizar análises exploratórias, modelagem de dados, visualizações e, finalmente, obter inferências a partir dos dados.\n","\n"],"metadata":{"id":"aItu2Kgr5ska"}},{"cell_type":"markdown","source":["### Lidando com dados ausentes\n","\n","Lidar com dados ausentes, também conhecidos como dados \"missing\" em Pandas, é uma parte importante da análise de dados, pois muitas vezes os conjuntos de dados reais contêm valores ausentes em algumas observações ou colunas. O Pandas oferece várias ferramentas e métodos para lidar com dados ausentes de forma eficaz. Aqui estão algumas estratégias comuns:\n","\n","\n","* 1 - Identificação de Dados Ausentes:\n","\n","isna() e isnull(): Essas funções permitem verificar se os valores são nulos. Por exemplo, df.isna() retornará um DataFrame booleano indicando quais valores são nulos.\n","\n","notna() e notnull(): Permitem verificar se os valores não são nulos.\n"],"metadata":{"id":"03gB_pBS6jev"}},{"cell_type":"code","source":["dados = pd.Series([1,2,3,np.nan,10])\n","\n","dados.isna()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F3qEsYps6v9p","executionInfo":{"status":"ok","timestamp":1693071403916,"user_tz":180,"elapsed":235,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"417ea507-3010-45bf-b5af-a8789de8ffe5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    False\n","1    False\n","2    False\n","3     True\n","4    False\n","dtype: bool"]},"metadata":{},"execution_count":241}]},{"cell_type":"markdown","source":["* 2 - Remoção de Dados Ausentes\n","\n","dropna(): Esse método permite remover linhas ou colunas com valores ausentes. Por padrão, ele remove qualquer linha que contenha pelo menos um valor ausente."],"metadata":{"id":"AiEEmzf864hv"}},{"cell_type":"code","source":["dados.dropna()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xRelvbnK68nX","executionInfo":{"status":"ok","timestamp":1693071437812,"user_tz":180,"elapsed":2,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"17d412be-263e-4bb3-ce29-476a666c5b7c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0     1.0\n","1     2.0\n","2     3.0\n","4    10.0\n","dtype: float64"]},"metadata":{},"execution_count":242}]},{"cell_type":"markdown","source":["dropna(subset=['coluna']): Você pode especificar uma coluna específica para verificar e remover apenas as linhas que contêm valores ausentes nessa coluna."],"metadata":{"id":"yDYLBNpJ7CMI"}},{"cell_type":"code","source":["dados = {\n","    \"Nome\": [\"Alice\", \"Bob\", \"Carol\", \"David\", \"Eva\"],\n","    \"Idade\": [25, 30, 22, 35, np.nan],\n","    \"Cidade\": [\"São Paulo\", \"Rio de Janeiro\", \"Belo Horizonte\", \"São Paulo\", \"Rio de Janeiro\"]\n","}\n","\n","dados = pd.DataFrame(dados)\n","\n","dados"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"Be9Zu7dn7DKO","executionInfo":{"status":"ok","timestamp":1693071596815,"user_tz":180,"elapsed":219,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"f2f05118-ac19-4862-f860-63854a7225d7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Nome  Idade          Cidade\n","0  Alice   25.0       São Paulo\n","1    Bob   30.0  Rio de Janeiro\n","2  Carol   22.0  Belo Horizonte\n","3  David   35.0       São Paulo\n","4    Eva    NaN  Rio de Janeiro"],"text/html":["\n","  <div id=\"df-f3f54eba-bb72-4cb0-8934-a6713b8945a6\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    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Por exemplo, df.fillna(0) preencherá todos os valores ausentes com zero.\n","\n","fillna(metodo): Você também pode usar métodos como 'ffill' (preenchimento para frente) ou 'bfill' (preenchimento para trás) para preencher os valores ausentes com base nos valores próximos na mesma coluna."],"metadata":{"id":"TvPff-047tyW"}},{"cell_type":"code","source":["dados = {\n","    \"Nome\": [\"Alice\", \"Bob\", \"Carol\", \"David\", \"Eva\"],\n","    \"Idade\": [25, 30, 22, 35, np.nan],\n","    \"Cidade\": [\"São Paulo\", \"Rio de Janeiro\", \"Belo Horizonte\", \"São Paulo\", \"Rio de Janeiro\"]\n","}\n","\n","dados = pd.DataFrame(dados, columns=['Nome', 'Idade', 'Cidade', 'Salário'])\n","\n","dados"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"91_sGmIf78Y7","executionInfo":{"status":"ok","timestamp":1693071714370,"user_tz":180,"elapsed":4,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"1aec78c5-f043-40c6-ce7c-14c5809c5662"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Nome  Idade          Cidade Salário\n","0  Alice   25.0       São Paulo     