#%%
import pandas as pd

#%%
df = pd.read_csv('Ecommerce Purchases')

#%%
df.head()

#%%
df.info()

#%%
df['Purchase Price'].mean()

#%%
df['Purchase Price'].max()

#%%
df['Purchase Price'].min()

#%%
df[df['Language'] == 'en'].count()

#%%
df[df['Job'] == 'Lawyer'].count()

#%%
df['AM or PM'].value_counts()

#%%
df['Job'].value_counts().head(5)

#%%
df[df['Lot'] == '90 WT']['Purchase Price']

#%%
df[df["Credit Card"] == 4926535242672853]['Email']

#%%
df[(df['CC Provider'] == 'American Express') & (df['Purchase Price'] > 95)].count()

#%%
sum(df['CC Exp Date'].apply(lambda x: x[3:]) == '25')

#%%
df['Email'].apply(lambda x: x.split('@')[1]).value_counts().head(5)

#%%

a2e486697556d685ff98185714f66ddbde012a29
<mask token>

<mask token>
df.head()
df.info()
df['Purchase Price'].mean()
df['Purchase Price'].max()
df['Purchase Price'].min()
df[df['Language'] == 'en'].count()
df[df['Job'] == 'Lawyer'].count()
df['AM or PM'].value_counts()
df['Job'].value_counts().head(5)
df[df['Lot'] == '90 WT']['Purchase Price']
df[df['Credit Card'] == 4926535242672853]['Email']
df[(df['CC Provider'] == 'American Express') & (df['Purchase Price'] > 95)
    ].count()
sum(df['CC Exp Date'].apply(lambda x: x[3:]) == '25')
df['Email'].apply(lambda x: x.split('@')[1]).value_counts().head(5)

<mask token>
df = pd.read_csv('Ecommerce Purchases')
df.head()
df.info()
df['Purchase Price'].mean()
df['Purchase Price'].max()
df['Purchase Price'].min()
df[df['Language'] == 'en'].count()
df[df['Job'] == 'Lawyer'].count()
df['AM or PM'].value_counts()
df['Job'].value_counts().head(5)
df[df['Lot'] == '90 WT']['Purchase Price']
df[df['Credit Card'] == 4926535242672853]['Email']
df[(df['CC Provider'] == 'American Express') & (df['Purchase Price'] > 95)
    ].count()
sum(df['CC Exp Date'].apply(lambda x: x[3:]) == '25')
df['Email'].apply(lambda x: x.split('@')[1]).value_counts().head(5)

import pandas as pd
df = pd.read_csv('Ecommerce Purchases')
df.head()
df.info()
df['Purchase Price'].mean()
df['Purchase Price'].max()
df['Purchase Price'].min()
df[df['Language'] == 'en'].count()
df[df['Job'] == 'Lawyer'].count()
df['AM or PM'].value_counts()
df['Job'].value_counts().head(5)
df[df['Lot'] == '90 WT']['Purchase Price']
df[df['Credit Card'] == 4926535242672853]['Email']
df[(df['CC Provider'] == 'American Express') & (df['Purchase Price'] > 95)
    ].count()
sum(df['CC Exp Date'].apply(lambda x: x[3:]) == '25')
df['Email'].apply(lambda x: x.split('@')[1]).value_counts().head(5)

#%%
import pandas as pd

#%%
df = pd.read_csv('Ecommerce Purchases')

#%%
df.head()

#%%
df.info()

#%%
df['Purchase Price'].mean()

#%%
df['Purchase Price'].max()

#%%
df['Purchase Price'].min()

#%%
df[df['Language'] == 'en'].count()

#%%
df[df['Job'] == 'Lawyer'].count()

#%%
df['AM or PM'].value_counts()

#%%
df['Job'].value_counts().head(5)

#%%
df[df['Lot'] == '90 WT']['Purchase Price']

#%%
df[df["Credit Card"] == 4926535242672853]['Email']

#%%
df[(df['CC Provider'] == 'American Express') & (df['Purchase Price'] > 95)].count()

#%%
sum(df['CC Exp Date'].apply(lambda x: x[3:]) == '25')

#%%
df['Email'].apply(lambda x: x.split('@')[1]).value_counts().head(5)

#%%
