RangeIndex: 344 entries, 0 to 343
Data columns (total 7 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   species            344 non-null    object 
 1   island             344 non-null    object 
 2   bill_length_mm     342 non-null    float64
 3   bill_depth_mm      342 non-null    float64
 4   flipper_length_mm  342 non-null    float64
 5   body_mass_g        342 non-null    float64
 6   sex                333 non-null    object 
dtypes: float64(4), object(3)
memory usage: 18.9+ KB
None
bill_length_mm           29.807054
bill_depth_mm             3.899808
flipper_length_mm       197.731792
body_mass_g          643131.077327
dtype: float64

In sklearn, we can use the class VarianceThreshold to select features that are more than the threshold value. We can use the following Python code for that purpose:

from sklearn.feature_selection import VarianceThreshold
import seaborn

df = seaborn.load_dataset("penguins")
print(df.info())

features = df.drop(["species", "island", "sex"], axis=1)
print(features.var())

feature_selection = VarianceThreshold(threshold=4)
feature_selection.fit(features)

selected_features = features.columns[feature_selection.get_support()]
print(type(selected_features))
df2 = df.filter(selected_features, axis=1)
print(df2.head())

The following Python statements select the features that have a threshold of more than 4…