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