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The main content of this article is to explain "Python how to achieve data box technology", interested friends may wish to have a look. The method introduced in this paper is simple, fast and practical. Next let the editor to take you to learn "Python how to achieve data box technology" bar!

1 data sub-box

The official definition of data box technology in Pandas: Bin values into discrete intervals, refers to the division of values into discrete intervals. For example, apples of different sizes are grouped into several pre-arranged boxes; people of different ages are divided into several age groups.

This technique can be very useful in data processing.

2 exampl

Let's look at the example first.

Import numpy as npimport pandas as pd

Ages = np.array ([5, 10, 36, 36, 12, 77, 89, 100, 30, 1) # Age data

The data are now divided into three sections and labeled as old, middle and young. Pandas provides an easy-to-use API that can be easily implemented.

Pd.cut (ages, 3, labels= ['green', 'middle', 'old'])

The result is as follows, and one line of code is implemented.

[green, medium, green, old, green, green]

During the operation of cut, the minimum and maximum values of the one-dimensional array are counted, and an interval length is obtained. Because three intervals need to be divided, three uniform intervals are obtained, as shown below.

Pd.cut (ages, 3) > interval is as follows: Categories (3, interval [float64]): [(0.901) 34.0] < (34.067.0) < (67.0100.0]]

The minimum value of the given data is 1, and the interval is left open and right closed by default, so in order to include 1, you need to extend the leftmost interval to the left by 0.1% (the total interval length). The default precision is 3 decimal places.

3 function prototype

After a preliminary understanding of cut through the above examples, it is easier to analyze the cut prototype.

The meaning of the parameter is as follows:

X: the class array data to be segmented. Note that it must be 1D.

Bins: simply understood as the sub-box rule, that is, the bucket. Support for int scalars and sequences

Right: indicates whether the right boundary of the interval is included. By default, it contains

Labels: tagged bins after segmentation

Retbins: indicates whether the split bins will be returned. It is not returned by default. If True, then:

Array ([0.901, 34. , 67. , 100. ]))

Include_lowest: whether the left side of the interval is open or closed. Default is on.

Duplicates: whether repeating intervals are allowed. Raise: not allowed, drop: allowed.

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