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1.熊猫cut（）函数 (1. Pandas cut() Function)

Pandas cut() function is used to segregate array elements into separate bins. The cut() function works only on one-dimensional array-like objects.

Pandas cut（）函数用于将数组元素分离到单独的bin中。 cut（）函数仅适用于一维类似数组的对象。

2.熊猫cut（）函数的用法 (2. Usage of Pandas cut() Function)

The cut() function is useful when we have a large number of scalar data and we want to perform some statistical analysis on it.

当我们有大量标量数据并且想要对其进行一些统计分析时，cut（）函数很有用。

For example, let’s say we have an array of numbers between 1 and 20. We want to divide them into two bins of (1, 10] and (10, 20] and add labels such as “Lows” and “Highs”. We can easily perform this using the pandas cut() function.

例如，假设我们有一个1到20之间的数字数组。我们想将它们分为（1，10]和（10，20]的两个bin，并添加标签，例如“ Lows”和“ Highs”。可以使用pandas cut（）函数轻松地执行此操作。

Furthermore, we can perform functions on the elements of a specific bin and label elements.

此外，我们可以对特定bin元素和label元素执行功能。

3. Pandas cut（）函数语法 (3. Pandas cut() function syntax)

The cut() function sytax is:

cut（）函数的语法为：

cut(x,bins,right=True,labels=None,retbins=False,precision=3,include_lowest=False,duplicates="raise",
)

• x is the input array to be binned. It must be one-dimensional.x是要合并的输入数组。 它必须是一维的。
• bins defines the bin edges for the segmentation.bin定义用于分割的bin边缘。
• right indicates whether to include the rightmost edge or not, default value is True.right表示是否包括最右边，默认值为True。
• labels is used to specify the labels for the returned bins.标签用于为返回的垃圾箱指定标签。
• retbins specifies whether to return the bins or not.retbins指定是否返回垃圾箱。
• precision specifies the precision at which to store and display the bins labels.precision指定存储和显示垃圾箱标签的精度。
• include_lowest specifies whether the first interval should be left-inclusive or not.include_lowest指定第一个间隔是否应为左包含。
• duplicates speicifies what to do if the bins edges are not unique, whether to raise ValueError or drop non-uniques.重复项专门说明如果垃圾箱边缘不唯一时该怎么做，是引发ValueError还是丢弃非唯一变量。

4. Pandas cut（）函数示例 (4. Pandas cut() function examples)

Let’s look into some examples of pandas cut() function. I will use NumPy to generate random numbers to populate the DataFrame object.

让我们看一下pandas cut（）函数的一些示例。 我将使用NumPy生成随机数来填充DataFrame对象。

4.1）将段号划分为垃圾箱 (4.1) Segment Numbers into Bins)

import pandas as pd
import numpy as npdf_nums = pd.DataFrame({'num': np.random.randint(1, 100, 10)})
print(df_nums)df_nums['num_bins'] = pd.cut(x=df_nums['num'], bins=[1, 25, 50, 75, 100])
print(df_nums)print(df_nums['num_bins'].unique())

Output:

输出：

num
0   80
1   40
2   25
3    9
4   66
5   13
6   63
7   33
8   20
9   60num   num_bins
0   80  (75, 100]
1   40   (25, 50]
2   25    (1, 25]
3    9    (1, 25]
4   66   (50, 75]
5   13    (1, 25]
6   63   (50, 75]
7   33   (25, 50]
8   20    (1, 25]
9   60   (50, 75][(75, 100], (25, 50], (1, 25], (50, 75]]
Categories (4, interval[int64]): [(1, 25] < (25, 50] < (50, 75] < (75, 100]]

Notice that 25 is part of the bin (1, 25]. It’s because the rightmost edge is included by default. If you don’t want that then pass the right=False parameter to the cut() function.

注意25是bin（1，25]的一部分。这是因为默认情况下包括了最右边。如果您不希望这样做，则将right=False参数传递给cut（）函数。

4.2）将标签添加到垃圾箱 (4.2) Adding Labels to Bins)

import pandas as pd
import numpy as npdf_nums = pd.DataFrame({'num': np.random.randint(1, 20, 10)})
print(df_nums)df_nums['nums_labels'] = pd.cut(x=df_nums['num'], bins=[1, 10, 20], labels=['Lows', 'Highs'], right=False)print(df_nums)print(df_nums['nums_labels'].unique())

Since we want 10 to be part of Highs, we are specifying right=False in the cut() function call.

由于我们希望10成为高点的一部分，因此我们在cut（）函数调用中指定right = False 。

Output:

输出：

num
0    5
1   16
2    6
3   13
4    2
5   10
6   18
7   10
8    2
9   18num nums_labels
0    5        Lows
1   16       Highs
2    6        Lows
3   13       Highs
4    2        Lows
5   10       Highs
6   18       Highs
7   10       Highs
8    2        Lows
9   18       Highs[Lows, Highs]
Categories (2, object): [Lows < Highs]

5.参考 (5. References)

• pandas cut() API Doc熊猫cut（）API文档
• Python Pandas TutorialPython熊猫教程

翻译自: https://www.journaldev.com/33394/pandas-cut-function-examples

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