From 71d966ed8982f907b8429568892d495be276a917 Mon Sep 17 00:00:00 2001 From: Detecprof Date: Fri, 23 Aug 2024 23:29:39 -0300 Subject: [PATCH 1/2] Atividade-para-casa-S12-Pandas-Numpy-II_Denise_Carvalho.ipynb --- exercicios/para-casa/README.md | 24 +- exercicios/para-casa/dados_vendas.csv | 366 +++++++++++ .../para-casa/exercicio-para-casa.ipynb | 606 ++++++++++++++++++ exercicios/para-casa/gerar_dados_falsos.py | 26 + 4 files changed, 1006 insertions(+), 16 deletions(-) create mode 100644 exercicios/para-casa/dados_vendas.csv create mode 100644 exercicios/para-casa/exercicio-para-casa.ipynb create mode 100644 exercicios/para-casa/gerar_dados_falsos.py diff --git a/exercicios/para-casa/README.md b/exercicios/para-casa/README.md index 3750f22..1822914 100644 --- a/exercicios/para-casa/README.md +++ b/exercicios/para-casa/README.md @@ -39,15 +39,7 @@ Neste exercício, você vai praticar a manipulação, consulta, filtragem, agreg datas = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D') num_registros = len(datas) - # Criando DataFrame - data = { - 'Data': np.random.choice(datas, num_registros), - 'Categoria': np.random.choice(categorias, num_registros), - 'Quantidade_Vendida': np.random.randint(1, 100, num_registros), - 'Preco_Unitario': np.round(np.random.uniform(10, 500, num_registros), 2) - } - df = pd.DataFrame(data) - df['Valor_Total'] = df['Quantidade_Vendida'] * df['Preco_Unitario'] + # Salvando o DataFrame em um arquivo CSV df.to_csv('dados_vendas.csv', index=False) @@ -157,10 +149,10 @@ Bom trabalho! Terminou o exercício? Dá uma olhada nessa checklist e confere se tá tudo certinho, combinado?! -- [ ] Fiz o fork do repositório. -- [ ] Clonei o fork na minha máquina (`git clone url-do-meu-fork`). -- [ ] Resolvi o exercício. -- [ ] Adicionei as mudanças. (`git add .` para adicionar todos os arquivos, ou `git add nome_do_arquivo` para adicionar um arquivo específico) -- [ ] Commitei a cada mudança significativa ou na finalização do exercício (`git commit -m "Mensagem do commit"`) -- [ ] Pushei os commits na minha branch (`git push origin nome-da-branch`) -- [ ] Criei um Pull Request seguindo as orientaçoes que estao nesse [documento](https://github.com/mflilian/repo-example/blob/main/exercicios/para-casa/instrucoes-pull-request.md). +- [x] Fiz o fork do repositório. +- [x] Clonei o fork na minha máquina (`git clone url-do-meu-fork`). +- [x] Resolvi o exercício. +- [x] Adicionei as mudanças. (`git add .` para adicionar todos os arquivos, ou `git add nome_do_arquivo` para adicionar um arquivo específico) +- [x] Commitei a cada mudança significativa ou na finalização do exercício (`git commit -m "Mensagem do commit"`) +- [x] Pushei os commits na minha branch (`git push origin nome-da-branch`) +- [x] Criei um Pull Request seguindo as orientaçoes que estao nesse [documento](https://github.com/mflilian/repo-example/blob/main/exercicios/para-casa/instrucoes-pull-request.md). diff --git a/exercicios/para-casa/dados_vendas.csv b/exercicios/para-casa/dados_vendas.csv new file mode 100644 index 0000000..c98fa34 --- /dev/null +++ b/exercicios/para-casa/dados_vendas.csv @@ -0,0 +1,366 @@ +Data,Categoria,Quantidade_Vendida,Preco_Unitario,Valor_Total +2023-04-13,Roupas,74,60.43,4471.82 +2023-12-15,Alimentos,83,272.88,22649.04 +2023-09-28,Roupas,17,195.62,3325.54 +2023-04-17,Roupas,85,233.93,19884.05 +2023-03-13,Roupas,78,305.94,23863.32 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+2023-08-15,Alimentos,9,14.65,131.85 +2023-04-22,Alimentos,34,35.82,1217.88 +2023-04-09,Livros,76,53.39,4057.64 +2023-06-02,Eletrônicos,99,28.42,2813.5800000000004 +2023-12-15,Roupas,35,245.09,8578.15 +2023-12-04,Eletrônicos,1,294.57,294.57 +2023-07-13,Eletrônicos,40,143.13,5725.2 +2023-11-06,Eletrônicos,64,205.16,13130.24 +2023-06-12,Brinquedos,22,54.93,1208.46 +2023-07-27,Alimentos,60,174.82,10489.199999999999 +2023-06-18,Eletrônicos,64,266.03,17025.92 +2023-06-10,Alimentos,93,368.81,34299.33 +2023-03-09,Livros,72,11.64,838.08 +2023-10-16,Roupas,11,238.98,2628.7799999999997 +2023-10-04,Livros,14,155.53,2177.42 +2023-10-31,Livros,60,427.88,25672.8 +2023-05-15,Brinquedos,30,360.62,10818.6 +2023-07-14,Roupas,35,298.66,10453.1 +2023-05-08,Livros,85,145.86,12398.1 +2023-02-02,Livros,37,456.07,16874.59 +2023-06-25,Roupas,5,32.1,160.5 +2023-01-22,Roupas,83,63.64,5282.12 +2023-08-26,Livros,78,201.9,15748.2 +2023-06-07,Roupas,26,70.97,1845.22 +2023-02-07,Livros,62,478.64,29675.68 +2023-08-18,Livros,4,401.01,1604.04 +2023-12-31,Brinquedos,89,136.9,12184.1 +2023-02-20,Eletrônicos,42,298.39,12532.38 +2023-09-21,Livros,89,491.34,43729.259999999995 +2023-10-10,Alimentos,18,443.26,7978.68 +2023-01-27,Eletrônicos,40,304.39,12175.599999999999 +2023-08-14,Eletrônicos,72,452.82,32603.04 +2023-10-04,Eletrônicos,39,494.7,19293.3 +2023-10-13,Brinquedos,14,374.24,5239.360000000001 +2023-04-07,Livros,32,41.84,1338.88 +2023-10-11,Brinquedos,51,206.77,10545.27 +2023-11-13,Livros,38,420.53,15980.14 +2023-10-31,Brinquedos,97,122.98,11929.06 +2023-05-27,Brinquedos,23,417.01,9591.23 +2023-01-04,Alimentos,63,68.97,4345.11 +2023-02-04,Brinquedos,15,33.0,495.0 +2023-07-11,Roupas,97,198.2,19225.399999999998 +2023-02-18,Alimentos,25,28.0,700.0 +2023-01-17,Brinquedos,17,479.06,8144.02 +2023-06-21,Eletrônicos,97,414.84,40239.479999999996 +2023-08-08,Roupas,66,402.38,26557.079999999998 +2023-06-07,Roupas,78,318.59,24850.019999999997 +2023-02-15,Roupas,53,116.0,6148.0 +2023-01-06,Alimentos,51,263.95,13461.449999999999 +2023-04-09,Brinquedos,39,302.66,11803.740000000002 +2023-08-21,Brinquedos,51,267.27,13630.769999999999 +2023-02-06,Eletrônicos,70,137.51,9625.699999999999 +2023-10-07,Eletrônicos,6,262.47,1574.8200000000002 +2023-12-15,Roupas,67,250.68,16795.56 +2023-10-29,Eletrônicos,7,498.2,3487.4 +2023-06-30,Alimentos,51,449.74,22936.74 +2023-04-05,Brinquedos,72,237.06,17068.32 +2023-04-09,Roupas,42,315.24,13240.08 +2023-07-07,Eletrônicos,64,376.46,24093.44 +2023-04-26,Alimentos,15,27.08,406.2 +2023-07-10,Alimentos,29,448.51,13006.789999999999 +2023-09-10,Eletrônicos,33,431.46,14238.179999999998 +2023-06-10,Brinquedos,94,234.14,22009.16 +2023-09-13,Eletrônicos,27,201.67,5445.089999999999 +2023-11-19,Roupas,36,143.75,5175.0 +2023-05-08,Eletrônicos,29,243.57,7063.53 +2023-01-18,Alimentos,38,31.74,1206.12 +2023-10-08,Eletrônicos,57,427.57,24371.489999999998 +2023-08-11,Brinquedos,97,27.36,2653.92 +2023-02-23,Livros,27,171.24,4623.4800000000005 +2023-02-27,Eletrônicos,55,466.48,25656.4 +2023-11-19,Brinquedos,33,370.25,12218.25 +2023-12-26,Brinquedos,68,147.88,10055.84 +2023-06-23,Alimentos,86,133.8,11506.800000000001 +2023-10-07,Brinquedos,66,12.49,824.34 +2023-04-24,Brinquedos,10,350.9,3509.0 +2023-10-15,Brinquedos,5,33.9,169.5 +2023-12-08,Brinquedos,74,270.98,20052.52 +2023-05-31,Roupas,97,481.44,46699.68 diff --git a/exercicios/para-casa/exercicio-para-casa.ipynb b/exercicios/para-casa/exercicio-para-casa.ipynb new file mode 100644 index 0000000..0f03660 --- /dev/null +++ b/exercicios/para-casa/exercicio-para-casa.ipynb @@ -0,0 +1,606 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercício de Casa 🏠 Análise e Visualização de Dados com Pandas\n", + "\n", + "**Semana 12 - Pandas-Numpy-II**\n", + "\n", + "**Professora: Daviny Letícia**\n", + "\n", + "\n", + "**Objetivo: Neste exercício, você vai praticar a manipulação, consulta, filtragem, agregação e visualização de dados usando a biblioteca Pandas e o Matplotlib em Python. Vamos trabalhar com um conjunto de dados fictícios que contém informações sobre vendas de uma loja.