pandas数据映射、聚合和绘图函数

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我是靠谱客的博主细心大船，最近开发中收集的这篇文章主要介绍pandas数据映射、聚合和绘图函数，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

import pandas as pd
pd.__version__
# 就是pandas模块，对一些汉字，处理不好，版本会继续升级的

输出

‘0.22.0’

# pandas数据处理

import numpy as np
from pandas import Series, DataFrame

## 1、删除重复元素 ### 使用duplicated()函数检测重复的行，返回元素为布尔类型的Series对象，每个元素对应一行，如果该行不是第一次出现，则元素为True

# 在mysql中有属性表，城市，省份 市 市的名字只能是唯一，唯一键的效率低
# 如果有属性数据重复，那么pandas拿到的就是一个带有数据冗余的表
df = DataFrame({'color': ['white', 'red', 'white'], 'size': [10, 20, 10]})
df

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	color	size
0	white	10
1	red	20
2	white	10

# 
df.duplicated()

输出

0 False
1 False
2 True
dtype: bool

### 使用drop_duplicates()函数删除重复的行

df.drop_duplicates()

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	color	size
0	white	10
1	red	20

### 如果使用pd.concat([df1,df2],axis = 1)生成新的DataFrame，新的df中columns相同，使用pandas低版本使用duplicate()和drop_duplicates()都会出问题

df2 = pd.concat([df, df], axis=1)
df2

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	color	size	color	size
0	white	10	white	10
1	red	20	red	20
2	white	10	white	10

df2.duplicated()

输出

0 False
1 False
2 True
dtype: bool

df2.drop_duplicates()

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	color	size	color	size
0	white	10	white	10
1	red	20	red	20

# 如果真的出现了两列完全相同，那么我们该怎么删除列
# drop 会删除所有的
df2.T.drop_duplicates().T

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	color	size
0	white	10
1	red	20
2	white	10

## 2. 映射映射的含义：创建一个映射关系列表，把values元素和一个特定的标签或者字符串绑定需要使用字典： `map = { ‘label1’:’value1’, ‘label2’:’value2’, … } ` 包含三种操作： - replace()函数：替换元素 - 最重要：map()函数：新建一列 - rename()函数：替换索引 ### 1) replace()函数：替换元素

df

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	color	size
0	white	10
1	red	20
2	white	10

df.mean()

输出

size 13.333333
dtype: float64

d = {'white': 255, 'red': 128}
df.replace(d)

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	color	size
0	255	10
1	128	20
2	255	10

d = {'white': 255, 'gray': 128}
df.replace(d)
# map也可以用来检索文章中敏感词

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	color	size
0	255	10
1	red	20
2	255	10

### 使用replace()函数，对values进行替换操作作用：数据处理时，需要将，String类型转换成int，然后才可以进行计算机运算性别，男：0；女：1 ### 首先定义一个字典 ### 调用.replace() ### replace还经常用来替换NaN元素

df

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	color	size
0	white	10
1	red	20
2	white	10

df['size'][0] = np.nan

C:UsersAdministratorAnaconda3libsite-packagesipykernel_launcher.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy “”“Entry point for launching an IPython kernel. C:UsersAdministratorAnaconda3libsite-packagespandascoreindexing.py:194: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy self._setitem_with_indexer(indexer, value)

# 我们的表中有中如果nan,我们想填充0， fillna()
d = {np.nan: 0}
df.replace(d)

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	color	size
0	white	0.0
1	red	20.0
2	white	10.0

### 2) map()函数：新建一列使用map()函数，由已有的列生成一个新列适合处理某一单独的列。

df2 = DataFrame(np.random.randint(0, 150, size=(4, 2)),
columns=['Python', 'Java'],
index=list('abcd'))
df2

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	Python	Java
a	58	1
b	99	71
c	66	7
d	104	143

#使用map函数新建一列
# 生成一个Math的列
df2['Math'] = df2['Python'].map(lambda x: x + 20)
df2

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	Python	Java	Math
a	58	1	78
b	99	71	119
c	66	7	86
d	104	143	124

def level(x):
if x > 100:
return '完美'
elif x > 100 and x >= 80:
return '优秀'
elif x < 80 and x >= 60:
return '及格'
else:
return '不及格'

#用数学成绩判断这个人的等级，然后将判断得到的等级添加为一列
df2['level'] = df2['Math'].map(level)
df2

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	Python	Java	Math	level
a	58	1	78	不及格
b	99	71	119	完美
c	66	7	86	不及格
d	104	143	124	完美

### 仍然是新建一个字典 ### map()函数中可以使用lambda函数 ### transform()和map()类似 transform是变形的意思 transform中的值只能是一个function

df2['Math'] = df2['Math'].transform(lambda x: x + np.random.randint(0, 50, size=1)[0])
df2

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	Python	Java	Math	level
a	58	1	121	不及格
b	99	71	161	完美
c	66	7	134	不及格
d	104	143	158	完美

使用map()函数新建一个新列

df2.drop('level', axis=1, inplace=True)
df2

# 我们在pandas中map中最大区别一个使用dict一个不可以使用
df2['level'] = df2['Java'].transform(level)
df2

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	Python	Java	Math	level
a	58	1	121	不及格
b	99	71	161	及格
c	66	7	134	不及格
d	104	143	158	完美

### 3) rename()函数：替换索引仍然是新建一个字典使用rename()函数替换行索引

df2

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	Python	Java	Math	level
a	58	1	121	不及格
b	99	71	161	及格
c	66	7	134	不及格
d	104	143	158	完美

#这个是不行的
df2.index[0] = '张三'

# rename 要替换的索引存在则替换，不存在就不管
df2.rename({'a':'张三', 'Python': '大蟒蛇'},axis=1)

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	大蟒蛇	Java	Math	level
a	58	1	121	不及格
b	99	71	161	及格
c	66	7	134	不及格
d	104	143	158	完美

# rename中的参数值可以是一个func
def index_rename(item):
if item == 'a':
return '张三'
else:
return '李四'

df2.rename(index_rename)

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	Python	Java	Math	level
张三	58	1	121	不及格
李四	99	71	161	及格
李四	66	7	134	不及格
李四	104	143	158	完美

## 3. 异常值检测和过滤使用describe()函数查看每一列的描述性统计量

# 数据中比较大或特别小的都可以认为是异常值
# 大头子和小头爸爸都算是人类中的异常值
# NaN也是异常值
df3 = DataFrame(np.random.randint(0, 150, size=(10, 4)),
columns=['Python', 'Java', 'PHP', 'VR'])
df3

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	Python	Java	PHP	VR
0	122	19	146	88
1	82	105	120	146
2	134	19	81	127
3	0	78	75	39
4	3	115	19	8
5	16	147	71	44
6	16	92	124	131
7	42	141	64	114
8	31	103	64	13
9	103	83	128	108

df3.describe()

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	Python	Java	PHP	VR
count	10.000000	10.000000	10.000000	10.000000
mean	54.900000	90.200000	89.200000	81.800000
std	50.858081	43.649615	39.000855	51.410764
min	0.000000	19.000000	19.000000	8.000000
25%	16.000000	79.250000	65.750000	40.250000
50%	36.500000	97.500000	78.000000	98.000000
75%	97.750000	112.500000	123.000000	123.750000
max	134.000000	147.000000	146.000000	146.000000

使用std()函数可以求得DataFrame对象每一列的标准差

df3.std()

输出

Python 50.858081
Java 43.649615
PHP 39.000855
VR 51.410764
dtype: float64

根据每一列的标准差，对DataFrame元素进行过滤。

借助any()函数, 测试是否有True，有一个或以上返回True，反之返回False

对每一列应用筛选条件,去除标准差太大的数据

df3

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	Python	Java	PHP	VR
0	122	19	146	88
1	82	105	120	146
2	134	19	81	127
3	0	78	75	39
4	3	115	19	8
5	16	147	71	44
6	16	92	124	131
7	42	141	64	114
8	31	103	64	13
9	103	83	128	108

# 大于70的数据我们认定为异常值
cond1 = df3 >= 70
cond2 = cond1.all(axis=1)
df3[cond2]

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	Python	Java	PHP	VR
1	82	105	120	146
9	103	83	128	108

删除特定索引df.drop(labels,inplace = True)

## 4. 排序使用.take()函数排序可以借助np.random.permutation()函数随机排序

# 生成指定大小的矩阵
np.random.permutation([4, 2])

输出

array([4, 2])

df2

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	Python	Java	Math	level
a	58	1	121	不及格
b	99	71	161	及格
c	66	7	134	不及格
d	104	143	158	完美

df2.iloc[::-1]

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	Python	Java	Math	level
d	104	143	158	完美
c	66	7	134	不及格
b	99	71	161	及格
a	58	1	121	不及格

# 通过手写的方式将矩阵进行颠倒
df2.take([3, 2, 1, 0])

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	Python	Java	Math	level
d	104	143	158	完美
c	66	7	134	不及格
b	99	71	161	及格
a	58	1	121	不及格

# 怎么进行一个随机的排序
df2.take(np.random.permutation(4), axis=1)

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	Python	level	Java	Math
a	58	不及格	1	121
b	99	及格	71	161
c	66	不及格	7	134
d	104	完美	143	158