NaN\n","1    Bob   30.0  Rio de Janeiro     NaN\n","2  Carol   22.0  Belo Horizonte     NaN\n","3  David   35.0       São Paulo     NaN\n","4    Eva    NaN  Rio de Janeiro     NaN"],"text/html":["\n","  <div id=\"df-71f6c1d6-38ee-4436-ae74-8069dd653506\" class=\"colab-df-container\">\n","    <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>Nome</th>\n","      <th>Idade</th>\n","      <th>Cidade</th>\n","      <th>Salário</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Alice</td>\n","      <td>25.0</td>\n","      <td>São Paulo</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Bob</td>\n","      <td>30.0</td>\n","      <td>Rio de Janeiro</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Carol</td>\n","      <td>22.0</td>\n","      <td>Belo Horizonte</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>David</td>\n","      <td>35.0</td>\n","      <td>São Paulo</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Eva</td>\n","      <td>NaN</td>\n","      <td>Rio de Janeiro</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71f6c1d6-38ee-4436-ae74-8069dd653506')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-71f6c1d6-38ee-4436-ae74-8069dd653506 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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O termo \"ffill\" significa \"forward fill\", o que indica que ele preenche os valores ausentes avançando na direção dos índices maiores."],"metadata":{"id":"jOchjY9G8tRk"}},{"cell_type":"code","source":["df.fillna(method='ffill')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"dtiBGTvF8hUe","executionInfo":{"status":"ok","timestamp":1693071892431,"user_tz":180,"elapsed":226,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"939a5522-1172-400c-e8ea-2d4cf0373d3e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["          0         1         2\n","0  1.524942  1.720838  0.907236\n","1 -0.614782  0.420707 -1.283050\n","2  0.704547  0.420707 -1.186298\n","3  0.120769  0.420707  1.313455\n","4  0.934829  0.420707  1.313455\n","5  0.454356  0.420707  1.313455"],"text/html":["\n","  <div id=\"df-e7ff068d-c4fa-4436-90d4-e86f3a66e090\" class=\"colab-df-container\">\n","    <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>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.524942</td>\n","      <td>1.720838</td>\n","      <td>0.907236</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-0.614782</td>\n","      <td>0.420707</td>\n","      <td>-1.283050</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.704547</td>\n","      <td>0.420707</td>\n","      <td>-1.186298</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.120769</td>\n","      <td>0.420707</td>\n","      <td>1.313455</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.934829</td>\n","      <td>0.420707</td>\n","      <td>1.313455</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.454356</td>\n","      <td>0.420707</td>\n","      <td>1.313455</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7ff068d-c4fa-4436-90d4-e86f3a66e090')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-e7ff068d-c4fa-4436-90d4-e86f3a66e090 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":250}]},{"cell_type":"markdown","source":["Você também pode interpolar pela média"],"metadata":{"id":"V5HbDYkj8y-A"}},{"cell_type":"code","source":["df.fillna(df.mean())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"XnGyONLY89Hk","executionInfo":{"status":"ok","timestamp":1693071962798,"user_tz":180,"elapsed":229,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"b4846250-622a-488d-909d-8671019102f1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["          0         1         2\n","0  1.524942  1.720838  0.907236\n","1 -0.614782  0.420707 -1.283050\n","2  0.704547  1.070772 -1.186298\n","3  0.120769  1.070772  1.313455\n","4  0.934829  1.070772 -0.062164\n","5  0.454356  1.070772 -0.062164"],"text/html":["\n","  <div id=\"df-1916d4a6-4022-4db5-a599-51038fd47d99\" class=\"colab-df-container\">\n","    <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>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.524942</td>\n","      <td>1.720838</td>\n","      <td>0.907236</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-0.614782</td>\n","      <td>0.420707</td>\n","      <td>-1.283050</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.704547</td>\n","      <td>1.070772</td>\n","      <td>-1.186298</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.120769</td>\n","      <td>1.070772</td>\n","      <td>1.313455</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.934829</td>\n","      <td>1.070772</td>\n","      <td>-0.062164</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.454356</td>\n","      <td>1.070772</td>\n","      <td>-0.062164</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1916d4a6-4022-4db5-a599-51038fd47d99')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-1916d4a6-4022-4db5-a599-51038fd47d99 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":251}]},{"cell_type":"markdown","source":["É importante destacar que o uso de métodos de preenchimento de valores ausentes, como ffill (preenchimento para frente) e interpolação pela média, tem suas limitações e pode introduzir vieses nos dados ou até mesmo distorcer as análises, dependendo do contexto."],"metadata":{"id":"Vx2rwfE29ILH"}},{"cell_type":"markdown","source":["### Transformação dos Dados\n","\n","A transformação de dados em Pandas envolve a modificação dos dados originais para prepará-los para análise. Vamos discutir algumas operações comuns de transformação de dados em Pandas:\n"],"metadata":{"id":"ag6nVrSZ77n0"}},{"cell_type":"markdown","source":["* 1 - Remoção de observações duplicadas:\n","\n","Para remover linhas duplicadas em um DataFrame, você pode usar o método drop_duplicates():"],"metadata":{"id":"UVtqXFDQ9jEM"}},{"cell_type":"code","source":["df.drop_duplicates() # Como não há obs duplicadas, nada acontece"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"oJo6rCUi9ov2","executionInfo":{"status":"ok","timestamp":1693072141464,"user_tz":180,"elapsed":318,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"895384c2-89bc-4713-9f69-040b5e990b33"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["          0         1         2\n","0  1.524942  1.720838  0.907236\n","1 -0.614782  0.420707 -1.283050\n","2  0.704547       NaN -1.186298\n","3  0.120769       NaN  1.313455\n","4  0.934829       NaN       NaN\n","5  0.454356       NaN       NaN"],"text/html":["\n","  <div id=\"df-654afae5-b643-455b-9547-f73ab0ec94b0\" class=\"colab-df-container\">\n","    <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>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.524942</td>\n","      <td>1.720838</td>\n","      <td>0.907236</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-0.614782</td>\n","      <td>0.420707</td>\n","      <td>-1.283050</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.704547</td>\n","      <td>NaN</td>\n","      <td>-1.186298</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.120769</td>\n","      <td>NaN</td>\n","      <td>1.313455</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.934829</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.454356</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-654afae5-b643-455b-9547-f73ab0ec94b0')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-654afae5-b643-455b-9547-f73ab0ec94b0 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Suponha que temos um DataFrame com informações sobre produtos e queremos substituir nomes de categorias específicas por nomes abreviados."],"metadata":{"id":"v8ZZyPBa97h-"}},{"cell_type":"code","source":["dados = {\n","    'Produto': ['Laptop', 'Mouse', 'Teclado', 'Monitor', 'Impressora'],\n","    'Categoria': ['Eletrônicos', 'Periféricos', 'Periféricos', 'Eletrônicos', 'Outros']\n","}\n","\n","df = pd.DataFrame(dados)\n","\n","# Substituir a categoria 'Eletrônicos' por 'Elec' e 'Periféricos' por 'Perif'\n","df['Categoria'].replace({'Eletrônicos': 'Elec', 'Periféricos': 'Perif'}, inplace=True)\n","\n","print(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3acvJIO59_Wo","executionInfo":{"status":"ok","timestamp":1693072234047,"user_tz":180,"elapsed":204,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"450059dd-7679-4448-940c-0b093ac5707b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["      Produto Categoria\n","0      Laptop      Elec\n","1       Mouse     Perif\n","2     Teclado     Perif\n","3     Monitor      Elec\n","4  Impressora    Outros\n"]}]},{"cell_type":"markdown","source":["Neste exemplo, usamos o método replace() para substituir os valores da coluna 'Categoria'. O dicionário passado como argumento para replace() mapeia os valores antigos (chaves) para os novos valores (valores correspondentes). Os valores 'Eletrônicos' foram substituídos por 'Elec' e 'Periféricos' foram substituídos por 'Perif'. O uso de inplace=True faz a substituição diretamente no DataFrame original.