Access a list of columns to be evaluated in a Comprehension Expression-CodePudding

There is this dataframe called frame with columns: Age, Maturity, Gender, Height which has values 'PASSED' or 'FAILED'.

I want to create a new column called result and count the number of a subset of the overall columns as seen in check_columns which has the value PASSED.

I tried to use a comprehension expression which as seen in this case I wanted it to be evaluated to 2 since the columns Age and Maturity have PASSED while Gender has FAILED which are present in a subset of columns in check_columns.

frame = pd.DataFrame(
    data = {'Age':['PASSED'], 'Maturity':['PASSED'],'Gender':['FAILED'], 'Height':['PASSED']}
)
check_columns = ['Age','Maturity','Gender']
frame['result'] = sum([1  if frame[column] =='PASSED' else 0 for column in check_columns ])

I tried to used a comprehension with a list but it says this error:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

CodePudding user response：

Select only the desired columns, check which ones are equal to 'PASSED' using DataFrame.eq, and count the True values using DataFrame.sum (pass axis=1 to count column-wise)

import pandas as pd

frame = pd.DataFrame(
    data = {'Age':['PASSED'], 'Maturity':['PASSED'],'Gender':['FAILED'], 'Height':['PASSED']}
)
check_columns = ['Age','Maturity','Gender']

frame['results'] = frame[check_columns].eq('PASSED').sum(axis=1)

Output:

>>> frame 

      Age Maturity  Gender  Height  results
0  PASSED   PASSED  FAILED  PASSED        2