### 随机抽样当DataFrame规模足够大时，直接使用np.random.randint()函数，就配合take()函数实现随机抽样

df4 = DataFrame(np.random.randint(0, 150, size=(1000, 4)),
columns=['Python', 'Java', 'Math', 'China'])
df4

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	Python	Java	Math	China
0	0	94	45	52
1	130	44	120	60
2	45	134	149	14
3	18	62	4	55
4	73	66	112	27
5	17	90	61	136
6	106	101	29	100
7	60	58	142	60
8	47	94	70	24
9	9	115	101	80
10	92	2	135	35
11	149	57	39	92
12	131	143	149	142
13	35	68	12	119
14	137	62	47	68
15	67	74	140	34
16	85	139	88	104
17	32	122	31	145
18	65	24	33	17
19	68	41	60	99
20	76	104	29	78
21	61	9	26	58
22	65	23	12	22
23	27	90	122	93
24	55	119	49	27
25	92	95	116	127
26	127	96	39	60
27	116	15	103	130
28	86	53	11	128
29	120	91	138	124
…	…	…	…	…
970	100	60	17	66
971	122	148	30	22
972	33	133	121	110
973	34	66	62	133
974	88	14	93	131
975	109	86	143	28
976	69	45	86	31
977	55	92	57	138
978	12	63	72	23
979	100	40	116	142
980	113	87	74	80
981	44	62	93	39
982	144	63	130	111
983	38	57	105	55
984	10	18	94	20
985	86	144	12	35
986	96	6	8	54
987	10	93	61	127
988	7	61	79	110
989	28	141	128	76
990	136	136	13	66
991	80	129	61	101
992	141	143	51	38
993	29	46	118	129
994	40	64	69	100
995	92	70	142	54
996	115	71	62	55
997	28	10	93	107
998	31	137	88	75
999	76	36	44	119

1000 rows × 4 columns

# 抽取1000行中50个
#公司年会可以做一个抽奖系统
df4.take(np.random.randint(0, 1000, size=50))

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	Python	Java	Math	China
110	11	145	121	64
799	9	4	42	21
333	36	3	134	145
998	31	137	88	75
143	6	146	87	23
602	115	109	59	53
903	132	32	147	81
74	102	57	146	84
500	124	116	84	17
218	4	99	91	126
490	74	56	39	134
771	1	75	57	6
632	39	10	145	106
615	75	113	82	37
369	46	17	139	106
227	50	144	134	67
518	27	99	42	15
866	68	30	79	43
626	47	15	27	111
748	91	111	70	89
135	84	46	131	110
926	1	56	129	148
905	31	136	25	32
387	136	24	103	131
837	145	50	10	18
804	60	75	70	139
727	6	57	98	14
907	142	63	88	25
538	145	41	89	18
81	51	11	33	69
646	130	7	43	15
131	148	117	103	17
846	141	84	99	48
923	111	148	40	78
96	108	128	41	108
275	4	41	93	41
612	15	7	16	81
757	103	79	48	70
581	13	10	73	37
709	53	40	117	121
641	146	17	127	23
724	22	68	16	32
671	134	34	140	80
445	108	45	134	55
883	137	114	112	90
300	139	104	121	9
451	66	134	138	127
711	39	92	147	74
466	14	99	34	70
322	108	86	122	62

## 5. 数据聚合【重点】数据聚合是数据处理的最后一步，通常是要使每一个数组生成一个单一的数值。数据分类处理： - 分组：先把数据分为几组 - 用函数处理：为不同组的数据应用不同的函数以转换数据 - 合并：把不同组得到的结果合并起来数据分类处理的核心： groupby()函数 groupby()在pandas中也担任分组的重担如果想使用color列索引，计算price1的均值，可以先获取到price1列，然后再调用groupby函数，用参数指定color这一列使用.groups属性查看各行的分组情况：

df5 = DataFrame({'item': ['萝卜', '白菜', '西红柿', '辣椒', '冬瓜','萝卜', '西红柿', '白菜',
'西红柿', '辣椒', '冬瓜'],
'seller': ['李大妈', '李大妈', '李大妈', '王大妈', '王大妈', '王大妈', '王大妈', '赵大妈', '赵大妈', '赵大妈', '赵大妈'],
'price': np.random.randint(3, 10, size=11)},
columns = ['item', 'seller', 'price'])
df5

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	item	seller	price
0	萝卜	李大妈	9
1	白菜	李大妈	7
2	西红柿	李大妈	8
3	辣椒	王大妈	6
4	冬瓜	王大妈	8
5	萝卜	王大妈	5
6	西红柿	王大妈	4
7	白菜	赵大妈	8
8	西红柿	赵大妈	4
9	辣椒	赵大妈	6
10	冬瓜	赵大妈	6

# 首先是分组，然后是找出一个最小值
# 找出各蔬菜中最便宜的
df5.groupby(['item']).min()

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	seller	price
item
冬瓜	王大妈	6
白菜	李大妈	7
萝卜	李大妈	5
西红柿	李大妈	4
辣椒	王大妈	6

df5.groupby(['item']).max()

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	seller	price
item
冬瓜	赵大妈	8
白菜	赵大妈	8
萝卜	王大妈	9
西红柿	赵大妈	8
辣椒	赵大妈	6

# 求平均值，并且添加前缀
mean_price = df5.groupby(['item']).mean().add_prefix('mean_')
mean_price
# select avg('item) as asd

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	mean_price
item
冬瓜	7.000000
白菜	7.500000
萝卜	7.000000
西红柿	5.333333
辣椒	6.000000

# 把得到的平均值融合到原表中
# left_on 设定左边表的关联列，右表对齐，多对多
df6 = pd.merge(df5, mean_price, left_on='item', right_index=True)
df6

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	item	seller	price	mean_price
0	萝卜	李大妈	9	7.000000
5	萝卜	王大妈	5	7.000000
1	白菜	李大妈	7	7.500000
7	白菜	赵大妈	8	7.500000
2	西红柿	李大妈	8	5.333333
6	西红柿	王大妈	4	5.333333
8	西红柿	赵大妈	4	5.333333
3	辣椒	王大妈	6	6.000000
9	辣椒	赵大妈	6	6.000000
4	冬瓜	王大妈	8	7.000000
10	冬瓜	赵大妈	6	7.000000

# 先求一个平方差，求各个的蔬菜价格波动
price_std = df5.groupby(['item']).std().add_prefix('std_')
price_std

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	std_price
item
冬瓜	1.414214
白菜	0.707107
萝卜	2.828427
西红柿	2.309401
辣椒	0.000000

# 将蔬菜的波动值融合到原数据中
df7 = pd.merge(df6, price_std, left_on='item', right_index=True)
df7

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	item	seller	price	mean_price	std_price
0	萝卜	李大妈	9	7.000000	2.828427
5	萝卜	王大妈	5	7.000000	2.828427
1	白菜	李大妈	7	7.500000	0.707107
7	白菜	赵大妈	8	7.500000	0.707107
2	西红柿	李大妈	8	5.333333	2.309401
6	西红柿	王大妈	4	5.333333	2.309401
8	西红柿	赵大妈	4	5.333333	2.309401
3	辣椒	王大妈	6	6.000000	0.000000
9	辣椒	赵大妈	6	6.000000	0.000000
4	冬瓜	王大妈	8	7.000000	1.414214
10	冬瓜	赵大妈	6	7.000000	1.414214

# std()标准平方差，看数据的波动，描述情况
def std_price(std_p):
if std_p > 2.5:
return '价格很坑'
elif std_p > 1:
return '价格稳定'
else:
return '良心菜价'

# map
# 将各个蔬菜的价格情况添加为一列
df7['std_p'] = df7['std_price'].map(std_price)
df7

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	item	seller	price	mean_price	std_price	std_p
0	萝卜	李大妈	9	7.000000	2.828427	价格很坑
5	萝卜	王大妈	5	7.000000	2.828427	价格很坑
1	白菜	李大妈	7	7.500000	0.707107	良心菜价
7	白菜	赵大妈	8	7.500000	0.707107	良心菜价
2	西红柿	李大妈	8	5.333333	2.309401	价格稳定
6	西红柿	王大妈	4	5.333333	2.309401	价格稳定
8	西红柿	赵大妈	4	5.333333	2.309401	价格稳定
3	辣椒	王大妈	6	6.000000	0.000000	良心菜价
9	辣椒	赵大妈	6	6.000000	0.000000	良心菜价
4	冬瓜	王大妈	8	7.000000	1.414214	价格稳定
10	冬瓜	赵大妈	6	7.000000	1.414214	价格稳定

## 6.0 高级数据聚合可以使用pd.merge()函数将聚合操作的计算结果添加到df的每一行使用groupby分组后调用加和等函数进行运算，让后最后可以调用add_prefix()，来修改列名 ### 可以使用transform和apply实现相同功能在transform或者apply中传入函数即可

df7

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	item	seller	price	mean_price	std_price	std_p
0	萝卜	李大妈	9	7.000000	2.828427	价格很坑
5	萝卜	王大妈	5	7.000000	2.828427	价格很坑
1	白菜	李大妈	7	7.500000	0.707107	良心菜价
7	白菜	赵大妈	8	7.500000	0.707107	良心菜价
2	西红柿	李大妈	8	5.333333	2.309401	价格稳定
6	西红柿	王大妈	4	5.333333	2.309401	价格稳定
8	西红柿	赵大妈	4	5.333333	2.309401	价格稳定
3	辣椒	王大妈	6	6.000000	0.000000	良心菜价
9	辣椒	赵大妈	6	6.000000	0.000000	良心菜价
4	冬瓜	王大妈	8	7.000000	1.414214	价格稳定
10	冬瓜	赵大妈	6	7.000000	1.414214	价格稳定

