2020-06-06 阅读量: 1119
连续数据离散化场景：
数据分析和统计的预处理阶段，经常的会碰到年龄、消费等连续型数值，我们希望将数值进行离散化分段统计，提高数据区分度，那么下面介绍一个简单使用的pandas中的 cut() 方法
函数用法：
**cut(series, bins, right=True, labels=NULL)**
1
series (类似数组排列，必须是一维的)
bins (表示分段数或分类区间，可以是数字，比如说4，就是分成4段，也可以是列表，表示各段的间隔点)
right=True（表示分组右边闭合，right=False表示分组左边闭合，）
labels(表示结果标签，一般最好添加，方便阅读和后续统计)
另外，请注意：
如果 cut_1 = pd.cut ()
cut_1.codes： 获得分组的codes码，即0，1，2，3，4…
pd.value_counts(cut_1): 返回分段计数的结果
如下成绩代码：
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)) # <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)) # 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
'''
