python - pandas GroupBy columns with NaN (missing) values

Question

I have a DataFrame with many missing values in columns which I wish to groupby:

import pandas as pd
import numpy as np
df = pd.DataFrame({'a': ['1', '2', '3'], 'b': ['4', np.NaN, '6']})

In [4]: df.groupby('b').groups
Out[4]: {'4': [0], '6': [2]}

see that Pandas has dropped the rows with NaN target values. (I want to include these rows!)

Since I need many such operations (many cols have missing values), and use more complicated functions than just medians (typically random forests), I want to avoid writing too complicated pieces of code.

Any suggestions? Should I write a function for this or is there a simple solution?

Answer 1

pandas >= 1.1

From pandas 1.1 you have better control over this behavior, NA values are now allowed in the grouper using dropna=False:

pd.__version__
# '1.1.0.dev0+2004.g8d10bfb6f'

# Example from the docs
df

   a    b  c
0  1  2.0  3
1  1  NaN  4
2  2  1.0  3
3  1  2.0  2

# without NA (the default)
df.groupby('b').sum()

     a  c
b        
1.0  2  3
2.0  2  5

# with NA
df.groupby('b', dropna=False).sum()

     a  c
b        
1.0  2  3
2.0  2  5
NaN  1  4