How to do proper imputation in Python / Sklearn

Question

I have the following data below. Notice the Age has Nan. My goal is to impute all columns properly.

+----+-------------+----------+--------+------+-------+-------+---------+
| ID | PassengerId | Survived | Pclass | Age  | SibSp | Parch |  Fare   |
+----+-------------+----------+--------+------+-------+-------+---------+
|  0 |           1 |        0 |      3 | 22.0 |     1 |     0 | 7.2500  |
|  1 |           2 |        1 |      1 | 38.0 |     1 |     0 | 71.2833 |
|  2 |           3 |        1 |      3 | 26.0 |     0 |     0 | 7.9250  |
|  3 |           4 |        1 |      1 | 35.0 |     1 |     0 | 53.1000 |
|  4 |           5 |        0 |      3 | 35.0 |     0 |     0 | 8.0500  |
|  5 |           6 |        0 |      3 | NaN  |     0 |     0 | 8.4583  |
+----+-------------+----------+--------+------+-------+-------+---------+

I have a working code that imputes all columns. The results are below. The results looks problematic.

+----+-------------+----------+--------+-----------+-------+-------+---------+
| ID | PassengerId | Survived | Pclass |    Age    | SibSp | Parch |  Fare   |
+----+-------------+----------+--------+-----------+-------+-------+---------+
|  0 | 1.0         | 0.0      | 3.0    | 22.000000 | 1.0   | 0.0   | 7.2500  |
|  1 | 2.0         | 1.0      | 1.0    | 38.000000 | 1.0   | 0.0   | 71.2833 |
|  2 | 3.0         | 1.0      | 3.0    | 26.000000 | 0.0   | 0.0   | 7.9250  |
|  3 | 4.0         | 1.0      | 1.0    | 35.000000 | 1.0   | 0.0   | 53.1000 |
|  4 | 5.0         | 0.0      | 3.0    | 35.000000 | 0.0   | 0.0   | 8.0500  |
|  5 | 6.0         | 0.0      | 3.0    | 2.909717  | 0.0   | 0.0   | 8.4583  |
+----+-------------+----------+--------+-----------+-------+-------+---------+

My code is below:

import pandas as pd
import numpy as np

#https://www.kaggle.com/shivamp629/traincsv/downloads/traincsv.zip/1
data = pd.read_csv("train.csv")

data2 = data[['PassengerId', 'Survived','Pclass','Age','SibSp','Parch','Fare']].copy()

from sklearn.preprocessing import Imputer

fill_NaN = Imputer(missing_values=np.nan, strategy='mean', axis=1)
data2_im = pd.DataFrame(fill_NaN.fit_transform(data2), columns = data2.columns)

data2_im

It's weird the age is 2.909717. Is there a proper way to do simple mean imputation. I am okay doing column by column but I am not clear with syntax/approach. Thanks for any help.

Answer 1

The problem is that you use the wrong axis. The correct code should be:

fill_NaN = Imputer(missing_values=np.nan, strategy='mean', axis=0)

Note the axis=0 .

Answer 2

The root of your problem is this line:

fill_NaN = Imputer(missing_values=np.nan, strategy='mean', axis=1)

, which means you're averaging over rows (oranges and apples).

Try changing it to:

fill_NaN = Imputer(missing_values=np.nan, strategy='mean', axis=0) # axis=0

and you will have the expected behaviour.

strategy='median' could be even better, as it's robust against outliers:

fill_NaN = Imputer(missing_values=np.nan, strategy='median', axis=0)

Answer 3

Try like

fill_NaN = Imputer(missing_values=np.nan, strategy='mean', axis=0)

or

data2.fillna(data2.mean())