Apply A Function To Pandas Dataframe With A Condition To Check For `nans`

I have Null and NaNs in one of the pandas DataFrame columns. I'd like to apply with a condition to check for NaN in the column and store the return from the function into a new col

Solution 1:

You can first filter for the rows you want, apply the function and then assign to the new column. Pandas will fill the missing rows with NaN. This is usually more efficient than running apply for each row.

import pandas as pd
from numpy import NaN

df = pd.DataFrame({'Col1': [1, 9, NaN],
                   'Col2': [1, 3, 5]}) 

defsample_func(v1, v2, token):
    # call API 
    r = cl_apicall(v1, v2, token)
    return r

# mock api calldefcl_apicall(v1, v2, token):
    returnf"{v1},-{v2}-{token}"# Apply function#df['new_col'] = df.apply(lambda x: sample_func(x['Col1'], x['Col2'], 'xxxxxx'), axis = 1)
df['new_col'] = df[df['Col1'].isnull()].apply(lambda x: sample_func(x['Col1'], x['Col2'], 'xxxxxx'), axis = 1)
print(df)

Result

   Col1  Col2          new_col
01.01NaN19.03NaN2NaN5  nan,-5.0-xxxxxx