pandas-08 pd.cut()的功能和作用

pd.cut()的作用，有點類似給成績設定優良中差，比如：0-59分為差，60-70分為中，71-80分為優秀等等，在pandas中，也提供了這樣一個方法來處理這些事兒。直接上代碼：

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]

#　指定多個區間
bins = [0, 59, 70, 80, 100]

score_cut = pd.cut(score_list, bins)
print(type(score_cut)) # <class 'pandas.core.arrays.categorical.Categorical'>
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)) # 統計每個區間人數
'''
(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方法進行分箱
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]
'''

# 但是這樣的方法不是很適合閱讀，可以使用cut方法中的label參數
# 為每個區間指定一個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
'''