How to replace missing values with group mode in Pandas?

Question

I follow the method in this post to replace missing values with the group mode, but encounter the "IndexError: index out of bounds".

 df['SIC'] = df.groupby('CIK').SIC.apply(lambda x: x.fillna(x.mode()[0]))

I guess this is probably because some groups have all missing values and do not have a mode. Is there a way to get around this? Thank you!

Answer 1

mode is quite difficult, given that there really isn't any agreed upon way to deal with ties. Plus it's typically very slow. Here's one way that will be "fast". We'll define a function that calculates the mode for each group, then we can fill the missing values afterwards with a map. We don't run into issues with missing groups, though for ties we arbitrarily choose the modal value that comes first when sorted:

def fast_mode(df, key_cols, value_col):
    """ 
    Calculate a column mode, by group, ignoring null values. 

    Parameters
    ----------
    df : pandas.DataFrame
        DataFrame over which to calcualate the mode. 
    key_cols : list of str
        Columns to groupby for calculation of mode.
    value_col : str
        Column for which to calculate the mode. 

    Return
    ------ 
    pandas.DataFrame
        One row for the mode of value_col per key_cols group. If ties, 
        returns the one which is sorted first. 
    """
    return (df.groupby(key_cols + [value_col]).size() 
              .to_frame('counts').reset_index() 
              .sort_values('counts', ascending=False) 
              .drop_duplicates(subset=key_cols)).drop(columns='counts')

Sample data df:

   CIK  SIK
0    C  2.0
1    C  1.0
2    B  NaN
3    B  3.0
4    A  NaN
5    A  3.0
6    C  NaN
7    B  NaN
8    C  1.0
9    A  2.0
10   D  NaN
11   D  NaN
12   D  NaN

Code:

df.loc[df.SIK.isnull(), 'SIK'] = df.CIK.map(fast_mode(df, ['CIK'], 'SIK').set_index('CIK').SIK)

Output df:

   CIK  SIK
0    C  2.0
1    C  1.0
2    B  3.0
3    B  3.0
4    A  2.0
5    A  3.0
6    C  1.0
7    B  3.0
8    C  1.0
9    A  2.0
10   D  NaN
11   D  NaN
12   D  NaN