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Parte 1: Preparação do Ambiente\n", + "1. Instalação das bibliotecas necessárias:\n", + "\n", + "**Bibliotecas instaladas ☑**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Geração de Dados Fictícios\n", + "\n", + " Use o script abaixo para gerar um conjunto de dados falso que simula informações de vendas de produtos em uma loja.\n", + "\n", + " **Dados de vendas csv gerado ☑**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Parte 2: Consulta e Filtragem de Dados\n", + "1. Carregar os dados" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('dados_vendas.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Pergunta 1: Quantas linhas e colunas existem no DataFrame carregado?\n", + " \n", + "**São 365 linhas e 5 colunas**" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(365, 5)\n" + ] + } + ], + "source": [ + "# Exibe o formato do DataFrame - shape: exibe a quantidade de linhas e colunas do DataFrame\n", + "print(df.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Pergunta 2: Quais são os tipos de dados (dtypes) das colunas?\n", + "\n", + "**Os tipos de dados são: object, inteiro e float.**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data object\n", + "Categoria object\n", + "Quantidade_Vendida int64\n", + "Preco_Unitario float64\n", + "Valor_Total float64\n", + "dtype: object\n" + ] + } + ], + "source": [ + "# Exibe os tipos de dados das colunas - dtypes: retorna os tipos de dados da coluna\n", + "print(df.dtypes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Exibir as primeiras 5 linhas do DataFrame\n", + "\n", + "**Utilizei a função df.head() para exibir as 5 primeiras linhas.**\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Data Categoria Quantidade_Vendida Preco_Unitario Valor_Total\n", + "0 2023-04-13 Roupas 74 60.43 4471.82\n", + "1 2023-12-15 Alimentos 83 272.88 22649.04\n", + "2 2023-09-28 Roupas 17 195.62 3325.54\n", + "3 2023-04-17 Roupas 85 233.93 19884.05\n", + "4 2023-03-13 Roupas 78 305.94 23863.32\n" + ] + } + ], + "source": [ + "#Exibir as 5 primeiras linhas do DataFrame\n", + "print(df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Pergunta 3: Quantas categorias únicas de produtos estão presentes no DataFrame?\n", + "\n", + "** São 5 categorias únicas na coluna 'Categoria'**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Categorias únicas: ['Roupas' 'Alimentos' 'Eletrônicos' 'Brinquedos' 'Livros']\n", + "Número de categorias únicas: 5\n" + ] + } + ], + "source": [ + "#Retornar os valores das categorias unicas da coluna 'Categoria' e a quantidade\n", + "categorias_unicas = df['Categoria'].unique()\n", + "print(\"Categorias únicas:\", categorias_unicas)\n", + "\n", + "\n", + "num_categorias = len(categorias_unicas)\n", + "print('Número de categorias únicas:', num_categorias)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "6. Filtrar os dados para mostrar apenas as vendas da categoria 'Eletrônicos'" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Data Categoria Quantidade_Vendida Preco_Unitario Valor_Total\n", + "5 2023-07-08 Eletrônicos 73 256.12 18696.76\n", + "6 2023-01-21 Eletrônicos 1 274.53 274.53\n", + "7 2023-04-13 Eletrônicos 51 248.32 12664.32\n", + "14 2023-06-01 Eletrônicos 34 303.52 10319.68\n", + "18 2023-09-15 Eletrônicos 39 121.40 4734.60\n", + ".. ... ... ... ... ...