Pandas 数据分析，高中体测练习
分析体测成绩
体侧成绩转变成分数
开卷考试
excel完成可以
pandas读取excel代码中 完成
一个手输入
进一步，画图，分布，体重正常，肥胖，偏瘦比例，绘制饼图
男生跑步1000成绩，不及格，及格，中等，良好，优秀，柱状图绘制
导包、读取文件
import numpy as np
import pandas as pd
from pandas import Series,DataFrame
import matplotlib.pyplot as plt
%matplotlib inline
data = pd.read_excel('18级高一体测成绩汇总.xls')
data
班级
性别
姓名
1000米
50米
跳远
体前屈
引体
肺活量
身高
体重
BMI
0
1
男
高孜阳0611
4'13
8.88
195
12
1
2785
170
72.6
NaN
1
1
男
郝少杰1013
4'16
7.70
225
11
7
3133
174
52.7
NaN
2
1
男
郝梓烨0619
4'09
8.45
218
14
1
3901
169
46.5
NaN
3
1
男
何弘源1010
4'21
8.05
206
13
1
4946
183
79.7
NaN
4
1
男
刘硕鹏1212
3'44
7.52
210
13
9
3538
171
54.7
NaN
…
…
…
…
…
…
…
…
…
…
…
…
…
488
17
男
张乔楠0311
4'23
8.27
208
10
0
4647
176
69.5
NaN
489
17
男
郭泽森0333
5'19
9.55
210
15
6
7042
177
76
NaN
490
17
男
陈子龙061X
3'25
7.5
252
13
13
5755
181
65
NaN
491
17
男
王丹龙0636
4'39
7.81
208
14
11
5688
172
51.7
NaN
492
17
男
王玉涵0636
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
# 清洗数据
cond = data['班级'] != '班级' # 把有班级那一行的数据给删除
data = data[cond] # 得到新的数据
data[:45]
班级
性别
姓名
1000米
50米
跳远
体前屈
引体
肺活量
身高
体重
BMI
0
1
男
高孜阳0611
4'13
8.88
195
12
1
2785
170
72.6
NaN
1
1
男
郝少杰1013
4'16
7.70
225
11
7
3133
174
52.7
NaN
2
1
男
郝梓烨0619
4'09
8.45
218
14
1
3901
169
46.5
NaN
3
1
男
何弘源1010
4'21
8.05
206
13
1
4946
183
79.7
NaN
4
1
男
刘硕鹏1212
3'44
7.52
210
13
9
3538
171
54.7
NaN
5
1
男
刘运硕0314
3'49
7.94
190
20
7
3970
175
66.4
NaN
6
1
男
吕晓瑶0314
3'54
7.75
186
11
7
3710
173
53.9
NaN
7
1
男
米孜聪0636
4'3
8.06
195
3
1
5578
178
83.1
NaN
8
1
男
聂浩然2719
4'01
7.75
220
15
10
3821
175
66.5
NaN
9
1
男
牛苗嘉1211
4'12
7.38
245
17
11
4423
167
53.9
NaN
10
1
男
牛砚哲2813
4
7.82
219
13
11
4031
173
57.4
NaN
11
1
男
齐子涵185x
4'13
7.37
228
9
15
4354
163
54.6
NaN
12
1
男
乔一甲0616
3'45
7.66
202
7
3
2238
179
61.1
NaN
13
1
男
任晓波0311
3'46
7.66
245
3
7
4811
177
63.9
NaN
14
1
男
戎小龙2633
0
0
0
0
0
0
0
NaN
NaN
15
1
男
桑淳熙0616
3'57
7.60
192
7
5
4147
174
59.2
NaN
16
1
男
田晓龙2411
4'18
8.14
210
8
4
4241
179
61.9
NaN
17
1
男
田玉聪2716
3'32
7.20
255
22
12
5324
183
63.4
NaN
18
1
男
王晨宇0613
3'56
8.15
207
13
12
4363
173
60.5
NaN
19
1
男
王家梁0630
3'47
8.15
202
13
16
5364
174
56
NaN
20
1
男
王乐天3331
3'53
7.85
210
3
7
3445
177
56.9
NaN
21
1
男
王一钊1213
3'57
7.85
220
9
2
5670
177
55.5
NaN
22
1
男
王子天0634
3'42
7.23
212
12
15
5709
185
72.3
NaN
23
1
男
王子鑫0012
4'3
7.68
218
15
3
4780
177
83.7
NaN
24
1
男
未晓锟1214
4'14
