From a8d55724eb8ce56c8e85ce0a07bb7a80beb48e46 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?An=C3=A1lia=20Alves?= Date: Fri, 23 Aug 2024 21:30:29 -0300 Subject: [PATCH] Exercicio S10 --- "An\303\241lia/Employee.csv" | 4654 +++++++++++++++++++++++++++++++ "An\303\241lia/exercicioS10.py" | 71 + exercicios/para-sala/teste.py | 2 + 3 files changed, 4727 insertions(+) create mode 100644 "An\303\241lia/Employee.csv" create mode 100644 "An\303\241lia/exercicioS10.py" create mode 100644 exercicios/para-sala/teste.py diff --git "a/An\303\241lia/Employee.csv" "b/An\303\241lia/Employee.csv" new file mode 100644 index 0000000..42bd8f1 --- /dev/null +++ "b/An\303\241lia/Employee.csv" @@ -0,0 +1,4654 @@ +Education,JoiningYear,City,PaymentTier,Age,Gender,EverBenched,ExperienceInCurrentDomain,LeaveOrNot +Bachelors,2017,Bangalore,3,34,Male,No,0,0 +Bachelors,2013,Pune,1,28,Female,No,3,1 +Bachelors,2014,New Delhi,3,38,Female,No,2,0 +Masters,2016,Bangalore,3,27,Male,No,5,1 +Masters,2017,Pune,3,24,Male,Yes,2,1 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+Bachelors,2017,Bangalore,3,38,Male,No,3,0 +Bachelors,2013,Pune,3,37,Male,No,3,0 +Bachelors,2016,Bangalore,3,22,Male,No,0,0 +PHD,2012,Bangalore,3,30,Female,No,4,0 +Bachelors,2013,Bangalore,3,39,Female,No,5,0 +Bachelors,2017,Bangalore,2,35,Male,No,0,0 +Bachelors,2012,Pune,3,25,Female,No,3,1 +Bachelors,2018,Bangalore,3,32,Male,Yes,1,1 +Bachelors,2018,Bangalore,3,31,Female,No,2,1 +Bachelors,2016,Bangalore,3,37,Male,No,2,0 +Bachelors,2016,New Delhi,1,38,Female,No,2,1 +Bachelors,2014,Bangalore,3,22,Female,No,0,0 +Masters,2014,New Delhi,3,37,Female,No,0,1 +Bachelors,2018,Bangalore,3,32,Male,Yes,2,1 +Bachelors,2013,Bangalore,3,26,Male,No,4,0 +Bachelors,2017,Bangalore,3,28,Male,No,0,1 +Bachelors,2015,Bangalore,3,35,Male,No,0,0 +Bachelors,2016,Bangalore,1,33,Female,No,0,1 +Bachelors,2012,Bangalore,3,36,Female,No,4,0 +Bachelors,2013,Bangalore,3,31,Female,No,5,0 +Bachelors,2015,Pune,3,32,Female,Yes,1,1 +Masters,2017,Pune,2,31,Female,No,2,0 +Bachelors,2013,Bangalore,3,25,Female,No,3,0 +Bachelors,2016,Pune,3,30,Male,No,2,0 +Bachelors,2013,Bangalore,3,26,Female,No,4,0 +Masters,2013,Pune,2,37,Male,No,2,1 +Masters,2018,New Delhi,3,27,Male,No,5,1 +Bachelors,2012,Bangalore,3,30,Male,Yes,2,0 +Bachelors,2015,Bangalore,3,33,Male,Yes,4,0 diff --git "a/An\303\241lia/exercicioS10.py" "b/An\303\241lia/exercicioS10.py" new file mode 100644 index 0000000..fc82dd7 --- /dev/null +++ "b/An\303\241lia/exercicioS10.py" @@ -0,0 +1,71 @@ +import pandas as pd +from datetime import datetime +import matplotlib.pyplot as plt + +df = pd.read_csv("Employee.csv") + +# Faça a limpeza do seu dataframe excluindo linhas duplicadas ou preenchendo valores nulos + +df_atualizado = df.drop_duplicates() + +print(df_atualizado) + +valores_nulos = df.isnull().sum() + +print(valores_nulos) + +# Crie um dataframe que tenha os empregados que trabalham na empresa a mais de 5 anos. + +ano_atual = datetime.now().year +mais_de_5_anos = df_atualizado [(ano_atual - df_atualizado ['JoiningYear']) > 5] + +# Agrupe os empregados por gênero e idade e crie um gráfico para cada caso. + +agrupamento_genero_idade = mais_de_5_anos.groupby(['Gender', 'Age']).size().reset_index(name='Contagem') + +print(agrupamento_genero_idade ) + +masculino = agrupamento_genero_idade[agrupamento_genero_idade['Gender'] == 'Male'] +plt.bar(masculino['Age'], masculino['Contagem'], color='blue') +plt.title('Número de Empregados por Idade (Masculino)') +plt.xlabel('Idade') +plt.ylabel('Número de Empregados') +plt.show() + +feminino = agrupamento_genero_idade[agrupamento_genero_idade['Gender'] == 'Female'] +plt.bar(feminino['Age'], feminino['Contagem'], color='pink') +plt.title('Número de Empregados por Idade (Feminino)') +plt.xlabel('Idade') +plt.ylabel('Número de Empregados') +plt.show() + +# Veja qual a cidade que mais tem empregados e faça uma média do tempo de serviço dos empregados por cidade + +agrupamento_cidade = mais_de_5_anos.groupby('City').agg({'JoiningYear':'count', 'ExperienceInCurrentDomain':'mean'}).reset_index() +agrupamento_cidade.columns = ['Cidade', 'Numero De Empregados', 'Media Experiencia'] + +# Faça a porcentagem de quantos empregados ainda trabalham na empresa (use a coluna LeaveOrNot do dataframe) Conte quantos empregados existem por PaymentTier + +porcentagem_ainda_trabalhando = df_atualizado['LeaveOrNot'].mean() * 100 + +contagem_payment_tier = df_atualizado['PaymentTier'].value_counts() + +# Substitua os valores da coluna EverBenched para True ou False + +df['EverBenched'] = df['EverBenched'].map({'Yes': True, 'No': False}) + +# Crie um gráfico de pizza com o resultado da coluna EverBenched e outro com LeaveOrNot + +resultado_everbenched = df['EverBenched'].value_counts() + +plt.figure(figsize=(6, 6)) +plt.pie(resultado_everbenched, labels=resultado_everbenched.index, autopct='%1.1f%%', colors=['Green', 'Red']) +plt.title('Distribuição de EverBenched (True/False)') +plt.show() + +resultado_leaveornot = df['LeaveOrNot'].value_counts() + +plt.figure(figsize=(6, 6)) +plt.pie(resultado_leaveornot, labels=['0', '1'], autopct='%1.1f%%', colors=['lightgreen', 'lightcoral']) +plt.title('Distribuição de LeaveOrNot (0/1)') +plt.show() \ No newline at end of file diff --git a/exercicios/para-sala/teste.py b/exercicios/para-sala/teste.py new file mode 100644 index 0000000..daabf90 --- /dev/null +++ b/exercicios/para-sala/teste.py @@ -0,0 +1,2 @@ +import matplotlib.pyplot as pl +