\n", + "347 2023-09-10 Eletrônicos 33 431.46 14238.18\n", + "349 2023-09-13 Eletrônicos 27 201.67 5445.09\n", + "351 2023-05-08 Eletrônicos 29 243.57 7063.53\n", + "353 2023-10-08 Eletrônicos 57 427.57 24371.49\n", + "356 2023-02-27 Eletrônicos 55 466.48 25656.40\n", + "\n", + "[73 rows x 5 columns]\n" + ] + } + ], + "source": [ + "#Filtrar Dados de Colunas no DataFrame\n", + "eletronicos = df[df['Categoria'] == 'Eletrônicos']\n", + "print(eletronicos)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "7. Pergunta 4: Qual é a média do valor total das vendas na categoria 'Eletrônicos'?\n", + " \n", + " * A média do valor total das vendas na categoria 'Eletrônicos': **12291.256301369864**" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Média do valor total categoria 'Eletrônicos': 12291.256301369864\n" + ] + } + ], + "source": [ + "#Primeiro filtrar o DataFrame para pegar os dados das linhas da coluna categoria 'Eletrônicos'\n", + "##Calcular a média: valor total das vendas - categoria 'Eletrônicos' do DataFrame \n", + "###Exibir a média da coluna 'Valor_Total', categoria 'Eletrônicos'\n", + "eletronicos_df = df[df['Categoria'] == 'Eletrônicos']\n", + "media_valor_total_eletronicos = eletronicos_df['Valor_Total'].mean()\n", + "print(\"Média do valor total categoria 'Eletrônicos':\", media_valor_total_eletronicos)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "8. Filtrar as vendas acima de R$ 1000:\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Data Categoria Quantidade_Vendida Preco_Unitario Valor_Total\n", + "0 2023-04-13 Roupas 74 60.43 4471.82\n", + "1 2023-12-15 Alimentos 83 272.88 22649.04\n", + "2 2023-09-28 Roupas 17 195.62 3325.54\n", + "3 2023-04-17 Roupas 85 233.93 19884.05\n", + "4 2023-03-13 Roupas 78 305.94 23863.32\n", + ".. ... ... ... ... ...\n", + "358 2023-12-26 Brinquedos 68 147.88 10055.84\n", + "359 2023-06-23 Alimentos 86 133.80 11506.80\n", + "361 2023-04-24 Brinquedos 10 350.90 3509.00\n", + "363 2023-12-08 Brinquedos 74 270.98 20052.52\n", + "364 2023-05-31 Roupas 97 481.44 46699.68\n", + "\n", + "[330 rows x 5 columns]\n" + ] + } + ], + "source": [ + "#Definir a variável vendas>1000\n", + "vendas_acima_1000 = df[df['Valor_Total'] > 1000]\n", + "print(vendas_acima_1000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "9. Pergunta 5: Quantas vendas acima de R$ 1000 ocorreram?\n", + "\n", + "** Há 330 registros de vendas acima de R$ 1000,00**" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantidade de vendas acima de R$ 1000: 330\n", + " Data Categoria Quantidade_Vendida Preco_Unitario Valor_Total\n", + "0 2023-04-13 Roupas 74 60.43 4471.82\n", + "1 2023-12-15 Alimentos 83 272.88 22649.04\n", + "2 2023-09-28 Roupas 17 195.62 3325.54\n", + "3 2023-04-17 Roupas 85 233.93 19884.05\n", + "4 2023-03-13 Roupas 78 305.94 23863.32\n", + "5 2023-07-08 Eletrônicos 73 256.12 18696.76\n", + "7 2023-04-13 Eletrônicos 51 248.32 12664.32\n", + "8 2023-05-02 Alimentos 45 210.39 9467.55\n", + "9 2023-08-03 Brinquedos 77 388.22 29892.94\n", + "11 2023-03-29 Roupas 62 303.24 18800.88\n" + ] + } + ], + "source": [ + "#Filtrar os valores de vendas da coluna Valor_Total > R$ 1000\n", + "vendas_maior_1000 = df[df['Valor_Total'] > 1000]\n", + "\n", + "#Ulizar a função 'shape' para verificar a quantidade de registros\n", + "quantidade_vendas_acima_1000 = len(vendas_maior_1000)\n", + "\n", + "#Exibindo os resultados (quantidades de registros e as 10 primeiras linhas)\n", + "print(f'Quantidade de vendas acima de R$ 1000: {quantidade_vendas_acima_1000}')\n", + "print(vendas_maior_1000.head(10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Parte 3: Agrupamento e Agregação**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Agrupar os dados por categoria e calcular o total de vendas por categoria.