\n","\n","Essa é uma maneira simples de realizar substituições em um DataFrame do Pandas, útil para modificar valores com base em critérios específicos durante a preparação dos dados para análise."],"metadata":{"id":"gcPTf4_U-EmT"}},{"cell_type":"markdown","source":["* 3 - Discretização e *Binning*:\n","\n","A discretização é o processo de dividir variáveis contínuas em intervalos ou categorias discretas. Você pode usar o método cut() para criar intervalos (bins) em seus dados. Suponha que temos um DataFrame com informações sobre idades de pessoas e queremos criar faixas etárias com base nas idades.\n","\n"],"metadata":{"id":"MLyhuq6k-IfH"}},{"cell_type":"code","source":["dados = {\n","    'Nome': ['Alice', 'Bob', 'Carol', 'David', 'Eva'],\n","    'Idade': [25, 30, 22, 35, 28]\n","}\n","\n","df = pd.DataFrame(dados)\n","\n","# Definindo os limites dos bins e as categorias correspondentes\n","limites_bins = [0, 18, 25, 35, float('inf')]  # Faixas etárias: 0-18, 19-25, 26-35, 36+\n","categorias = ['0-18', '19-25', '26-35', '36+']\n","\n","# Usando cut() para criar uma nova coluna 'Faixa Etária' com base nas idades\n","df['Faixa Etária'] = pd.cut(df['Idade'], bins=limites_bins, labels=categorias)\n","\n","print(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6uN_rD1o-TIN","executionInfo":{"status":"ok","timestamp":1693072314138,"user_tz":180,"elapsed":295,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"b9a3c87c-ec92-4cfb-97f3-3c9ae67409a8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Nome  Idade Faixa Etária\n","0  Alice     25        19-25\n","1    Bob     30        26-35\n","2  Carol     22        19-25\n","3  David     35        26-35\n","4    Eva     28        26-35\n"]}]},{"cell_type":"markdown","source":["Neste exemplo, usamos o método cut() para criar uma nova coluna chamada 'Faixa Etária' com base nas idades das pessoas. Definimos os limites dos bins (faixas etárias) em limites_bins e as categorias correspondentes em categorias. O método cut() atribui automaticamente a cada idade a categoria apropriada com base nos limites especificados.\n","\n","Isso é útil quando você deseja transformar variáveis contínuas, como idades, em categorias discretas para análise. O Pandas faz o trabalho pesado de atribuir os valores corretos a cada categoria com base nos limites especificados."],"metadata":{"id":"6GdlItwc-aY5"}},{"cell_type":"markdown","source":["* 4 - Criando variáveis *dummy* (Ou binárias)"],"metadata":{"id":"_UoFTHAC-Pyf"}},{"cell_type":"markdown","source":["Elas são usada quando você deseja converter variáveis categóricas em um formato numérico que possa ser utilizado em algoritmos de machine learning ou em análises estatísticas que requerem entradas numéricas.\n","\n","As variáveis dummy são variáveis binárias (0 ou 1) que representam a presença ou ausência de uma categoria particular de uma variável categórica. Variáveis dummy são usadas quando você tem variáveis categóricas (como cores, países, categorias de produtos, etc.) que não podem ser diretamente usadas em algoritmos de machine learning ou análises estatísticas. Algoritmos e modelos matemáticos requerem entradas numéricas. Para calcular variáveis dummy, você cria uma nova coluna binária para cada categoria da variável categórica original. Cada coluna binária representa uma categoria específica e é preenchida com 1 (verdadeiro) se a categoria estiver presente e 0 (falso) caso contrário.\n","\n","A biblioteca Pandas do Python possui funções para criar variáveis dummy, como pd.get_dummies(). Exemplo:"],"metadata":{"id":"rv3AdAt--t8u"}},{"cell_type":"code","source":["# Dados de exemplo\n","dados = {'Cor': ['Vermelho', 'Verde', 'Azul', 'Vermelho', 'Azul', 'Verde', 'Verde']}\n","\n","# Criando um DataFrame a partir dos dados\n","df = pd.DataFrame(dados)\n","\n","# Exibindo o DataFrame original\n","print(\"DataFrame Original:\")\n","print(df)\n","\n","# Calculando as variáveis dummy\n","df_dummies = pd.get_dummies(df, columns=['Cor'], prefix=['Cor'])\n","\n","# Exibindo o DataFrame com as variáveis dummy\n","print(\"\\nDataFrame com Variáveis Dummy:\")\n","print(df_dummies)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jyNse_dh-7UB","executionInfo":{"status":"ok","timestamp":1693072495935,"user_tz":180,"elapsed":222,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"43396c8a-48d9-4b67-9942-6da2b80ad26e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["DataFrame