How to check if values in column are NOT null and then output a specific value to another column

Question

I am currently writing a function in pandas to try to check rows in a column to see if they are not null. If they are not null, I want something to be outputed to a new column and for this case it would be 'Financing'. Basically if a row has a value for loan funded date, I want the phrase Financing to be printed a new column called Payment Type.

def typepayment(x):
    if x['Loan Funded Date'] != np.nan:
        x['Payment Type'] = 'Financing'
    return x
df2 = df1.apply(typepayment, axis = 1)
df2

The output for the code above still outputs Financing in Payment Type for rows that are null in Loan Funded Date. What is causing this problem?

Ynjxsjmh · Accepted Answer · 2022-07-18 20:04:33Z

1

x['Loan Funded Date'] != np.nan will be True if x['Loan Funded Date'] is null (NaN is not equal with itself). In this case, x['Payment Type'] will be set to 'Financing'. As a alternative, you can use

m = df1['Loan Funded Date'].notna()
df1.loc[m, 'Payment Type'] = 'Financing'
# or
df1['Payment Type'] = df1['Payment Type'].mask(m, 'Financing')

edited Jul 18, 2022 at 20:04

answered Jul 18, 2022 at 19:58

Ynjxsjmh

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