\n", + " \n", + "Categoria Valor_Total\n", + "* 0 Alimentos 742457.74\n", + "* 1 Brinquedos 1187964.04\n", + "* 2 Eletrônicos 897261.71\n", + "* 3 Livros 963685.46\n", + "* 4 Roupas 991215.29\n", + "\n", + " \n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Categoria Valor_Total\n", + "0 Alimentos 742457.74\n", + "1 Brinquedos 1187964.04\n", + "2 Eletrônicos 897261.71\n", + "3 Livros 963685.46\n", + "4 Roupas 991215.29\n" + ] + } + ], + "source": [ + "total_por_categoria = df.groupby('Categoria')['Valor_Total'].sum().reset_index()\n", + "print(total_por_categoria)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Pergunta 6: Qual categoria teve o maior valor total de vendas?\n", + "\n", + "**Categoria 'Brinquedos' com vendas totais de R$ 1187964.04**" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A categoria com o maior valor de vendas é: Brinquedos, com vendas totais de R$ 1187964.04\n" + ] + } + ], + "source": [ + "#Calcular o valor máximo por categoria\n", + "##Identificar a maior categoria em vendas\n", + "###Determinar (função max) o valor máximo em vendas por categorias\n", + "vendas_por_categoria = df.groupby('Categoria')['Valor_Total'].sum()\n", + "categoria_maior_venda = vendas_por_categoria.idxmax()\n", + "valor_maximo_venda = vendas_por_categoria.max()\n", + "print(f'A categoria com o maior valor de vendas é: {categoria_maior_venda}, com vendas totais de R$ {valor_maximo_venda:.2f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Calcular a quantidade média vendida por categoria" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Categoria Quantidade_Vendida\n", + "0 Alimentos 55.618182\n", + "1 Brinquedos 47.247191\n", + "2 Eletrônicos 46.780822\n", + "3 Livros 50.917808\n", + "4 Roupas 47.200000\n" + ] + } + ], + "source": [ + "#Média por categoria\n", + "media_quantidade_categoria = df.groupby('Categoria')['Quantidade_Vendida'].mean().reset_index()\n", + "print(media_quantidade_categoria)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Pergunta 7: Qual categoria teve a maior quantidade média vendida?\n", + " \n", + "**A categoria com a maior quantidade média vendida é: Alimentos, com uma média de 55.62 unidades vendidas**" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A categoria com a maior quantidade média vendida é: Alimentos, com uma média de 55.62 unidades vendidas.\n" + ] + } + ], + "source": [ + "vendas_por_categoria = df.groupby('Categoria')['Quantidade_Vendida'].mean()\n", + "categoria_maior_quantidade_media = vendas_por_categoria.idxmax()\n", + "quantidade_media_vendida = vendas_por_categoria.max()\n", + "print(f'A categoria com a maior quantidade média vendida é: {categoria_maior_quantidade_media}, com uma média de {quantidade_media_vendida:.2f} unidades vendidas.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Parte 4: Visualização de Dados**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Criar um gráfico de barras mostrando o total de vendas por categoria" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "total_por_categoria = df.groupby('Categoria')['Valor_Total'].sum().reset_index()\n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(total_por_categoria['Categoria'], total_por_categoria['Valor_Total'], color='skyblue')\n", + "plt.xlabel('Categoria')\n", + "plt.ylabel('Valor Total de Vendas (R$)')\n", + "plt.title('Total de Vendas por Categoria')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Pergunta 8: Com base no gráfico, qual categoria visualmente se destaca em termos de vendas totais?