Pandas_text

impor pandas as pd 

df[ 'Sales_ID'][0]
Out: 'AU_1382578        !'

df['Sales_ID'] = df['Sales_ID'].str.rstrip('!').str.strip()
###cleans whole column

‘AU_1382578’ in Sales_ID → ‘AU’ in Sales_Branch.

start: where to start with a default of 0
stop: where to stop (exclusive)
step: how far a step with a default of 1


df['Sales_Branch'] = df['Sales_ID'].str.slice(stop=2)
###take the first two letters and create a new column called Sales_ID

df['Product_Description'][0]
Out: 'The Fruit, a company that sells fruits from all over the world, has branches in Australia, the United Kingdom, the United States, and Taiwan. 
Product: Apple Mango Banana Watermelon Orange Blueberry Banana Watermelon Kiwifruit'
###contains description and product, need to split

split() contains 3 parameters:
pat: what to split on with a default of white space ‘ ’
n: the users can specify how many splits they want
expand: when expand=True, the splits are put into individual columns

df['Product_Description'].str.split(' Product: ', expand=True)

df['Description'] = df['Product_Description'].str.split(' Product: ', expand=True)[0]
df['Product'] = df['Product_Description'].str.split(' Product: ', expand=True)[1]
or
df[['Description', 'Product']] = df['Product_Description'].str.split(': ', expand=True)

df['Product'] = df['Product'].str.split().apply(sorted)

df['Product_Description'].str.split(' Product: ', expand=True)

###renme columns
Products = df['Product'].apply(pd.Series)
Products = Products.rename(columns=lambda x: 'Product_'+str(x))