Original:\n","        Cor\n","0  Vermelho\n","1     Verde\n","2      Azul\n","3  Vermelho\n","4      Azul\n","5     Verde\n","6     Verde\n","\n","DataFrame com Variáveis Dummy:\n","   Cor_Azul  Cor_Verde  Cor_Vermelho\n","0         0          0             1\n","1         0          1             0\n","2         1          0             0\n","3         0          0             1\n","4         1          0             0\n","5         0          1             0\n","6         0          1             0\n"]}]},{"cell_type":"markdown","source":["### Manipulação de Strings"],"metadata":{"id":"AGMDXBhH9-nK"}},{"cell_type":"markdown","source":["Nas aulas anteriores, já falamos um pouco sobre manipulação de strings em Python. Agora, aprofundaremos um pouco os métodos existentes para esses tipos de dados. Como forma de refrescar a memória, aqui estão os principais métodos padrões de manipulação de strings no Python"],"metadata":{"id":"fs1ucpme__01"}},{"cell_type":"markdown","source":["![image.png](data:image/png;base64,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)"],"metadata":{"id":"oQTCIsktAvF-"}},{"cell_type":"markdown","source":["#### Expressões Regulares"],"metadata":{"id":"6ZcExai0CAOD"}},{"cell_type":"markdown","source":["Expressões regulares, também conhecidas como \"regex\" ou \"regexp\", são uma poderosa ferramenta para manipulação de strings em Python (e em muitas outras linguagens de programação). As expressões regulares permitem realizar pesquisa, correspondência e manipulação de padrões de texto complexos de maneira eficiente. O módulo re do Python é amplamente utilizado para trabalhar com expressões regulares. Aqui estão alguns conceitos importantes relacionados a expressões regulares em Python:\n","\n","\n","* 1 - Padrões de Correspondência:\n","\n","As expressões regulares são usadas para especificar padrões de texto que você deseja encontrar em uma string. Por exemplo, você pode usar uma expressão regular para encontrar todas as ocorrências de endereços de e-mail em um texto.\n","\n","* 2 - Módulo re:\n","\n","O módulo re é a biblioteca padrão do Python para trabalhar com expressões regulares. Ele fornece funções e classes para compilar e usar expressões regulares.\n","\n","\n","* 3 - Funções Principais do Módulo re:\n","\n","re.match(): Verifica se o padrão ocorre no início da string.\n","\n","re.search(): Procura por qualquer local onde o padrão ocorre na string.\n","\n","re.findall(): Retorna todas as ocorrências do padrão na string.\n","\n","re.finditer(): Retorna um iterador de todas as correspondências do padrão na string.\n","\n","re.sub(): Substitui todas as ocorrências do padrão por outra string.\n","\n","* 4 - Padrões Básicos:\n","\n","Os padrões de expressões regulares podem incluir caracteres literais (como letras e números) e caracteres especiais que têm significados especiais (por exemplo, . para qualquer caractere, * para zero ou mais ocorrências, [ ] para especificar um conjunto de caracteres, etc.).\n","\n","\n","* 5 - Caracteres de Escape:\n","\n","Se você precisar corresponder a caracteres especiais como ., *, [, ], etc., como literais, é necessário escapá-los usando a barra invertida \\.\n","\n","\n","* 6 - Exemplos de Expressões Regulares:\n","\n","Alguns exemplos de padrões de expressões regulares comuns incluem:\n","\n","  \\d+: Corresponde a um ou mais dígitos.\n","\n","  [A-Za-z]+: Corresponde a uma ou mais letras maiúsculas ou minúsculas.\n","\n","  .*: Corresponde a qualquer sequência de caracteres.\n","\n","  \\b\\w+\\b: Corresponde a palavras completas em uma string.\n","\n","  [0-9]{2,4}: Corresponde a um número que tem de 2 a 4 dígitos.\n","\n","* 7 - Exemplo de Uso:\n","\n","Aqui está um exemplo simples de uso do módulo re para encontrar endereços de e-mail em uma string:"],"metadata":{"id":"Z19QorArB_lP"}},{"cell_type":"code","source":["import re\n","\n","texto = \"Meu email é joao@exemplo.com e o outro é maria@exemplo.org\"\n","padrao = r'\\S+@\\S+'\n","\n","emails = re.findall(padrao, texto)\n","print(emails)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oje3ZEOjCcz8","executionInfo":{"status":"ok","timestamp":1693073405861,"user_tz":180,"elapsed":241,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"21ea760a-304b-471b-a033-f66cad585c44"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["['joao@exemplo.com', 'maria@exemplo.org']\n"]}]},{"cell_type":"markdown","source":["* 8 - O módulo re permite usar flags para modificar o comportamento das expressões regulares. Por exemplo, a flag re.IGNORECASE torna a correspondência de texto maiúsculo/minúsculo insensível.