How to use SimpleImputer Class to replace missing values with mean values using Python?

Question

This is my code

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#Importing Dataset
dataset = pd.read_csv('C:/Users/Rupali Singh/Desktop/ML A-Z/Machine Learning A-Z Template Folder/Part 1 - Data Preprocessing/Data.csv')
print(dataset)
X = dataset.iloc[:, :-1].values
Y = dataset.iloc[:, 3].values
#Missing Data

from sklearn.impute import SimpleImputer

imputer = SimpleImputer(missing_values= np.nan, strategy='mean')
X.fit[:, 1:3] = imputer.fit_transform(X[:, 1:3])
print(X)

My data set:

Country   Age   Salary Purchased
0   France  44.0  72000.0        No
1    Spain  27.0  48000.0       Yes
2  Germany  30.0  54000.0        No
3    Spain  38.0  61000.0        No
4  Germany  40.0      NaN       Yes
5   France  35.0  58000.0       Yes
6    Spain   NaN  52000.0        No
7   France  48.0  79000.0       Yes
8  Germany  50.0  83000.0        No
9   France  37.0  67000.0       Yes

Error Message:

File "C:/Users/Rupali Singh/PycharmProjects/Machine_Learning/data_preprocessing_Template.py", line 15, in <module>
    X.fit[:, 1:3] = imputer.fit_transform(X[:, 1:3])
AttributeError: 'numpy.ndarray' object has no attribute 'fit'

Answer 1

Your error is due to using Simple Imputer's fit and fit_transform on a numpy array. Here's how i used it on a Dataframe

imr = Imputer(missing_values='NaN', strategy='median', axis=0)
imr = imr.fit(data[['age']])
data['age'] = imr.transform(data[['age']]).ravel()

X.fit = impute.fit_transform().. this is wrong. you can't assign a value to a X.fit() just simply because .fit() is an imputer function, you can't use the method fit() on a numpy array, hence your error!

Use x[:, 1:3] = imputer.fit_transform(x[:, 1:3]) instead

Hope this helps!

Answer 2

SimpleImputer also works fine.

from sklearn.impute import SimpleImputer 
imputer=SimpleImputer(missing_values=np.nan,strategy='mean')
imputer=imputer.fit(X[:,1:3])
X[:,1:3]=imputer.transform(X[:,1:3])

which gives result

array([['France', 44.0, 72000.0],
       ['Spain', 27.0, 48000.0],
       ['Germany', 30.0, 54000.0],
       ['Spain', 38.0, 61000.0],
       ['Germany', 40.0, 63777.77777777778],
       ['France', 35.0, 58000.0],
       ['Spain', 38.77777777777778, 52000.0],
       ['France', 48.0, 79000.0],
       ['Germany', 50.0, 83000.0],
       ['France', 37.0, 67000.0]], dtype=object)

Answer 3

#Taking care of missing data

from sklearn.impute import SimpleImputer 
imputer=SimpleImputer(missing_values=np.nan,strategy='mean')
imputer=imputer.fit(X[:,1:3])
X[:,1:3]=imputer.transform(X[:,1:3])

Result

array([['France', 44.0, 72000.0],
       ['Spain', 27.0, 48000.0],
       ['Germany', 30.0, 54000.0],
       ['Spain', 38.0, 61000.0],
       ['Germany', 40.0, 63777.77777777778],
       ['France', 35.0, 58000.0],
       ['Spain', 38.77777777777778, 52000.0],
       ['France', 48.0, 79000.0],
       ['Germany', 50.0, 83000.0],
       ['France', 37.0, 67000.0]], dtype=object)

Answer 4

You can also simplify your code to

from sklearn.impute import SimpleImputer 
imputer=SimpleImputer(missing_values=np.nan,strategy='mean')
imputer=imputer.fit(X[:,1:])
X[:,1:]=imputer.transform(X[:,1:])

so that you start from the second column with index 1 and end with the last one.