8.30
206
15
1
3358
173
46.6
NaN
25
1
男
张国瑞033x
4'04
8.15
205
9
5
3494
169
48.3
NaN
26
1
男
张皓天0632
4'04
7.55
190
12
5
3286
169
50.1
NaN
27
1
男
张泽地0310
4'02
7.55
240
5
12
4483
171
58.4
NaN
28
1
男
张智贤0318
3'57
7.89
220
9
11
4254
166
54.8
NaN
29
1
男
赵博翰101x
4'16
8.19
212
27
7
3498
169
68
NaN
30
1
男
赵泽凯0311
4'01
7.89
213
5
11
4322
174
55.9
NaN
31
1
男
赵泽宇0616
4'08
8.21
208
19
20
3917
166
51.9
NaN
32
1
男
左晶川1217
4'06
8.71
206
11
4
3970
172
47.8
NaN
34
2
男
贾和0633
4'22
7.97
215
9
9
3865
175
58.7
NaN
35
2
男
李森0636
0
0
0
0
0
0
0
NaN
NaN
36
2
男
李一帆1812
4'46
8.79
172
7
1
4750
174
88.6
NaN
37
2
男
李子阳0618
4'01
7.37
210
2
7
4714
182
62.5
NaN
38
2
男
吕星繁0312
0
0
0
0
0
0
0
NaN
NaN
39
2
男
赵凌云105x
4'13
7.77
208
8
7
4327
173
56
NaN
40
2
男
赵鹏悦2612
4'27
8.8
185
10
5
4745
164
74.8
NaN
42
3
男
宫诚博0612
3'43
6.89
276
16
12
5212
1.84
73.1
NaN
43
3
男
郭亚浩181X
4'04
7.25
240
13
8
4756
1.76
72
NaN
44
3
男
郝晓辰0013
3'38
7.36
246
22
11
4433
1.84
62.5
NaN
45
3
男
李国玺2310
4'19
8.17
220
18
1
4438
1.74
72.2
NaN
46
3
男
李一帆1218
4'08
7.8
227
15
1
6033
1.77
85.6
NaN
data.fillna(0, inplace=True) # 没有参加体侧的同学分数都填充成0
# 没有空数据了
data.isnull().any() #查询是否还有空数据
班级 False
性别 False
姓名 False
1000米 False
50米 False
跳远 False
体前屈 False
引体 False
肺活量 False
身高 False
体重 False
BMI False
dtype: bool
data.head()
班级
性别
姓名
1000米
50米
跳远
体前屈
引体
肺活量
身高
体重
BMI
0
1
男
高孜阳0611
4'13
8.88
195.0
12
1
2785
170.0
72.6
0.0
1
1
男
郝少杰1013
4'16
7.70
225.0
11
7
3133
174.0
52.7
0.0
2
1
男
郝梓烨0619
4'09
8.45
218.0
14
1
3901
169.0
46.5
0.0
3
1
男
何弘源1010
4'21
8.05
206.0
13
1
4946
183.0
79.7
0.0
4
1
男
刘硕鹏1212
3'44
7.52
210.0
13
9
3538
171.0
54.7
0.0
把数据中的 4'13 转换成 小数
def convert(x):
if isinstance(x, str):
minute, second = x.split("'")
minute = int(minute)
second = int(second)
return minute + second / 100.0
else:
return x
data['1000米'] = data['1000米'].map(convert) # 映射
data.head()
班级 性别 姓名 1000米 50米 跳远 体前屈 引体 肺活量 身高 体重 BMI 0 1 男 高孜阳0611 4.13 8.88 195.0 12 1 2785 170.0 72.6 0.0 1 1 男 郝少杰1013 4.16 7.70 225.0 11 7 3133 174.0 52.7 0.0 2 1 男 郝梓烨0619 4.09 8.45 218.0 14 1 3901 169.0 46.5 0.0 3 1 男 何弘源1010 4.21 8.05 206.0 13 1 4946 183.0 79.7 0.0 4 1 男 刘硕鹏1212 3.44 7.52 210.0 13 9 3538 171.0 54.7 0.0
加载体侧成绩评分表
score = pd.read_excel('体侧成绩评分表.xls', header = [0,1])
score
