How to impute missing values with means in Python?

This recipe helps you impute missing values with means in Python
In [2]:
## How to impute missing values with means in Python 
def Kickstarter_Example_35():
    print()
    print(format('How to impute missing values with means in Python', '*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    import pandas as pd
    import numpy as np
    from sklearn.preprocessing import Imputer

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

    # Create two variables called x0 and x1. Make the first value of x1 a missing value
    df['V0'] = [0.3051,0.4949,0.6974,0.3769,0.2231,
                0.341,0.4436,0.5897,0.6308,0.5]
    df['V1'] = [np.nan,np.nan,0.2615,0.5846,0.4615,
                0.8308,0.4962,np.nan,0.5346,0.6731]

    # View the dataset
    print(); print(df)

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

    # View the data
    print(); print(imputed_df)

Kickstarter_Example_35()
****************How to impute missing values with means in Python*****************

       V0      V1
0  0.3051     NaN
1  0.4949     NaN
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     NaN
8  0.6308  0.5346
9  0.5000  0.6731

[[0.3051 0.5489]
 [0.4949 0.5489]
 [0.6974 0.2615]
 [0.3769 0.5846]
 [0.2231 0.4615]
 [0.341  0.8308]
 [0.4436 0.4962]
 [0.5897 0.5489]
 [0.6308 0.5346]
 [0.5    0.6731]]