\n", + " \n", + "**Categoria Brinquedos**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Criar um gráfico de linha mostrando a variação diária das vendas de 'Eletrônicos'" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vendas_diarias = eletronicos.groupby('Data')['Valor_Total'].sum().reset_index()\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(vendas_diarias['Data'], vendas_diarias['Valor_Total'], color='purple')\n", + "plt.xlabel('Data')\n", + "plt.ylabel('Valor Total de Vendas (R$)')\n", + "plt.title('Variação Diária das Vendas de Eletrônicos')\n", + "plt.xticks(ticks=vendas_diarias['Data'][::5], rotation=45)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Pergunta 9: Existe algum padrão ou tendência observável nas vendas diárias de eletrônicos?\n", + "\n", + "**Análise de gráficos de linhas - Categoria \"Eletrônicos\"**\n", + "\n", + "* Após a análise do gráfico de linhas referente a variação diária de vendas de eletrônicos, foi observado que há uma alta variação diária, com picos de alta e baixa ao longo do período. Isso indica que as vendas não são constantes e podem variar de um dia para o outro;\n", + "* Existe picos elevados e quedas significativas, que podem ter ocorrido por diversos contextos.\n", + "* Não há um padrão observado ao longo do período.\n", + "**Conclusão: De um modo geral, não há um padrão ou tendência observável.**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/exercicios/para-casa/gerar_dados_falsos.py b/exercicios/para-casa/gerar_dados_falsos.py new file mode 100644 index 0000000..b89b883 --- /dev/null +++ b/exercicios/para-casa/gerar_dados_falsos.py @@ -0,0 +1,26 @@ +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +# Configurando a seed para reprodutibilidade +np.random.seed(42) + +# Gerando dados falsos +categorias = ['Eletrônicos', 'Roupas', 'Alimentos', 'Livros', 'Brinquedos'] +datas = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D') +num_registros = len(datas) + +# Criando DataFrame +data = { + 'Data': np.random.choice(datas, num_registros), + 'Categoria': np.random.choice(categorias, num_registros), + 'Quantidade_Vendida': np.random.randint(1, 100, num_registros), + 'Preco_Unitario': np.round(np.random.uniform(10, 500, num_registros), 2) +} +df = pd.DataFrame(data) +df['Valor_Total'] = df['Quantidade_Vendida'] * df['Preco_Unitario'] + +# Salvando o DataFrame em um arquivo CSV +df.to_csv('dados_vendas.csv', index=False) + +print("Conjunto de dados gerado e salvo como 'dados_vendas.csv'.") \ No newline at end of file From 6e4c39532411f34c40b46cac7b0e6cae8e6f8d02 Mon Sep 17 00:00:00 2001 From: Detecprof Date: Fri, 23 Aug 2024 23:35:03 -0300 Subject: [PATCH 2/2] =?UTF-8?q?atualiza=C3=A7=C3=A3o?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- exercicios/para-sala/dados_ficticios.csv | 1001 +++++++++++++++++ exercicios/para-sala/exercicio-S12.ipynb | 261 +++++ .../para-sala/exercicio-extra-s12.ipynb | 635 +++++++++++ 3 files changed, 1897 insertions(+) create mode 100644 exercicios/para-sala/dados_ficticios.csv create mode 100644 exercicios/para-sala/exercicio-S12.ipynb create mode 100644 exercicios/para-sala/exercicio-extra-s12.ipynb diff --git a/exercicios/para-sala/dados_ficticios.csv b/exercicios/para-sala/dados_ficticios.csv new file mode 100644 index 0000000..bead322 --- /dev/null +++ b/exercicios/para-sala/dados_ficticios.csv @@ -0,0 +1,1001 @@ +Categoria,Valor_1,Valor_2,Valor_3,Data +C,53.41755975777159,126.03482578797596,1,2023-01-01 +D,68.76170839215887,131.23022393453707,2,2023-01-02 +A,59.5042383818605,100.64008298131533,6,2023-01-03 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+D,43.94285076996662,99.02695296859098,6,2025-09-24 +D,57.42095372009307,100.65593990823173,2,2025-09-25 +C,52.99292580406492,84.83009330599862,8,2025-09-26 diff --git a/exercicios/para-sala/exercicio-S12.ipynb b/exercicios/para-sala/exercicio-S12.ipynb new file mode 100644 index 0000000..3b02b6a --- /dev/null +++ b/exercicios/para-sala/exercicio-S12.ipynb @@ -0,0 +1,261 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Exercício de Sala 🏫 17/08/2024 \n", + "# Primeira do Dia" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Categoria Valor_1 Valor_2 Valor_3 Data\n", + "0 C 53.417560 126.034826 1 2023-01-01\n", + "1 D 68.761708 131.230224 2 2023-01-02\n", + "2 A 59.504238 100.640083 6 2023-01-03\n", + "3 C 44.230963 84.931643 5 2023-01-04\n", + "4 C 41.015853 109.199443 9 2023-01-05\n", + "5 D 54.919192 86.445693 5 2023-01-06\n", + "6 A 36.797668 140.267745 2 2023-01-07\n", + "7 A 68.314588 102.730707 4 2023-01-08\n", + "8 C 61.794401 92.693569 9 2023-01-09\n", + "9 B 45.308243 103.693606 1 2023-01-10\n", + "\n", + "RangeIndex: 1000 entries, 0 to 999\n", + "Data columns (total 5 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Categoria 1000 non-null object \n", + " 1 Valor_1 1000 non-null float64\n", + " 2 Valor_2 1000 non-null float64\n", + " 3 Valor_3 1000 non-null int64 \n", + " 4 Data 1000 non-null object \n", + "dtypes: float64(2), int64(1), object(2)\n", + "memory usage: 39.2+ KB\n", + "None\n", + " Valor_1 Valor_2 Valor_3\n", + "count 1000.000000 1000.000000 1000.000000\n", + "mean 50.401664 100.759179 5.014000\n", + "std 10.012925 19.673028 2.588533\n", + "min 21.037446 39.609757 1.000000\n", + "25% 43.749954 87.668313 3.000000\n", + "50% 50.366335 100.416802 5.000000\n", + "75% 56.815984 113.760305 7.000000\n", + "max 80.788808 163.862151 9.000000\n" + ] + } + ], + "source": [ + "# Carregar os dados fictícios\n", + "df = pd.read_csv('dados_ficticios.csv')\n", + "\n", + "# Exibir as 10 primeiras linhas\n", + "print(df.head(10))\n", + "\n", + "# Exibir informações gerais\n", + "print(df.info())\n", + "\n", + "# Exibir estatísticas descritivas\n", + "print(df.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Categoria Valor_1 Valor_2 Valor_3 Data\n", + "7 A 68.314588 102.730707 4 2023-01-08\n", + "30 A 65.511520 101.140262 2 2023-01-31\n", + "35 A 67.553408 121.450147 6 2023-02-05\n", + "74 A 69.647251 97.230880 5 2023-03-16\n", + "87 A 63.668743 100.293764 1 2023-03-29\n" + ] + } + ], + "source": [ + "# Filtrar os registros\n", + "filtro = df[(df['Categoria'] == 'A') & (df['Valor_1'] > 60)]\n", + "print(filtro.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Converter a coluna 'Data' para datetime se ainda não estiver\n", + "df['Data'] = pd.to_datetime(df['Data'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Categoria Valor_1 Valor_2 Valor_3 Data\n", + "120 D 44.076061 113.927749 6 2023-05-01\n", + "121 A 41.360092 119.106104 5 2023-05-02\n", + "122 B 50.485216 101.768138 9 2023-05-03\n", + "123 C 41.690499 129.550602 2 2023-05-04\n", + "124 A 52.704568 77.166218 8 2023-05-05\n" + ] + } + ], + "source": [ + "# Filtrar pelo intervalo de datas\n", + "filtro_data = df[(df['Data'] >= '2023-05-01') & (df['Data'] <= '2023-05-31')]\n", + "print(filtro_data.