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This article mainly explains "how to use qcut in Python analysis package". Interested friends may wish to have a look at it. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn how to use qcut in the Python analysis package.

Its function is to determine the interval of boxes according to the frequency of the value, so as to satisfy the equal number of samples in each box as much as possible.

Let's look at the example first:

Ages = np.array ([5, 10, 36, 36, 12, 77, 89, 100, 30, 1]) # Age data pd.qcut (ages, 3, labels= ['green', 'middle', 'old']). Value_counts ()

# results: green No. 3 Middle School No. 3 Old 3dtype: int64

As you can see, the number of samples in each interval is 3. 5%. However, the lengths of the three intervals obtained by qcut are not necessarily equal. This is the biggest difference from cut, which is divided into equal intervals by cut.

# these are the three intervals obtained after qcut:

Categories (3, interval [float64]): [(0.999, 11.333] < (11.333, 49.667] < (49.667, 100.0)]

Obviously, the length of the interval is different.

At this point, I believe you have a deeper understanding of "how to use qcut in the Python analysis package". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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