Python中的 Pandas.cut() 方法

Pandas cut()函数用于将数组元素分隔到不同的 bin 中。 cut函数主要用于对标量数据进行统计分析。

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

Parameters:

x: The input array to be binned. Must be 1-dimensional.

bins: defines the bin edges for the segmentation.

right : (bool, default True )  Indicates whether bins includes the rightmost edge or not. If right == True (the default), then the bins [1, 2, 3, 4] indicate (1,2], (2,3], (3,4]. 

labels : (array or bool, optional)  Specifies the labels for the returned bins. Must be the same length as the resulting bins. If False, returns only integer indicators of the bins. 

retbins : (bool, default False) Whether to return the bins or not. Useful when bins is provided as a scalar.

示例 1：假设我们有一个包含 10 个从 1 到 100 的随机数的数组，我们希望将数据分成 5 个 bin (1,20] , (20,40] , (40,60] , (60,80] , (80,100] )。

Python3

import pandas as pd
import numpy as np
 
 
df= pd.DataFrame({'number': np.random.randint(1, 100, 10)})
df['bins'] = pd.cut(x=df['number'], bins=[1, 20, 40, 60,
                                          80, 100])
print(df)
 
# We can check the frequency of each bin
print(df['bins'].unique())

输出：

示例 2：我们也可以给我们的 bins 添加标签，例如让我们看看前面的示例并为其添加一些标签

Python3

import pandas as pd
import numpy as np
 
df = pd.DataFrame({'number': np.random.randint(1, 100, 10)})
df['bins'] = pd.cut(x=df['number'], bins=[1, 20, 40, 60, 80, 100],
                    labels=['1 to 20', '21 to 40', '41 to 60',
                            '61 to 80', '81 to 100'])
 
print(df)
 
# We can check the frequency of each bin
print(df['bins'].unique())

输出：