男肺活量 女肺活量 男50米跑 女50米跑 男体前屈 … 女跳远 男引体 女仰卧 男1000 女800 成绩 分数 成绩 分数 成绩 分数 成绩 分数 成绩 分数 … 成绩 分数 成绩 分数 成绩 分数 成绩 分数 成绩 分数 0 4540 100 3150 100 7.1 100 7.8 100 23.6 100 … 204 100 16.0 100 53 100 3'30" 100 3'24" 100 1 4420 95 3100 95 7.2 95 7.9 95 21.5 95 … 198 95 15.0 95 51 95 3'35" 95 3'30" 95 2 4300 90 3050 90 7.3 90 8.0 90 19.4 90 … 192 90 14.0 90 49 90 3'40" 90 3'36" 90 3 4050 85 2900 85 7.4 85 8.3 85 17.2 85 … 185 85 13.0 85 46 85 3'47" 85 3'43" 85 4 3800 80 2750 80 7.5 80 8.6 80 15.0 80 … 178 80 12.0 80 43 80 3'55" 80 3'50" 80
把男1000 成绩 3'30 转换成小数
def convert(item):
m, s = item.strip('"').split("'")
m, s = int(m),int(s)
return m + s / 100.0
score.iloc[:,-4] = score.iloc[:,-4].map(convert) # 获取它的索引
把女800 成绩 3'30 转换成小数
def convert(item):
m, s = item.strip('"').split("'")
m, s = int(m),int(s)
return m + s / 100.0
score.iloc[:,-2] = score.iloc[:,-2].map(convert) # 获取它的索引
对应索引 男1000 男50米跑
data.columns =['班级', '性别', '姓名', '男1000', '男50米跑', '跳远', '体前屈', '引体', '肺活量', '身高', '体重',
'BMI']
data
s.dtypes
成绩 float64
分数 int64
dtype: object
data.dtypes
data['男50米跑'] = data['男50米跑'].astype(np.float) # 转换数据类型
data
for col in [ '男1000', '男50米跑']:
s = score[col] # 获取成绩的标准
def convert(x):
for i in range(len(s)): # 获取长度循环
if x <= s['成绩'].iloc[0]:
if x == 0: # 判断是否没有成绩
return 0
return 100
elif x > s['成绩'].iloc[-1]:
return 0 # 跑得太慢
elif (x > s['成绩'].iloc[i - 1]) and (x <= s['成绩'].iloc[i]):
return s['分数'].iloc[i]
data[col + '成绩'] = data[col].map(convert) # 增加一列
# 这里会报错 数据类型不对 我们在上面转换一下数据类型
转换 '跳远', '体前屈', '引体', '肺活量'
data.columns
score.head()
for col in ['跳远', '体前屈', '引体', '肺活量']:
s = score['男' + col]
def convert(x):
for i in range(len(s)):
if x >= s['成绩'].iloc[i]:
return s['分数'].iloc[i]
return 0
data[col + '成绩'] = data[col].map(convert)
data.head()
班级 性别 姓名 男1000 男50米跑 跳远 体前屈 引体 肺活量 身高 体重 BMI 男1000成绩 男50米跑成绩 跳远成绩 体前屈成绩 引体成绩 肺活量成绩 0 1 男 高孜阳0611 4.13 8.88 195.0 12 1 2785 170.0 72.6 0.0 72 66 60 74 0 62 1 1 男 郝少杰1013 4.16 7.70 225.0 11 7 3133 174.0 52.7 0.0 70 78 74 74 60 68 2 1 男 郝梓烨0619 4.09 8.45 218.0 14 1 3901 169.0 46.5 0.0 74 70 70 78 0 80 3 1 男 何弘源1010 4.21 8.05 206.0 13 1 4946 183.0 79.7 0.0 68 74 64 76 0 100 4 1 男 刘硕鹏1212 3.44 7.52 210.0 13 9 3538 171.0 54.7 0.0 85 78 66 76 68 74
data.columns
cols = ['班级', '性别', '姓名', '男1000','男1000成绩', '男50米跑', '男50米跑成绩', '跳远', '跳远成绩','体前屈', '体前屈成绩', '引体','引体成绩', '肺活量', '肺活量成绩', '身高', '体重', 'BMI', ]
