数据科学和人工智能技术笔记十九、数据整理（上）十九、数据整理（上）将列表拆分为大小为 N 的分块

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我是靠谱客的博主小巧大地，最近开发中收集的这篇文章主要介绍数据科学和人工智能技术笔记十九、数据整理（上）十九、数据整理（上）将列表拆分为大小为 N 的分块，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

十九、数据整理（上）

作者：Chris Albon

译者：飞龙

协议：CC BY-NC-SA 4.0

在 Pandas 中通过分组应用函数

import pandas as pd

# 创建示例数据帧
data = {'Platoon': ['A','A','A','A','A','A','B','B','B','B','B','C','C','C','C','C'],
       'Casualties': [1,4,5,7,5,5,6,1,4,5,6,7,4,6,4,6]}
df = pd.DataFrame(data)
df

	Casualties	Platoon
0	1	A
1	4	A
2	5	A
3	7	A
4	5	A
5	5	A
6	6	B
7	1	B
8	4	B
9	5	B
10	6	B
11	7	C
12	4	C
13	6	C
14	4	C
15	6	C

# 按照 df.platoon 对 df 分组
# 然后将滚动平均 lambda 函数应用于 df.casualties
df.groupby('Platoon')['Casualties'].apply(lambda x:x.rolling(center=False,window=2).mean())

'''
0     NaN
1     2.5
2     4.5
3     6.0
4     6.0
5     5.0
6     NaN
7     3.5
8     2.5
9     4.5
10    5.5
11    NaN
12    5.5
13    5.0
14    5.0
15    5.0
dtype: float64
'''

在 Pandas 中向分组应用操作

# 导入模块
import pandas as pd

# 创建数据帧
raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], 
        'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], 
        'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], 
        'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'name', 'preTestScore', 'postTestScore'])
df

	regiment	company	name	preTestScore	postTestScore
0	Nighthawks	1st	Miller	4	25
1	Nighthawks	1st	Jacobson	24	94
2	Nighthawks	2nd	Ali	31	57
3	Nighthawks	2nd	Milner	2	62
4	Dragoons	1st	Cooze	3	70
5	Dragoons	1st	Jacon	4	25
6	Dragoons	2nd	Ryaner	24	94
7	Dragoons	2nd	Sone	31	57
8	Scouts	1st	Sloan	2	62
9	Scouts	1st	Piger	3	70
10	Scouts	2nd	Riani	2	62
11	Scouts	2nd	Ali	3	70

# 创建一个 groupby 变量，按团队（regiment）对 preTestScores 分组
groupby_regiment = df['preTestScore'].groupby(df['regiment'])
groupby_regiment

# <pandas.core.groupby.SeriesGroupBy object at 0x113ddb550>

“这个分组变量现在是GroupBy对象。除了分组的键df ['key1']的一些中间数据之外，它实际上还没有计算任何东西。我们的想法是，该对象具有将所有操作应用于每个分组所需的所有信息。” – PyDA

使用list()显示分组的样子。

list(df['preTestScore'].groupby(df['regiment']))

'''
[('Dragoons', 4     3
  5     4
  6    24
  7    31
  Name: preTestScore, dtype: int64), ('Nighthawks', 0     4
  1    24
  2    31
  3     2
  Name: preTestScore, dtype: int64), ('Scouts', 8     2
  9     3
  10    2
  11    3
  Name: preTestScore, dtype: int64)] 
'''

df['preTestScore'].groupby(df['regiment']).describe()

	count	mean	std	min	25%	50%	75%	max
regiment
Dragoons	4.0	15.50	14.153916	3.0	3.75	14.0	25.75	31.0
Nighthawks	4.0	15.25	14.453950	2.0	3.50	14.0	25.75	31.0
Scouts	4.0	2.50	0.577350	2.0	2.00	2.5	3.00	3.0

# 每个团队的 preTestScore 均值
groupby_regiment.mean()

'''
regiment
Dragoons      15.50
Nighthawks    15.25
Scouts         2.50
Name: preTestScore, dtype: float64 
'''

df['preTestScore'].groupby([df['regiment'], df['company']]).mean()

'''
regiment    company
Dragoons    1st         3.5
            2nd        27.5
Nighthawks  1st        14.0
            2nd        16.5
Scouts      1st         2.5
            2nd         2.5
Name: preTestScore, dtype: float64 
'''

df['preTestScore'].groupby([df['regiment'], df['company']]).mean().unstack()

company	1st	2nd
regiment
Dragoons	3.5	27.5
Nighthawks	14.0	16.5
Scouts	2.5	2.5

# 按团队和公司（company）对整个数据帧分组
df.groupby(['regiment', 'company']).mean()

		preTestScore	postTestScore
regiment	company
Dragoons	1st	3.5	47.5
2nd	27.5	75.5
Nighthawks	1st	14.0	59.5
2nd	16.5	59.5
Scouts	1st	2.5	66.0
2nd	2.5	66.0

# 每个团队和公司的观测数量
df.groupby(['regiment', 'company']).size()

'''
regiment    company
Dragoons    1st        2
            2nd        2
Nighthawks  1st        2
            2nd        2
Scouts      1st        2
            2nd        2
dtype: int64 
'''

# 按团队对数据帧分组，对于每个团队，
for name, group in df.groupby('regiment'): 
    # 打印团队名称
    print(name)
    # 打印它的数据
    print(group)


'''
Dragoons
   regiment company    name  preTestScore  postTestScore
4  Dragoons     1st   Cooze             3             70
5  Dragoons     1st   Jacon             4             25
6  Dragoons     2nd  Ryaner            24             94
7  Dragoons     2nd    Sone            31             57
Nighthawks
     regiment company      name  preTestScore  postTestScore
0  Nighthawks     1st    Miller             4             25
1  Nighthawks     1st  Jacobson            24             94
2  Nighthawks     2nd       Ali            31             57
3  Nighthawks     2nd    Milner             2             62
Scouts
   regiment company   name  preTestScore  postTestScore
8    Scouts     1st  Sloan             2             62
9    Scouts     1st  Piger             3             70
10   Scouts     2nd  Riani             2             62
11   Scouts     2nd    Ali             3             70 
'''

按列分组：

特别是在这种情况下：按列对数据类型（即axis = 1）分组，然后使用list()查看该分组的外观。

list(df.groupby(df.dtypes, axis=1))

'''
[(dtype('int64'),     preTestScore  postTestScore
  0              4             25
  1             24             94
  2             31             57
  3              2             62
  4              3             70
  5              4             25
  6             24             94
  7             31             57
  8              2             62
  9              3             70
  10             2             62
  11             3             70),
 (dtype('O'),       regiment company      name
  0   Nighthawks     1st    Miller
  1   Nighthawks     1st  Jacobson
  2   Nighthawks     2nd       Ali
  3   Nighthawks     2nd    Milner
  4     Dragoons     1st     Cooze
  5     Dragoons     1st     Jacon
  6     Dragoons     2nd    Ryaner
  7     Dragoons     2nd      Sone
  8       Scouts     1st     Sloan
  9       Scouts     1st     Piger
  10      Scouts     2nd     Riani
  11      Scouts     2nd       Ali)] 

df.groupby('regiment').mean().add_prefix('mean_')

	mean_preTestScore	mean_postTestScore
regiment
Dragoons	15.50	61.5
Nighthawks	15.25	59.5
Scouts	2.50	66.0

# 创建获取分组状态的函数
def get_stats(group):
    return {'min': group.min(), 'max': group.max(), 'count': group.count(), 'mean': group.mean()}

bins = [0, 25, 50, 75, 100]
group_names = ['Low', 'Okay', 'Good', 'Great']
df['categories'] = pd.cut(df['postTestScore'], bins, labels=group_names)

df['postTestScore'].groupby(df['categories']).apply(get_stats).unstack()

	count	max	mean	min
categories
Good	8.0	70.0	63.75	57.0
Great	2.0	94.0	94.00	94.0
Low	2.0	25.0	25.00	25.0
Okay	0.0	NaN	NaN	NaN

在 Pandas 数据帧上应用操作

# 导入模型
import pandas as pd
import numpy as np

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'reports': [4, 24, 31, 2, 3],
        'coverage': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df

	coverage	name	reports	year
Cochice	25	Jason	4	2012
Pima	94	Molly	24	2012
Santa Cruz	57	Tina	31	2013
Maricopa	62	Jake	2	2014
Yuma	70	Amy	3	2014

# 创建大写转换的 lambda 函数
capitalizer = lambda x: x.upper()

将capitalizer函数应用于name列。

apply()可以沿数据帧的任意轴应用函数。

df['name'].apply(capitalizer)

'''
Cochice       JASON
Pima          MOLLY
Santa Cruz     TINA
Maricopa       JAKE
Yuma            AMY
Name: name, dtype: object 
'''

将capitalizer lambda 函数映射到序列name中的每个元素。

map()对序列的每个元素应用操作。

df['name'].map(capitalizer)

'''
Cochice       JASON
Pima          MOLLY
Santa Cruz     TINA
Maricopa       JAKE
Yuma            AMY
Name: name, dtype: object 
'''

将平方根函数应用于整个数据帧中的每个单元格。

applymap()将函数应用于整个数据帧中的每个元素。

# 删除字符串变量，以便 applymap() 可以运行
df = df.drop('name', axis=1)

# 返回数据帧每个单元格的平方根
df.applymap(np.sqrt)

	coverage	reports	year
Cochice	5.000000	2.000000	44.855323
Pima	9.695360	4.898979	44.855323
Santa Cruz	7.549834	5.567764	44.866469
Maricopa	7.874008	1.414214	44.877611
Yuma	8.366600	1.732051	44.877611

在数据帧上应用函数。

# 创建叫做 times100 的函数
def times100(x):
    # 如果 x 是字符串，
    if type(x) is str:
        # 原样返回它
        return x
    # 如果不是，返回它乘上 100
    elif x:
        return 100 * x
    # 并留下其它东西
    else:
        return

df.applymap(times100)

	coverage	reports	year
Cochice	2500	400	201200
Pima	9400	2400	201200
Santa Cruz	5700	3100	201300
Maricopa	6200	200	201400
Yuma	7000	300	201400

向 Pandas 数据帧赋予新列

import pandas as pd

# 创建空数据帧
df = pd.DataFrame()

# 创建一列
df['name'] = ['John', 'Steve', 'Sarah']

# 查看数据帧
df

	name
0	John
1	Steve
2	Sarah

# 将一个新列赋予名为 age 的 df，它包含年龄列表
df.assign(age = [31, 32, 19])

	name	age
0	John	31
1	Steve	32
2	Sarah	19

将列表拆分为大小为 N 的分块

在这个片段中，我们接受一个列表并将其分解为大小为 n 的块。在处理具有最大请求大小的 API 时，这是一种非常常见的做法。

这个漂亮的函数由 Ned Batchelder 贡献，发布于 StackOverflow。

# 创建名称列表
first_names = ['Steve', 'Jane', 'Sara', 'Mary','Jack','Bob', 'Bily', 'Boni', 'Chris','Sori', 'Will', 'Won','Li']

# 创建叫做 chunks 的函数，有两个参数 l 和 n
def chunks(l, n):
    # 对于长度为 l 的范围中的项目 i
    for i in range(0, len(l), n):
        # 创建索引范围
        yield l[i:i+n]

