Issue
I have a data set as below
ID A1 A2
0 A123 1234
1 1234 5568
2 5568 NaN
3 Zabc NaN
4 3456 3456
5 3456 3456
6 NaN NaN
7 NaN NaN
Intention is to go through each column (A1 and A2), identify where both the columns are blank as in row 6 and 7, create a new column and categorise as "Both A1 and A2 are blank"
I used the below code,
df['Z_Tax No Not Mapped'] = np.NaN
df['Z_Tax No Not Mapped'] = np.where((df['A1'] == np.NaN) & (df['A2'] == np.NaN), 1, 0)
However the output captures all the rows as 0 under new column 'Z_Tax No Not Mapped', but the data have instances where both the columns are blank. Not sure where i'm making a mistake to filter such cases.
Note: Columns A1 and A2 are sometimes alphanumeric or just numeric
Idea is to place a category in a separate column as "IDs are not updated" or "IDs are updated", so that by placing a simple filter on "IDs are not updated" we can identify cases that are blank in both columns
Help would be much appreciated.
Solution
Use DataFrame.isna
with DataFrame.all
for test if all columns are True
s - missing values:
df['Z_Tax No Not Mapped'] = np.where(df[['A1','A2']].isna().all(axis=1),
'Both A1 and A2 are blank',
'')
Answered By - jezrael
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