I Want To Use Fillna Mean To Fill The Missing Value. I Want To Do That According To Product Id

product_ID Prodcut_Price Product_monthly_sale 1 24 2000.00 1 Nan 2500.00 1 26 Nan 1

Solution 1:

Create data

df = pd.DataFrame({'product_ID':[1,1,3,3,3], 
                   'Prodcut_Price':[1,np.nan,5,np.nan, 9],
                   'Product_monthly_sale':[1,np.nan,5,np.nan, 5]})
df

Result:

    product_ID  Prodcut_Price   Product_monthly_sale
0   1           1.0             1.0
1   1           NaN             NaN
2   3           5.0             5.0
3   3           NaN             NaN
4   3           9.0             5.0

Fill nan with grouped means

df = df[['product_ID']].join(df.groupby("product_ID")
        .transform(lambda x: x.fillna(x.mean())))
df

Result:

    product_ID  Prodcut_Price   Product_monthly_sale
0   1           1.0             1.0
1   1           1.0             1.0
2   3           5.0             5.0
3   3           7.0             5.0
4   3           9.0             5.0

Solution 2:

For improve performance avoid lambda function, rather use GroupBy.transform for means per groups with DataFrame.fillna:

df = df.fillna(df.groupby("product_ID").transform('mean'))
print (df)
   product_ID  Prodcut_Price  Product_monthly_sale
0           1           24.0                2000.0
1           1           26.0                2500.0
2           1           26.0                2400.0
3           1           28.0                2700.0
4           2           25.0                2400.0
5           2           26.0                2500.0
6           2           27.0                2600.0