# 将各个蔬菜的价格求和
df7.groupby(['item'])['price'].sum()

输出

item
冬瓜 14
白菜 15
萝卜 14
西红柿 16
辣椒 12
Name: price, dtype: int32

df7.groupby(['item'])['price'].transform(sum)

输出

0 14
5 14
1 15
7 15
2 16
6 16
8 16
3 12
9 12
4 14
10 14
Name: price, dtype: int32

transform()与apply()函数还能传入一个函数或者lambda

df = DataFrame({'color':['white','black','white','white','black','black'], 'status':['up','up','down','down','down','up'], 'value1':[12.33,14.55,22.34,27.84,23.40,18.33], 'value2':[11.23,31.80,29.99,31.18,18.25,22.44]})

apply的操作对象，也就是传给lambda的参数是整列的数组

# np.mean()
# apply 与 transform的最大区别，在于transform做了循环(交叉表)CROSS JOIN , apply比较简洁，调用原本的功能
df7.groupby(['item'])['price'].apply(sum)

输出

item
冬瓜 14
白菜 15
萝卜 14
西红柿 16
辣椒 12
Name: price, dtype: int64

df7.groupby(['item'])['price'].apply(np.mean)

- 输出 item 冬瓜 7.000000 白菜 7.500000 萝卜 7.000000 西红柿 5.333333 辣椒 6.000000 Name: price, dtype: float64 Series和DataFrame都有一个都有一个生成各类图标的plot方法，默认情况下锁生成的都是线形图

import numpy as np
import pandas as pd
from pandas import Series, DataFrame
# matplotlib不引入，在调用plot（）的时候不会报错，但是图片也显示不出
# pandas和matplotlib进行了深度合作
import matplotlib.pyplot as plt

# 线形图简单示例Series图例表示.plot()

s = Series(np.random.randint(0 ,20, size=10))
s

输出

0 14
1 6
2 19
3 5
4 7
5 16
6 1
7 10
8 11
9 17
dtype: int32

s.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x9fe0240>

这里写图片描述

简单的DataFrame图标实例.plot()

df = DataFrame(np.random.randint(0, 150, size=(5, 4)),columns = ['Python', 'Java', 'PHP', 'Ruby'])
df.plot()

<matplotlib.axes._subplots.AxesSubplot at 0xa106390>

这里写图片描述

### 柱状图 DataFrame柱状图例

df.plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0xa4c5240>

这里写图片描述

df.plot(kind='barh')

<matplotlib.axes._subplots.AxesSubplot at 0xa430be0>

这里写图片描述

读取tips.csv，查看每天聚会人数，每天各种聚会规模的比例饿
求和并df.sum()，注意灵活使用axis()

tip = pd.read_csv('./tips.csv')
tip

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	day	1	2	3	4	5	6
0	Fri	1	16	1	1	0	0
1	Stat	2	53	18	13	1	0
2	Sun	0	39	15	18	3	1
3	Thur	1	48	4	5	1	3

将第一列day数据编程行索引set_index

tip.set_index('day',inplace=True)
tip
# 行 代表是星期几
# 列 代表的事几个人一同去吃饭

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	1	2	3	4	5	6
day
Fri	1	16	1	1	0	0
Stat	2	53	18	13	1	0
Sun	0	39	15	18	3	1
Thur	1	48	4	5	1	3

tip.plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0xc1ded68>

这里写图片描述

#将不重要的数据给去除掉
tip.drop(axis=1,labels=['1', '6'], inplace=True)

# 还能用什么方法保留2-5的数据
tip.iloc[:,1:-1].plot(kind='bar')
# 下课自己去尝试一下

<matplotlib.axes._subplots.AxesSubplot at 0xc481128>

这里写图片描述

tip.plot(kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0xc7a9668>

这里写图片描述

## 直方图 random生成随机直方图，调用hist()方法

nd = np.random.randint(0, 10, size=10)
nd

输出

array([2, 3, 5, 4, 7, 0, 4, 4, 0, 5])

s = Series(nd)
# bins 默认值是10 值越大条越细
s.hist(bins=50)