cols = ['班级', '性别', '姓名', '男1000','男1000成绩', '男50米跑', '男50米跑成绩', '跳远', '跳远成绩','体前屈',
'体前屈成绩', '引体','引体成绩', '肺活量', '肺活量成绩', '身高',
'体重', 'BMI', ]
# 根据索引的顺序去DataFrame中取值
data[cols]
计算体重
h = data['身高']
h[:50]
def convert(x):
if x >100:
return x/100
return x
data['身高'] = data['身高'].map(convert)
data['BMI'] = (data['体重'] / data['身高']**2).round(1) # 保留1位小数
data.head()
班级 性别 姓名 男1000 男50米跑 跳远 体前屈 引体 肺活量 身高 体重 BMI 男1000成绩 男50米跑成绩 跳远成绩 体前屈成绩 引体成绩 肺活量成绩
0 1 男 高孜阳0611 4.13 8.88 195.0 12 1 2785 1.70 72.6 25.12 72 66 60 74 0 62 1 1 男 郝少杰1013 4.16 7.70 225.0 11 7 3133 1.74 52.7 17.41 70 78 74 74 60 68 2 1 男 郝梓烨0619 4.09 8.45 218.0 14 1 3901 1.69 46.5 16.28 74 70 70 78 0 80 3 1 男 何弘源1010 4.21 8.05 206.0 13 1 4946 1.83 79.7 23.80 68 74 64 76 0 100 4 1 男 刘硕鹏1212 3.44 7.52 210.0 13 9 3538 1.71 54.7 18.71 85 78 66 76 68 74 … … … … … … … … … … … … … … … … … … … 488 17 男 张乔楠0311 4.23 8.27 208.0 10 0 4647 1.76 69.5 22.44 68 72 66 72 0 100 489 17 男 郭泽森0333 5.19 9.55 210.0 15 6 7042 1.77 76.0 24.26 40 50 66 80 50 100 490 17 男 陈子龙061X 3.25 7.50 252.0 13 13 5755 1.81 65.0 19.84 100 80 90 76 85 100 491 17 男 王丹龙0636 4.39 7.81 208.0 14 11 5688 1.72 51.7 17.48 62 76 66 78 76 100 492 17 男 王玉涵0636 0.00 0.00 0.0 0 0 0 0.00 0.0 NaN 0 0 0 50 0 0
def convert_bmi(x):
if x >= 26.4:
return 60
elif (x <= 16.4) or (x > 23.3 and x < 26.3):
return 80
elif x >= 16.5 and x <= 23.2:
return 100
return 0
data['BMI_score'] = data['BMI'].map(convert_bmi)
班级
性别
姓名
男1000
男50米跑
跳远
体前屈
引体
肺活量
身高
体重
BMI
男1000成绩
男50米跑成绩
跳远成绩
体前屈成绩
引体成绩
肺活量成绩
BMI_score
0
1
男
高孜阳0611
4.13
8.88
195.0
12
1
2785
1.70
72.6
25.12
72
66
60
74
0
62
80
1
1
男
郝少杰1013
4.16
7.70
225.0
11
7
3133
1.74
52.7
17.41
70
78
74
74
60
68
100
2
1
男
郝梓烨0619
4.09
8.45
218.0
14
1
3901
1.69
46.5
16.28
74
70
70
78
0
80
80
3
1
男
何弘源1010
4.21
8.05
206.0
13
1
4946
1.83
79.7
23.80
68
74
64
76
0
100
80
4
1
男
刘硕鹏1212
3.44
7.52
210.0
13
9
3538
1.71
54.7
18.71
85
78
66
76
68
74
100
5
1
男
刘运硕0314
3.49
7.94
190.0
20
7
3970
1.75
66.4
21.68
80
74
50
90
60
80
100
6
1
男
吕晓瑶0314
3.54
7.75
186.0
11
7
3710
1.73
53.9
18.01
80
76
40
74
60
78
100
7
1
男
米孜聪0636
4.03
8.06
195.0
3
1
5578
1.78
83.1
26.23
76
74
60
62
0
100
80
8
1
男
聂浩然2719
4.01
7.75
220.0
15
10
3821
1.75
66.5
21.71
76
76
72
80
72
80
100
9
1
男
牛苗嘉1211
4.12
7.38
245.0
17
11
4423
1.67
53.9
19.33
72
85
85
80
76
95
100
10
1
男
牛砚哲2813
4.00
