67 ECommerse csv Qustions Part 03

In [1]:

#ecom = pd.read_csv('Ecommerce.csv')
import pandas as pd
ecom=pd.read_csv("Ecommerce.csv")

Hard: How many people have a credit card that expires in 2025?

In [44]:

ecom['CC Exp Date'].iloc[0][3:]

Out[44]:

'20'

In [47]:

sum(ecom['CC Exp Date'].apply(lambda X: X[3:]) == '25')

Out[47]:

1033

In [37]:

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

Out[37]:

1033

In [6]:

ecom['CC Exp Date'].apply(lambda x: x[3:]).head(5)

Out[6]:

0    20
1    18
2    19
3    24
4    25
Name: CC Exp Date, dtype: object

In [8]:

ecom['CC Exp Date'].head(1)

Out[8]:

0    02/20
Name: CC Exp Date, dtype: object

In [35]:

ecom['CC Exp Date'].iloc[0]

Out[35]:

'02/20'

In [36]:

ecom['CC Exp Date'].iloc[0][3:]

Out[36]:

'20'

Hard: What are the top 5 most popular email providers/hosts (e.g. gmail.com, yahoo.com, etc...)

ecom['Email'].apply(lambda x: x.split('@')[1]).value_counts().head(5)¶

In [53]:

ram=ecom["Email"].iloc[0]
ram.split('@')[1]

Out[53]:

'yahoo.com'

In [30]:

ecom["Email"].iloc[0]

Out[30]:

'pdunlap@yahoo.com'

In [31]:

ep=ecom["Email"].iloc[0]

In [32]:

ep.split('@')

Out[32]:

['pdunlap', 'yahoo.com']

In [33]:

ep.split('@')[1]

Out[33]:

'yahoo.com'

In [56]:

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

Out[56]:

hotmail.com     1638
yahoo.com       1616
gmail.com       1605
smith.com         42
williams.com      37
Name: Email, dtype: int64