# 从函数 chunks 的结果创建一个列表
list(chunks(first_names, 5))

'''
[['Steve', 'Jane', 'Sara', 'Mary', 'Jack'],
 ['Bob', 'Bily', 'Boni', 'Chris', 'Sori'],
 ['Will', 'Won', 'Li']] 
'''

在 Pandas 中使用正则表达式将字符串分解为列

# 导入模块
import re
import pandas as pd

# 创建带有一列字符串的数据帧
data = {'raw': ['Arizona 1 2014-12-23       3242.0',
                'Iowa 1 2010-02-23       3453.7',
                'Oregon 0 2014-06-20       2123.0',
                'Maryland 0 2014-03-14       1123.6',
                'Florida 1 2013-01-15       2134.0',
                'Georgia 0 2012-07-14       2345.6']}
df = pd.DataFrame(data, columns = ['raw'])
df

	raw
0	Arizona 1 2014-12-23 3242.0
1	Iowa 1 2010-02-23 3453.7
2	Oregon 0 2014-06-20 2123.0
3	Maryland 0 2014-03-14 1123.6
4	Florida 1 2013-01-15 2134.0
5	Georgia 0 2012-07-14 2345.6

# df['raw'] 的哪些行包含 'xxxx-xx-xx'？
df['raw'].str.contains('....-..-..', regex=True)

'''
0    True
1    True
2    True
3    True
4    True
5    True
Name: raw, dtype: bool 
'''

# 在 raw 列中，提取字符串中的单个数字
df['female'] = df['raw'].str.extract('(d)', expand=True)
df['female']

'''
0    1
1    1
2    0
3    0
4    1
5    0
Name: female, dtype: object 
'''

# 在 raw 列中，提取字符串中的 xxxx-xx-xx
df['date'] = df['raw'].str.extract('(....-..-..)', expand=True)
df['date']

'''
0    2014-12-23
1    2010-02-23
2    2014-06-20
3    2014-03-14
4    2013-01-15
5    2012-07-14
Name: date, dtype: object 
'''

# 在 raw 列中，提取字符串中的 ####.##
df['score'] = df['raw'].str.extract('(dddd.d)', expand=True)
df['score']

'''
0    3242.0
1    3453.7
2    2123.0
3    1123.6
4    2134.0
5    2345.6
Name: score, dtype: object 
'''

# 在 raw 列中，提取字符串中的单词
df['state'] = df['raw'].str.extract('([A-Z]w{0,})', expand=True)
df['state']

'''
0     Arizona
1        Iowa
2      Oregon
3    Maryland
4     Florida
5     Georgia
Name: state, dtype: object 
'''

df

	raw	female	date	score	state
0	Arizona 1 2014-12-23 3242.0	1	2014-12-23	3242.0	Arizona
1	Iowa 1 2010-02-23 3453.7	1	2010-02-23	3453.7	Iowa
2	Oregon 0 2014-06-20 2123.0	0	2014-06-20	2123.0	Oregon
3	Maryland 0 2014-03-14 1123.6	0	2014-03-14	1123.6	Maryland
4	Florida 1 2013-01-15 2134.0	1	2013-01-15	2134.0	Florida
5	Georgia 0 2012-07-14 2345.6	0	2012-07-14	2345.6	Georgia

由两个数据帧贡献列

# 导入库
import pandas as pd

# 创建数据帧
dataframe_one = pd.DataFrame()
dataframe_one['1'] = ['1', '1', '1']
dataframe_one['B'] = ['b', 'b', 'b']

# 创建第二个数据帧
dataframe_two = pd.DataFrame()
dataframe_two['2'] = ['2', '2', '2']
dataframe_two['B'] = ['b', 'b', 'b']

# 将每个数据帧的列转换为集合，
# 然后找到这两个集合的交集。
# 这将是两个数据帧共享的列的集合。
set.intersection(set(dataframe_one), set(dataframe_two))

# {'B'}

从多个列表构建字典

# 创建官员名称的列表
officer_names = ['Sodoni Dogla', 'Chris Jefferson', 'Jessica Billars', 'Michael Mulligan', 'Steven Johnson']

# 创建官员军队的列表
officer_armies = ['Purple Army', 'Orange Army', 'Green Army', 'Red Army', 'Blue Army']

# 创建字典，它是两个列表的 zip
dict(zip(officer_names, officer_armies))

'''
{'Chris Jefferson': 'Orange Army',
 'Jessica Billars': 'Green Army',
 'Michael Mulligan': 'Red Army',
 'Sodoni Dogla': 'Purple Army',
 'Steven Johnson': 'Blue Army'} 
'''

将 CSV 转换为 Python 代码来重建它

# 导入 pandas 包
import pandas as pd

# 将 csv 文件加载为数据帧
df_original = pd.read_csv('http://vincentarelbundock.github.io/Rdatasets/csv/datasets/iris.csv')
df = pd.read_csv('http://vincentarelbundock.github.io/Rdatasets/csv/datasets/iris.csv')

# 打印创建数据帧的代码
print('==============================')
print('RUN THE CODE BELOW THIS LINE')
print('==============================')
print('raw_data =', df.to_dict(orient='list'))
print('df = pd.DataFrame(raw_data, columns = ' + str(list(df_original)) + ')')

'''
==============================
RUN THE CODE BELOW THIS LINE
==============================
raw_data = {'Sepal.Length': [5.0999999999999996, 4.9000000000000004, 4.7000000000000002, 4.5999999999999996, 5.0, 5.4000000000000004, 4.5999999999999996, 5.0, 4.4000000000000004, 4.9000000000000004, 5.4000000000000004, 4.7999999999999998, 4.7999999999999998, 4.2999999999999998, 5.7999999999999998, 5.7000000000000002, 5.4000000000000004, 5.0999999999999996, 5.7000000000000002, 5.0999999999999996, 5.4000000000000004, 5.0999999999999996, 4.5999999999999996, 5.0999999999999996, 4.7999999999999998, 5.0, 5.0, 5.2000000000000002, 5.2000000000000002, 4.7000000000000002, 4.7999999999999998, 5.4000000000000004, 5.2000000000000002, 5.5, 4.9000000000000004, 5.0, 5.5, 4.9000000000000004, 4.4000000000000004, 5.0999999999999996, 5.0, 4.5, 4.4000000000000004, 5.0, 5.0999999999999996, 4.7999999999999998, 5.0999999999999996, 4.5999999999999996, 5.2999999999999998, 5.0, 7.0, 6.4000000000000004, 6.9000000000000004, 5.5, 6.5, 5.7000000000000002, 6.2999999999999998, 4.9000000000000004, 6.5999999999999996, 5.2000000000000002, 5.0, 5.9000000000000004, 6.0, 6.0999999999999996, 5.5999999999999996, 6.7000000000000002, 5.5999999999999996, 5.7999999999999998, 6.2000000000000002, 5.5999999999999996, 5.9000000000000004, 6.0999999999999996, 6.2999999999999998, 6.0999999999999996, 6.4000000000000004, 6.5999999999999996, 6.7999999999999998, 6.7000000000000002, 6.0, 5.7000000000000002, 5.5, 5.5, 5.7999999999999998, 6.0, 5.4000000000000004, 6.0, 6.7000000000000002, 6.2999999999999998, 5.5999999999999996, 5.5, 5.5, 6.0999999999999996, 5.7999999999999998, 5.0, 5.5999999999999996, 5.7000000000000002, 5.7000000000000002, 6.2000000000000002, 5.0999999999999996, 5.7000000000000002, 6.2999999999999998, 5.7999999999999998, 7.0999999999999996, 6.2999999999999998, 6.5, 7.5999999999999996, 4.9000000000000004, 7.2999999999999998, 6.7000000000000002, 7.2000000000000002, 6.5, 6.4000000000000004, 6.7999999999999998, 5.7000000000000002, 5.7999999999999998, 6.4000000000000004, 6.5, 7.7000000000000002, 7.7000000000000002, 6.0, 6.9000000000000004, 5.5999999999999996, 7.7000000000000002, 6.2999999999999998, 6.7000000000000002, 7.2000000000000002, 6.2000000000000002, 6.0999999999999996, 6.4000000000000004, 7.2000000000000002, 7.4000000000000004, 7.9000000000000004, 6.4000000000000004, 6.2999999999999998, 6.0999999999999996, 7.7000000000000002, 6.2999999999999998, 6.4000000000000004, 6.0, 6.9000000000000004, 6.7000000000000002, 6.9000000000000004, 5.7999999999999998, 6.7999999999999998, 6.7000000000000002, 6.7000000000000002, 6.2999999999999998, 6.5, 6.2000000000000002, 5.9000000000000004], 'Petal.Width': [0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.40000000000000002, 0.29999999999999999, 0.20000000000000001, 0.20000000000000001, 0.10000000000000001, 0.20000000000000001, 0.20000000000000001, 0.10000000000000001, 0.10000000000000001, 0.20000000000000001, 0.40000000000000002, 