7.82
219.0
13
11
4031
1.73
57.4
19.18
78
76
72
76
76
80
100
11
1
男
齐子涵185x
4.13
7.37
228.0
9
15
4354
1.63
54.6
20.55
72
85
76
70
95
90
100
12
1
男
乔一甲0616
3.45
7.66
202.0
7
3
2238
1.79
61.1
19.07
85
78
62
68
20
30
100
13
1
男
任晓波0311
3.46
7.66
245.0
3
7
4811
1.77
63.9
20.40
85
78
85
62
60
100
100
14
1
男
戎小龙2633
0.00
0.00
0.0
0
0
0
0.00
0.0
NaN
0
0
0
50
0
0
0
15
1
男
桑淳熙0616
3.57
7.60
192.0
7
5
4147
1.74
59.2
19.55
78
78
50
68
40
85
100
16
1
男
田晓龙2411
4.18
8.14
210.0
8
4
4241
1.79
61.9
19.32
70
72
66
70
30
85
100
17
1
男
田玉聪2716
3.32
7.20
255.0
22
12
5324
1.83
63.4
18.93
95
95
90
95
80
100
100
18
1
男
王晨宇0613
3.56
8.15
207.0
13
12
4363
1.73
60.5
20.21
78
72
64
76
80
90
100
19
1
男
王家梁0630
3.47
8.15
202.0
13
16
5364
1.74
56.0
18.50
85
72
62
76
100
100
100
20
1
男
王乐天3331
3.53
7.85
210.0
3
7
3445
1.77
56.9
18.16
80
76
66
62
60
74
100
21
1
男
王一钊1213
3.57
7.85
220.0
9
2
5670
1.77
55.5
17.72
78
76
72
70
10
100
100
22
1
男
王子天0634
3.42
7.23
212.0
12
15
5709
1.85
72.3
21.12
85
90
68
74
95
100
100
23
1
男
王子鑫0012
4.03
7.68
218.0
15
3
4780
1.77
83.7
26.72
76
78
70
80
20
100
60
24
1
男
未晓锟1214
4.14
8.30
206.0
15
1
3358
1.73
46.6
15.57
72
72
64
80
0
72
80
25
1
男
张国瑞033x
4.04
8.15
205.0
9
5
3494
1.69
48.3
16.91
76
72
64
70
40
74
100
26
1
男
张皓天0632
4.04
7.55
190.0
12
5
3286
1.69
50.1
17.54
76
78
50
74
40
70
100
27
1
男
张泽地0310
4.02
7.55
240.0
5
12
4483
1.71
58.4
19.97
76
78
80
64
80
95
100
28
1
男
张智贤0318
3.57
7.89
220.0
9
11
4254
1.66
54.8
19.89
78
76
72
70
76
85
100
29
1
男
赵博翰101x
4.16
8.19
212.0
27
7
3498
1.69
68.0
23.81
70
72
68
100
60
74
80
30
1
男
赵泽凯0311
4.01
7.89
213.0
5
11
4322
1.74
55.9
18.46
76
76
68
64
76
90
100
31
1
男
赵泽宇0616
4.08
8.21
208.0
19
20
3917
1.66
51.9
18.83
74
72
66
85
100
80
100
32
1
男
左晶川1217
4.06
8.71
206.0
11
4
3970
1.72
47.8
16.16
74
66
64
74
30
80
80
34
2
男
贾和0633
4.22
7.97
215.0
9
9
3865
1.75
58.7
19.17
68
74
70
70
68
80
100
35
2
男
李森0636
0.00
0.00
0.0
0
0
0
0.00
0.0
NaN
0
0
0
50
0
0
0
36
2
男
李一帆1812
4.46
8.79
172.0
7
1
4750
1.74
88.6
29.26
50
66
10
68
0
100
60
37
2
男
李子阳0618
4.01
7.37
210.0
2
7
4714
1.82
62.5
18.87
76
85
66
60
60
100
100
38
2
男
吕星繁0312
0.00
0.00
0.0
0
0
0
0.00
0.0
NaN
0
0
0
50
0
0
0
39
2
男
赵凌云105x
4.13
7.77
208.0
8
7
4327
1.73
56.0
18.71
72
76
66
70
60
90
100
40
2
男
赵鹏悦2612
4.27
8.80
185.0
10
5
4745
1.64
74.8
27.81
66
66
40
72
40