<matplotlib.axes._subplots.AxesSubplot at 0xc9296d8>

这里写图片描述

随机百分比密度图

# density 密度
s.plot(kind='kde')

<matplotlib.axes._subplots.AxesSubplot at 0xca69710>

这里写图片描述

这两张表经常会被画在一起，直方图以规格形式给出（以便画出密度图），然后在再其上绘制核密度估计。

接下来看看一个由两个不同de 标准正太正太分布组成的双峰分布。

np.random.normal()正太分布函数

直方图hist，函数中心必须添加属性normed = True

nd1 = np.random.normal(loc=15, scale=5, size=1000)
nd1

输出

array([16.50002167, 21.95871306, 22.75958416, 17.31162324, 21.11362045,
18.26581647, 14.3677174 , 14.04193695, 24.47250718, 12.15495935,
9.27291491, 12.31342298, 10.2092411 , 26.50106733, 15.73813638,
15.96716855, 16.69108033, 17.47009465, 23.25585844, 3.75013434,
25.12165759, 9.2131128 , 20.80536815, 15.5412481 , 28.90305594,
20.53246725, 11.80165575, 21.60802209, 11.17585822, 16.32740781,
15.18755747, 18.45527818, 20.69591603, 21.49051133, 17.71510416,
18.80558295, 24.17867724, 20.85117481, 20.24591299, 10.10623473,
14.24666052, 17.52448709, 19.22814077, 15.72357144, 20.43921318,
8.0917059 , 22.00146547, 23.50631652, 9.19318319, 9.66151146,
15.75132368, 15.32207848, 15.48579172, 25.26377042, 9.0799947 ,
7.36254758, 2.42731825, 18.10300724, 10.88015804, 13.39732585,
8.91052017, 24.09794363, 18.02868005, 9.901521 , 13.21712227,
19.5068725 , 10.50911924, 14.89803695, 15.50261478, 17.15580065,
14.98757024, 26.86473339, 12.68019989, 15.02038962, 17.42206335,
11.8994741 , 14.31309029, 13.75152275, 6.47324371, 14.73115004,
21.72693826, 20.54387112, 21.36295281, 25.51458225, 7.25840778,
13.76897525, 19.77348404, 15.09407833, 18.76318749, 5.43239314,
15.45835862, 15.40969353, 16.28667652, 13.07865591, 8.28808502,
13.42694347, 18.38122404, 17.6814759 , 19.28344426, 9.53901169,
14.46634917, 11.84352671, 11.09474199, 4.75771378, 15.53622797,
13.13240292, 11.15249509, 8.30862199, 17.21214064, 17.02251594,
16.28650651, 8.21461943, 14.76498248, 15.98369367, 11.98718219,
17.02526983, 19.97020523, 16.44969423, 13.71055375, 22.77569864,
8.51357972, 6.61518919, 14.84898364, 17.58370265, 17.49575552,
12.93721125, 14.53553582, 18.22937262, 15.59990324, 10.94411136,
15.15079863, 7.75436718, 19.64155892, 8.05933151, 14.14798446,
25.93909358, 9.48265882, 16.06401139, 17.17927089, 16.88383318,
19.95283715, 12.02420859, 18.16494132, 14.31856533, 17.48454134,
19.70450889, 16.15431485, 14.97920007, 12.75705767, 21.13982655,
23.76846867, 20.43796706, 17.5593216 , 13.81091458, 19.4130765 ,
22.30718067, 18.30679605, 18.73435888, 21.1085248 , 16.17991617,
17.66907561, 9.3648496 , 12.53151174, 18.74196939, 14.72622698,
21.91666941, 10.68175022, 28.06536912, 16.92750333, 10.48199841,
16.95819675, 11.6035696 , 17.37937365, 12.65895274, 11.54480806,
18.3616206 , 18.02565635, 15.90571496, 6.96905143, 23.91371845,
18.72841836, 2.36636948, 15.66448126, 19.13919071, 14.37065233,
14.82159192, 13.72377931, 16.78926541, 20.13959017, 16.77379021,
14.84332182, 17.34148733, 18.78528991, 7.46478532, 21.50181241,
7.9833104 , 17.80479141, 16.70124722, 13.64582571, 13.577898 ,
16.37998434, 19.03837668, 16.89253034, 20.42677538, 11.88847021,
11.38631853, 15.50082332, 16.7030249 , 16.088621 , 12.51561343,
15.73055771, 19.48992889, 13.06485701, 8.0678333 , 18.05531125,
5.43515846, 19.04760736, 7.879961 , 20.12691794, 8.43035117,
17.22251733, 8.03678616, 16.0078554 , 16.15014473, 8.21224197,
19.38089222, 9.01029769, 11.26062042, 14.76521656, 17.35078069,
22.80520652, 14.46913352, 17.42879149, 13.37647894, 17.25338735,
18.73637238, 2.88421749, 20.40011706, 10.39826822, 15.05082296,
19.06963941, 16.91706731, 13.82272106, 7.27754931, 16.11283426,
15.5246287 , 13.81157399, 6.09658837, 15.6001777 , 18.92162163,
13.38838482, 12.3894269 , 25.61593369, 14.51006631, 24.07157909,
16.8478554 , 19.06337918, 6.89606982, 15.89648191, 9.94890574,
4.1741199 , 19.96662183, 22.11552452, 20.07915682, 11.81127282,
10.01155602, 16.06106413, 13.72389102, 13.58895892, 6.38356799,
15.51871295, 18.04894363, 6.91148171, 15.76145194, 11.06726095,
15.0972117 , 13.89722476, 17.2514969 , 13.71947839, 15.46657072,
9.57084451, 8.69629316, 18.11964659, 13.78259735, 25.01368229,
9.83800798, 9.04644566, 9.01788547, 13.94342442, 19.78553559,
14.88336783, 7.98179993, 12.39448036, 17.1408828 , 19.33147132,
14.05125452, 17.23008172, 13.65371068, 16.17567752, 12.23122378,
12.10680811, 2.95619838, 6.89529116, 15.51517282, 15.19482832,
12.01639161, 18.7831425 , 17.0239132 , 1.38190082, 13.69724912,
13.06217886, 26.94699552, 22.59459457, 11.97867369, 13.93633583,
17.10552488, 22.58992482, 19.40934193, 19.53641763, 15.88442231,
7.9295681 , 21.77780904, 12.47072318, 13.81039032, 17.39736673,
13.15453916, 21.36911849, 10.70201259, 10.59387373, 12.65426619,
11.54376933, 13.14155316, 16.87773098, 11.76657509, 14.89503279,
11.12283823, 21.28503492, 19.51000396, 16.66250962, 12.52950223,
20.49618371, 16.8304768 , 13.58145978, 5.67632775, 12.51501766,
16.40642439, 17.349106 , 7.89876048, 23.19479679, 10.2073971 ,
14.0566871 , 14.78658926, 20.44095559, 15.54838402, 14.89340323,
12.12392313, 8.92467267, 12.74334742, 17.47857181, 19.19866611,
2.45721744, 8.89357759, 8.14078952, 20.12140264, 13.08791183,
15.15808184, 11.52595765, 9.67495873, 8.53996197, 14.04442005,
22.79638213, 13.1821865 , 12.72650507, 9.90846988, 11.41972578,
17.93019223, 17.29881884, 12.55815741, 14.25266947, 10.33093622,
7.142058 , 21.69019735, 17.25592536, 13.08063266, 8.6764044 ,
10.24371983, 15.41889379, 4.28811294, 11.39411962, 26.56646754,
16.96888119, 18.83606664, 15.01419964, 15.34971026, 22.02537937,
4.36119692, 25.28447139, 10.48726479, 13.59826026, 14.06848502,
12.99063847, 16.78391206, 9.74175223, 19.30650068, 22.54410113,
24.98914811, 9.94762802, 21.40109447, 13.6695364 , 7.72764777,
9.64404477, 11.17475083, 12.60055258, 20.97721358, 23.34476622,
18.61854549, 15.52419453, 18.91436655, 9.62666948, 10.61228808,
18.06891452, 19.51675254, 13.23077434, 19.00453996, 13.32899195,
12.26702829, 15.6820842 , 8.11843937, 21.44353167, 15.52251699,
11.4997531 , 24.72088535, 16.28930122, 8.0863221 , 14.2955109 ,
7.17201489, 13.87547583, 17.67027528, 19.13974024, 22.68693614,
24.18678422, 6.06103468, 24.76111537, 10.97781017, 15.81552381,
10.84972314, 21.14140714, 13.76751962, 6.04277574, 24.58010336,
8.68660216, 16.87910212, 26.79462555, 16.53919855, 12.56473841,
12.48541333, 14.29182684, 10.48183018, 17.41734179, 10.97468455,
11.04737238, 11.98624708, 6.25940958, 14.55275986, 10.96969175,
14.44198921, 12.22024554, 21.06729722, 18.63578895, 15.01203526,
9.78234677, 12.69823694, 18.53826221, 18.33718908, 8.67841207,
12.40316973, 6.25257103, 13.12125998, 23.65467315, 8.48692567,
16.48741407, 22.88008283, 9.74399847, 24.23586658, 20.85856245,
