How to impute missing values with means in Python?

## 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()