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replace all missing value with mean pandas

column_means = df.mean()
df = df.fillna(column_means)

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Replace with missing values for numerical columns with mean

IN:
#Replace the missing values for numerical columns with mean
train_df['LoanAmount'] = train_df['LoanAmount'].fillna(train_df['LoanAmount'].mean())
train_df['Credit_History'] = train_df[‘Credit_History'].fillna(train_df['Credit_History'].mean())

OUT:
Loan_ID  0
Gender  13
Married  3
Dependents  15
Education  0
Self_Employed 32
ApplicantIncome  0
CoapplicantIncome  0
LoanAmount 0
Loan_Amount_Term 0
Credit_History 0
Property_Area  0
Loan_Status  0
dtype: int64

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replace all missing value with mean pandas

Replace with missing values for numerical columns with mean

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