How can I select the rows with null values in respect of columns name?
What I have:
| ID | A | B |
|---|---|---|
| 1 | a | b |
| 2 | v | |
| 3 | y | |
| 4 | w | j |
| 5 | w |
What I want:
Select rows with null in respect with e.g. column B:
| ID | B |
|---|---|
| 3 | y |
| 5 | w |
Use pandas.DataFrame.pop and pandas.Series.isna:
>>> df
ID A B
0 1 a b
1 2 NaN v
2 3 y NaN
3 4 w j
4 5 w NaN
>>> df[df.pop('B').isna()]
ID A
2 3 y
4 5 w
Use pop if you do not need column 'B' in the original dataframe afterwards. Otherwise, use pandas.DataFrame.drop:
>>> df[df['B'].isna()].drop('B', axis=1)
B!df[df['B'].isna()]