how to fill missing values in dataset-scikit learn imputation

In this video we will learn how to fill missing values in dataset
#missing values in machine learning
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#Missing Data in categorical features
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#Pandas tutorial
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link to mean median imputation : https://youtu.be/7ayFsqb-96c
link to Mode imputation for categorical features: https://youtu.be/jJ9gdOezo3U

Missing Data is something no Data scientist want to come across but there are many reasons for Data to be missing. E.g lets say the customer does not want to give data or we can say that who ever is collecting the data might have missed it or we can say in verbal communication a lot of data is missed.

Steps:
import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer

numeric_cols = columns_to_use = ['MSSubClass','LotFrontage', 'LotArea',
'OverallQual', 'OverallCond','YearBuilt','YearRemodAdd', 'MasVnrArea','BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF']

imputer.fit(data)
imputer.statistics_
data = imputer.transform(data)

data_1 = pd.DataFrame(data, columns=numeric_cols)

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Thanks.