6.93278193, 9.39064606, 11.74417199, 17.26023393, 17.4899952 ,
15.60605781, 17.23917663, 16.29546289, 10.05711149, 7.27520158,
13.98169832, 14.95431197, 15.24916955, 14.78272085, 21.01423717,
11.47976526, 16.05894889, 16.30783588, 11.30069306, 21.44509951,
21.12234178, 13.63871927, 8.67404016, 19.55244501, 10.47571836,
21.03142778, 19.22024405, 17.56288251, 11.91033324, 11.17856874,
11.01553264, 14.59588468, 8.70018864, 12.13660946, 24.77501267,
10.5752547 , 8.6157514 , 6.26227458, 14.93877643, 21.75858592,
17.27632436, 13.10800654, 21.44729017, 11.76678708, 21.22406953,
16.94976341, 5.29558133, 21.19612368, 9.19780541, 18.17468954,
16.2252154 , 19.59557946, 11.66578002, 19.06531132, 21.36638746,
20.6541794 , 15.53407109, 14.31871485, 8.88234657, 19.95083641,
15.33807678, 23.35312598, -0.41594355, 12.99684309, 17.81847784,
8.65620742, 11.11906853, 14.72213943, 10.96787938, 19.0607508 ,
15.06548159, 10.73352375, 7.80835499, 18.45434514, 10.94886255,
17.02581022, 15.74533205, 16.83597387, 15.6272432 , 15.07400084,
21.43340505, 19.8596281 , 15.10743665, 9.8524759 , 18.4157017 ,
8.71576747, 8.81978059, 18.74270385, 20.19611499, 7.78305738,
11.12512739, 11.0320895 , 4.08279734, 19.50883865, 18.3420021 ,
16.38436628, 15.41935401, 14.88358696, 21.28094706, 8.07498229,
5.44891281, 22.96052191, 4.82706329, 18.64935207, 6.81372616,
21.48792701, 10.81966699, 16.51777675, 13.66587892, 5.15167454,
19.6746364 , 18.92921522, 11.80793426, 15.99329862, 13.5428769 ,
17.09127653, 18.0650194 , 16.9966228 , 15.88075782, 10.01499427,
15.53643766, 14.82116696, 19.64805793, 13.38260529, 17.55275748,
12.79123029, 1.60388552, 9.64273317, 14.10328556, 12.80516354,
16.13765219, 24.82846188, 12.40167473, 14.37984687, 7.17152562,
3.57487493, 18.68028442, 22.02231231, 13.08430578, 15.36424363,
12.58209423, 19.16279834, 15.5310864 , 6.74056615, 18.95227059,
9.62725175, 24.77371694, 18.72404572, 22.33711919, 13.33348445,
10.82508722, 9.09203216, 2.25354049, 11.87985931, 9.93989723,
11.64604036, 13.04827512, 20.30953976, 12.18749003, 13.07841278,
16.33515174, 18.03769259, 16.79215097, 25.2966099 , 25.94685352,
5.69546044, 11.92172349, 19.8342691 , 12.6440724 , 20.28437939,
16.35062192, 14.67498508, 18.03873384, 11.98190638, 12.48304321,
17.18353178, 11.6280484 , 14.99820737, 18.62490662, 21.36806158,
10.33361186, 20.65887684, 18.14897646, 13.90046949, 11.97019342,
12.86450733, 14.79502629, 7.12492396, 14.02432214, 7.74633751,
20.42791153, 4.51691996, 12.45499258, 26.46562207, 25.43928357,
16.02156583, 15.77301938, 11.19124392, 20.2017068 , 11.38777201,
15.18199219, 13.06922751, 10.84513727, 16.72115341, 18.10468922,
3.27337482, 15.77864477, 11.69318446, 16.11939922, 5.75250131,
11.70259621, 9.96164663, 14.04692601, 16.66432923, 13.02730275,
22.14321073, 9.97324488, 20.89977249, 16.61452685, 14.98716636,
24.34831533, 12.64388541, 24.29123835, 18.62064405, 14.92635478,
20.6628145 , 12.35438856, 16.62651241, 15.23094509, 15.17337696,
6.55103708, 19.01998175, 23.25536204, 17.41528444, 21.16583531,
18.9232849 , 19.15163022, 7.24348554, 10.57811529, 19.75374939,
18.20943943, 22.28176618, 18.12091144, 12.23462022, 25.14515739,
13.50928872, 8.66583537, 15.73187863, 15.96399654, 12.59117191,
23.55583777, 17.89899462, 18.94571775, 11.52067712, 13.39007944,
12.59774377, 11.15893922, 10.95135673, 14.42674691, 17.96761055,
16.49122727, 12.35296522, 15.12034443, 15.74986747, 12.51710365,
9.03581021, 13.25291267, 10.15981987, 25.01072793, 18.97541121,
19.75442295, 15.72658347, 15.0584492 , 17.99054063, 5.09522099,
12.27876186, 16.19625502, 11.07278001, 20.34912201, 13.00536634,
16.99162583, 13.84421155, 14.38223744, 14.4279466 , 20.38561836,
11.8408168 , 13.61657858, 20.71889558, 13.34126163, 14.15019112,
25.77991025, 22.55302383, 19.96159611, 13.83428248, 10.01061803,
12.17573564, 12.90200549, 13.5309241 , 19.83578067, 9.19966323,
15.90839431, 10.42371807, 13.24996161, 11.84023659, 2.7949252 ,
18.58428929, 14.08771667, 11.87739049, 12.94936314, 5.72137785,
9.00511033, 24.56904689, 12.63663553, 17.76632913, 23.15421102,
17.48732613, 15.18508386, 16.55137541, 21.34679622, 16.29028048,
11.45359196, 20.79345061, 2.00181134, 15.79546808, 20.83970512,
22.25406941, 10.11771432, 8.67460536, 22.40106521, 8.87171 ,
15.65385265, 11.95693078, 17.83327425, 9.60459978, 8.27386886,
13.12833899, 11.16273179, 19.53131749, 23.54517063, 24.42752656,
23.96310917, 18.4784533 , 7.23297108, 10.54061606, 20.86058868,
11.84239435, 18.31885798, 11.17861973, 10.97931156, 16.58560191,
9.07837253, 15.33024139, 23.10263136, 14.38242525, 20.74808168,
14.23372606, 15.70755858, 21.23669843, 1.35217317, 12.37976423,
7.14600676, 18.39477579, 17.74507314, 18.47339341, 15.85955812,
12.03810285, 19.7152867 , 15.52989343, 9.58677829, 12.8273546 ,
13.12251889, 11.9852327 , 9.64979431, 4.67254736, 5.83485027,
25.58650984, 22.28051372, 10.58979922, 18.30552752, 13.38469023,
15.49887756, 18.9906719 , 15.28172592, 13.98672976, 22.84162786,
11.16412929, 28.13020788, 14.53176387, 10.58195681, 13.62227215,
19.39882903, 16.4404991 , 16.22472679, 21.69188583, 9.39591894,
13.51732061, 12.43309456, 24.88460179, 18.21701532, 12.22188839,
17.66401711, 18.73947878, 19.96433349, 12.44923092, 16.2503667 ,
13.93141598, 20.68562248, 13.22824968, 14.49476981, 14.65557226,
13.50932288, 18.78651139, 13.11609163, 13.72922394, 5.48391225,
21.74546083, 4.54749793, 9.61640779, 12.76025987, 16.6814269 ,
7.54157478, 14.12360955, 20.48599741, 7.25144564, 18.90874243,
18.4448861 , 9.92069343, 16.25574177, 6.89835745, 13.93865526,
21.31573413, 13.36223344, 15.66061316, 19.88795267, 17.3822145 ,
18.74051049, 18.76796461, 4.8938116 , 15.55699742, 13.40461 ,
15.95881726, 18.13730597, 24.84545955, -0.18746347, 15.51857936,
14.85838324, 9.53991868, 8.24792235, 14.38302382, 16.59971307,
19.10383007, 16.70314952, 14.83957597, 19.67174747, 21.01421162,
16.47244826, 11.66594006, 17.90942654, 12.45942559, 11.75413215,
15.8393784 , 16.96650233, 21.34297276, 16.89814547, 17.18400988,
23.06562578, 9.31042315, 17.07230418, 14.9523615 , 15.95823228,
21.05921299, 16.08593307, 19.09498751, 10.39794857, 25.96392485,
14.69617158, 17.66141256, 10.20026414, 6.2382827 , 16.86058806,
19.25205008, 13.70010653, 2.27012525, 16.92507514, 24.39321811,
10.85770341, 23.02441749, 19.2417343 , 19.05590996, 22.87654077,
20.41296773, 23.48198919, 16.00712785, 7.06656844, 15.13328042,
12.70216947, 7.8267746 , 23.43791089, 28.6641215 , 14.95792843,
10.89927121, 8.22974538, 6.62927352, 10.62186059, 12.56804649,
13.18951132, 14.61382172, 23.37917646, 20.56671153, 16.86438089,
18.11011443, 20.30088321, 17.27765895, 7.96486177, 9.15479929])

