I have dataframe where column 'Score' is calculated from values in other columns. I would need to have missing value in Score column if any of other columns has missing value for that row.
df = pd.DataFrame({'Score': [71, 63, 23],
'Factor_1': [nan, '15', '23'],
'Factor_2': ['12', nan, '45'],
'Factor_3': ['3', '5', '7']})
Expected values for column Score: nan, nan, 23 (because Factor 1 is missing in 1st row and Factor 2 is missing in 2nd row). So, I should replace existing values with NAs.
Thank you for your help.
Use DataFrame.filter for Factor column, test if missing values by DataFrame.isna for at least one value per row by DataFrame.any and set NaN by DataFrame.loc:
df.loc[df.filter(like='Factor').isna().any(axis=1), 'Score'] = np.nan
Or use Series.mask:
df['Score'] = df['Score'].mask(df.filter(like='Factor').isna().any(axis=1))
If need explicit columns names:
cols = ['Factor_1', 'Factor_2', 'Factor_3']
df.loc[df[cols].isna().any(axis=1), 'Score'] = np.nan
df['Score'] = df['Score'].mask(df[cols].isna().any(axis=1))
print (df)
Score Factor_1 Factor_2 Factor_3
0 NaN NaN 12 3
1 NaN 15 NaN 5
2 23.0 23 45 7
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