How to impute missing values with means in Python?
0

# How to impute missing values with means in Python?

This recipe helps you impute missing values with means in Python
In :
```## 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")

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]]
```