100
60
42
3
男
宫诚博0612
3.43
6.89
276.0
16
12
5212
1.84
73.1
21.59
85
100
100
80
80
100
100
43
3
男
郭亚浩181X
4.04
7.25
240.0
13
8
4756
1.76
72.0
23.24
76
90
80
76
64
100
0
44
3
男
郝晓辰0013
3.38
7.36
246.0
22
11
4433
1.84
62.5
18.46
90
85
85
95
76
95
100
45
3
男
李国玺2310
4.19
8.17
220.0
18
1
4438
1.74
72.2
23.85
70
72
72
85
0
95
80
46
3
男
李一帆1218
4.08
7.80
227.0
15
1
6033
1.77
85.6
27.32
74
76
76
80
0
100
60
47
3
男
刘凡1218
4.09
8.06
208.0
10
2
4106
1.70
68.7
23.77
74
74
66
72
10
85
80
48
3
男
刘哲垚1217
4.09
8.16
190.0
2
6
4214
1.67
60.7
21.76
74
72
50
60
50
85
100
49
3
男
米卓凡241X
4.05
8.16
200.0
13
9
3857
1.72
51.4
17.37
76
72
62
76
68
80
100
50
3
男
牛卓凡0614
4.02
8.27
228.0
14
12
3266
1.62
52.2
19.89
76
72
76
78
80
70
100
51
3
男
苏仕一1233
4.01
8.50
215.0
6
9
3578
1.64
49.9
18.55
76
70
70
66
68
76
100
定义需求，画图，对比分析
(data['BMI_score'].value_counts()).plot(kind = 'pie', autopct = "%0.2f%%")
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" alt="img">
(data['BMI_score'].value_counts()).plot(kind = 'bar')
aaarticlea/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXcAAAEBCAYAAACDu+UiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAARKElEQVR4nO3dfcyddX3H8fdHivhELA83rLZlZVpUXKSQe0jmH1PA8eAfxUUm/CEdwRQzSDQxm+j+UJOxYDIlMdlYykDK4kAUDY3iAyLMmA2wsFKpBamAUNu1t/KgzoxJ/e6Pc3XctPfpfXo/9HB+vF/Jybmu3/W7zvmeK3c+57p/53edk6pCktSWlw27AEnS3DPcJalBhrskNchwl6QGGe6S1KAFwy4A4Mgjj6xly5YNuwxJGin33nvvz6tqbKptL4pwX7ZsGevXrx92GZI0UpL8tN82h2UkqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWrQtOGe5BVJ7klyf5JNST7VtV+X5NEkG7rbiq49ST6XZEuSjUlOmu8XIUl6oUEuYnoWOLWqfp3kYOD7Sb7RbfurqvryHv3PApZ3t7cBV3X3kqQDZNpwr96vefy6Wz24u+3rFz5WAtd3+92VZGGSRVW1fdbV7odll339QD7djD12xbuHXYKkBg005p7koCQbgJ3AbVV1d7fp8m7o5cokh3Rti4EnJu2+tWvb8zFXJ1mfZP3ExMQsXoIkaU8DhXtV7aqqFcAS4OQkfwh8DHgT8EfA4cBHu+6Z6iGmeMw1VTVeVeNjY1N+740kaYb2a7ZMVT0N3AmcWVXbq+dZ4PPAyV23rcDSSbstAbbNQa2SpAENMltmLMnCbvmVwOnAg0kWdW0BzgEe6HZZB1zQzZo5BXjmQI+3S9JL3SCzZRYBa5McRO/N4Kaq+lqS7yYZozcMswH4YNf/VuBsYAvwG+DCuS9bkrQvg8yW2QicOEX7qX36F3DJ7EuTJM2UV6hKUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalB04Z7klckuSfJ/Uk2JflU135skruTPJzki0le3rUf0q1v6bYvm9+XIEna0yBn7s8Cp1bVCcAK4MwkpwCfBq6squXAU8BFXf+LgKeq6g3AlV0/SdIBNG24V8+vu9WDu1sBpwJf7trXAud0yyu7dbrtpyXJnFUsSZrWQGPuSQ5KsgHYCdwG/AR4uqqe67psBRZ3y4uBJwC67c8AR0zxmKuTrE+yfmJiYnavQpL0AgOFe1XtqqoVwBLgZODNU3Xr7qc6S6+9GqrWVNV4VY2PjY0NWq8kaQD7NVumqp4G7gROARYmWdBtWgJs65a3AksBuu2vBZ6ci2IlSYMZZLbMWJKF3fIrgdOBzcAdwHu7bquAW7rldd063fbvVtVeZ+6SpPmzYPouLALWJjmI3pvBTVX1tSQ/Am5M8rfAfwLXdP2vAf4lyRZ6Z+znzUPdkqR9mDbcq2ojcOIU7Y/QG3/fs/1/gHPnpDpJ0ox4haokNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSg6YN9yRLk9yRZHOSTUk+1LV/MsnPkmzobmdP2udjSbYkeSjJGfP5AiRJe1swQJ/ngI9U1X1JDgXuTXJbt+3Kqvr7yZ2THA+cB7wFeB3wnSTHVdWuuSxcktTftGfuVbW9qu7rln8FbAYW72OXlcCNVfVsVT0KbAFOnotiJUmD2a8x9yTLgBOBu7umS5NsTHJtksO6tsXAE5N228oUbwZJVidZn2T9xMTEfhcuSepv4HBP8hrgZuDDVfVL4Crg9cAKYDvwmd1dp9i99mqoWlNV41U1PjY2tt+FS5L6GyjckxxML9i/UFVfAaiqHVW1q6p+B1zN80MvW4Glk3ZfAmybu5IlSdMZZLZMgGuAzVX12UntiyZ1ew/wQLe8DjgvySFJjgWWA/fMXcmSpOkMMlvm7cD7gR8m2dC1fRw4P8kKekMujwEXA1TVpiQ3AT+iN9PmEmfKSNKBNW24V9X3mXoc/dZ97HM5cPks6pIkzYJXqEpSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUHThnuSpUnuSLI5yaYkH+raD09yW5KHu/vDuvYk+VySLUk2Jjlpvl+EJOmFBjlzfw74SFW9GTgFuCTJ8cBlwO1VtRy4vVsHOAtY3t1WA1fNedWSpH2aNtyrantV3dct/wrYDCwGVgJru25rgXO65ZXA9dVzF7AwyaI5r1yS1Nd+jbknWQacCNwNHF1V26H3BgAc1XVbDDwxabetXduej7U6yfok6ycmJva/cklSXwOHe5LXADcDH66qX+6r6xRttVdD1ZqqGq+q8bGxsUHLkCQNYKBwT3IwvWD/QlV9pWvesXu4pbvf2bVvBZZO2n0JsG1uypUkDWKQ2TIBrgE2V9VnJ21aB6zqllcBt0xqv6CbNXMK8Mzu4RtJ0oGxYIA+bwfeD/wwyYau7ePAFcBNSS4CHgfO7bbdCpwNbAF+A1w4pxVLkqY1bbhX1feZehwd4LQp+hdwySzrkiTNgleoSlKDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQdOGe5Jrk+xM8sCktk8m+VmSDd3t7EnbPpZkS5KHkpwxX4VLkvob5Mz9OuDMKdqvrKoV3e1WgCTHA+cBb+n2+cckB81VsZKkwUwb7lX1PeDJAR9vJXBjVT1bVY8CW4CTZ1GfJGkGZjPmfmmSjd2wzWFd22LgiUl9tnZtkqQDaKbhfhXwemAFsB34TNeeKfrWVA+QZHWS9UnWT0xMzLAMSdJUZhTuVbWjqnZV1e+Aq3l+6GUrsHRS1yXAtj6PsaaqxqtqfGxsbCZlSJL6mFG4J1k0afU9wO6ZNOuA85IckuRYYDlwz+xKlCTtrwXTdUhyA/AO4MgkW4FPAO9IsoLekMtjwMUAVbUpyU3Aj4DngEuqatf8lC5J6mfacK+q86dovmYf/S8HLp9NUZKk2fEKVUlqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGTRvuSa5NsjPJA5PaDk9yW5KHu/vDuvYk+VySLUk2JjlpPouXJE1tkDP364Az92i7DLi9qpYDt3frAGcBy7vbauCquSlTkrQ/pg33qvoe8OQezSuBtd3yWuCcSe3XV89dwMIki+aqWEnSYGY65n50VW0H6O6P6toXA09M6re1a9tLktVJ1idZPzExMcMyJElTmesPVDNFW03VsarWVNV4VY2PjY3NcRmS9NI203DfsXu4pbvf2bVvBZZO6rcE2Dbz8iRJMzHTcF8HrOqWVwG3TGq/oJs1cwrwzO7hG0nSgbNgug5JbgDeARyZZCvwCeAK4KYkFwGPA+d23W8Fzga2AL8BLpyHmiVJ05g23Kvq/D6bTpuibwGXzLYoSdLseIWqJDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUoGm/W0YCWHbZ14ddwkAeu+Ldwy