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This article introduces the relevant knowledge of "what is the role of Pandas method in Python". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Pandas.cut (x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False)

The explanation found on the Internet is too illusory, simple to understand, the main function is to classify a number (x) according to a given evaluation group (bins), determine which group the number belongs to, and return it. If you define the name of the judgment group (labels), it will be returned by name.

For example, there is a group of examinee scores, not directly given the deal, but given according to the description; then the evaluation group bins is 0-60 is a group, 60-80 is a group, 80-100 is a group, describing a labels of 0-60 points is a failure, 60-80 is a pass, 80-100 is excellent.

Then it is as follows

Import numpy as np

Import pandas as pd

Grade = [80pr 75pr 32.5100]

Bins = [0th 60th 80100]

Group_names = ['fail', 'pass', 'excellent']

Cats = pd.cut (grade, bins,labels = group_names)

Output:

[pass, fail, excellent]

Categories (3, object): [failing < passing < excellent]

The first is what we want, and the next two are additional definitions of the judging group. If we want to return to the original criteria, retbins=True is fine. The last thing to note is that the input data x must be a queue or numpy.array type

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