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Python – Removing Constant Features From the Dataset
  • Last Updated : 02 Sep, 2020

Those features which contain constant values (i.e. only one value for all the outputs or target values) in the dataset are known as Constant Features. These features don’t provide any information to the target feature. These are redundant data available in the dataset. Presence of this feature has no effect on the target, so it is good to remove these features from the dataset. This process of removing redundant features and keeping only the necessary features in the dataset comes under the filter method of Feature Selection Methods.

Now Let’s see how we can remove constant features in Python.

Consider the self created dataset for the article:

PortalArticle’s_categoryViews
GeeksforGeeksPython545
GeeksforGeeksData Science1505
GeeksforGeeksData Science1157
GeeksforGeeksData Science2541
GeeksforGeeksMathematics5726
GeeksforGeeksPython3125
GeeksforGeeksData Science3131
GeeksforGeeksMathematics6525
GeeksforGeeksMathematics15000

Code: Create DataFrame of the above data




# Import pandas to create DataFrame
import pandas as pd
  
# Make DataFrame of the given data
data = pd.DataFrame({"Portal":['GeeksforGeeks', 'GeeksforGeeks', 'GeeksforGeeks', 'GeeksforGeeks', 'GeeksforGeeks'
                               'GeeksforGeeks', 'GeeksforGeeks', 'GeeksforGeeks', 'GeeksforGeeks'],
                    "Article's_category":['Python', 'Data Science', 'Data Science', 'Data Science', 'Mathematics', 
                                          'Python', 'Data Science', 'Mathematics', 'Mathematics'],
                    "Views":[545, 1505, 1157, 2541, 5726, 3125, 3131, 6525, 15000]})

Code: Convert the categorical data to numerical data






# import ordinal encoder from sklearn
from sklearn.preprocessing import OrdinalEncoder
ord_enc = OrdinalEncoder()
  
# Transform the data
data[["Portal","Article's_category"]] = ord_enc.fit_transform(data[["Portal","Article's_category"]])

Code: Fit the data to VarianceThreshold.




# import VarianceThreshold
from sklearn.feature_selection import VarianceThreshold
var_threshold = VarianceThreshold(threshold=0)   # threshold = 0 for constant
  
# fit the data
var_threshold.fit(data)
  
# We can check the variance of different features as
print(var_threshold.variances_)

Output: Variance of different features:

[0.00000000e+00 6.17283951e-01 1.76746269e+07]

Code: Transform the data




print(var_threshold.transform(data))
print('*' * 10,"Separator",'*' * 10)
  
# shapes of data before transformed and after transformed
print("Earlier shape of data: ", data.shape)
print("Shape after transformation: ", var_threshold.transform(data).shape)

Output:

[[2.000e+00 5.450e+02]
 [0.000e+00 1.505e+03]
 [0.000e+00 1.157e+03]
 [0.000e+00 2.541e+03]
 [1.000e+00 5.726e+03]
 [2.000e+00 3.125e+03]
 [0.000e+00 3.131e+03]
 [1.000e+00 6.525e+03]
 [1.000e+00 1.500e+04]]
********** Separator **********
Earlier shape of data:  (9, 3)
Shape after transformation:  (9, 2)

As you can observe earlier we had 9 observations with 3 features.
After transformation we have 9 observations with 2 features. We can clearly observe that the removed feature is ‘Portal’.

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