List column names that are NULL/Empty for a Dataframe in each row

Question

I have a dataframe with null/empty values in it.
I can easily get the count for each row of the null values by doing this:

df['NULL_COUNT'] = len(df[fields] - df.count(axis=1)

Which will put the number of columns that are NULL in the field NULL_COUNT.

Is there a way to write the column headers the same way to another field if it is null?

df['NULL_FIELD_NAMES'] = "<some query expression>"

Example:

df = pd.DataFrame([range(3), [0, np.NaN, 0], [0, 0, np.NaN], range(3), range(3)], columns=['A', 'B', 'C'])

In the df above, the 2nd row should have df['NULL_FIELD_NAME'] = 'B' and 3rd row should have df['NULL_FIELD_NAME'] = 'C'

Answer 1

You can use:

df['new'] = (df.isnull() * df.columns.to_series()).apply(','.join,axis=1).str.strip(',')

Another solution:

df['new'] = df.apply(lambda x: ','.join(x[x.isnull()].index),axis=1)

Sample:

df = pd.DataFrame([range(3), [np.NaN, np.NaN, 0], [0, 0, np.NaN], range(3), range(3)], 
                  columns=['A', 'B', 'C'])
print (df)
     A    B    C
0  0.0  1.0  2.0
1  NaN  NaN  0.0
2  0.0  0.0  NaN
3  0.0  1.0  2.0
4  0.0  1.0  2.0

df['new'] = df.apply(lambda x: ','.join(x[x.isnull()].index),axis=1)
print (df)
     A    B    C  new
0  0.0  1.0  2.0     
1  NaN  NaN  0.0  A,B
2  0.0  0.0  NaN    C
3  0.0  1.0  2.0     
4  0.0  1.0  2.0