0.40000000000000002, 0.29999999999999999, 0.29999999999999999, 0.29999999999999999, 0.20000000000000001, 0.40000000000000002, 0.20000000000000001, 0.5, 0.20000000000000001, 0.20000000000000001, 0.40000000000000002, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.40000000000000002, 0.10000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.10000000000000001, 0.20000000000000001, 0.20000000000000001, 0.29999999999999999, 0.29999999999999999, 0.20000000000000001, 0.59999999999999998, 0.40000000000000002, 0.29999999999999999, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 1.3999999999999999, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6000000000000001, 1.0, 1.3, 1.3999999999999999, 1.0, 1.5, 1.0, 1.3999999999999999, 1.3, 1.3999999999999999, 1.5, 1.0, 1.5, 1.1000000000000001, 1.8, 1.3, 1.5, 1.2, 1.3, 1.3999999999999999, 1.3999999999999999, 1.7, 1.5, 1.0, 1.1000000000000001, 1.0, 1.2, 1.6000000000000001, 1.5, 1.6000000000000001, 1.5, 1.3, 1.3, 1.3, 1.2, 1.3999999999999999, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1000000000000001, 1.3, 2.5, 1.8999999999999999, 2.1000000000000001, 1.8, 2.2000000000000002, 2.1000000000000001, 1.7, 1.8, 1.8, 2.5, 2.0, 1.8999999999999999, 2.1000000000000001, 2.0, 2.3999999999999999, 2.2999999999999998, 1.8, 2.2000000000000002, 2.2999999999999998, 1.5, 2.2999999999999998, 2.0, 2.0, 1.8, 2.1000000000000001, 1.8, 1.8, 1.8, 2.1000000000000001, 1.6000000000000001, 1.8999999999999999, 2.0, 2.2000000000000002, 1.5, 1.3999999999999999, 2.2999999999999998, 2.3999999999999999, 1.8, 1.8, 2.1000000000000001, 2.3999999999999999, 2.2999999999999998, 1.8999999999999999, 2.2999999999999998, 2.5, 2.2999999999999998, 1.8999999999999999, 2.0, 2.2999999999999998, 1.8], 'Petal.Length': [1.3999999999999999, 1.3999999999999999, 1.3, 1.5, 1.3999999999999999, 1.7, 1.3999999999999999, 1.5, 1.3999999999999999, 1.5, 1.5, 1.6000000000000001, 1.3999999999999999, 1.1000000000000001, 1.2, 1.5, 1.3, 1.3999999999999999, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.8999999999999999, 1.6000000000000001, 1.6000000000000001, 1.5, 1.3999999999999999, 1.6000000000000001, 1.6000000000000001, 1.5, 1.5, 1.3999999999999999, 1.5, 1.2, 1.3, 1.3999999999999999, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6000000000000001, 1.8999999999999999, 1.3999999999999999, 1.6000000000000001, 1.3999999999999999, 1.5, 1.3999999999999999, 4.7000000000000002, 4.5, 4.9000000000000004, 4.0, 4.5999999999999996, 4.5, 4.7000000000000002, 3.2999999999999998, 4.5999999999999996, 3.8999999999999999, 3.5, 4.2000000000000002, 4.0, 4.7000000000000002, 3.6000000000000001, 4.4000000000000004, 4.5, 4.0999999999999996, 4.5, 3.8999999999999999, 4.7999999999999998, 4.0, 4.9000000000000004, 4.7000000000000002, 4.2999999999999998, 4.4000000000000004, 4.7999999999999998, 5.0, 4.5, 3.5, 3.7999999999999998, 3.7000000000000002, 3.8999999999999999, 5.0999999999999996, 4.5, 4.5, 4.7000000000000002, 4.4000000000000004, 4.0999999999999996, 4.0, 4.4000000000000004, 4.5999999999999996, 4.0, 3.2999999999999998, 4.2000000000000002, 4.2000000000000002, 4.2000000000000002, 4.2999999999999998, 3.0, 4.0999999999999996, 6.0, 5.0999999999999996, 5.9000000000000004, 5.5999999999999996, 5.7999999999999998, 6.5999999999999996, 4.5, 6.2999999999999998, 5.7999999999999998, 6.0999999999999996, 5.0999999999999996, 5.2999999999999998, 5.5, 5.0, 5.0999999999999996, 5.2999999999999998, 5.5, 6.7000000000000002, 6.9000000000000004, 5.0, 5.7000000000000002, 4.9000000000000004, 6.7000000000000002, 4.9000000000000004, 5.7000000000000002, 6.0, 4.7999999999999998, 4.9000000000000004, 5.5999999999999996, 5.7999999999999998, 6.0999999999999996, 6.4000000000000004, 5.5999999999999996, 5.0999999999999996, 5.5999999999999996, 6.0999999999999996, 5.5999999999999996, 5.5, 4.7999999999999998, 5.4000000000000004, 5.5999999999999996, 5.0999999999999996, 5.0999999999999996, 5.9000000000000004, 5.7000000000000002, 5.2000000000000002, 5.0, 5.2000000000000002, 5.4000000000000004, 5.0999999999999996], 'Species': ['setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica'], 'Sepal.Width': [3.5, 3.0, 3.2000000000000002, 3.1000000000000001, 3.6000000000000001, 3.8999999999999999, 3.3999999999999999, 3.3999999999999999, 2.8999999999999999, 3.1000000000000001, 3.7000000000000002, 3.3999999999999999, 3.0, 3.0, 4.0, 4.4000000000000004, 3.8999999999999999, 3.5, 3.7999999999999998, 3.7999999999999998, 3.3999999999999999, 3.7000000000000002, 3.6000000000000001, 3.2999999999999998, 3.3999999999999999, 3.0, 3.3999999999999999, 3.5, 3.3999999999999999, 3.2000000000000002, 3.1000000000000001, 3.3999999999999999, 4.0999999999999996, 4.2000000000000002, 3.1000000000000001, 3.2000000000000002, 3.5, 3.6000000000000001, 3.0, 3.3999999999999999, 3.5, 2.2999999999999998, 3.2000000000000002, 3.5, 3.7999999999999998, 3.0, 3.7999999999999998, 3.2000000000000002, 3.7000000000000002, 3.2999999999999998, 3.2000000000000002, 3.2000000000000002, 3.1000000000000001, 2.2999999999999998, 2.7999999999999998, 2.7999999999999998, 3.2999999999999998, 2.3999999999999999, 2.8999999999999999, 2.7000000000000002, 2.0, 3.0, 2.2000000000000002, 2.8999999999999999, 2.8999999999999999, 3.1000000000000001, 3.0, 2.7000000000000002, 2.2000000000000002, 2.5, 3.2000000000000002, 2.7999999999999998, 2.5, 2.7999999999999998, 2.8999999999999999, 3.0, 2.7999999999999998, 3.0, 2.8999999999999999, 2.6000000000000001, 2.3999999999999999, 2.3999999999999999, 2.7000000000000002, 2.7000000000000002, 3.0, 3.3999999999999999, 3.1000000000000001, 2.2999999999999998, 3.0, 2.5, 2.6000000000000001, 3.0, 2.6000000000000001, 2.2999999999999998, 2.7000000000000002, 3.0, 2.8999999999999999, 2.8999999999999999, 2.5, 2.7999999999999998, 3.2999999999999998, 2.7000000000000002, 3.0, 2.8999999999999999, 3.0, 3.0, 2.5, 2.8999999999999999, 2.5, 3.6000000000000001, 3.2000000000000002, 2.7000000000000002, 3.0, 2.5, 2.7999999999999998, 3.2000000000000002, 3.0, 3.7999999999999998, 2.6000000000000001, 2.2000000000000002, 3.2000000000000002, 2.7999999999999998, 2.7999999999999998, 2.7000000000000002, 3.2999999999999998, 3.2000000000000002, 2.7999999999999998, 3.0, 2.7999999999999998, 3.0, 2.7999999999999998, 3.7999999999999998, 2.7999999999999998, 2.7999999999999998, 2.6000000000000001, 3.0, 3.3999999999999999, 3.1000000000000001, 3.0, 3.1000000000000001, 3.1000000000000001, 3.1000000000000001, 2.7000000000000002, 3.2000000000000002, 3.2999999999999998, 3.0, 2.5, 3.0, 3.3999999999999999, 3.0], 'Unnamed: 0': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150]}
'''

