【数据分析可视化】数据分箱技术Binning

分箱：抽象理解为苹果根据大小不同分级分箱

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

# 模拟成绩分箱
score_list = np.random.randint(35, 100, size=20)
score_list

array([93, 35, 83, 44, 56, 62, 37, 86, 44, 82, 49, 91, 49, 82, 53, 89, 47,
       56, 38, 86])

# 成绩评级分段
bins = [0,59,70,80,100]

# 分箱（返回Categories类型）
score_cut = pd.cut(score_list, bins)
score_cut

[(80, 100], (0, 59], (80, 100], (0, 59], (0, 59], ..., (80, 100], (0, 59], (0, 59], (0, 59], (80, 100]]
Length: 20
Categories (4, interval[int64]): [(0, 59] < (59, 70] < (70, 80] < (80, 100]]

# 每一个分箱多少人
pd.value_counts(score_cut)

(0, 59]      11
(80, 100]     8
(59, 70]      1
(70, 80]      0
dtype: int64

# 将模拟的成绩 放入DataFrame
df = DataFrame()
df['score'] = score_list
df

	score
0	93
1	35
2	83
3	44
4	56
5	62
6	37
7	86
8	44
9	82
10	49
11	91
12	49
13	82
14	53
15	89
16	47
17	56
18	38
19	86

# 填充长度为3的随机字符串
df['student'] = [pd.util.testing.rands(3) for i in range(20)]
df

	score	student
0	93	8c1
1	35	cHy
2	83	6xy
3	44	6gY
4	56	tc5
5	62	r5T
6	37	3z3
7	86	vsy
8	44	F6h
9	82	hgC
10	49	xA9
11	91	iLZ
12	49	BVK
13	82	E9C
14	53	rbE
15	89	hSL
16	47	AIt
17	56	Gdk
18	38	AFX
19	86	JhU

# 利用pd.cut将数据处理并填充到DataFrame
pd.cut(df['score'], bins)

0     (80, 100]
1       (0, 59]
2     (80, 100]
3       (0, 59]
4       (0, 59]
5      (59, 70]
6       (0, 59]
7     (80, 100]
8       (0, 59]
9     (80, 100]
10      (0, 59]
11    (80, 100]
12      (0, 59]
13    (80, 100]
14      (0, 59]
15    (80, 100]
16      (0, 59]
17      (0, 59]
18      (0, 59]
19    (80, 100]
Name: score, dtype: category
Categories (4, interval[int64]): [(0, 59] < (59, 70] < (70, 80] < (80, 100]]

# cut的标签化
df['Categories'] = pd.cut(df['score'], bins, labels=['low','ok','good','great'])
df

	score	student	Categories
0	93	8c1	great
1	35	cHy	low
2	83	6xy	great
3	44	6gY	low
4	56	tc5	low
5	62	r5T	ok
6	37	3z3	low
7	86	vsy	great
8	44	F6h	low
9	82	hgC	great
10	49	xA9	low
11	91	iLZ	great
12	49	BVK	low
13	82	E9C	great
14	53	rbE	low
15	89	hSL	great
16	47	AIt	low
17	56	Gdk	low
18	38	AFX	low
19	86	JhU	great

bennyrhys

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	score
0	93
1	35
2	83
3	44
4	56
5	62
6	37
7	86
8	44
9	82
10	49
11	91
12	49
13	82
14	53
15	89
16	47
17	56
18	38
19	86

	score	student
0	93	8c1
1	35	cHy
2	83	6xy
3	44	6gY
4	56	tc5
5	62	r5T
6	37	3z3
7	86	vsy
8	44	F6h
9	82	hgC
10	49	xA9
11	91	iLZ
12	49	BVK
13	82	E9C
14	53	rbE
15	89	hSL
16	47	AIt
17	56	Gdk
18	38	AFX
19	86	JhU

	score
0	93
1	35
2	83
3	44
4	56
5	62
6	37
7	86
8	44
9	82
10	49
11	91
12	49
13	82
14	53
15	89
16	47
17	56
18	38
19	86

	score	student
0	93	8c1
1	35	cHy
2	83	6xy
3	44	6gY
4	56	tc5
5	62	r5T
6	37	3z3
7	86	vsy
8	44	F6h
9	82	hgC
10	49	xA9
11	91	iLZ
12	49	BVK
13	82	E9C
14	53	rbE
15	89	hSL
16	47	AIt
17	56	Gdk
18	38	AFX
19	86	JhU

【数据分析可视化】数据分箱技术Binning

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