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

# Create an empty dataset
df = pd.DataFrame()

# Create two variables called x0 and x1. Make the first value of x1 a missing value
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]

# View the dataset
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

# Create an imputer object that looks for 'Nan' values, then replaces them with the mean value of the feature by columns (axis=0)
mean_imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)

# Train the imputor on the df dataset
mean_imputer = mean_imputer.fit(df)

Apply Imputer

# Apply the imputer to the df dataset
imputed_df = mean_imputer.transform(df.values)

View Data

# View the 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.