df = pd.DataFrame(raw_data, columns = ['Unnamed: 0', 'Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width', 'Species']) 

# 如果你打算检查结果
# 1. 输入此单元格中上面单元格生成的代码
raw_data = {'Petal.Width': [0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.40000000000000002, 0.29999999999999999, 0.20000000000000001, 0.20000000000000001, 0.10000000000000001, 0.20000000000000001, 0.20000000000000001, 0.10000000000000001, 0.10000000000000001, 0.20000000000000001, 0.40000000000000002, 0.40000000000000002, 0.29999999999999999, 0.29999999999999999, 0.29999999999999999, 0.20000000000000001, 0.40000000000000002, 0.20000000000000001, 0.5, 0.20000000000000001, 0.20000000000000001, 0.40000000000000002, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.40000000000000002, 0.10000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.10000000000000001, 0.20000000000000001, 0.20000000000000001, 0.29999999999999999, 0.29999999999999999, 0.20000000000000001, 0.59999999999999998, 0.40000000000000002, 0.29999999999999999, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 0.20000000000000001, 1.3999999999999999, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6000000000000001, 1.0, 1.3, 1.3999999999999999, 1.0, 1.5, 1.0, 1.3999999999999999, 1.3, 1.3999999999999999, 1.5, 1.0, 1.5, 1.1000000000000001, 1.8, 1.3, 1.5, 1.2, 1.3, 1.3999999999999999, 1.3999999999999999, 1.7, 1.5, 1.0, 1.1000000000000001, 1.0, 1.2, 1.6000000000000001, 1.5, 1.6000000000000001, 1.5, 1.3, 1.3, 1.3, 1.2, 1.3999999999999999, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1000000000000001, 1.3, 2.5, 1.8999999999999999, 2.1000000000000001, 1.8, 2.2000000000000002, 2.1000000000000001, 1.7, 1.8, 1.8, 2.5, 2.0, 1.8999999999999999, 2.1000000000000001, 2.0, 2.3999999999999999, 2.2999999999999998, 1.8, 2.2000000000000002, 2.2999999999999998, 1.5, 2.2999999999999998, 2.0, 2.0, 1.8, 2.1000000000000001, 1.8, 1.8, 1.8, 2.1000000000000001, 1.6000000000000001, 1.8999999999999999, 2.0, 2.2000000000000002, 1.5, 1.3999999999999999, 2.2999999999999998, 2.3999999999999999, 1.8, 1.8, 2.1000000000000001, 2.3999999999999999, 2.2999999999999998, 1.8999999999999999, 2.2999999999999998, 2.5, 2.2999999999999998, 1.8999999999999999, 2.0, 2.2999999999999998, 1.8], 'Sepal.Width': [3.5, 3.0, 3.2000000000000002, 3.1000000000000001, 3.6000000000000001, 3.8999999999999999, 3.3999999999999999, 3.3999999999999999, 2.8999999999999999, 3.1000000000000001, 3.7000000000000002, 3.3999999999999999, 3.0, 3.0, 4.0, 4.4000000000000004, 3.8999999999999999, 3.5, 3.7999999999999998, 3.7999999999999998, 3.3999999999999999, 3.7000000000000002, 3.6000000000000001, 3.2999999999999998, 3.3999999999999999, 3.0, 3.3999999999999999, 3.5, 3.3999999999999999, 3.2000000000000002, 3.1000000000000001, 3.3999999999999999, 4.0999999999999996, 4.2000000000000002, 3.1000000000000001, 3.2000000000000002, 3.5, 3.6000000000000001, 3.0, 3.3999999999999999, 3.5, 2.2999999999999998, 3.2000000000000002, 3.5, 3.7999999999999998, 3.0, 3.7999999999999998, 3.2000000000000002, 3.7000000000000002, 3.2999999999999998, 3.2000000000000002, 3.2000000000000002, 3.1000000000000001, 2.2999999999999998, 2.7999999999999998, 2.7999999999999998, 3.2999999999999998, 2.3999999999999999, 2.8999999999999999, 2.7000000000000002, 2.0, 3.0, 2.2000000000000002, 2.8999999999999999, 2.8999999999999999, 3.1000000000000001, 3.0, 2.7000000000000002, 2.2000000000000002, 2.5, 3.2000000000000002, 2.7999999999999998, 2.5, 2.7999999999999998, 2.8999999999999999, 3.0, 2.7999999999999998, 3.0, 2.8999999999999999, 2.6000000000000001, 2.3999999999999999, 2.3999999999999999, 2.7000000000000002, 2.7000000000000002, 3.0, 3.3999999999999999, 3.1000000000000001, 2.2999999999999998, 3.0, 2.5, 2.6000000000000001, 3.0, 2.6000000000000001, 2.2999999999999998, 2.7000000000000002, 3.0, 2.8999999999999999, 2.8999999999999999, 2.5, 2.7999999999999998, 3.2999999999999998, 2.7000000000000002, 3.0, 2.8999999999999999, 3.0, 3.0, 2.5, 2.8999999999999999, 2.5, 3.6000000000000001, 3.2000000000000002, 2.7000000000000002, 3.0, 2.5, 2.7999999999999998, 3.2000000000000002, 3.0, 3.7999999999999998, 2.6000000000000001, 2.2000000000000002, 3.2000000000000002, 2.7999999999999998, 2.7999999999999998, 2.7000000000000002, 3.2999999999999998, 3.2000000000000002, 2.7999999999999998, 3.0, 2.7999999999999998, 3.0, 2.7999999999999998, 3.7999999999999998, 2.7999999999999998, 2.7999999999999998, 2.6000000000000001, 3.0, 3.3999999999999999, 3.1000000000000001, 3.0, 3.1000000000000001, 3.1000000000000001, 3.1000000000000001, 2.7000000000000002, 3.2000000000000002, 3.2999999999999998, 3.0, 2.5, 3.0, 3.3999999999999999, 3.0], 'Species': ['setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'versicolor', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica', 'virginica'], 'Unnamed: 0': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150], 'Sepal.Length': [5.0999999999999996, 4.9000000000000004, 4.7000000000000002, 4.5999999999999996, 5.0, 5.4000000000000004, 4.5999999999999996, 5.0, 4.4000000000000004, 4.9000000000000004, 5.4000000000000004, 4.7999999999999998, 4.7999999999999998, 4.2999999999999998, 5.7999999999999998, 5.7000000000000002, 5.4000000000000004, 5.0999999999999996, 5.7000000000000002, 5.0999999999999996, 5.4000000000000004, 5.0999999999999996, 4.5999999999999996, 5.0999999999999996, 4.7999999999999998, 5.0, 5.0, 5.2000000000000002, 5.2000000000000002, 4.7000000000000002, 4.7999999999999998, 5.4000000000000004, 5.2000000000000002, 5.5, 4.9000000000000004, 5.0, 5.5, 4.9000000000000004, 4.4000000000000004, 5.0999999999999996, 5.0, 4.5, 4.4000000000000004, 5.0, 5.0999999999999996, 4.7999999999999998, 5.0999999999999996, 4.5999999999999996, 5.2999999999999998, 5.0, 7.0, 6.4000000000000004, 6.9000000000000004, 5.5, 6.5, 5.7000000000000002, 6.2999999999999998, 4.9000000000000004, 6.5999999999999996, 5.2000000000000002, 5.0, 5.9000000000000004, 6.0, 6.0999999999999996, 5.5999999999999996, 6.7000000000000002, 5.5999999999999996, 5.7999999999999998, 6.2000000000000002, 5.5999999999999996, 5.9000000000000004, 6.0999999999999996, 6.2999999999999998, 6.0999999999999996, 6.4000000000000004, 6.5999999999999996, 6.7999999999999998, 6.7000000000000002, 6.0, 5.7000000000000002, 5.5, 5.5, 5.7999999999999998, 6.0, 5.4000000000000004, 6.0, 6.7000000000000002, 6.2999999999999998, 5.5999999999999996, 5.5, 5.5, 6.0999999999999996, 5.7999999999999998, 5.0, 5.5999999999999996, 5.7000000000000002, 5.7000000000000002, 6.2000000000000002, 5.0999999999999996, 5.7000000000000002, 6.2999999999999998, 5.7999999999999998, 7.0999999999999996, 6.2999999999999998, 6.5, 7.5999999999999996, 4.9000000000000004, 7.2999999999999998, 6.7000000000000002, 7.2000000000000002, 6.5, 6.4000000000000004, 6.7999999999999998, 5.7000000000000002, 5.7999999999999998, 6.4000000000000004, 6.5, 7.7000000000000002, 7.7000000000000002, 6.0, 6.9000000000000004, 5.5999999999999996, 7.7000000000000002, 6.2999999999999998, 6.7000000000000002, 7.2000000000000002, 6.2000000000000002, 6.0999999999999996, 6.4000000000000004, 7.2000000000000002, 7.4000000000000004, 7.9000000000000004, 6.4000000000000004, 6.2999999999999998, 6.0999999999999996, 7.7000000000000002, 6.2999999999999998, 6.4000000000000004, 6.0, 6.9000000000000004, 6.7000000000000002, 6.9000000000000004, 5.7999999999999998, 6.7999999999999998, 6.7000000000000002, 6.7000000000000002, 6.2999999999999998, 6.5, 6.2000000000000002, 5.9000000000000004], 'Petal.Length': [1.3999999999999999, 1.3999999999999999, 1.3, 1.5, 1.3999999999999999, 1.7, 1.3999999999999999, 1.5, 1.3999999999999999, 1.5, 1.5, 1.6000000000000001, 1.3999999999999999, 1.1000000000000001, 1.2, 1.5, 1.3, 1.3999999999999999, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.8999999999999999, 1.6000000000000001, 1.6000000000000001, 1.5, 1.3999999999999999, 1.6000000000000001, 1.6000000000000001, 1.5, 1.5, 1.3999999999999999, 1.5, 1.2, 1.3, 1.3999999999999999, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6000000000000001, 1.8999999999999999, 1.3999999999999999, 1.6000000000000001, 1.3999999999999999, 1.5, 1.3999999999999999, 4.7000000000000002, 4.5, 4.9000000000000004, 4.0, 4.5999999999999996, 4.5, 4.7000000000000002, 3.2999999999999998, 4.5999999999999996, 3.8999999999999999, 3.5, 4.2000000000000002, 4.0, 4.7000000000000002, 3.6000000000000001, 4.4000000000000004, 4.5, 4.0999999999999996, 4.5, 3.8999999999999999, 4.7999999999999998, 4.0, 4.9000000000000004, 4.7000000000000002, 4.2999999999999998, 4.4000000000000004, 4.7999999999999998, 5.0, 4.5, 3.5, 3.7999999999999998, 3.7000000000000002, 3.8999999999999999, 5.0999999999999996, 4.5, 4.5, 4.7000000000000002, 4.4000000000000004, 4.0999999999999996, 4.0, 4.4000000000000004, 4.5999999999999996, 4.0, 3.2999999999999998, 4.2000000000000002, 4.2000000000000002, 4.2000000000000002, 4.2999999999999998, 3.0, 4.0999999999999996, 6.0, 5.0999999999999996, 5.9000000000000004, 5.5999999999999996, 5.7999999999999998, 6.5999999999999996, 4.5, 6.2999999999999998, 5.7999999999999998, 6.0999999999999996, 5.0999999999999996, 5.2999999999999998, 5.5, 5.0, 5.0999999999999996, 5.2999999999999998, 5.5, 6.7000000000000002, 6.9000000000000004, 5.0, 5.7000000000000002, 4.9000000000000004, 6.7000000000000002, 4.9000000000000004, 5.7000000000000002, 6.0, 4.7999999999999998, 4.9000000000000004, 5.5999999999999996, 5.7999999999999998, 6.0999999999999996, 6.4000000000000004, 5.5999999999999996, 5.0999999999999996, 5.5999999999999996, 6.0999999999999996, 5.5999999999999996, 5.5, 4.7999999999999998, 5.4000000000000004, 5.5999999999999996, 5.0999999999999996, 5.0999999999999996, 5.9000000000000004, 5.7000000000000002, 5.2000000000000002, 5.0, 5.2000000000000002, 5.4000000000000004, 5.0999999999999996]}
df = pd.DataFrame(raw_data, columns = ['Unnamed: 0', 'Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width', 'Species'])

# 查看原始数据帧的前几行
df.head()

	Unnamed: 0	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
0	1	5.1	3.5	1.4	0.2	setosa
1	2	4.9	3.0	1.4	0.2	setosa
2	3	4.7	3.2	1.3	0.2	setosa
3	4	4.6	3.1	1.5	0.2	setosa
4	5	5.0	3.6	1.4	0.2	setosa

# 查看使用我们的代码创建的，数据帧的前几行
df_original.head()

	Unnamed: 0	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
0	1	5.1	3.5	1.4	0.2	setosa
1	2	4.9	3.0	1.4	0.2	setosa
2	3	4.7	3.2	1.3	0.2	setosa
3	4	4.6	3.1	1.5	0.2	setosa
4	5	5.0	3.6	1.4	0.2	setosa

将分类变量转换为虚拟变量

# 导入模块
import pandas as pd

# 创建数据帧
raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], 
        'sex': ['male', 'female', 'male', 'female', 'female']}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'sex'])
df

	first_name	last_name	sex
0	Jason	Miller	male
1	Molly	Jacobson	female
2	Tina	Ali	male
3	Jake	Milner	female
4	Amy	Cooze	female

# 从 sex 变量创建一组虚拟变量
df_sex = pd.get_dummies(df['sex'])

# 将虚拟变量连接到主数据帧
df_new = pd.concat([df, df_sex], axis=1)
df_new

	first_name	last_name	sex	female	male
0	Jason	Miller	male	0.0	1.0
1	Molly	Jacobson	female	1.0	0.0
2	Tina	Ali	male	0.0	1.0
3	Jake	Milner	female	1.0	0.0
4	Amy	Cooze	female	1.0	0.0

# 连接新列的替代方案
df_new = df.join(df_sex)
df_new

	first_name	last_name	sex	female	male
0	Jason	Miller	male	0.0	1.0
1	Molly	Jacobson	female	1.0	0.0
2	Tina	Ali	male	0.0	1.0
3	Jake	Milner	female	1.0	0.0
4	Amy	Cooze	female	1.0	0.0

将分类变量转换为虚拟变量

# 导入模块
import pandas as pd
import patsy

# 创建数据帧
raw_data = {'countrycode': [1, 2, 3, 2, 1]} 
df = pd.DataFrame(raw_data, columns = ['countrycode'])
df

	countrycode
0	1
1	2
2	3
3	2
4	1

# 将 countrycode 变量转换为三个二元变量
patsy.dmatrix('C(countrycode)-1', df, return_type='dataframe')

	C(countrycode)[1]	C(countrycode)[2]	C(countrycode)[3]
0	1.0	0.0	0.0
1	0.0	1.0	0.0
2	0.0	0.0	1.0
3	0.0	1.0	0.0
4	1.0	0.0	0.0

将字符串分类变量转换为数字变量

# 导入模块
import pandas as pd

raw_data = {'patient': [1, 1, 1, 2, 2], 
        'obs': [1, 2, 3, 1, 2], 
        'treatment': [0, 1, 0, 1, 0],
        'score': ['strong', 'weak', 'normal', 'weak', 'strong']} 
df = pd.DataFrame(raw_data, columns = ['patient', 'obs', 'treatment', 'score'])
df

	patient	obs	treatment	score
0	1	1	0	strong
1	1	2	1	weak
2	1	3	0	normal
3	2	1	1	weak
4	2	2	0	strong

# 创建一个函数，将 df['score'] 的所有值转换为数字
def score_to_numeric(x):
    if x=='strong':
        return 3
    if x=='normal':
        return 2
    if x=='weak':
        return 1

df['score_num'] = df['score'].apply(score_to_numeric)
df

	patient	obs	treatment	score	score_num
0	1	1	0	strong	3
1	1	2	1	weak	1
2	1	3	0	normal	2
3	2	1	1	weak	1
4	2	2	0	strong	3

将变量转换为时间序列

# 导入库
import pandas as pd

# 创建索引为一组名称的数据集
raw_data = {'date': ['2014-06-01T01:21:38.004053', '2014-06-02T01:21:38.004053', '2014-06-03T01:21:38.004053'],
        'score': [25, 94, 57]}
df = pd.DataFrame(raw_data, columns = ['date', 'score'])
df

	date	score
0	2014-06-01T01:21:38.004053	25
1	2014-06-02T01:21:38.004053	94
2	2014-06-03T01:21:38.004053	57

# 转置数据集，使索引（在本例中为名称）为列
df["date"] = pd.to_datetime(df["date"])

df = df.set_index(df["date"])

df

	date	score
date
—	—	—
2014-06-01 01:21:38.004053	2014-06-01 01:21:38.004053	25
2014-06-02 01:21:38.004053	2014-06-02 01:21:38.004053	94
2014-06-03 01:21:38.004053	2014-06-03 01:21:38.004053	57