\n","\n","Expressões regulares podem ser incrivelmente úteis para análise de texto, validação de entradas, extração de informações e muito mais. No entanto, elas também podem ser complexas e difíceis de ler. Portanto, é aconselhável estudar e praticar expressões regulares para aproveitar ao máximo essa poderosa ferramenta.\n"],"metadata":{"id":"alM31zCqCjE3"}},{"cell_type":"markdown","source":["####  Funções de Strings em Pandas"],"metadata":{"id":"Tk7T_zo3DDDJ"}},{"cell_type":"markdown","source":["O Pandas oferece uma variedade de funções para manipulação de strings em DataFrames. Essas funções são extremamente úteis quando você precisa trabalhar com dados de texto em colunas de um DataFrame. Aqui estão algumas das funções de string mais comuns em Pandas, juntamente com exemplos:"],"metadata":{"id":"RpgBweQmDM7c"}},{"cell_type":"markdown","source":["* 1-  str.lower() e str.upper():\n","\n","Converte todos os caracteres para minúsculas ou maiúsculas, respectivamente.\n","Exemplo:"],"metadata":{"id":"cQeXS0n7DOKj"}},{"cell_type":"code","source":["import pandas as pd\n","\n","data = {'Nome': ['Alice', 'Bob', 'Carol', 'David'],\n","        'Cidade': ['New York', 'Los Angeles', 'San Francisco', 'Chicago']}\n","df = pd.DataFrame(data)\n","\n","df['Cidade'] = df['Cidade'].str.lower()\n","print(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zMfAQwFxDPH-","executionInfo":{"status":"ok","timestamp":1693073606150,"user_tz":180,"elapsed":219,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"b2cd7a6b-7530-4197-d57f-f7f2ba57f9ab"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Nome         Cidade\n","0  Alice       new york\n","1    Bob    los angeles\n","2  Carol  san francisco\n","3  David        chicago\n"]}]},{"cell_type":"markdown","source":["* 2 - str.len():\n","\n"],"metadata":{"id":"6XqJ8CVzDS1e"}},{"cell_type":"code","source":["df['Tamanho do Nome'] = df['Nome'].str.len()\n","print(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LfsjlYR1DYf5","executionInfo":{"status":"ok","timestamp":1693073641782,"user_tz":180,"elapsed":478,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"552ca5a0-fb25-4eab-d6e3-4b5c89066abe"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Nome         Cidade  Tamanho do Nome\n","0  Alice       new york                5\n","1    Bob    los angeles                3\n","2  Carol  san francisco                5\n","3  David        chicago                5\n"]}]},{"cell_type":"markdown","source":["* 3 - str.contains():\n","\n","Verifica se uma string contém uma determinada substring e retorna uma série de booleanos."],"metadata":{"id":"jpWEOU-RDaX0"}},{"cell_type":"code","source":["df['Contém \"San\"'] = df['Cidade'].str.contains('san', case=False)\n","print(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"n0b1gQjVDfIc","executionInfo":{"status":"ok","timestamp":1693073668731,"user_tz":180,"elapsed":406,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"5112e730-db2e-4c2a-9d3b-7a1c2ba505e7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Nome         Cidade  Tamanho do Nome  Contém \"San\"\n","0  Alice       new york                5         False\n","1    Bob    los angeles                3         False\n","2  Carol  san francisco                5          True\n","3  David        chicago                5         False\n"]}]},{"cell_type":"markdown","source":["* 4 - str.replace():\n","\n","Substitui uma substring por outra em cada string da coluna."],"metadata":{"id":"w1tIUATYDirx"}},{"cell_type":"code","source":["df['Cidade'] = df['Cidade'].str.replace(' ', '-') # Substituindo espaço vazio por '-'\n","print(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kCjk3ZpBDli0","executionInfo":{"status":"ok","timestamp":1693073698205,"user_tz":180,"elapsed":270,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"f8af7ce4-6382-4045-ead4-6e93fa173eb5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Nome         Cidade  Tamanho do Nome  Contém \"San\"\n","0  Alice       new-york                5         False\n","1    Bob    los-angeles                3         False\n","2  Carol  san-francisco                5          True\n","3  David        chicago                5         False\n"]}]},{"cell_type":"markdown","source":["* 5 - str.extract():\n","\n","Extrai informações usando