nd2 = np.random.normal(loc=5, scale=1, size=1000)
nd2

输出

array([4.16357325, 5.62772375, 3.9552129 , 5.01722184, 5.94728292,
3.87611474, 6.40455754, 7.46665515, 3.38515994, 5.34430971,
3.92116982, 5.26760858, 3.87753152, 4.91594112, 2.28170611,
5.22466842, 4.24448323, 5.5262917 , 5.51089455, 6.09550044,
6.8681638 , 4.88041697, 4.89141158, 4.26124332, 5.4022748 ,
4.61356982, 4.62460457, 7.67978794, 4.57731853, 6.67464937,
5.10280295, 4.33677994, 4.19373832, 5.36394475, 4.27075858,
5.39031978, 4.30670864, 3.93891792, 6.19342666, 4.07486768,
6.27780597, 4.55468073, 6.73625441, 5.86779949, 5.49083393,
4.58853995, 5.6148867 , 3.48363662, 6.63894092, 5.22175345,
4.39575909, 4.6296257 , 5.13293428, 4.63102278, 5.83887074,
5.23785433, 5.8753402 , 3.73502303, 5.15459362, 4.57097034,
5.67559993, 4.69832925, 5.65150331, 4.33270153, 3.92274046,
5.70642851, 5.60828203, 4.59928909, 4.16696389, 5.27554203,
6.56415018, 5.11148937, 5.33306229, 4.79096096, 7.14275288,
3.24127949, 5.42305971, 4.8503601 , 4.73999867, 3.30017711,
5.48859061, 8.6720081 , 4.61849386, 5.82255506, 4.94013434,
4.29599778, 3.64307498, 5.91702747, 4.53273311, 2.86494732,
4.83081915, 4.66762299, 5.51913047, 4.86923194, 5.64247402,
6.92941377, 5.80016722, 4.71514671, 4.36175335, 5.29760305,
4.03054667, 5.77336301, 4.27461778, 5.39664654, 2.95356186,
5.69516542, 5.65799556, 5.59469922, 6.1820571 , 3.33644612,
5.01553752, 5.61859069, 5.76889055, 5.33857628, 4.44932627,
5.44414466, 6.69054537, 4.95445522, 3.93424904, 6.14749563,
5.1360424 , 4.9205245 , 6.27270956, 3.402828 , 6.63289999,
4.53090779, 4.06289069, 5.68322406, 4.82075541, 4.18470219,
4.23731251, 5.784845 , 4.69988419, 5.60999033, 3.89867943,
4.91213246, 6.32055125, 3.32742804, 4.387534 , 4.22955403,
5.10986226, 5.31619754, 5.26842713, 4.30329285, 4.02172716,
5.12122162, 5.74783566, 4.89342586, 4.14319473, 4.89207417,
5.17976012, 4.92222856, 4.41448012, 6.11759185, 6.20346597,
6.30937222, 4.45736507, 4.19464601, 5.95110871, 5.39087317,
4.56342672, 5.41105951, 5.19021551, 4.44773853, 6.43024933,
4.2240709 , 3.10760838, 4.10843011, 3.49803467, 4.13008625,
4.93954846, 5.36686046, 4.94802037, 5.92916376, 3.6722152 ,
5.59956637, 2.26681606, 4.98058069, 5.52226543, 4.3678841 ,
5.79127271, 7.01911131, 5.54222329, 5.1736914 , 5.12307703,
3.64086435, 4.49529974, 3.09617049, 5.23362009, 4.83003444,
5.97855068, 4.90465821, 3.3488062 , 5.68806677, 3.10608318,
3.59533022, 4.1582079 , 5.42337516, 5.18032938, 3.80619133,
6.16036382, 7.10779899, 5.79528339, 4.20739267, 5.7849504 ,
6.35741234, 4.87889289, 4.67202464, 5.59772812, 3.89601316,
3.81930584, 3.86334162, 2.12820156, 6.03205986, 3.93786607,
4.69154114, 5.01000487, 5.48795451, 5.64960885, 3.28107906,
6.44071533, 5.84515172, 3.77795634, 4.1543535 , 4.76779398,
6.46011012, 4.20959715, 6.12791645, 3.13111379, 6.32404882,
4.01307035, 7.04022176, 5.88297238, 4.77726627, 5.81734169,
4.5834276 , 4.32792513, 4.78572659, 6.2394519 , 4.95323565,
3.76792869, 5.04695662, 4.6782468 , 4.26210285, 2.68574935,
4.87784877, 4.16379962, 4.89492512, 5.29077423, 6.63540688,
5.91601612, 5.79836639, 4.46454219, 5.59057577, 5.64159953,
3.73283891, 5.76728448, 5.59155539, 4.62458597, 4.59258478,
4.26568473, 4.13624814, 5.45664658, 3.20651961, 4.408289 ,
5.91191617, 6.42389566, 5.3310918 , 5.16260576, 5.08216533,
4.23972288, 5.4333913 , 5.52618567, 3.53352888, 6.18801528,
5.09545601, 5.47251721, 4.52145652, 4.48497876, 6.13442928,
5.46427115, 2.29920696, 5.96166798, 6.86041016, 4.64935911,
4.05211169, 5.22005598, 5.03828418, 3.11720228, 6.5067688 ,
4.16690752, 4.31972409, 7.66002522, 6.29075211, 6.54033656,
4.73315998, 4.48450546, 6.9592562 , 3.47862276, 5.30881486,
4.82692032, 5.57301637, 4.19444764, 5.20859468, 4.50189408,
4.34951492, 4.63316205, 5.55343187, 3.70853791, 6.23107709,
4.27639353, 3.43623509, 3.70621964, 4.20023314, 4.38700297,
6.0247284 , 4.10301279, 1.73386839, 3.8811813 , 5.95340252,
3.32468732, 5.33303337, 5.91283534, 6.00924919, 3.6032262 ,
4.59126025, 4.29693335, 6.78247321, 7.06582257, 3.79111525,
3.78370384, 5.66027207, 4.96055824, 5.70154465, 4.65007777,
4.5035654 , 5.61986658, 4.58564207, 6.36564777, 5.77875425,
4.70467287, 5.17829573, 5.82797022, 3.48804111, 5.03259832,
3.43797394, 5.4791678 , 4.29187316, 5.43345946, 2.97895257,
4.17941581, 6.46108478, 4.16631229, 3.82299511, 4.88063746,
5.99597437, 4.2566243 , 5.61937518, 4.45414788, 5.92053089,
3.49520851, 7.42316601, 5.29250923, 4.28792588, 7.36984588,
4.04040733, 4.26886281, 3.77000458, 4.56072738, 4.63605669,
4.95127483, 3.53601143, 3.72015063, 4.80868551, 3.67868907,
4.44574129, 5.95172132, 5.8080427 , 5.30838712, 5.24493577,
5.19000903, 4.74896707, 5.44316688, 3.96915616, 4.68391686,
7.5726147 , 4.91730717, 5.45240095, 5.9629974 , 2.47171197,
3.69924953, 4.68509439, 4.43821199, 4.99523136, 4.40460386,
4.96303014, 4.71679413, 4.57039619, 5.33357121, 5.15079823,
6.49139364, 3.86845536, 4.98871996, 3.7839628 , 4.273599 ,
5.95315578, 3.74863635, 4.11147589, 5.53989483, 4.80278295,
4.90408198, 6.95778251, 5.19522392, 5.5683528 , 5.98498926,
5.37911824, 6.28442773, 6.06750223, 6.05986868, 4.71704863,
3.99302845, 4.95290817, 2.49207559, 4.87651285, 5.15237529,
3.49276428, 4.99630332, 4.53049613, 4.93972966, 5.14459539,
6.43671059, 3.6443922 , 3.62322561, 4.93096589, 4.03285254,
5.12044196, 4.7470407 , 5.05114539, 4.98548726, 3.84715856,
4.38895987, 5.02571047, 4.27669533, 5.80634551, 3.74243646,
5.74672054, 4.30977187, 5.07860367, 5.36591131, 6.49938734,
4.5655998 , 6.72658129, 5.24367282, 4.48870661, 4.71499478,
6.42220582, 6.1114622 , 5.48564748, 5.57308475, 6.15891602,
5.9758781 , 5.85915079, 3.91862602, 2.12821654, 3.87280873,
3.66024374, 4.50284895, 5.21899411, 5.38545771, 7.16073212,
6.59658302, 5.00053088, 6.18178777, 3.63806579, 5.01496147,
5.90991082, 6.97094723, 4.83097336, 5.30165196, 4.661253 ,
5.86529545, 5.02720143, 4.84598703, 4.33055532, 3.21912353,
4.45419335, 4.77518495, 5.14216553, 6.36418059, 6.26262535,
4.59296046, 5.03454922, 6.00619901, 4.18621875, 4.91093635,
3.29685616, 3.79522521, 6.9291816 , 6.4750611 , 4.89884067,
5.78038694, 4.02603052, 5.70340682, 3.66391833, 6.78237457,
5.45288145, 4.96589685, 4.6344795 , 3.73365996, 4.93605937,
6.30098803, 6.15180329, 3.62245617, 4.31080067, 4.89444726,
4.46041046, 6.46807424, 6.69713398, 5.37660044, 3.82659646,
5.74103172, 4.49821583, 5.42852421, 2.79327574, 4.5965607 ,
4.89687084, 5.06394347, 3.76460552, 5.573225 , 3.72458122,
3.05024013, 5.17225729, 4.97343936, 4.62345663, 6.6316699 ,
3.89960346, 4.6849749 , 5.38701991, 5.70404233, 4.69017532,
5.876532 , 4.74405497, 5.44952709, 4.89016766, 6.21227894,
5.01569238, 6.63113838, 6.08459047, 4.42572288, 4.23403788,
4.58841822, 5.13660595, 3.78069157, 4.95872333, 5.94666079,
6.99971432, 5.57830349, 5.19189896, 3.35896715, 5.36527688,
5.72545598, 3.56843588, 4.57611497, 5.73088564, 6.5592119 ,
5.78983926, 4.76242211, 5.51854665, 5.37074764, 4.80702089,
5.68180577, 6.68828184, 3.97815013, 3.86441813, 5.50323984,
6.2954246 , 6.81212302, 4.34765753, 5.38604839, 3.57545063,
4.2153493 , 6.40788719, 5.04704768, 5.71393966, 5.46348939,
4.36487098, 5.72428558, 3.64913714, 5.09075194, 5.10808268,
4.53924537, 3.95084051, 5.80156225, 3.95139318, 6.40185987,
5.10152297, 5.9083863 , 6.77868656, 4.93491808, 6.87341423,
5.00020607, 5.37300145, 4.94257872, 4.05335049, 5.65874513,
7.05585192, 3.07188578, 6.47767778, 5.15548659, 6.61809565,
4.63849661, 4.00552794, 7.4990428 , 5.55064212, 3.95348289,
6.77047746, 5.17235527, 4.08495527, 5.69626916, 5.27694599,
6.5455655 , 5.03440539, 3.79535853, 3.88216299, 5.66687272,
5.42595946, 3.98806174, 5.38019678, 4.85600623, 6.60249457,
4.43772189, 7.31528327, 5.45132995, 6.46431198, 5.05253691,
5.30463532, 5.59352554, 4.46884691, 4.4371996 , 5.15883358,
5.31511306, 6.16704347, 6.77732211, 6.10656385, 5.15621815,
5.46977566, 4.51159292, 3.94227825, 5.31667496, 5.67738107,
5.28083731, 6.15320115, 3.42747691, 5.72918766, 5.91724691,
4.83514468, 4.19425714, 4.59213095, 4.23491379, 4.44307898,
4.85002062, 3.68601655, 3.34430793, 4.44669012, 5.21651207,
4.39399604, 6.1542975 , 5.18053303, 4.56594122, 5.82571705,
5.27565334, 5.06206244, 3.30770505, 3.91693396, 6.98565476,
3.4926006 , 5.1485241 , 6.21774637, 4.28257422, 6.52841365,
4.62857032, 4.23136567, 3.82420662, 3.74394523, 5.0192126 ,
5.16862418, 5.14095923, 6.43276907, 5.84756387, 5.30127721,
5.2689874 , 3.70364905, 6.55048716, 6.30709324, 3.52150307,
5.95410199, 5.04133322, 4.25300762, 4.66354312, 5.79064765,
4.40433573, 4.45537903, 5.89482175, 5.87593757, 5.68857767,
4.86160107, 4.19193785, 5.17753232, 3.9977945 , 4.9442369 ,
6.07835905, 5.02438589, 6.22749511, 5.24584004, 3.63975901,
4.68905444, 4.78441954, 6.4812497 , 3.69047389, 4.88141576,
5.79649154, 5.04203019, 5.83245323, 3.9658845 , 3.81575922,
4.46942339, 3.53117414, 4.055562 , 5.39131185, 4.68754333,
4.53087311, 4.20410031, 6.27197988, 6.85397581, 4.85151187,
5.02835855, 5.27851599, 5.17836394, 4.06129462, 4.4375603 ,
7.08798937, 5.5102644 , 3.81629454, 5.56699322, 5.10566699,
4.86234081, 3.8165508 , 4.40874826, 3.41589827, 4.29403851,
2.93109546, 4.71142032, 4.21331257, 3.94113757, 4.79319043,
4.51598912, 5.84379645, 5.3311838 , 5.62069354, 4.56616148,
3.78008828, 4.12170311, 3.47164841, 6.45149467, 7.07439161,
5.15775446, 4.99605674, 3.68084454, 4.81140656, 5.31432729,
5.44268601, 4.28726986, 7.01091418, 3.61892038, 4.94733244,
4.32424272, 4.0247057 , 5.80500818, 5.6972797 , 5.65320886,
6.39972657, 3.3030268 , 5.06080049, 2.94627967, 5.61769648,
4.40227402, 3.50383268, 5.70211839, 4.11933258, 5.08979386,
4.2676073 , 4.36874 , 5.60255516, 4.0650777 , 3.69051336,
6.11521605, 5.13445683, 5.62023694, 5.67238026, 4.62919193,
6.29114967, 5.76445703, 5.33025919, 3.04761616, 5.74038603,
6.90571771, 4.88536199, 4.37548828, 4.03133291, 3.98986754,
5.18311004, 3.60657778, 4.68029741, 6.77247176, 4.05498816,
5.81458552, 5.68999805, 4.49644808, 7.23364798, 4.78154564,
4.30177813, 4.15465457, 4.53973133, 5.27878305, 4.19131211,
3.85860472, 6.69376269, 6.05901324, 5.08240134, 4.48290616,
4.74503704, 4.56161557, 4.76352048, 5.58432118, 6.72418505,
4.58035456, 6.77123094, 5.92590072, 5.92009114, 3.80774064,
7.94398176, 4.16758862, 4.72331011, 5.78362765, 3.21055947,
5.76164498, 6.4397337 , 4.44481261, 3.33294915, 6.01488334,
6.81204549, 2.42044915, 4.4403678 , 5.47945654, 4.07128056,
5.30664905, 6.41306649, 6.01389555, 4.4295987 , 7.10258643,
4.65877207, 4.96947419, 5.99469993, 6.2051869 , 5.70905434,
4.61033172, 5.73426407, 4.35125811, 4.53512318, 3.95150925,
6.78339022, 4.30245366, 5.76650037, 5.15131781, 5.52825882,
6.8477806 , 5.18687599, 4.53926858, 7.22687264, 5.63437343,
5.86334083, 5.63117323, 3.69713011, 6.21429763, 4.22379533,
4.58421616, 4.20044833, 4.88802996, 6.11973732, 4.91362631,
5.99532401, 5.23794912, 4.71085432, 5.65410814, 5.13094576,
4.77431904, 5.86111277, 5.20227685, 5.20100846, 3.51333093,
5.61254166, 5.17714846, 4.34431496, 4.04362843, 5.32914837,
4.45681308, 5.11813353, 6.10079781, 5.20358212, 3.62758645,
5.52902048, 4.01751996, 5.43673173, 4.99834851, 4.23534757,
4.06770858, 5.3580892 , 4.40710573, 5.43241561, 4.60487781,
6.48997503, 3.70168528, 5.23138227, 6.23080636, 4.74835939,
3.80599272, 3.29946934, 6.34557388, 4.93872346, 6.47946449,
6.41594052, 6.73218324, 3.04289943, 4.15115282, 5.10402299,
4.50899173, 4.5168367 , 4.65360808, 6.01393715, 6.4251474 ,
4.46519491, 5.05383961, 4.87180319, 3.27026279, 5.01396013,
4.34003878, 5.76289112, 5.32605534, 4.25412225, 5.12852744,
3.58240371, 3.23632483, 5.32664125, 4.8313264 , 3.84089868,
6.76103641, 4.67086501, 4.974855 , 5.90858545, 4.11203174,
3.47689329, 4.59854682, 3.79011385, 5.67780065, 4.04810594,
5.67560683, 4.41887216, 5.47302958, 3.9217647 , 4.22237868,
5.03075115, 5.40360709, 5.63968412, 5.95374395, 5.32658004,
5.41877689, 4.8850843 , 4.61497329, 4.62041275, 5.04798966,
5.41226352, 4.7350273 , 2.27956637, 3.28210161, 4.90247233,
5.97151358, 3.03264944, 4.59598116, 5.61863583, 5.10906549,
5.896816 , 5.0875292 , 6.55366492, 6.06902742, 4.61753771,
4.35189183, 4.70411494, 6.3450411 , 4.79457834, 4.91535206])