在 Pandas 数据帧中计数

# 导入库
import pandas as pd

year = pd.Series([1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 
                  1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894])
guardCorps = pd.Series([0,2,2,1,0,0,1,1,0,3,0,2,1,0,0,1,0,1,0,1])
corps1 = pd.Series([0,0,0,2,0,3,0,2,0,0,0,1,1,1,0,2,0,3,1,0])
corps2 = pd.Series([0,0,0,2,0,2,0,0,1,1,0,0,2,1,1,0,0,2,0,0])
corps3 = pd.Series([0,0,0,1,1,1,2,0,2,0,0,0,1,0,1,2,1,0,0,0])
corps4 = pd.Series([0,1,0,1,1,1,1,0,0,0,0,1,0,0,0,0,1,1,0,0])
corps5 = pd.Series([0,0,0,0,2,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0])
corps6 = pd.Series([0,0,1,0,2,0,0,1,2,0,1,1,3,1,1,1,0,3,0,0])
corps7 = pd.Series([1,0,1,0,0,0,1,0,1,1,0,0,2,0,0,2,1,0,2,0])
corps8 = pd.Series([1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,1,0,1])
corps9 = pd.Series([0,0,0,0,0,2,1,1,1,0,2,1,1,0,1,2,0,1,0,0])
corps10 = pd.Series([0,0,1,1,0,1,0,2,0,2,0,0,0,0,2,1,3,0,1,1])
corps11 = pd.Series([0,0,0,0,2,4,0,1,3,0,1,1,1,1,2,1,3,1,3,1])
corps14 = pd.Series([ 1,1,2,1,1,3,0,4,0,1,0,3,2,1,0,2,1,1,0,0])
corps15 = pd.Series([0,1,0,0,0,0,0,1,0,1,1,0,0,0,2,2,0,0,0,0])

variables = dict(guardCorps = guardCorps, corps1 = corps1, 
                 corps2 = corps2, corps3 = corps3, corps4 = corps4, 
                 corps5 = corps5, corps6 = corps6, corps7 = corps7, 
                 corps8 = corps8, corps9 = corps9, corps10 = corps10, 
                 corps11 = corps11 , corps14 = corps14, corps15 = corps15)

horsekick = pd.DataFrame(variables, columns = ['guardCorps', 
                                                    'corps1', 'corps2', 
                                                    'corps3', 'corps4', 
                                                    'corps5', 'corps6', 
                                                    'corps7', 'corps8', 
                                                    'corps9', 'corps10', 
                                                    'corps11', 'corps14', 
                                                    'corps15'])

horsekick.index = [1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 
                  1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894]

horsekick

	guardCorps	corps1	corps2	corps3	corps4	corps5	corps6	corps7	corps8	corps9	corps10	corps11	corps14	corps15
1875	0	0	0	0	0	0	0	1	1	0	0	0	1	0
1876	2	0	0	0	1	0	0	0	0	0	0	0	1	1
1877	2	0	0	0	0	0	1	1	0	0	1	0	2	0
1878	1	2	2	1	1	0	0	0	0	0	1	0	1	0
1879	0	0	0	1	1	2	2	0	1	0	0	2	1	0
1880	0	3	2	1	1	1	0	0	0	2	1	4	3	0
1881	1	0	0	2	1	0	0	1	0	1	0	0	0	0
1882	1	2	0	0	0	0	1	0	1	1	2	1	4	1
1883	0	0	1	2	0	1	2	1	0	1	0	3	0	0
1884	3	0	1	0	0	0	0	1	0	0	2	0	1	1
1885	0	0	0	0	0	0	1	0	0	2	0	1	0	1
1886	2	1	0	0	1	1	1	0	0	1	0	1	3	0
1887	1	1	2	1	0	0	3	2	1	1	0	1	2	0
1888	0	1	1	0	0	1	1	0	0	0	0	1	1	0
1889	0	0	1	1	0	1	1	0	0	1	2	2	0	2
1890	1	2	0	2	0	1	1	2	0	2	1	1	2	2
1891	0	0	0	1	1	1	0	1	1	0	3	3	1	0
1892	1	3	2	0	1	1	3	0	1	1	0	1	1	0
1893	0	1	0	0	0	1	0	2	0	0	1	3	0	0
1894	1	0	0	0	0	0	0	0	1	0	1	1	0	0

# 计算每个团队中每个死亡人数的次数
result = horsekick.apply(pd.value_counts).fillna(0); result

| | guardCorps | corps1 | corps2 | corps3 | corps4 | corps5 | corps6 | corps7 | corps8 | corps9 | corps10 | corps11 | corps14 | corps15 |
| 0 | 9.0 | 11.0 | 12.0 | 11.0 | 12.0 | 10.0 | 9.0 | 11.0 | 13.0 | 10.0 | 10.0 | 6 | 6 | 14.0 |
| 1 | 7.0 | 4.0 | 4.0 | 6.0 | 8.0 | 9.0 | 7.0 | 6.0 | 7.0 | 7.0 | 6.0 | 8 | 8 | 4.0 |
| 2 | 3.0 | 3.0 | 4.0 | 3.0 | 0.0 | 1.0 | 2.0 | 3.0 | 0.0 | 3.0 | 3.0 | 2 | 3 | 2.0 |
| 3 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 1.0 | 3 | 2 | 0.0 |
| 4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 | 1 | 0.0 |

# 计算每个月死亡总数出现在 guardCorps 的次数
pd.value_counts(horsekick['guardCorps'].values, sort=False)

'''
0    9
1    7
2    3
3    1
dtype: int64 
'''

horsekick['guardCorps'].unique()

# array([0, 2, 1, 3])

在 Pandas 中创建流水线

Pandas 的流水线功能允许你将 Python 函数串联在一起，来构建数据处理流水线。

import pandas as pd

# 创建空数据帧
df = pd.DataFrame()

# Create a column
df['name'] = ['John', 'Steve', 'Sarah']
df['gender'] = ['Male', 'Male', 'Female']
df['age'] = [31, 32, 19]

# 查看数据帧
df

	name	gender	age
0	John	Male	31
1	Steve	Male	32
2	Sarah	Female	19

# 创建函数，
def mean_age_by_group(dataframe, col):
    # 它按列分组数据，并返回每组的均值
    return dataframe.groupby(col).mean()

# 创建函数，
def uppercase_column_name(dataframe):
    # 它大写所有列标题
    dataframe.columns = dataframe.columns.str.upper()
    # 并返回它
    return dataframe

# 创建流水线，它应用 mean_age_by_group 函数
(df.pipe(mean_age_by_group, col='gender')
   # 之后应用 uppercase_column_name 函数
   .pipe(uppercase_column_name)
)

	AGE
gender
Female	19.0
Male	31.5

使用`for`循环创建 Pandas 列

import pandas as pd
import numpy as np

raw_data = {'student_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], 
        'test_score': [76, 88, 84, 67, 53, 96, 64, 91, 77, 73, 52, np.NaN]}
df = pd.DataFrame(raw_data, columns = ['student_name', 'test_score'])

# 创建列表来储存数据
grades = []

# 对于列中的每一行
for row in df['test_score']:
    # 如果大于某个值
    if row > 95:
        # 添加字母分数
        grades.append('A')
    # 或者，如果大于某个值
    elif row > 90:
        # 添加字母分数
        grades.append('A-')
    # 或者，如果大于某个值
    elif row > 85:
        # 添加字母分数
        grades.append('B')
    # 或者，如果大于某个值
    elif row > 80:
        # 添加字母分数
        grades.append('B-')
    # 或者，如果大于某个值
    elif row > 75:
        # 添加字母分数
        grades.append('C')
    # 或者，如果大于某个值
    elif row > 70:
        # 添加字母分数
        grades.append('C-')
    # 或者，如果大于某个值
    elif row > 65:
        # 添加字母分数
        grades.append('D')
    # 或者，如果大于某个值
    elif row > 60:
        # 添加字母分数
        grades.append('D-')
    # 否则
    else:
        # 添加不及格分数
        grades.append('Failed')

# 从列表创建一列
df['grades'] = grades

# 查看新数据帧
df

	student_name	test_score	grades
0	Miller	76.0	C
1	Jacobson	88.0	B
2	Ali	84.0	B-
3	Milner	67.0	D
4	Cooze	53.0	Failed
5	Jacon	96.0	A
6	Ryaner	64.0	D-
7	Sone	91.0	A-
8	Sloan	77.0	C
9	Piger	73.0	C-
10	Riani	52.0	Failed
11	Ali	NaN	Failed

创建项目计数

from collections import Counter

# 创建一个今天吃的水果的计数器
fruit_eaten = Counter(['Apple', 'Apple', 'Apple', 'Banana', 'Pear', 'Pineapple'])

# 查看计数器
fruit_eaten

# Counter({'Apple': 3, 'Banana': 1, 'Pear': 1, 'Pineapple': 1}) 

# 更新菠萝的计数（因为你只吃菠萝）
fruit_eaten.update(['Pineapple'])

# 查看计数器
fruit_eaten

# Counter({'Apple': 3, 'Banana': 1, 'Pear': 1, 'Pineapple': 2}) 

# 查看计数最大的三个项目
fruit_eaten.most_common(3)

# [('Apple', 3), ('Pineapple', 2), ('Banana', 1)]

基于条件创建一列

# 导入所需模块
import pandas as pd
import numpy as np

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, columns = ['name', 'age', 'preTestScore', 'postTestScore'])
df

	name	age	preTestScore	postTestScore
0	Jason	42	4	25
1	Molly	52	24	94
2	Tina	36	31	57
3	Jake	24	2	62
4	Amy	73	3	70

# 创建一个名为 df.elderly 的新列
# 如果 df.age 大于 50 则值为 yes，否则为 no
df['elderly'] = np.where(df['age']>=50, 'yes', 'no')

# 查看数据帧
df

	name	age	preTestScore	postTestScore	elderly
0	Jason	42	4	25	no
1	Molly	52	24	94	yes
2	Tina	36	31	57	no
3	Jake	24	2	62	no
4	Amy	73	3	70	yes

从词典键和值创建列表

# 创建字典
dict = {'county': ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'fireReports': [4, 24, 31, 2, 3]}

# 创建键的列表
list(dict.keys())

# ['fireReports', 'year', 'county'] 

# 创建值的列表
list(dict.values())

'''
[[4, 24, 31, 2, 3],
 [2012, 2012, 2013, 2014, 2014],
 ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma']] 
'''

Pandas 中的交叉表

# 导入库
import pandas as pd

raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], 
        'company': ['infantry', 'infantry', 'cavalry', 'cavalry', 'infantry', 'infantry', 'cavalry', 'cavalry','infantry', 'infantry', 'cavalry', 'cavalry'], 
        'experience': ['veteran', 'rookie', 'veteran', 'rookie', 'veteran', 'rookie', 'veteran', 'rookie','veteran', 'rookie', 'veteran', 'rookie'],
        'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], 
        'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'experience', 'name', 'preTestScore', 'postTestScore'])
df

	regiment	company	experience	name	preTestScore	postTestScore
0	Nighthawks	infantry	veteran	Miller	4	25
1	Nighthawks	infantry	rookie	Jacobson	24	94
2	Nighthawks	cavalry	veteran	Ali	31	57
3	Nighthawks	cavalry	rookie	Milner	2	62
4	Dragoons	infantry	veteran	Cooze	3	70
5	Dragoons	infantry	rookie	Jacon	4	25
6	Dragoons	cavalry	veteran	Ryaner	24	94
7	Dragoons	cavalry	rookie	Sone	31	57
8	Scouts	infantry	veteran	Sloan	2	62
9	Scouts	infantry	rookie	Piger	3	70
10	Scouts	cavalry	veteran	Riani	2	62
11	Scouts	cavalry	rookie	Ali	3	70

