import pandas as pd df = pd.read_csv('merged/olist_dataset_train.csv') df.head()

df.shape

df[['order_purchase_timestamp', 'order_approved_at', 'order_delivered_carrier_date', 'order_delivered_customer_date', 'order_estimated_delivery_date', 'review_creation_date', 'review_answer_timestamp']] = df[['order_purchase_timestamp', 'order_approved_at', 'order_delivered_carrier_date', 'order_delivered_customer_date', 'order_estimated_delivery_date', 'review_creation_date', 'review_answer_timestamp']].apply(pd.to_datetime)

from pandas_profiling import ProfileReport profile = ProfileReport(df, title="Análise de vendas") profile

df.drop(columns=['customer_id','customer_unique_id','order_id','review_id', 'review_comment_title'],inplace=True)

df['year_month_purchase'] = df['order_purchase_timestamp'].dt.strftime('%Y-%m')

def embeleze(fig): fig.update_layout( font_family = 'Calibri', template = 'plotly_white' )

df['2017-11_purchase'] = df['order_purchase_timestamp'].dt.strftime('%Y-%m-%d') df7 = df[(df['2017-11_purchase'] > '2017-11-01') & (df['2017-11_purchase'] < '2017-11-30')] df_SP2 = df7[df7['customer_state'] == 'SP'] df_RJ2 = df7[df7['customer_state'] == 'RJ'] df_MG2 = df7[df7['customer_state'] == 'MG'] df7 = pd.concat([df_SP2, df_RJ2, df_MG2])

df7 = df7.groupby(['2017-11_purchase', 'customer_state']).sum().reset_index()

df['review_score'].min()

df['review_score'].max()

df.max()

df[df['payment_value'] == 13664.08]