nd3 = np.concatenate([nd1, nd2])
nd3

输出

array([16.50002167, 21.95871306, 22.75958416, …, 6.3450411 ,
4.79457834, 4.91535206])

s8 =Series(nd3)
s8

输出

0 16.500022
1 21.958713
2 22.759584
3 17.311623
4 21.113620
5 18.265816
6 14.367717
7 14.041937
8 24.472507
9 12.154959
10 9.272915
11 12.313423
12 10.209241
13 26.501067
14 15.738136
15 15.967169
16 16.691080
17 17.470095
18 23.255858
19 3.750134
20 25.121658
21 9.213113
22 20.805368
23 15.541248
24 28.903056
25 20.532467
26 11.801656
27 21.608022
28 11.175858
29 16.327408
…
1970 5.030751
1971 5.403607
1972 5.639684
1973 5.953744
1974 5.326580
1975 5.418777
1976 4.885084
1977 4.614973
1978 4.620413
1979 5.047990
1980 5.412264
1981 4.735027
1982 2.279566
1983 3.282102
1984 4.902472
1985 5.971514
1986 3.032649
1987 4.595981
1988 5.618636
1989 5.109065
1990 5.896816
1991 5.087529
1992 6.553665
1993 6.069027
1994 4.617538
1995 4.351892
1996 4.704115
1997 6.345041
1998 4.794578
1999 4.915352
Length: 2000, dtype: float64

# 密度图
s8.plot(kind='kde')

<matplotlib.axes._subplots.AxesSubplot at 0xda06e48>

这里写图片描述

s8.hist(bins=70)

<matplotlib.axes._subplots.AxesSubplot at 0xd8187f0>

这里写图片描述

s8.plot(kind='kde')
# 怎么解决密度线趴下来的问题
# normed 统一的，将数据归一化
s8.hist(bins= 70, normed=True)

<matplotlib.axes._subplots.AxesSubplot at 0xd568a58>

这里写图片描述

## 散布图

df = DataFrame(np.random.randint(0, 150, size=(10, 3)),columns=['Python', 'Java', 'PHP'])
df

.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }

	Python	Java	PHP
0	50	148	144
1	43	104	98
2	16	139	91
3	37	54	79
4	120	111	147
5	2	90	132
6	104	33	130
7	59	32	82
8	39	17	52
9	147	49	29

df['C++'] = df['Python'].map(lambda x : 0.7 * x + 20)
df

.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }

	Python	Java	PHP	C++
0	50	148	144	55.0
1	43	104	98	50.1
2	16	139	91	31.2
3	37	54	79	45.9
4	120	111	147	104.0
5	2	90	132	21.4
6	104	33	130	92.8
7	59	32	82	61.3
8	39	17	52	47.3
9	147	49	29	122.9

散布图散布图是观察两个一维数据列之间的关系的有效方法

注意是用kind=’scatter’ ，给标签columns

# scatter 需要x和y两个参数，作用是对比
df.plot(kind='scatter',x='Python', y='Java')

<matplotlib.axes._subplots.AxesSubplot at 0xf094be0>

这里写图片描述

df.plot(kind='scatter',x='Python', y='C++')

<matplotlib.axes._subplots.AxesSubplot at 0xfaccda0>

这里写图片描述

散布图矩阵，当有多个时，两两点之间的联系

函数：pd.plotting.scatter_matrix(),注意参数diagnol:对角线

pd.plotting.scatter_matrix(df, figsize=(12, 9))

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000FE00198>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000000FE38C50>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000000FE5D860>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000000FE86550>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000FE4A978>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000000FE4A7F0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000000FEF17F0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000000FF1B470>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000FF4C160>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000000FF76550>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000000FFAD0F0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000000FFD9C50>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000100137F0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000001002AC18>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000001007A9B0>,
<matplotlib.axes._subplots.AxesSubplot object at 0x000000001008FE80>]],
dtype=object)

这里写图片描述

pd.plotting.scatter_matrix(df, figsize=(12, 9), diagonal='kde')
# 散点图可以让我们找到两列数据之间的关系
# 一，如果数据量太小，两列数据没有关系，加大数据量，又会形成关系
# 二，并不是说数据量越大，两列之间的关系就越明显，在数据过大的情况下，
# 关系会发生改变

最后

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本文分类：Other
浏览次数：66 次浏览
发布日期：2024-01-23 20:25:25
本文链接：https://www.kaopuke.com/article/k-p-k_13_u_23_okf0_12__7_g2.html

pandas数据映射、聚合和绘图函数

概述

transform()与apply()函数还能传入一个函数或者lambda

最后

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	Python	Java	PHP	VR
0	122	19	146	88
1	82	105	120	146
2	134	19	81	127
3	0	78	75	39
4	3	115	19	8
5	16	147	71	44
6	16	92	124	131
7	42	141	64	114
8	31	103	64	13
9	103	83	128	108

	Python	Java	PHP	VR
0	122	19	146	88
1	82	105	120	146
2	134	19	81	127
3	0	78	75	39
4	3	115	19	8
5	16	147	71	44
6	16	92	124	131
7	42	141	64	114
8	31	103	64	13
9	103	83	128	108

	Python	Java	Math	China
0	0	94	45	52
1	130	44	120	60
2	45	134	149	14
3	18	62	4	55
4	73	66	112	27
5	17	90	61	136
6	106	101	29	100
7	60	58	142	60
8	47	94	70	24
9	9	115	101	80
10	92	2	135	35
11	149	57	39	92
12	131	143	149	142
13	35	68	12	119
14	137	62	47	68
15	67	74	140	34
16	85	139	88	104
17	32	122	31	145
18	65	24	33	17
19	68	41	60	99
20	76	104	29	78
21	61	9	26	58
22	65	23	12	22
23	27	90	122	93
24	55	119	49	27
25	92	95	116	127
26	127	96	39	60
27	116	15	103	130
28	86	53	11	128
29	120	91	138	124
…	…	…	…	…
970	100	60	17	66
971	122	148	30	22
972	33	133	121	110
973	34	66	62	133
974	88	14	93	131
975	109	86	143	28
976	69	45	86	31
977	55	92	57	138
978	12	63	72	23
979	100	40	116	142
980	113	87	74	80
981	44	62	93	39
982	144	63	130	111
983	38	57	105	55
984	10	18	94	20
985	86	144	12	35
986	96	6	8	54
987	10	93	61	127
988	7	61	79	110
989	28	141	128	76
990	136	136	13	66
991	80	129	61	101
992	141	143	51	38
993	29	46	118	129
994	40	64	69	100
995	92	70	142	54
996	115	71	62	55
997	28	10	93	107
998	31	137	88	75
999	76	36	44	119

