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Machine Learning for Beginners in Python: How to Impute Missing Values With Means

By on Friday, May 21, 2021

Impute Missing Values With Means

Mean imputation replaces missing values with the mean value of that feature/variable. Mean imputation is one of the most ‘naive’ imputation methods because unlike more complex methods like k-nearest neighbors imputation, it does not use the information we have about an observation to estimate a value for it.

Preliminaries


import pandas as pd
import numpy as np
from sklearn.preprocessing import Imputer

Create Data


df = pd.DataFrame()


df['x0'] = [0.3051,0.4949,0.6974,0.3769,0.2231,0.341,0.4436,0.5897,0.6308,0.5]
df['x1'] = [np.nan,0.2654,0.2615,0.5846,0.4615,0.8308,0.4962,0.3269,0.5346,0.6731]


df
x0 x1
0 0.3051 NaN
1 0.4949 0.2654
2 0.6974 0.2615
3 0.3769 0.5846
4 0.2231 0.4615
5 0.3410 0.8308
6 0.4436 0.4962
7 0.5897 0.3269
8 0.6308 0.5346
9 0.5000 0.6731

Fit Imputer


mean_imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)

mean_imputer = mean_imputer.fit(df)

Apply Imputer


imputed_df = mean_imputer.transform(df.values)

View Data


imputed_df
array([[ 0.3051    ,  0.49273333],
       [ 0.4949    ,  0.2654    ],
       [ 0.6974    ,  0.2615    ],
       [ 0.3769    ,  0.5846    ],
       [ 0.2231    ,  0.4615    ],
       [ 0.341     ,  0.8308    ],
       [ 0.4436    ,  0.4962    ],
       [ 0.5897    ,  0.3269    ],
       [ 0.6308    ,  0.5346    ],
       [ 0.5       ,  0.6731    ]])

Notice that 0.49273333 is the imputed value, replacing the np.NaN value.

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

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Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
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