Fill missing values (na) with an list/series after modelling missing values

Question

I am trying to plug the predicted missing values into original df (of course to the column with missing value). How could I do so?

The predicted missing values are basically stored in a list/series whose length is the number of missing values in the original df. The order in the list matches with the order that missing values appear in the df, I think, since I split the test_set from the df using nonull() at the missing series.

I have been trying pd.Series.fillna, but that just allows one value to replace.

Welcome to SO. Please provide a **[mcve]**. – jpp Apr 05 '18 at 14:02 — jpp, Apr 05 '18 at 14:02
Thank you. Will definitely do so next times – Hieu Tran Apr 07 '18 at 02:08 — Hieu Tran, Apr 07 '18 at 02:08

P.Tillmann · Answer 1 · 2018-04-06T08:45:06.720

0

You can use numpy where and pandas isnull function to do that.

df['relevant_column'] = np.where(df['relevant_column'].isnull(),
                                 predicted_values,
                                 df['relevant_column'])

predicted_values should be a pandas series or 1d numpy array with the same lenght as the dataframe.

edited Apr 06 '18 at 08:45

answered Apr 05 '18 at 15:29

P.Tillmann

2,090
10
17

Fill missing values (na) with an list/series after modelling missing values

1 Answers1