Pandas：数据的离散化

import numpy as np
import pandas as pd
from pandas import Series,DataFrame

一、cut的使用

将ages按照“18到25”、“26到35”、“35到60”以及“60以上”进行划分

ages = [20,22,25,27,21,23,37,31,61,45,41,32]
bins = [18,25,35,60,100]
r = pd.cut(ages,bins)
r

[(18, 25], (18, 25], (18, 25], (25, 35], (18, 25], ..., (25, 35], (60, 100], (35, 60], (35, 60], (25, 35]]
Length: 12
Categories (4, interval[int64]): [(18, 25] < (25, 35] < (35, 60] < (60, 100]]

codes返回划分组的编号

r.codes

array([0, 0, 0, 1, 0, 0, 2, 1, 3, 2, 2, 1], dtype=int8)

categories返回划分的组

r.categories

IntervalIndex([(18, 25], (25, 35], (35, 60], (60, 100]]
              closed='right',
              dtype='interval[int64]')

二、使用value_counts可以对划分在各个组的数据进行值统计

pd.value_counts(r)

(18, 25]     5
(35, 60]     3
(25, 35]     3
(60, 100]    1
dtype: int64

三、使用labels参数对分组命名

pd.cut(ages,bins,labels=['Youth',' YoungAdult','MiddleAged','Senior'])

[Youth, Youth, Youth, YoungAdult, Youth, ..., YoungAdult, Senior, MiddleAged, MiddleAged, YoungAdult]
Length: 12
Categories (4, object): [YoungAdult < MiddleAged < Senior < Youth]

四、指定分组数量，不指定分组的边界

pd.cut(ages,5)

[(19.959, 28.2], (19.959, 28.2], (19.959, 28.2], (19.959, 28.2], (19.959, 28.2], ..., (28.2, 36.4], (52.8, 61.0], (44.6, 52.8], (36.4, 44.6], (28.2, 36.4]]
Length: 12
Categories (5, interval[float64]): [(19.959, 28.2] < (28.2, 36.4] < (36.4, 44.6] < (44.6, 52.8] < (52.8, 61.0]]

五、根据分位数进行划分–qcut

pd.qcut(ages,4)

[(19.999, 22.75], (19.999, 22.75], (22.75, 29.0], (22.75, 29.0], (19.999, 22.75], ..., (29.0, 38.0], (38.0, 61.0], (38.0, 61.0], (38.0, 61.0], (29.0, 38.0]]
Length: 12
Categories (4, interval[float64]): [(19.999, 22.75] < (22.75, 29.0] < (29.0, 38.0] < (38.0, 61.0]]

自己设定分位数

pd.qcut(ages,[0,0.1,0.5,0.8,1.])

[(19.999, 21.1], (21.1, 29.0], (21.1, 29.0], (21.1, 29.0], (19.999, 21.1], ..., (29.0, 40.2], (40.2, 61.0], (40.2, 61.0], (40.2, 61.0], (29.0, 40.2]]
Length: 12
Categories (4, interval[float64]): [(19.999, 21.1] < (21.1, 29.0] < (29.0, 40.2] < (40.2, 61.0]]