expressões regulares."],"metadata":{"id":"bTCXOAYVDsCd"}},{"cell_type":"code","source":["df['Estado'] = df['Cidade'].str.extract(r'([A-Za-z]+)') # é um pouco obscuro ainda, mas futuramente retomaremos expressões regulares\n","print(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TxSMzfe2EBrj","executionInfo":{"status":"ok","timestamp":1693073863269,"user_tz":180,"elapsed":221,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"d543f303-a4e8-40cb-9a02-af8a08970d23"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Nome         Cidade  Tamanho do Nome  Contém \"San\"   Estado\n","0  Alice       new-york                5         False      new\n","1    Bob    los-angeles                3         False      los\n","2  Carol  san-francisco                5          True      san\n","3  David        chicago                5         False  chicago\n"]}]},{"cell_type":"markdown","source":["### Exercícios"],"metadata":{"id":"mJdwlxZQLF7k"}},{"cell_type":"markdown","source":["1 - Temos o seguinte banco de dados com valores missing. Preencha os valores faltantes com zeros."],"metadata":{"id":"65wEEgD4LNC3"}},{"cell_type":"code","source":["data = {'A': [1, 2, np.nan, 4, 5],\n","        'B': [np.nan, 2, 3, np.nan, 5],\n","        'C': [1, np.nan, 3, 4, np.nan]}\n","\n","df = pd.DataFrame(data)\n","\n","\n","# Preencher os valores ausentes com zeros\n","df_filled = df.fillna(0)\n","\n","# Exibir o DataFrame com os valores preenchidos\n","print(df_filled)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xGUBtDtSLQOm","executionInfo":{"status":"ok","timestamp":1693075737721,"user_tz":180,"elapsed":242,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"b3a05998-cdaa-413a-abd1-f146b9622df5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["     A    B    C\n","0  1.0  0.0  1.0\n","1  2.0  2.0  0.0\n","2  0.0  3.0  3.0\n","3  4.0  0.0  4.0\n","4  5.0  5.0  0.0\n"]}]},{"cell_type":"markdown","source":["2 - Novamente, temos um banco de dados com NAs. Conte os valores missing em cada coluna:"],"metadata":{"id":"EsXxk-WLLa8H"}},{"cell_type":"code","source":["data = {'A': [1, 2, np.nan, 4, 5],\n","        'B': [np.nan, 2, 3, np.nan, 5],\n","        'C': [1, np.nan, 3, 4, np.nan]}\n","\n","df = pd.DataFrame(data)\n","\n","\n","# Contar os valores ausentes em cada coluna\n","count_missing = df.isnull().sum()\n","\n","# Exibir o número de valores ausentes em cada coluna\n","print(count_missing)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"j3JdAuPZLou2","executionInfo":{"status":"ok","timestamp":1693075809754,"user_tz":180,"elapsed":207,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"ced37f40-9e37-4043-bc7e-e45db1204ce0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["A    1\n","B    2\n","C    2\n","dtype: int64\n"]}]},{"cell_type":"markdown","source":["3 - Remova os dados duplicados do seguinte banco de dados"],"metadata":{"id":"XuLEJFuVL0ep"}},{"cell_type":"code","source":["data = {'Nome': ['Alice', 'Bob', 'Alice', 'David', 'Eve'],\n","        'ID': [101, 102, 101, 103, 104]}\n","\n","df = pd.DataFrame(data)\n","\n","# Remova as entradas duplicadas com base no número de identificação\n","df_unique = df.drop_duplicates(subset='ID')\n","\n","# Exiba o novo DataFrame resultante\n","print(df_unique)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"n6aDFwq0L28s","executionInfo":{"status":"ok","timestamp":1693075863544,"user_tz":180,"elapsed":225,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"6a0ad43e-cb3f-4904-8336-04f80710a877"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["    Nome   ID\n","0  Alice  101\n","1    Bob  102\n","3  David  103\n","4    Eve  104\n"]}]},{"cell_type":"markdown","source":["4 - Para o seguinte banco de dados, faça um mapeamento que substitua 'alto' por 3, 'médio' por 2 e 'baixo' por 1. em seguida, utilize esse mapeamento no banco com o método replace"],"metadata":{"id":"GLSOagZiMKnH"}},{"cell_type":"code","source":["\n","data = {'Cidade': ['A', 'B', 'C', 'D'],\n","        'Poluição': ['Alto', 'Médio', 'Baixo', 'Alto']}\n","\n","df = pd.DataFrame(data)\n","\n","# Mapeie as representações de texto para valores numéricos\n","mapeamento = {'Alto': 3, 'Médio': 2, 'Baixo': 1}\n","df['Poluição'] = df['Poluição'].replace(mapeamento)\n","\n","# Exiba o DataFrame após a substituição\n","print(df)"],"metadata":{"id":"wPzGFEKlMSoV"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["5 - Para o seguinte banco de dados, crie bins de conceitos (i.e. 