	Python	Java	Math	China
110	11	145	121	64
799	9	4	42	21
333	36	3	134	145
998	31	137	88	75
143	6	146	87	23
602	115	109	59	53
903	132	32	147	81
74	102	57	146	84
500	124	116	84	17
218	4	99	91	126
490	74	56	39	134
771	1	75	57	6
632	39	10	145	106
615	75	113	82	37
369	46	17	139	106
227	50	144	134	67
518	27	99	42	15
866	68	30	79	43
626	47	15	27	111
748	91	111	70	89
135	84	46	131	110
926	1	56	129	148
905	31	136	25	32
387	136	24	103	131
837	145	50	10	18
804	60	75	70	139
727	6	57	98	14
907	142	63	88	25
538	145	41	89	18
81	51	11	33	69
646	130	7	43	15
131	148	117	103	17
846	141	84	99	48
923	111	148	40	78
96	108	128	41	108
275	4	41	93	41
612	15	7	16	81
757	103	79	48	70
581	13	10	73	37
709	53	40	117	121
641	146	17	127	23
724	22	68	16	32
671	134	34	140	80
445	108	45	134	55
883	137	114	112	90
300	139	104	121	9
451	66	134	138	127
711	39	92	147	74
466	14	99	34	70
322	108	86	122	62

	Python	Java	PHP
0	50	148	144
1	43	104	98
2	16	139	91
3	37	54	79
4	120	111	147
5	2	90	132
6	104	33	130
7	59	32	82
8	39	17	52
9	147	49	29

	Python	Java	PHP	VR
0	122	19	146	88
1	82	105	120	146
2	134	19	81	127
3	0	78	75	39
4	3	115	19	8
5	16	147	71	44
6	16	92	124	131
7	42	141	64	114
8	31	103	64	13
9	103	83	128	108

	Python	Java	PHP	VR
0	122	19	146	88
1	82	105	120	146
2	134	19	81	127
3	0	78	75	39
4	3	115	19	8
5	16	147	71	44
6	16	92	124	131
7	42	141	64	114
8	31	103	64	13
9	103	83	128	108

	Python	Java	Math	China
0	0	94	45	52
1	130	44	120	60
2	45	134	149	14
3	18	62	4	55
4	73	66	112	27
5	17	90	61	136
6	106	101	29	100
7	60	58	142	60
8	47	94	70	24
9	9	115	101	80
10	92	2	135	35
11	149	57	39	92
12	131	143	149	142
13	35	68	12	119
14	137	62	47	68
15	67	74	140	34
16	85	139	88	104
17	32	122	31	145
18	65	24	33	17
19	68	41	60	99
20	76	104	29	78
21	61	9	26	58
22	65	23	12	22
23	27	90	122	93
24	55	119	49	27
25	92	95	116	127
26	127	96	39	60
27	116	15	103	130
28	86	53	11	128
29	120	91	138	124
…	…	…	…	…
970	100	60	17	66
971	122	148	30	22
972	33	133	121	110
973	34	66	62	133
974	88	14	93	131
975	109	86	143	28
976	69	45	86	31
977	55	92	57	138
978	12	63	72	23
979	100	40	116	142
980	113	87	74	80
981	44	62	93	39
982	144	63	130	111
983	38	57	105	55
984	10	18	94	20
985	86	144	12	35
986	96	6	8	54
987	10	93	61	127
988	7	61	79	110
989	28	141	128	76
990	136	136	13	66
991	80	129	61	101
992	141	143	51	38
993	29	46	118	129
994	40	64	69	100
995	92	70	142	54
996	115	71	62	55
997	28	10	93	107
998	31	137	88	75
999	76	36	44	119

	Python	Java	Math	China
110	11	145	121	64
799	9	4	42	21
333	36	3	134	145
998	31	137	88	75
143	6	146	87	23
602	115	109	59	53
903	132	32	147	81
74	102	57	146	84
500	124	116	84	17
218	4	99	91	126
490	74	56	39	134
771	1	75	57	6
632	39	10	145	106
615	75	113	82	37
369	46	17	139	106
227	50	144	134	67
518	27	99	42	15
866	68	30	79	43
626	47	15	27	111
748	91	111	70	89
135	84	46	131	110
926	1	56	129	148
905	31	136	25	32
387	136	24	103	131
837	145	50	10	18
804	60	75	70	139
727	6	57	98	14
907	142	63	88	25
538	145	41	89	18
81	51	11	33	69
646	130	7	43	15
131	148	117	103	17
846	141	84	99	48
923	111	148	40	78
96	108	128	41	108
275	4	41	93	41
612	15	7	16	81
757	103	79	48	70
581	13	10	73	37
709	53	40	117	121
641	146	17	127	23
724	22	68	16	32
671	134	34	140	80
445	108	45	134	55
883	137	114	112	90
300	139	104	121	9
451	66	134	138	127
711	39	92	147	74
466	14	99	34	70
322	108	86	122	62

	Python	Java	PHP
0	50	148	144
1	43	104	98
2	16	139	91
3	37	54	79
4	120	111	147
5	2	90	132
6	104	33	130
7	59	32	82
8	39	17	52
9	147	49	29

pandas数据映射、聚合和绘图函数

概述

transform()与apply()函数还能传入一个函数或者lambda

最后

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	Python	Java	PHP	VR
0	122	19	146	88
1	82	105	120	146
2	134	19	81	127
3	0	78	75	39
4	3	115	19	8
5	16	147	71	44
6	16	92	124	131
7	42	141	64	114
8	31	103	64	13
9	103	83	128	108

	Python	Java	PHP	VR
0	122	19	146	88
1	82	105	120	146
2	134	19	81	127
3	0	78	75	39
4	3	115	19	8
5	16	147	71	44
6	16	92	124	131
7	42	141	64	114
8	31	103	64	13
9	103	83	128	108

	Python	Java	Math	China
0	0	94	45	52
1	130	44	120	60
2	45	134	149	14
3	18	62	4	55
4	73	66	112	27
5	17	90	61	136
6	106	101	29	100
7	60	58	142	60
8	47	94	70	24
9	9	115	101	80
10	92	2	135	35
11	149	57	39	92
12	131	143	149	142
13	35	68	12	119
14	137	62	47	68
15	67	74	140	34
16	85	139	88	104
17	32	122	31	145
18	65	24	33	17
19	68	41	60	99
20	76	104	29	78
21	61	9	26	58
22	65	23	12	22
23	27	90	122	93
24	55	119	49	27
25	92	95	116	127
26	127	96	39	60
27	116	15	103	130
28	86	53	11	128
29	120	91	138	124
…	…	…	…	…
970	100	60	17	66
971	122	148	30	22
972	33	133	121	110
973	34	66	62	133
974	88	14	93	131
975	109	86	143	28
976	69	45	86	31
977	55	92	57	138
978	12	63	72	23
979	100	40	116	142
980	113	87	74	80
981	44	62	93	39
982	144	63	130	111
983	38	57	105	55
984	10	18	94	20
985	86	144	12	35
986	96	6	8	54
987	10	93	61	127
988	7	61	79	110
989	28	141	128	76
990	136	136	13	66
991	80	129	61	101
992	141	143	51	38
993	29	46	118	129
994	40	64	69	100
995	92	70	142	54
996	115	71	62	55
997	28	10	93	107
998	31	137	88	75
999	76	36	44	119

	Python	Java	Math	China
110	11	145	121	64
799	9	4	42	21
333	36	3	134	145
998	31	137	88	75
143	6	146	87	23
602	115	109	59	53
903	132	32	147	81
74	102	57	146	84
500	124	116	84	17
218	4	99	91	126
490	74	56	39	134
771	1	75	57	6
632	39	10	145	106
615	75	113	82	37
369	46	17	139	106
227	50	144	134	67
518	27	99	42	15
866	68	30	79	43
626	47	15	27	111
748	91	111	70	89
135	84	46	131	110
926	1	56	129	148
905	31	136	25	32
387	136	24	103	131
837	145	50	10	18
804	60	75	70	139
727	6	57	98	14
907	142	63	88	25
538	145	41	89	18
81	51	11	33	69
646	130	7	43	15
131	148	117	103	17
846	141	84	99	48
923	111	148	40	78
96	108	128	41	108
275	4	41	93	41
612	15	7	16	81
757	103	79	48	70
581	13	10	73	37
709	53	40	117	121
641	146	17	127	23
724	22	68	16	32
671	134	34	140	80
445	108	45	134	55
883	137	114	112	90
300	139	104	121	9
451	66	134	138	127
711	39	92	147	74
466	14	99	34	70
322	108	86	122	62

	Python	Java	PHP
0	50	148	144
1	43	104	98
2	16	139	91
3	37	54	79
4	120	111	147
5	2	90	132
6	104	33	130
7	59	32	82
8	39	17	52
9	147	49	29