連續資料離散化場景：
資料分析和統計的預處理階段，經常的會碰到年齡、消費等連續型數值，我們希望將數值進行離散化分段統計，提高資料區分度，那麼下面介紹一個簡單使用的pandas中的 cut() 方法

函式用法：

**cut(series, bins, right=True, labels=NULL)**

series (類似陣列排列，必須是一維的)
bins (表示分段數或分類區間，可以是數字，比如說4，就是分成4段，也可以是列表，表示各段的間隔點)
right=True（表示分組右邊閉合，right=False表示分組左邊閉合，）
labels(表示結果標籤，一般最好新增，方便閱讀和後續統計)

另外，請注意：
如果 cut_1 = pd.cut ()
cut_1.codes： 獲得分組的codes碼，即0，1，2，3，4…
pd.value_counts(cut_1): 返回分段計數的結果

如下成績程式碼：

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引數
# 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
'''