diff --git a/exercicios/para-casa/ETL_employee.py b/exercicios/para-casa/ETL_employee.py
new file mode 100644
index 0000000..66d85bd
--- /dev/null
+++ b/exercicios/para-casa/ETL_employee.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\Lidi\Documents\{reprograma}\Git_on33\on33-python-s10-pandas-numpy-II\material\Employee.csv")
+
+print(df.info())
+print(df.isnull().sum())
+print(df.duplicated().sum())
+print(df.drop_duplicates(inplace=True))
+print(df.duplicated().sum())
+print(df.info())
+print(df.describe())
+
+current_year = dt.date.today().year
+five_years_ago = current_year - 5
+filtered_df = df[(df["JoiningYear"] <= five_years_ago)]
+print(filtered_df.describe())
+
+df["Age"].value_counts().sort_index().plot(kind="barh", title="Empregados por Idade", xlabel="Quantidade", ylabel="Idades", color="goldenrod")
+plt.show()
+
+df["Gender"].value_counts().plot(kind="pie", title="Empregados por Gênero", colors=["lightpink", "lightskyblue"], autopct="%.2f%%")
+plt.ylabel("")
+plt.legend()
+plt.show()
+
+most_employees_city = df["City"].max()
+print("A cidade com mais empregados é", most_employees_city)
+
+df["LenghtService"] = current_year - df["JoiningYear"]
+lenght_service_mean_by_city = df.groupby(["City"])["LenghtService"].mean()
+print(lenght_service_mean_by_city)
+
+total_employees = len(df)
+not_working = df["LeaveOrNot"].value_counts()
+not_working_pct = (not_working / total_employees) * 100
+print(f"Em {current_year} cerca de {not_working_pct[0]:.2f}% dos empregados ainda trabalham na empresa.")
+
+empolyees = len(df["PaymentTier"]) - not_working[1]
+print(f"Atualmente existem {empolyees} empregados na empresa.")
+
+def convertion(value):
+    if value == "Yes":
+        return True
+    if value == "No":
+        return False
+    else:
+        return "Não categorizado"
+
+df["EverBenched"] = df["EverBenched"].apply(convertion)
+print(df["EverBenched"])
+
+# incluir a porcentagem na legenda e rótulo
+df["EverBenched"].value_counts().plot(kind="pie", title="Empregados que já estiveram no banco", labels=["Não", "Sim"], colors=["peru", "sienna"], autopct="%.2f%%", explode=[0, 0.06])
+plt.ylabel("")
+plt.legend()
+plt.show()
+
+df["LeaveOrNot"].value_counts().plot(kind="pie", title="Empregados que já saíram da empresa", labels=["Permanecem na empresa", "Saíram da empresa"], colors=["seagreen", "lightgreen"], autopct="%.2f%%", explode=[0, 0.06])
+plt.ylabel("")
+plt.legend()
+plt.show()
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diff --git a/exercicios/para-sala/aula.py b/exercicios/para-sala/aula.py
deleted file mode 100644
index e69de29..0000000
diff --git a/exercicios/para-sala/aula_by_me.py b/exercicios/para-sala/aula_by_me.py
new file mode 100644
index 0000000..02d74f4
--- /dev/null
+++ b/exercicios/para-sala/aula_by_me.py
@@ -0,0 +1,60 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+
+df = pd.read_csv("../../material/desenvolvimento_paises.csv")
+
+# print(df.describe())
+# print(df.info())
+# # Fazer dentro de uma coluna específica
+# print(df["AveragScore"].value_counts())
+
+# Altera o df original. Para não alterar, não colocar o inplace
+# print(df.fillna(value=0, inplace=True))
+# print(df.isnull().sum())
+# print(df.duplicated().sum())
+# print(df.drop_duplicates(inplace=True))
+# print(df.duplicated().sum())
+
+# country_greater_security_value = df["SafetySecurity"].max()
+# country_lesser_security_value = df["SafetySecurity"].min()
+# print(country_greater_security_value)
+# print(country_lesser_security_value)
+# print("A diferença entre o maior país com SafetySecurity é de: ", country_greater_security_value - country_lesser_security_value)
+
+# line_higher_security_value = df[df["SafetySecurity"] == country_greater_security_value]
+# print(line_higher_security_value)
+
+# index_greater_value = df["SafetySecurity"].idxmax()
+# print(df.loc[index_greater_value])
+
+columns_name = df.columns
+columns_name = columns_name.drop(labels="Country")
+
+def categorizar_valores(valor):
+    if valor > 80:
+        return "Desenvolvido"
+    if valor < 70 and valor >= 50:
+        return "Em desenvolvimento"
+    if valor < 60:
+        return "Subdesenvolvido"
+    else:
+        return "Não categorizado"
+
+df["Media"] = df[columns_name].mean(axis=1)
+df["Categoria_Desenvolvimento"] = df["Media"].apply(categorizar_valores)
+
+# print(df["Media"])
+# print(df["Categoria_Desenvolvimento"].value_counts())
+
+development_category_counts = df["Categoria_Desenvolvimento"].value_counts()
+print(development_category_counts)
+
+development_category_counts.plot(kind="bar", title="QTD Países por Categoria")
+# plt.xticks(rotation=45)
+# plt.show()
+
+personel_freedom_filter = df.where((df["PersonelFreedom"] <= 30) & (df["Categoria_Desenvolvimento"] == "Subdesenvolvido"))
+
+personel_freedom_filter.dropna(inplace=True)
+
+print(personel_freedom_filter.info())
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