Pandas: Impute NaN's

Question

I have an incomplete dataframe, incomplete_df, as below. I want to impute the missing amounts with the average amount of the corresponding id. If the average for that specific id is itself NaN (see id=4), I want to use the overall average.

Below are the example data and my highly inefficient solution:

import pandas as pd
import numpy as np
incomplete_df = pd.DataFrame({'id': [1,2,3,2,2,3,1,1,1,2,4],
                              'type': ['one', 'one', 'two', 'three', 'two', 'three', 'one', 'two', 'one', 'three','one'],
                         'amount': [345,928,np.NAN,645,113,942,np.NAN,539,np.NAN,814,np.NAN] 
                         }, columns=['id','type','amount'])

# Forrest Gump Solution
for idx in incomplete_df.index[np.isnan(incomplete_df.amount)]: # loop through all rows with amount = NaN
    cur_id = incomplete_df.loc[idx, 'id']
    if (cur_id in means.index ):
        incomplete_df.loc[idx, 'amount'] = means.loc[cur_id]['amount'] # average amount of that specific id.
    else:
        incomplete_df.loc[idx, 'amount'] = np.mean(means.amount) # average amount across all id's

What is the fastest and the most pythonic/pandonic way to achieve this?

Answer 1

Disclaimer: I'm not really interested in the fastest solution but the most pandorable.

Here, I think that would be something like:

>>> df["amount"].fillna(df.groupby("id")["amount"].transform("mean"), inplace=True)
>>> df["amount"].fillna(df["amount"].mean(), inplace=True)

which produces

>>> df
    id   type  amount
0    1    one   345.0
1    2    one   928.0
2    3    two   942.0
3    2  three   645.0
4    2    two   113.0
5    3  three   942.0
6    1    one   442.0
7    1    two   539.0
8    1    one   442.0
9    2  three   814.0
10   4    one   615.2

[11 rows x 3 columns]

There are lots of obvious tweaks depending upon exactly how you want the chained imputation process to go.