约定第一行代码

import pandas as pd

离散化

Pandas为我们提供了方便的函数cut():

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

参数解释:

• x：需要离散化的数组、Series、DataFrame对象
• bins：分组的依据
• right：传入False则指定左边为闭端

1 离散化

bin=[0,18,25,35,60]
se1=pd.cut(se_ages,bin)
print(se1)

#代码结果
0    (18, 25]
1    (18, 25]
2    (25, 35]
3    (25, 35]
4    (18, 25]
5    (35, 60]
6    (35, 60]
dtype: category
Categories (4, interval[int64]): [(0, 18] < (18, 25] < (25, 35] < (35, 60]]

2 对分组计数

pd.value_counts(se1)

#代码结果
(18, 25]    3
(35, 60]    2
(25, 35]    2
(0, 18]     0
dtype: int64

3 指定左边为闭端

pd.cut(se_ages,bin,right=False)

#代码结果
0    [18, 25)
1    [18, 25)
2    [25, 35)
3    [25, 35)
4    [18, 25)
5    [35, 60)
6    [35, 60)
dtype: category
Categories (4, interval[int64]): [[0, 18) < [18, 25) < [25, 35) < [35, 60)]

4 为区间指定名称

pd.cut(se_ages,bin,labels=['Youth','YoungAdult','MiddleAge','Senior'])

0    YoungAdult
1    YoungAdult
2     MiddleAge
3     MiddleAge
4    YoungAdult
5        Senior
6        Senior
dtype: category
Categories (4, object): [Youth < YoungAdult < MiddleAge < Senior]