按公司和团队创建交叉表。按公司和团队计算观测数量。

pd.crosstab(df.regiment, df.company, margins=True)

company	cavalry	infantry	All
regiment
Dragoons	2	2	4
Nighthawks	2	2	4
Scouts	2	2	4
All	6	6	12

# 为每个团队创建公司和经验的交叉表
pd.crosstab([df.company, df.experience], df.regiment,  margins=True)

	regiment	Dragoons	Nighthawks	Scouts	All
company	experience
cavalry	rookie	1	1	1	3
	veteran	1	1	1	3
infantry	rookie	1	1	1	3
	veteran	1	1	1	3
All		4	4	4	12

删除重复

# 导入模块
import pandas as pd

raw_data = {'first_name': ['Jason', 'Jason', 'Jason','Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', 'Miller', 'Miller','Ali', 'Milner', 'Cooze'], 
        'age': [42, 42, 1111111, 36, 24, 73], 
        'preTestScore': [4, 4, 4, 31, 2, 3],
        'postTestScore': [25, 25, 25, 57, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
df

	first_name	last_name	age	preTestScore	postTestScore
0	Jason	Miller	42	4	25
1	Jason	Miller	42	4	25
2	Jason	Miller	1111111	4	25
3	Tina	Ali	36	31	57
4	Jake	Milner	24	2	62
5	Amy	Cooze	73	3	70

# 确定哪些观测是重复的
df.duplicated()

'''
0    False
1     True
2    False
3    False
4    False
5    False
dtype: bool 
'''

df.drop_duplicates()

	first_name	last_name	age	preTestScore	postTestScore
0	Jason	Miller	42	4	25
2	Jason	Miller	1111111	4	25
3	Tina	Ali	36	31	57
4	Jake	Milner	24	2	62
5	Amy	Cooze	73	3	70

# 删除 first_name 列中的重复项
# 但保留重复集中的最后一个观测
df.drop_duplicates(['first_name'], keep='last')

	first_name	last_name	age	preTestScore	postTestScore
2	Jason	Miller	1111111	4	25
3	Tina	Ali	36	31	57
4	Jake	Milner	24	2	62
5	Amy	Cooze	73	3	70

Pandas 数据帧的描述性统计

# 导入模块
import pandas as pd

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, columns = ['name', 'age', 'preTestScore', 'postTestScore'])
df

	name	age	preTestScore	postTestScore
0	Jason	42	4	25
1	Molly	52	24	94
2	Tina	36	31	57
3	Jake	24	2	62
4	Amy	73	3	70

5 rows × 4 columns

# 所有年龄之和
df['age'].sum()

# 227 

df['preTestScore'].mean()

# 12.800000000000001 

df['preTestScore'].cumsum()

'''
0     4
1    28
2    59
3    61
4    64
Name: preTestScore, dtype: int64 
'''

df['preTestScore'].describe()

'''
count     5.000000
mean     12.800000
std      13.663821
min       2.000000
25%       3.000000
50%       4.000000
75%      24.000000
max      31.000000
Name: preTestScore, dtype: float64 
'''

df['preTestScore'].count()

# 5 

df['preTestScore'].min()

# 2 

df['preTestScore'].max()

# 31 

df['preTestScore'].median()

# 4.0 

df['preTestScore'].var()

# 186.69999999999999 

df['preTestScore'].std()

# 13.663820841916802 

df['preTestScore'].skew()

# 0.74334524573267591 

df['preTestScore'].kurt()

# -2.4673543738411525 

df.corr()

	age	preTestScore	postTestScore
age	1.000000	-0.105651	0.328852
preTestScore	-0.105651	1.000000	0.378039
postTestScore	0.328852	0.378039	1.000000

3 rows × 3 columns

# 协方差矩阵
df.cov()

	age	preTestScore	postTestScore
age	340.80	-26.65	151.20
preTestScore	-26.65	186.70	128.65
postTestScore	151.20	128.65	620.30

3 rows × 3 columns

丢弃行或者列

# 导入模块
import pandas as pd

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'reports': [4, 24, 31, 2, 3]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df

	name	reports	year
Cochice	Jason	4	2012
Pima	Molly	24	2012
Santa Cruz	Tina	31	2013
Maricopa	Jake	2	2014
Yuma	Amy	3	2014

# 丢弃观测（行）
df.drop(['Cochice', 'Pima'])

	name	reports	year
Santa Cruz	Tina	31	2013
Maricopa	Jake	2	2014
Yuma	Amy	3	2014

# 丢弃变量（列）
# 注意：`axis = 1`表示我们指的是列，而不是行
df.drop('reports', axis=1)

	name	year
Cochice	Jason	2012
Pima	Molly	2012
Santa Cruz	Tina	2013
Maricopa	Jake	2014
Yuma	Amy	2014

如果它包含某个值（这里是Tina），丢弃一行。

具体来说：创建一个名为df的新数据框，名称列中的单元格的值不等于Tina。

df[df.name != 'Tina']

	name	reports	year
Cochice	Jason	4	2012
Pima	Molly	24	2012
Maricopa	Jake	2	2014
Yuma	Amy	3	2014

按照行号丢弃一行（在本例中为第 3 行）。

请注意，Pandas使用从零开始的编号，因此 0 是第一行，1 是第二行，等等。

df.drop(df.index[2])

	name	reports	year
Cochice	Jason	4	2012
Pima	Molly	24	2012
Maricopa	Jake	2	2014
Yuma	Amy	3	2014

可以扩展到范围。

df.drop(df.index[[2,3]])

	name	reports	year
Cochice	Jason	4	2012
Pima	Molly	24	2012
Yuma	Amy	3	2014

或相对于 DF 的末尾来丢弃。

df.drop(df.index[-2])

	name	reports	year
Cochice	Jason	4	2012
Pima	Molly	24	2012
Santa Cruz	Tina	31	2013
Yuma	Amy	3	2014

你也可以选择相对于起始或末尾的范围。

df[:3] # 保留前三个

	name	reports	year
Cochice	Jason	4	2012
Pima	Molly	24	2012
Santa Cruz	Tina	31	2013

df[:-3] # 丢掉后三个

	name	reports	year
Cochice	Jason	4	2012
Pima	Molly	24	2012

枚举列表

# 创建字符串列表
data = ['One','Two','Three','Four','Five']

# 对于 enumerate(data) 中的每个项目
for item in enumerate(data):
    # 打印整个枚举的元素
    print(item)
    # 只打印值（没有索引）
    print(item[1])

'''
(0, 'One')
One
(1, 'Two')
Two
(2, 'Three')
Three
(3, 'Four')
Four
(4, 'Five')
Five 
'''

在 Pandas 中将包含列表的单元扩展为自己的变量

# 导入 pandas
import pandas as pd

# 创建数据集
raw_data = {'score': [1,2,3], 
        'tags': [['apple','pear','guava'],['truck','car','plane'],['cat','dog','mouse']]}
df = pd.DataFrame(raw_data, columns = ['score', 'tags'])

# 查看数据集
df

	score	tags
0	1	[apple, pear, guava]
1	2	[truck, car, plane]
2	3	[cat, dog, mouse]

# 将 df.tags 扩展为自己的数据帧
tags = df['tags'].apply(pd.Series)

# 将每个变量重命名为标签
tags = tags.rename(columns = lambda x : 'tag_' + str(x))

# 查看 tags 数据帧
tags

	tag_0	tag_1	tag_2
0	apple	pear	guava
1	truck	car	plane
2	cat	dog	mouse

# 将 tags 数据帧添加回原始数据帧
pd.concat([df[:], tags[:]], axis=1)

	score	tags	tag_0	tag_1	tag_2
0	1	[apple, pear, guava]	apple	pear	guava
1	2	[truck, car, plane]	truck	car	plane
2	3	[cat, dog, mouse]	cat	dog	mouse

过滤 pandas 数据帧

# 导入模块
import pandas as pd

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'reports': [4, 24, 31, 2, 3],
        'coverage': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df

	coverage	name	reports	year
Cochice	25	Jason	4	2012
Pima	94	Molly	24	2012
Santa Cruz	57	Tina	31	2013
Maricopa	62	Jake	2	2014
Yuma	70	Amy	3	2014

# 查看列
df['name']

'''
Cochice       Jason
Pima          Molly
Santa Cruz     Tina
Maricopa       Jake
Yuma            Amy
Name: name, dtype: object 
'''

df[['name', 'reports']]

	name	reports
Cochice	Jason	4
Pima	Molly	24
Santa Cruz	Tina	31
Maricopa	Jake	2
Yuma	Amy	3

# 查看前两行
df[:2]

	coverage	name	reports	year
Cochice	25	Jason	4	2012
Pima	94	Molly	24	2012

# 查看 Coverage 大于 50 的行
df[df['coverage'] > 50]

	coverage	name	reports	year
Pima	94	Molly	24	2012
Santa Cruz	57	Tina	31	2013
Maricopa	62	Jake	2	2014
Yuma	70	Amy	3	2014

# 查看 Coverage 大于 50 并且 Reports 小于 4 的行
df[(df['coverage']  > 50) & (df['reports'] < 4)]

	coverage	name	reports	year
Maricopa	62	Jake	2	2014
Yuma	70	Amy	3	2014

寻找数据帧的列中的最大值

# 导入模块
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# 创建数据帧
raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
df

	first_name	last_name	age	preTestScore	postTestScore
0	Jason	Miller	42	4	25
1	Molly	Jacobson	52	24	94
2	Tina	Ali	36	31	57
3	Jake	Milner	24	2	62
4	Amy	Cooze	73	3	70

# 获取 preTestScore 列中的最大值的索引
df['preTestScore'].idxmax()

# 2

寻找数据帧中的唯一值

import pandas as pd
import numpy as np

raw_data = {'regiment': ['51st', '29th', '2nd', '19th', '12th', '101st', '90th', '30th', '193th', '1st', '94th', '91th'], 
            'trucks': ['MAZ-7310', np.nan, 'MAZ-7310', 'MAZ-7310', 'Tatra 810', 'Tatra 810', 'Tatra 810', 'Tatra 810', 'ZIS-150', 'Tatra 810', 'ZIS-150', 'ZIS-150'],
            'tanks': ['Merkava Mark 4', 'Merkava Mark 4', 'Merkava Mark 4', 'Leopard 2A6M', 'Leopard 2A6M', 'Leopard 2A6M', 'Arjun MBT', 'Leopard 2A6M', 'Arjun MBT', 'Arjun MBT', 'Arjun MBT', 'Arjun MBT'],
            'aircraft': ['none', 'none', 'none', 'Harbin Z-9', 'Harbin Z-9', 'none', 'Harbin Z-9', 'SH-60B Seahawk', 'SH-60B Seahawk', 'SH-60B Seahawk', 'SH-60B Seahawk', 'SH-60B Seahawk']}

df = pd.DataFrame(raw_data, columns = ['regiment', 'trucks', 'tanks', 'aircraft'])

