How does map() function work inside Lambda functions in Python?

Question

Can someone please explain what is actually going on in aggfunc here -

df.pivot_table(values='Loan_Status', index=['Credit_History'],
               aggfunc=lambda x: x.map({'Y':1,'N':0}).mean())

Thank you

It works the same as in any function... Although that is not the built-in map function, that is a method being accessed on the argument to the lambda function — juanpa.arrivillaga
– juanpa.arrivillaga, Commented Feb 1, 2018 at 1:05
I was confusing this with built-in map, this is actually pandas.Series.map. Thank you @Christian Dean — Yash
– Yash, Commented Feb 1, 2018 at 2:52

jpp · Accepted Answer · 2018-02-01 01:15:50Z

1

Below example should illustrate what's happening. The Loan_Status values are aggregated by Credit_History according to the logic "add up number of Y's and divide by total number of observations".

import pandas as pd

df = pd.DataFrame([['Y', 'A'], ['N', 'B'], ['Y', 'C'], ['N', 'A'], ['Y', 'C']],
                  columns=['Loan_Status', 'Credit_History'])

df.pivot_table(values='Loan_Status', index=['Credit_History'],
               aggfunc=lambda x: x.map({'Y':1,'N':0}).mean())

#                 Loan_Status
# Credit_History             
# A                       0.5
# B                       0.0
# C                       1.0

answered Feb 1, 2018 at 1:15

jpp

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