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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'
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4 Answers 4

Reset to default
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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!

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4
  • $\begingroup$ Thank You So much. $\endgroup$
    – Rupali Singh
    May 13, 2019 at 16:04
  • $\begingroup$ The latter half of this is correct, but you don't need dataframes as in the first half. (Of course, that will work as well.) $\endgroup$
    – Ben Reiniger ♦
    Dec 20, 2019 at 21:49
  • $\begingroup$ Sure, was only answering in this scope of his usage of the method which was applied to a dataframe $\endgroup$
    – Blenz
    Dec 21, 2019 at 0:05
  • 1
    $\begingroup$ Why do we need .ravel() at the end of the transform call? $\endgroup$
    – Psychotechnopath
    Jan 13, 2020 at 19:32
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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)
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#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)
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0
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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.

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