# 查看前几行
df.head()

	regiment	trucks	tanks	aircraft
0	51st	MAZ-7310	Merkava Mark 4	none
1	29th	NaN	Merkava Mark 4	none
2	2nd	MAZ-7310	Merkava Mark 4	none
3	19th	MAZ-7310	Leopard 2A6M	Harbin Z-9
4	12th	Tatra 810	Leopard 2A6M	Harbin Z-9

# 通过将 pandas 列转换为集合
# 创建唯一值的列表
list(set(df.trucks))

# [nan, 'Tatra 810', 'MAZ-7310', 'ZIS-150'] 

# 创建 df.trucks 中的唯一值的列表
list(df['trucks'].unique())

# ['MAZ-7310', nan, 'Tatra 810', 'ZIS-150']

地理编码和反向地理编码

在使用地理数据时，地理编码（将物理地址或位置转换为经纬度）和反向地理编码（将经纬度转换为物理地址或位置）是常见任务。

Python 提供了许多软件包，使任务变得异常简单。在下面的教程中，我使用 pygeocoder（Google 的 geo-API 的包装器）来进行地理编码和反向地理编码。

首先，我们要加载我们想要在脚本中使用的包。具体来说，我正在为地理函数加载 pygeocoder，为数据帧结构加载 pandas，为缺失值（np.nan）函数加载 numpy。

# 加载包
from pygeocoder import Geocoder
import pandas as pd
import numpy as np

地理数据有多种形式，在这种情况下，我们有一个 Python 字典，包含五个经纬度的字符串，每个坐标在逗号分隔的坐标对中。

# 创建原始数据的字典
data = {'Site 1': '31.336968, -109.560959',
        'Site 2': '31.347745, -108.229963',
        'Site 3': '32.277621, -107.734724',
        'Site 4': '31.655494, -106.420484',
        'Site 5': '30.295053, -104.014528'}

虽然技术上没必要，因为我最初使用 R，我是数据帧的忠实粉丝，所以让我们把模拟的数据字典变成数据帧。

# 将字典转换为 pandas 数据帧
df = pd.DataFrame.from_dict(data, orient='index')

# 查看数据帧
df

	0
Site 1	31.336968, -109.560959
Site 2	31.347745, -108.229963
Site 3	32.277621, -107.734724
Site 4	31.655494, -106.420484
Site 5	30.295053, -104.014528

你现在可以看到，我们有了包含五行的数据帧，每行包含一个经纬度字符串。在我们处理数据之前，我们需要1）将字符串分成纬度和经度，然后将它们转换为浮点数。以下代码就是这样。

# 为循环创建两个列表
lat = []
lon = []

# 对于变量中的每一行
for row in df[0]:
    # 尝试
    try:
        # 用逗号分隔行，转换为浮点
        # 并将逗号前的所有内容追加到 lat
        lat.append(float(row.split(',')[0]))
        # 用逗号分隔行，转换为浮点
        # 并将逗号后的所有内容追加到 lon
        lon.append(float(row.split(',')[1]))
    # 但是如果你得到了错误
    except:
        # 向 lat 添加缺失值
        lat.append(np.NaN)
        # 向 lon 添加缺失值
        lon.append(np.NaN)

# 从 lat 和 lon 创建新的两列
df['latitude'] = lat
df['longitude'] = lon

让我们看看现在有了什么。

# 查看数据帧
df

	0	latitude	longitude
Site 1	31.336968, -109.560959	31.336968	-109.560959
Site 2	31.347745, -108.229963	31.347745	-108.229963
Site 3	32.277621, -107.734724	32.277621	-107.734724
Site 4	31.655494, -106.420484	31.655494	-106.420484
Site 5	30.295053, -104.014528	30.295053	-104.014528

真棒。这正是我们想要看到的，一列用于纬度的浮点和一列用于经度的浮点。

为了反转地理编码，我们将特定的经纬度对（这里为第一行，索引为0）提供给 pygeocoder 的reverse_geocoder函数。

# 将经度和纬度转换为某个位置
results = Geocoder.reverse_geocode(df['latitude'][0], df['longitude'][0])

现在我们可以开始提取我们想要的数据了。

# 打印经纬度
results.coordinates

# (31.3372728, -109.5609559) 

# 打印城市
results.city

# 'Douglas' 

# 打印国家/地区
results.country

# 'United States' 

# 打印街道地址（如果可用）
results.street_address

# 打印行政区
results.administrative_area_level_1

# 'Arizona'

对于地理编码，我们需要将包含地址或位置（例如城市）的字符串，传入地理编码函数中。但是，并非所有字符串的格式都是 Google 的 geo-API 可以理解的。如果由.geocode().valid_address函数验证有效，我们可以转换。

# 验证地址是否有效（即在 Google 的系统中）
Geocoder.geocode("4207 N Washington Ave, Douglas, AZ 85607").valid_address

# True

因为输出是True，我们现在知道这是一个有效的地址，因此可以打印纬度和经度坐标。

# 打印经纬度
results.coordinates

# (31.3372728, -109.5609559)

但更有趣的是，一旦地址由 Google 地理 API 处理，我们就可以解析它并轻松地分隔街道号码，街道名称等。

# 寻找特定地址中的经纬度
result = Geocoder.geocode("7250 South Tucson Boulevard, Tucson, AZ 85756")

# 打印街道号码
result.street_number

# '7250' 

# 打印街道名
result.route

# 'South Tucson Boulevard'

你就实现了它。Python 使整个过程变得简单，只需几分钟即可完成分析。祝好运！

地理定位城市和国家

本教程创建一个函数，尝试获取城市和国家并返回其经纬度。但是当城市不可用时（通常是这种情况），则返回该国中心的经纬度。

from geopy.geocoders import Nominatim
geolocator = Nominatim()
import numpy as np

def geolocate(city=None, country=None):
    '''
    输入城市和国家，或仅输入国家。 如果可以的话，返回城市的经纬度坐标，否则返回该国家中心的经纬度。
    '''

    # 如果城市存在
    if city != None:
        # 尝试
        try:
            # 地理定位城市和国家
            loc = geolocator.geocode(str(city + ',' + country))
            # 并返回经纬度
            return (loc.latitude, loc.longitude)
        # 否则
        except:
            # 返回缺失值
            return np.nan
    # 如果城市不存在
    else:
        # 尝试
        try:
            # 地理定位国家中心
            loc = geolocator.geocode(country)
            # 返回经纬度
            return (loc.latitude, loc.longitude)
        # 否则
        except:
            # 返回缺失值
            return np.nan

# 地理定位城市和国家
geolocate(city='Austin', country='USA')

# (30.2711286, -97.7436995) 

# 仅仅地理定位国家
geolocate(country='USA')

# (39.7837304, -100.4458824)

使用 pandas 分组时间序列

# 导入所需模块
import pandas as pd
import numpy as np

df = pd.DataFrame()

df['german_army'] = np.random.randint(low=20000, high=30000, size=100)
df['allied_army'] = np.random.randint(low=20000, high=40000, size=100)
df.index = pd.date_range('1/1/2014', periods=100, freq='H')

df.head()

	german_army	allied_army
2014-01-01 00:00:00	28755	33938
2014-01-01 01:00:00	25176	28631
—	—	—
2014-01-01 02:00:00	23261	39685
—	—	—
2014-01-01 03:00:00	28686	27756
—	—	—
2014-01-01 04:00:00	24588	25681
—	—	—

Truncate the dataframe

df.truncate(before='1/2/2014', after='1/3/2014')

	german_army	allied_army
2014-01-02 00:00:00	26401	20189
2014-01-02 01:00:00	29958	23934
2014-01-02 02:00:00	24492	39075
2014-01-02 03:00:00	25707	39262
2014-01-02 04:00:00	27129	35961
2014-01-02 05:00:00	27903	25418
2014-01-02 06:00:00	20409	25163
2014-01-02 07:00:00	25736	34794
2014-01-02 08:00:00	24057	27209
2014-01-02 09:00:00	26875	33402
2014-01-02 10:00:00	23963	38575
2014-01-02 11:00:00	27506	31859
2014-01-02 12:00:00	23564	25750
2014-01-02 13:00:00	27958	24365
2014-01-02 14:00:00	24915	38866
2014-01-02 15:00:00	23538	33820
2014-01-02 16:00:00	23361	30080
2014-01-02 17:00:00	27284	22922
2014-01-02 18:00:00	24176	32155
2014-01-02 19:00:00	23924	27763
2014-01-02 20:00:00	23111	32343
2014-01-02 21:00:00	20348	28907
2014-01-02 22:00:00	27136	38634
2014-01-02 23:00:00	28649	29950
2014-01-03 00:00:00	21292	26395

# 设置数据帧的索引
df.index = df.index + pd.DateOffset(months=4, days=5)

df.head()

	german_army	allied_army
2014-05-06 00:00:00	28755	33938
2014-05-06 01:00:00	25176	28631
2014-05-06 02:00:00	23261	39685
2014-05-06 03:00:00	28686	27756
2014-05-06 04:00:00	24588	25681

# 将变量提前一小时
df.shift(1).head()

	german_army	allied_army
2014-05-06 00:00:00	NaN	NaN
2014-05-06 01:00:00	28755.0	33938.0
2014-05-06 02:00:00	25176.0	28631.0
2014-05-06 03:00:00	23261.0	39685.0
2014-05-06 04:00:00	28686.0	27756.0

# 将变量延后一小时
df.shift(-1).tail()

	german_army	allied_army
2014-05-09 23:00:00	26903.0	39144.0
2014-05-10 00:00:00	27576.0	39759.0
2014-05-10 01:00:00	25232.0	35246.0
2014-05-10 02:00:00	23391.0	21044.0
2014-05-10 03:00:00	NaN	NaN

# 对每小时观测值求和来按天汇总
df.resample('D').sum()

	german_army	allied_army
2014-05-06	605161	755962
2014-05-07	608100	740396
2014-05-08	589744	700297
2014-05-09	607092	719283
2014-05-10	103102	135193

# 对每小时观测值求平均来按天汇总
df.resample('D').mean()

	german_army	allied_army
2014-05-06	25215.041667	31498.416667
2014-05-07	25337.500000	30849.833333
2014-05-08	24572.666667	29179.041667
2014-05-09	25295.500000	29970.125000
2014-05-10	25775.500000	33798.250000

# 对每小时观测值求最小值来按天汇总
df.resample('D').min()

	german_army	allied_army
2014-05-06	24882.0	31310.0
2014-05-07	25311.0	30969.5
2014-05-08	24422.5	28318.0
2014-05-09	24941.5	32082.5
2014-05-10	26067.5	37195.0

# 对每小时观测值求中值来按天汇总
df.resample('D').median()

	german_army	allied_army
2014-05-06	24882.0	31310.0
2014-05-07	25311.0	30969.5
2014-05-08	24422.5	28318.0
2014-05-09	24941.5	32082.5
2014-05-10	26067.5	37195.0

# 对每小时观测值取第一个值来按天汇总
df.resample('D').first()

	german_army	allied_army
2014-05-06	28755	33938
2014-05-07	26401	20189
2014-05-08	21292	26395
2014-05-09	25764	22613
2014-05-10	26903	39144

# 对每小时观测值取最后一个值来按天汇总
df.resample('D').last()

	german_army	allied_army
2014-05-06	28214	32110
2014-05-07	28649	29950
2014-05-08	28379	32600
2014-05-09	26752	22379
2014-05-10	23391	21044