'A', 'B', etc.) com base na nota dos alunos"],"metadata":{"id":"DkuNY-6lM3wZ"}},{"cell_type":"code","source":["\n","data = {'Nome': ['Alice', 'Bob', 'Carol', 'David', 'Eve'],\n","        'Nota': [85, 72, 45, 60, 32]}\n","\n","df = pd.DataFrame(data)\n","\n","# Defina os intervalos das notas\n","faixas_etarias = [0, 18, 40, 60, 84, float('inf')]  # Intervalos\n","\n","# Crie uma nova coluna com as faixas etárias\n","df['Conceito'] = pd.cut(df['Nota'], bins=faixas_etarias, labels=['E', 'D', 'C', 'B', 'A'])\n","\n","\n","df\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"P2EIIhmZM-iz","executionInfo":{"status":"ok","timestamp":1693076273160,"user_tz":180,"elapsed":569,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"4121c563-54e9-44d7-e0e4-b1d4aea279d3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Nome  Nota Conceito\n","0  Alice    85        A\n","1    Bob    72        B\n","2  Carol    45        C\n","3  David    60        C\n","4    Eve    32        D"],"text/html":["\n","  <div id=\"df-5ae0ac4c-77d6-4a04-8b51-05dfb1b406e8\" class=\"colab-df-container\">\n","    <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>Nome</th>\n","      <th>Nota</th>\n","      <th>Conceito</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Alice</td>\n","      <td>85</td>\n","      <td>A</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Bob</td>\n","      <td>72</td>\n","      <td>B</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Carol</td>\n","      <td>45</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>David</td>\n","      <td>60</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Eve</td>\n","      <td>32</td>\n","      <td>D</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ae0ac4c-77d6-4a04-8b51-05dfb1b406e8')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-5ae0ac4c-77d6-4a04-8b51-05dfb1b406e8 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Sua tarefa é dividir essa coluna em duas colunas separadas, uma para o primeiro nome e outra para o último nome."],"metadata":{"id":"ydLkvQvzN-Fh"}},{"cell_type":"code","source":["import pandas as pd\n","\n","data = {'Nome_Completo': ['Alice Johnson', 'Bob Smith', 'Carol Brown', 'David White', 'Eve Anderson']}\n","\n","df = pd.DataFrame(data)\n","\n","# Divida a coluna \"Nome_Completo\" em \"Primeiro_Nome\" e \"Último_Nome\"\n","df[['Primeiro_Nome', 'Último_Nome']] = df['Nome_Completo'].str.split(' ', 1, expand=True)\n","\n","# Exiba o DataFrame com as novas colunas\n","print(df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2Q4Ah6ZEOBGY","executionInfo":{"status":"ok","timestamp":1693076433252,"user_tz":180,"elapsed":226,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"1500e901-fc0a-4cad-a1de-fd651cdebae9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["   Nome_Completo Primeiro_Nome Último_Nome\n","0  Alice Johnson         Alice     Johnson\n","1      Bob Smith           Bob       Smith\n","2    Carol Brown         Carol       Brown\n","3    David White         David       White\n","4   Eve Anderson           Eve    Anderson\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-276-012c5b8ebb7e>:8: FutureWarning: In a future version of pandas all arguments of StringMethods.split except for the argument 'pat' will be keyword-only.\n","  df[['Primeiro_Nome', 'Último_Nome']] = df['Nome_Completo'].str.split(' ', 1, expand=True)\n"]}]},{"cell_type":"markdown","source":["8 - Suponha que você tenha um DataFrame com uma coluna que contém nomes completos, mas alguns nomes têm espaços em branco extras no início ou no final. Sua tarefa é remover esses espaços em branco extras."],"metadata":{"id":"4VCdITq7OGst"}},{"cell_type":"code","source":["data = {'Nome_Completo': ['   Alice Johnson', 'Bob Smith   ', ' Carol Brown ', '  David White ', 'Eve  Anderson']}\n","\n","df = pd.DataFrame(data)\n","\n","# Remova espaços em branco extras no início e no final dos nomes\n","df['Nome_Completo'] = df['Nome_Completo'].str.strip()\n","\n","# Exiba o DataFrame com os nomes limpos\n","print(df)"],"metadata":{"id":"5cprzW1KOHiX","executionInfo":{"status":"ok","timestamp":1693076463871,"user_tz":180,"elapsed":240,"user":{"displayName":"Pedro Henrique de Santana Schmalz","userId":"15443225301388878262"}},"outputId":"12628f42-61ef-4474-ced7-13866986b652","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["   Nome_Completo\n","0  Alice Johnson\n","1      Bob Smith\n","2    Carol Brown\n","3    David White\n","4  Eve  Anderson\n"]}]}]}