From b82279563ff72e1c86aeb6b6ab0aed70e6b70d5d Mon Sep 17 00:00:00 2001 From: NogueiraBrenda Date: Wed, 21 Aug 2024 23:00:20 -0300 Subject: [PATCH 1/3] Atividade Semana 12 --- exercicios/para-casa/Atividade.py | 63 +++++++++++++++++++++++++++ exercicios/para-casa/Funcionarios.csv | 1 + 2 files changed, 64 insertions(+) create mode 100644 exercicios/para-casa/Atividade.py create mode 100644 exercicios/para-casa/Funcionarios.csv diff --git a/exercicios/para-casa/Atividade.py b/exercicios/para-casa/Atividade.py new file mode 100644 index 0000000..adaf2f4 --- /dev/null +++ b/exercicios/para-casa/Atividade.py @@ -0,0 +1,63 @@ +import pandas as pd +import datetime as dt +import matplotlib.pyplot as plt + +df = pd.read_csv(r"C:\Users\brend\OneDrive\Documentos\PYTHON\REPROGRAMA\Semana 12\on33-python-s10-pandas-numpy-II\material\Employee.csv") + +print(df.info()) +print(df.isnull().sum()) + +#Listar soma das duplicadas +print(df.duplicated().sum()) + +#Excluir duplicadas e salvar +print(df.drop_duplicates(inplace=True)) +print(df.duplicated().sum()) + +#Encontrar o ano atual +Ano_Atual = dt.datetime.now().year +print(Ano_Atual) + +#Cálculo quantidade de anos na empresa +df["Tempo de Empresa"] = (df["JoiningYear"] - Ano_Atual) +print(df.info()) + +#Filtro com os funcionários com mais de 5 anos +filtered_df = df[df["Tempo de Empresa"] > 5] +print(filtered_df.head()) + +#Salvando um novo DataFrame com os funcionários com mais de 5 anos +filtered_df.to_csv("./Funcionarios.csv", index=False) +#Veja qual a cidade que mais tem empregados e faça uma média do tempo de serviço dos empregados por cidade +Cidades = df["City"].value_counts().idxmax() +print(Cidades) +Mais_funcionarios = df["City"].value_counts().max() +print(Mais_funcionarios) +print('A cidade com Mais funcionários é', Cidades, 'com', Mais_funcionarios, 'funcionários') + +#Faça a porcentagem de quantos empregados ainda trabalham na empresa (use a coluna `LeaveOrNot` do dataframe) +Funcionarios = df["LeaveOrNot"].count() +#print(Funcionarios) +Ativos = (df["LeaveOrNot"] > 0).sum() +Percentual = (Ativos / Funcionarios) * 100 +print(format(round(Percentual,2))) + +#Conte quantos empregados existem por `PaymentTier` +Qtdade_Funcionários = df['PaymentTier'].count() +print(Qtdade_Funcionários) + + +#Substitua os valores da coluna `EverBenched` para `True` ou `False` +df['EverBenched'] = df['EverBenched'].apply(lambda x: "False" if x == 'No' else "True") +print(df["EverBenched"]) + +#Crie um gráfico de pizza com o resultado da coluna `EverBenched` e outro com `LeaveOrNot` +Grafico1 = df["EverBenched"].value_counts() +print(Grafico1) +Grafico1.plot(kind="pie") +plt.show + +Grafico2 = df["LeaveOrNot"].value_counts() +print(Grafico2) +Grafico2.plot(kind="pie") +plt.show \ No newline at end of file diff --git a/exercicios/para-casa/Funcionarios.csv b/exercicios/para-casa/Funcionarios.csv new file mode 100644 index 0000000..41c8a8b --- /dev/null +++ b/exercicios/para-casa/Funcionarios.csv @@ -0,0 +1 @@ +Education,JoiningYear,City,PaymentTier,Age,Gender,EverBenched,ExperienceInCurrentDomain,LeaveOrNot,Tempo de Empresa From 377902bb6f0b25572fc866b5b33b49ff79a7c7cb Mon Sep 17 00:00:00 2001 From: NogueiraBrenda Date: Thu, 22 Aug 2024 18:58:06 -0300 Subject: [PATCH 2/3] Atividade Semana 12 --- exercicios/para-casa/Atividade.py | 8 +- exercicios/para-casa/Funcionarios.csv | 2764 +++++++++++++++++++++++++ 2 files changed, 2768 insertions(+), 4 deletions(-) diff --git a/exercicios/para-casa/Atividade.py b/exercicios/para-casa/Atividade.py index adaf2f4..91929f7 100644 --- a/exercicios/para-casa/Atividade.py +++ b/exercicios/para-casa/Atividade.py @@ -19,7 +19,7 @@ print(Ano_Atual) #Cálculo quantidade de anos na empresa -df["Tempo de Empresa"] = (df["JoiningYear"] - Ano_Atual) +df["Tempo de Empresa"] = (Ano_Atual - df["JoiningYear"]) print(df.info()) #Filtro com os funcionários com mais de 5 anos @@ -28,6 +28,7 @@ #Salvando um novo DataFrame com os funcionários com mais de 5 anos filtered_df.to_csv("./Funcionarios.csv", index=False) + #Veja qual a cidade que mais tem empregados e faça uma média do tempo de serviço dos empregados por cidade Cidades = df["City"].value_counts().idxmax() print(Cidades) @@ -46,7 +47,6 @@ Qtdade_Funcionários = df['PaymentTier'].count() print(Qtdade_Funcionários) - #Substitua os valores da coluna `EverBenched` para `True` ou `False` df['EverBenched'] = df['EverBenched'].apply(lambda x: "False" if x == 'No' else "True") print(df["EverBenched"]) @@ -55,9 +55,9 @@ Grafico1 = df["EverBenched"].value_counts() print(Grafico1) Grafico1.plot(kind="pie") -plt.show +plt.show() Grafico2 = df["LeaveOrNot"].value_counts() print(Grafico2) Grafico2.plot(kind="pie") -plt.show \ No newline at end of file +plt.show() diff --git a/exercicios/para-casa/Funcionarios.csv b/exercicios/para-casa/Funcionarios.csv index 41c8a8b..cc07eaf 100644 --- a/exercicios/para-casa/Funcionarios.csv +++ b/exercicios/para-casa/Funcionarios.csv @@ -1 +1,2765 @@ 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filtered_df.to_csv("./Funcionarios.csv", index=False) -#Veja qual a cidade que mais tem empregados e faça uma média do tempo de serviço dos empregados por cidade +#Agrupe os empregados por gênero e idade +Genero = df["Gender"].value_counts +print(Genero) +Idade = df["Age"].value_counts +print(Idade) + +#crie um gráfico para cada caso acima +Genero.plot(kind="bar", title="Funcionários por Gênero") +plt.show() + +Idade.plot(kind="bar", title="Funcionários por Idade") +plt.show() + +#Veja qual a cidade que mais tem empregados Cidades = df["City"].value_counts().idxmax() print(Cidades) Mais_funcionarios = df["City"].value_counts().max() print(Mais_funcionarios) print('A cidade com Mais funcionários é', Cidades, 'com', Mais_funcionarios, 'funcionários') +#faça uma média do tempo de serviço dos empregados por cidade +df["Tempo_servico"] = (Ano_Atual - df["JoiningYear"]) +media_cidade = df.groupby("City")["Tempo_servico"].mean() +print(media_cidade) + #Faça a porcentagem de quantos empregados ainda trabalham na empresa (use a coluna `LeaveOrNot` do dataframe) Funcionarios = df["LeaveOrNot"].count() #print(Funcionarios)