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Valor_1 Valor_2\n", + "Categoria \n", + "A 50.290689 100.277620\n", + "B 50.749158 99.837544\n", + "C 50.652042 102.046140\n", + "D 50.011022 100.893619\n" + ] + } + ], + "source": [ + "# Agrupamento e cálculo da média\n", + "agrupado_media = df.groupby('Categoria')[['Valor_1', 'Valor_2']].mean()\n", + "print(agrupado_media)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Valor_3 Contagem\n", + "Categoria \n", + "A 1279 258\n", + "B 1188 230\n", + "C 1182 232\n", + "D 1365 280\n" + ] + } + ], + "source": [ + "agrupado_custom = df.groupby('Categoria').agg({\n", + " 'Valor_3': 'sum',\n", + " 'Categoria': 'count'\n", + "}).rename(columns={'Categoria': 'Contagem'})\n", + "print(agrupado_custom)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Agrupamento e cálculo da média\n", + "media_valor_1 = df.groupby('Categoria')['Valor_1'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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01João Silva2024-08-01Febre, TosseGripeAntitérmico, Repouso304
12Maria Oliveira2024-08-02Dor de CabeçaEnxaquecaAnalgésico456
23Carlos Souza2024-08-03Náusea, VômitoGastriteAntiácido, Dieta255
34Ana Paula2024-08-04Dor de GargantaAmigdaliteAntibiótico407
45Marcos Lima2024-08-05Cansaço, Falta de ArBronquiteBroncodilatador358
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12Maria Oliveira2024-08-02Dor de CabeçaEnxaquecaAnalgésico456
23Carlos Souza2024-08-03Náusea, VômitoGastriteAntiácido, Dieta255
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ID_ConsultaNome_PacienteData_ConsultaSintomasDiagnosticoPrescricaoDuracao_Consulta_MinutosGravidade_Sintomas
01João Silva2024-08-01Febre, TosseGripeAntitérmico, Repouso304
12Maria Oliveira2024-08-02Dor de CabeçaEnxaquecaAnalgésico456
23Carlos Souza2024-08-03Náusea, VômitoGastriteAntiácido, Dieta255
34Ana Paula2024-08-04Dor de GargantaAmigdaliteAntibiótico407
45Marcos Lima2024-08-05Cansaço, Falta de ArBronquiteBroncodilatador358
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" + ], + "text/plain": [ + " ID_Consulta Nome_Paciente Data_Consulta Sintomas \\\n", + "0 1 João Silva 2024-08-01 Febre, Tosse \n", + "1 2 Maria Oliveira 2024-08-02 Dor de Cabeça \n", + "2 3 Carlos Souza 2024-08-03 Náusea, Vômito \n", + "3 4 Ana Paula 2024-08-04 Dor de Garganta \n", + "4 5 Marcos Lima 2024-08-05 Cansaço, Falta de Ar \n", + "\n", + " Diagnostico Prescricao Duracao_Consulta_Minutos \\\n", + "0 Gripe Antitérmico, Repouso 30 \n", + "1 Enxaqueca Analgésico 45 \n", + "2 Gastrite Antiácido, Dieta 25 \n", + "3 Amigdalite Antibiótico 40 \n", + "4 Bronquite Broncodilatador 35 \n", + "\n", + " Gravidade_Sintomas \n", + "0 4 \n", + "1 6 \n", + "2 5 \n", + "3 7 \n", + "4 8 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Gráfico de dispersão" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df['Duracao_Consulta_Minutos'], df['Gravidade_Sintomas'], color='blue')\n", + "plt.title('Duração da Consulta x Gravidade dos Sintomas')\n", + "plt.xlabel('Duração da Consulta (minutos)')\n", + "plt.ylabel('Gravidade dos Sintomas (1 a 10)')\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Análise Storytelling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "De acorodo com a análise de dados do gráfico " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "colors = df['Gravidade_Sintomas']\n", + "plt.figure(figsize=(10, 6))\n", + "scatter = plt.scatter(df['Duracao_Consulta_Minutos'], df['Gravidade_Sintomas'], c=colors, cmap='viridis')\n", + "plt.title('Duração da Consulta x Gravidade dos Sintomas')\n", + "plt.xlabel('Duração da Consulta (minutos)')\n", + "plt.ylabel('Gravidade dos Sintomas (1 a 10)')\n", + "plt.colorbar(scatter, label='Gravidade dos Sintomas')\n", + "plt.grid(True)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}