Recipe: How to impute missing values with means in Python?
DATA MUNGING DATA CLEANING PYTHON

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]]


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