5BelHwzF2SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUoFldxJTkMeBXwC7guaoaT3I48EVgGfAY8OdV9dTsypQk7Y+5OHN/Z1WtqKrxbv0y4PaqWg7c3q1Lkg6g+RiWWQms7ZbXAufMw3NIkvZhtuFewLeT3Jtkddd2dFVtB+juj5rlc0iS9tNsvzjs7VW1LclRwG1JHhx0x+7NYDXAMcccM8syJEmTzerMvaq2dfc7ga8CJwM7kiwC6O539tl3TVWNV9X42NjYbMqQJO1hxuGe5NVJDt29DPwp8ACwDljVdVsF3DLbIiVJ+2c2wzJHA19Nsvtx/rWqvpnkB8BNSS4CHgfOnX2ZkqT9MeNwr6pHgBOmaP8FcNpsipIkzY5XqEpSgwx3SWqQv6EqDcEo/Catv0c72jxzl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KD/OIwSSNtFL6EDQ78F7F55i5JDTLcJalBhrskNchwl6QGGe6S1KB5C/ckZyZ5KMmWJJfN1/NIkvY2L+Ge5CDgH4CzgOOB85McPx/PJUna23yduZ8MbKmqR6rqf4EbgZXz9FySpD2kqub+QZP3AmdW1Qe69fcDb6uqSyf1WQ2s7lbfCDw054XMvSOBnw+7iIZ4POeOx3Jujcrx/P2qGptqw3xdoZop2l7wLlJVa4A18/T88yLJ+qoaH3YdrfB4zh2P5dxq4XjO17DMVmDppPUlwLZ5ei5J0h7mK9x/ACxPcmySlwPnAevm6bkkSXuYl2GZqnouyaXAt4CDgGuratN8PNcBNlLDSCPA4zl3PJZza+SP57x8oCpJGi6vUJWkBhnuktQgw12SGuQvMUl6yUvyJnpX0S+md03ONmBdVW0eamGz4Jm75l2SBUkuTvLNJBuT3J/kG0k+mOTgYdc3apK8NskVSR5M8ovutrlrWzjs+kZNko/S+4qUAPfQm8od4IZR/tJDZ8tMI8nRTHo3r6odQy5p5CS5AXgaWEvvAjfoXdi2Cji8qt43rNpGUZJvAd8F1lbVf3Vtv0fveJ5eVe8aZn2jJsmPgbdU1W/3aH85sKmqlg+nstkx3PtIsgL4J+C1wM+65iX0Quovq+q+YdU2apI8VFVv7LPtx1V13IGuaZRNczz7btPUkjwInFFVP92j/feBb4/q8XTMvb/rgIur6u7JjUlOAT4PnDCMokbUU0nOBW6uqt8BJHkZcC7w1FArG00/TfLX9M7cd8D//4f5F8ATwyxsRH0YuD3Jwzx//I4B3gBc2nevFznP3PtI8nC/f8eSbKmqNxzomkZVkmXAp4F30vvPB2AhcAdwWVU9OpzKRlOSw4DL6H0AeDS9IcMd9L7i49NV9eQQyxtJ3cnGyfSGYENv+PAHVbVrqIXNgmfu/X0jydeB63n+3XwpcAHwzaFVNZq2AbcC/wzcR+9HXP4Y2MTzY/Aa3HHA31XVR5O8il7Qn9RtG9kwGqbuP8q7hl3HXPLMfR+SnMXz06N2v5uvq6pbh1rYiEnyBXonEq8EngFeDXwVOI3e3+CqIZY3cpJsAk7ovsNpDfDfwM30jucJVfVnQy1QLwqGu+Zdko1V9dYkC+h9OP26qtqVJMD9VfXWIZc4UpJsrqo3d8v3VdVJk7ZtqKoVw6tOLxbOc+9j0lzizc4lnrWXddPKDgVeRW8GEsAhgPPc998DSS7slu9PMg6Q5Djgt/1300uJ4d7fTfRmcryzqo6oqiN4/gPBLw21stFzDfAgsAH4G+BLSa6md7HIjcMsbER9APiTJD+h9wP0/5HkEeDqbpvksEw/ziWeW0leB1BV27r/fE4HHq+qe4Zb2ehKcijwB/Q+z9jqBXaazHDvI8m3ge8w9Vzid1XV6UMsT5L2yWGZ/t4HHAH8W5InkzwJ3AkcTu/iG0l60fLMfQaSXFhVnx92HZLUj+E+A0ker6pjhl2HJPXjFap9JNnYbxO9S74l6UXLcO/vaOAM9v5iqwD/fuDLkaTBGe79fQ14TVVt2HNDkjsPfDmSNDjH3CWpQU6FlKQGGe6S1CDDXZIaZLhLUoP+D9vgmAcDpaG8AAAAAElFTkSuQmCC" alt="img">
data.groupby(['男1000成绩'])['BMI_score'].count().plot()
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" 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