# 对每小时观测值取第一个值，最后一个值，最高值，最低值来按天汇总
df.resample('D').ohlc()

	german_army	allied_army
	open	high
2014-05-06	28755	29206
2014-05-07	26401	29958
2014-05-08	21292	29786
2014-05-09	25764	29952
2014-05-10	26903	27576

按时间分组数据

2016 年 3 月 13 日，Pandas 版本 0.18.0 发布，重采样功能的运行方式发生了重大变化。本教程遵循 v0.18.0，不适用于以前版本的 pandas。

首先让我们加载我们关心的模块。

# 导入所需模块
import pandas as pd
import datetime
import numpy as np

接下来，让我们创建一些样例数据，我们可以将它们按时间分组作为样本。在这个例子中，我创建了一个包含两列 365 行的数据帧。一列是日期，第二列是数值。

# 为今天创建 datetime 变量
base = datetime.datetime.today()
# 创建一列变量
# 包含 365 天的 datetime 值
date_list = [base - datetime.timedelta(days=x) for x in range(0, 365)]

# 创建 365 个数值的列表
score_list = list(np.random.randint(low=1, high=1000, size=365))

# 创建空数据帧
df = pd.DataFrame()

# 从 datetime 变量创建一列
df['datetime'] = date_list
# 将列转换为 datetime 类型
df['datetime'] = pd.to_datetime(df['datetime'])
# 将 datetime 列设为索引
df.index = df['datetime'] 
# 为数值得分变量创建一列
df['score'] = score_list

# 让我们看看数据
df.head()

	datetime	score
datetime
2016-06-02 09:57:54.793972	2016-06-02 09:57:54.793972	900
2016-06-01 09:57:54.793972	2016-06-01 09:57:54.793972	121
2016-05-31 09:57:54.793972	2016-05-31 09:57:54.793972	547
2016-05-30 09:57:54.793972	2016-05-30 09:57:54.793972	504
2016-05-29 09:57:54.793972	2016-05-29 09:57:54.793972	304

在 pandas 中，按时间分组的最常用方法是使用.resample()函数。在 v0.18.0 中，此函数是两阶段的。这意味着df.resample('M')创建了一个对象，我们可以对其应用其他函数（mean，count，sum等）

# 按月对数据分组，并取每组（即每个月）的平均值
df.resample('M').mean()

	score
datetime
2015-06-30	513.629630
2015-07-31	561.516129
2015-08-31	448.032258
2015-09-30	548.000000
2015-10-31	480.419355
2015-11-30	487.033333
2015-12-31	499.935484
2016-01-31	429.193548
2016-02-29	520.413793
2016-03-31	349.806452
2016-04-30	395.500000
2016-05-31	503.451613
2016-06-30	510.500000

# 按月对数据分组，并获取每组（即每个月）的总和
df.resample('M').sum()

	score
datetime
2015-06-30	13868
2015-07-31	17407
2015-08-31	13889
2015-09-30	16440
2015-10-31	14893
2015-11-30	14611
2015-12-31	15498
2016-01-31	13305
2016-02-29	15092
2016-03-31	10844
2016-04-30	11865
2016-05-31	15607
2016-06-30	1021

分组有很多选项。你可以在 Pandas 的时间序列文档中了解它们的更多信息，但是，为了你的方便，我也在下面列出了它们。

值	描述
B	business day frequency
C	custom business day frequency (experimental)
D	calendar day frequency
W	weekly frequency
M	month end frequency
BM	business month end frequency
CBM	custom business month end frequency
MS	month start frequency
BMS	business month start frequency
Q	quarter end frequency
BQ	business quarter endfrequency
QS	quarter start frequency
BQS	business quarter start frequency
A	year end frequency
BA	business year end frequency
AS	year start frequency
BAS	business year start frequency
BH	business hour frequency
H	hourly frequency
T	minutely frequency
S	secondly frequency
L	milliseonds
U	microseconds
N	nanoseconds

按小时分组数据

# 导入库
import pandas as pd
import numpy as np

# 创建 2000 个元素的时间序列
# 每五分钟一个元素，起始于 2000.1.1
time = pd.date_range('1/1/2000', periods=2000, freq='5min')

# 创建 pandas 序列，带有 0 到 100 的随机值
# 将 time 用于索引
series = pd.Series(np.random.randint(100, size=2000), index=time)

# 查看前几行
series[0:10]

'''
2000-01-01 00:00:00    40
2000-01-01 00:05:00    13
2000-01-01 00:10:00    99
2000-01-01 00:15:00    72
2000-01-01 00:20:00     4
2000-01-01 00:25:00    36
2000-01-01 00:30:00    24
2000-01-01 00:35:00    20
2000-01-01 00:40:00    83
2000-01-01 00:45:00    44
Freq: 5T, dtype: int64 
'''

# 按索引的小时值对数据分组，然后按平均值进行汇总
series.groupby(series.index.hour).mean()

'''
0     50.380952
1     49.380952
2     49.904762
3     53.273810
4     47.178571
5     46.095238
6     49.047619
7     44.297619
8     53.119048
9     48.261905
10    45.166667
11    54.214286
12    50.714286
13    56.130952
14    50.916667
15    42.428571
16    46.880952
17    56.892857
18    54.071429
19    47.607143
20    50.940476
21    50.511905
22    44.550000
23    50.250000
dtype: float64 
'''

对行分组

# 导入模块
import pandas as pd

# 示例数据帧
raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], 
        'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], 
        'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], 
        'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'name', 'preTestScore', 'postTestScore'])
df

	regiment	company	name	preTestScore	postTestScore
0	Nighthawks	1st	Miller	4	25
1	Nighthawks	1st	Jacobson	24	94
2	Nighthawks	2nd	Ali	31	57
3	Nighthawks	2nd	Milner	2	62
4	Dragoons	1st	Cooze	3	70
5	Dragoons	1st	Jacon	4	25
6	Dragoons	2nd	Ryaner	24	94
7	Dragoons	2nd	Sone	31	57
8	Scouts	1st	Sloan	2	62
9	Scouts	1st	Piger	3	70
10	Scouts	2nd	Riani	2	62
11	Scouts	2nd	Ali	3	70

# 创建分组对象。 换句话说，
# 创建一个表示该特定分组的对象。 
# 这里，我们按照团队来分组 pre-test 得分。
regiment_preScore = df['preTestScore'].groupby(df['regiment'])

# 展示每个团队的 pre-test 得分的均值
regiment_preScore.mean()

'''
regiment
Dragoons      15.50
Nighthawks    15.25
Scouts         2.50
Name: preTestScore, dtype: float64 
'''

Pandas 中的分层数据

# 导入模块
import pandas as pd

# 创建数据帧
raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], 
        'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], 
        'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], 
        'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'name', 'preTestScore', 'postTestScore'])
df

	regiment	company	name	preTestScore	postTestScore
0	Nighthawks	1st	Miller	4	25
1	Nighthawks	1st	Jacobson	24	94
2	Nighthawks	2nd	Ali	31	57
3	Nighthawks	2nd	Milner	2	62
4	Dragoons	1st	Cooze	3	70
5	Dragoons	1st	Jacon	4	25
6	Dragoons	2nd	Ryaner	24	94
7	Dragoons	2nd	Sone	31	57
8	Scouts	1st	Sloan	2	62
9	Scouts	1st	Piger	3	70
10	Scouts	2nd	Riani	2	62
11	Scouts	2nd	Ali	3	70

# 设置分层索引但将列保留在原位
df = df.set_index(['regiment', 'company'], drop=False)
df

		regiment	company	name	preTestScore	postTestScore
regiment	company
	Nighthawks	1st	Nighthawks	1st	Miller	4
1st	Nighthawks	1st	Jacobson	24	94
2nd	Nighthawks	2nd	Ali	31	57
2nd	Nighthawks	2nd	Milner	2	62
	Dragoons	1st	Dragoons	1st	Cooze	3
1st	Dragoons	1st	Jacon	4	25
2nd	Dragoons	2nd	Ryaner	24	94
2nd	Dragoons	2nd	Sone	31	57
	Scouts	1st	Scouts	1st	Sloan	2
1st	Scouts	1st	Piger	3	70
2nd	Scouts	2nd	Riani	2	62
2nd	Scouts	2nd	Ali	3	70

# 将分层索引设置为团队然后公司
df = df.set_index(['regiment', 'company'])
df

		name	preTestScore	postTestScore
regiment	company
	Nighthawks	1st	Miller	4
1st	Jacobson	24	94
2nd	Ali	31	57
2nd	Milner	2	62
	Dragoons	1st	Cooze	3
1st	Jacon	4	25
2nd	Ryaner	24	94
2nd	Sone	31	57
	Scouts	1st	Sloan	2
1st	Piger	3	70
2nd	Riani	2	62
2nd	Ali	3	70

# 查看索引
df.index

MultiIndex(levels=[['Dragoons', 'Nighthawks', 'Scouts'], ['1st', '2nd']],
           labels=[[1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 2], [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1]],
           names=['regiment', 'company']) 

# 交换索引中的级别
df.swaplevel('regiment', 'company')

		name	preTestScore	postTestScore
company	regiment
1st	Nighthawks	Miller	4	25
	Nighthawks	Jacobson	24	94
2nd	Nighthawks	Ali	31	57
	Nighthawks	Milner	2	62
1st	Dragoons	Cooze	3	70
	Dragoons	Jacon	4	25
2nd	Dragoons	Ryaner	24	94
	Dragoons	Sone	31	57
1st	Scouts	Sloan	2	62
	Scouts	Piger	3	70
2nd	Scouts	Riani	2	62
	Scouts	Ali	3	70

# 按需求和数据
df.sum(level='regiment')

	preTestScore	postTestScore
regiment
Nighthawks	61	238
Dragoons	62	246
Scouts	10	264

最后

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What every programmer should know about memory 笔记每个程序员都应该了解的内存知识【第一部分】Memory part 2: CPU caches每个程序员都应该了解的 CPU 高速缓存 Memory part 4: NUMA supportMemory part 5: What programmers can doMemory part 6: More things programmers can do

数据科学和人工智能技术笔记 十九、数据整理（上）十九、数据整理（上）将列表拆分为大小为 N 的分块

概述

十九、数据整理（上）

在 Pandas 中通过分组应用函数

在 Pandas 中向分组应用操作

在 Pandas 数据帧上应用操作

向 Pandas 数据帧赋予新列

将列表拆分为大小为 N 的分块

在 Pandas 中使用正则表达式将字符串分解为列

由两个数据帧贡献列

从多个列表构建字典

将 CSV 转换为 Python 代码来重建它

将分类变量转换为虚拟变量

将分类变量转换为虚拟变量

将字符串分类变量转换为数字变量

将变量转换为时间序列

在 Pandas 数据帧中计数

在 Pandas 中创建流水线

使用for循环创建 Pandas 列

创建项目计数

基于条件创建一列

从词典键和值创建列表

Pandas 中的交叉表

删除重复

Pandas 数据帧的描述性统计

丢弃行或者列

枚举列表

在 Pandas 中将包含列表的单元扩展为自己的变量

过滤 pandas 数据帧

寻找数据帧的列中的最大值

寻找数据帧中的唯一值

地理编码和反向地理编码

地理定位城市和国家

使用 pandas 分组时间序列

Truncate the dataframe

按时间分组数据

按小时分组数据

对行分组

Pandas 中的分层数据

最后

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数据科学和人工智能技术笔记十九、数据整理（上）十九、数据整理（上）将列表拆分为大小为 N 的分块

使用`for`循环创建 Pandas 列

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