I am using pivot tables, trying to write code to display the number of consumer accounts for each customer. I have the following so far:
import pandas as pd
df1=pd.DataFrame({'custID':[1,1,2,2,2,3,3,4,4],
'accountID':[1,2,1,2,3,1,2,1,2],
'tenure_mo':[2,3,4,4,5,6,6,6,7],
'account_type':['BusiNESS','CONSUMER',
'consumer',
'BUSINESS',
'BuSIness',
'CONSUmer',
'consumer',
'CONSUMER',
'BUSINESS']},columns=['custID','accountID','tenure_mo','account_type'])
print(df1)
df2=pd.DataFrame({'custID':[1,2,3,4],
'cust_age':[20,35,50,85]},columns=['custID','cust_age'])
This is my desired output :
custID num_cons_accounts
1 1
2 1
3 2
4 1
How can I modify/expand my code to produce this output?
According to your description the following code should work:
df1=pd.DataFrame({'custID':[1,1,2,2,2,3,3,4,4],
'accountID':[1,2,1,2,3,1,2,1,2],
'tenure_mo':[2,3,4,4,5,6,6,6,7],
'account_type':['BusiNESS','CONSUMER',
'consumer',
'BUSINESS',
'BuSIness',
'CONSUmer',
'consumer',
'CONSUMER',
'BUSINESS']},columns=['custID','accountID','tenure_mo','account_type'])
df1 = df1[df1['account_type'].str.lower() == "consumer"]
print(df1.groupby("custID").count())
Select where lowercase version of account type is equal to "consumer"
and then get counts for each custID
.
The output:
accountID tenure_mo account_type
custID
1 1 1 1
2 1 1 1
3 2 2 2
4 1 1 1
A side note: if you want only the one column, drop the rest:)
use set to find the distinct count of accounts by account_type2 using an apply and lambda function
df1=pd.DataFrame({'custID':[1,1,2,2,2,3,3,4,4],
'accountID':[1,2,1,2,3,1,2,1,2],
'tenure_mo':[2,3,4,4,5,6,6,6,7],
'account_type':['BusiNESS','CONSUMER','consumer','BUSINESS','BuSIness','CONSUmer',
'consumer', 'CONSUMER','BUSINESS']},columns=['custID','accountID','tenure_mo','account_type'])
df1['account_type2']=df1['account_type'].apply(lambda row: row.lower())
grouped=df1.groupby('custID').apply(lambda row: len(set(row.account_type2)))
print(grouped)
output:
custID distinct count
1 2
2 2
3 1
4 2
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