How to realize Python Variance feature filtering

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Description

The main contents are as follows: 1. The features are screened by the variance of the features. The smaller the variance of the feature, the less obvious the change of the feature.

2. The features with less obvious changes do not have much effect on us to distinguish labels, so these features should be eliminated.

Example

Def variance_demo (): "" filter low variance feature: return: "" # 1. Get the data data = pd.read_csv ('factor_returns.csv') data = data.iloc [:, 1Rose Murray 2] print (' data:\ npictures, data) # 2. Instantiate a converter class transfer = VarianceThreshold (threshold=10) # 3. Call fit_transform () data_new = transfer.fit_transform (data) print ('data_new:\ nguys, data_new, data_new.shape) return None. Thank you for your reading! This is the end of this article on "how to achieve Python Variance feature filtering". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, you can share it out for more people to see!