pandas中pd.cut()的功能和作用

pd.cut()的作用，有点类似给成绩设定优良中差，比如：0-59分为差，60-70分为中，71-80分为优秀等等，在pandas中，也提供了这样一个方法来处理这些事儿。直接上代码：

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

np.random.seed(666)

score_list = np.random.randint(25, 100, size=20)
print(score_list)
# [27 70 55 87 95 98 55 61 86 76 85 53 39 88 41 71 64 94 38 94]

#　指定多个区间
bins = [0, 59, 70, 80, 100]

score_cut = pd.cut(score_list, bins)
print(type(score_cut)) # 
print(score_cut)
'''
[(0, 59], (59, 70], (0, 59], (80, 100], (80, 100], ..., (70, 80], (59, 70], (80, 100], (0, 59], (80, 100]]
Length: 20
Categories (4, interval[int64]): [(0, 59] < (59, 70] < (70, 80] < (80, 100]]
'''
print(pd.value_counts(score_cut)) # 统计每个区间人数
'''
(80, 100]    8
(0, 59]      7
(59, 70]     3
(70, 80]     2
dtype: int64
'''

df = DataFrame()
df['score'] = score_list
df['student'] = [pd.util.testing.rands(3) for i in range(len(score_list))]
print(df)
'''
    score student
0      27     1ul
1      70     yuK
2      55     WWK
3      87     EU6
4      95     Vqn
5      98     KAf
6      55     QNT
7      61     HaE
8      86     aBo
9      76     MMa
10     85     Ctc
11     53     5BI
12     39     wBp
13     88     WMB
14     41     q5t
15     71     MjZ
16     64     nTc
17     94     Kyx
18     38     Rlh
19     94     2uV
'''

# 使用cut方法进行分箱
print(pd.cut(df['score'], bins))
'''
0       (0, 59]
1      (59, 70]
2       (0, 59]
3     (80, 100]
4     (80, 100]
5     (80, 100]
6       (0, 59]
7      (59, 70]
8     (80, 100]
9      (70, 80]
10    (80, 100]
11      (0, 59]
12      (0, 59]
13    (80, 100]
14      (0, 59]
15     (70, 80]
16     (59, 70]
17    (80, 100]
18      (0, 59]
19    (80, 100]
Name: score, dtype: category
Categories (4, interval[int64]): [(0, 59] < (59, 70] < (70, 80] < (80, 100]]
'''

df['Categories'] = pd.cut(df['score'], bins)
print(df)
'''
    score student Categories
0      27     1ul    (0, 59]
1      70     yuK   (59, 70]
2      55     WWK    (0, 59]
3      87     EU6  (80, 100]
4      95     Vqn  (80, 100]
5      98     KAf  (80, 100]
6      55     QNT    (0, 59]
7      61     HaE   (59, 70]
8      86     aBo  (80, 100]
9      76     MMa   (70, 80]
10     85     Ctc  (80, 100]
11     53     5BI    (0, 59]
12     39     wBp    (0, 59]
13     88     WMB  (80, 100]
14     41     q5t    (0, 59]
15     71     MjZ   (70, 80]
16     64     nTc   (59, 70]
17     94     Kyx  (80, 100]
18     38     Rlh    (0, 59]
19     94     2uV  (80, 100]
'''

# 但是这样的方法不是很适合阅读，可以使用cut方法中的label参数
# 为每个区间指定一个label
df['Categories'] = pd.cut(df['score'], bins, labels=['low', 'middle', 'good', 'perfect'])
print(df)
'''
    score student Categories
0      27     1ul        low
1      70     yuK     middle
2      55     WWK        low
3      87     EU6    perfect
4      95     Vqn    perfect
5      98     KAf    perfect
6      55     QNT        low
7      61     HaE     middle
8      86     aBo    perfect
9      76     MMa       good
10     85     Ctc    perfect
11     53     5BI        low
12     39     wBp        low
13     88     WMB    perfect
14     41     q5t        low
15     71     MjZ       good
16     64     nTc     middle
17     94     Kyx    perfect
18     38     Rlh        low
19     94     2uV    perfect
'''