数据科学和人工智能技术笔记十九、数据整理（上）-摩杜云开发者社区

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('(\d\d\d\d\.\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

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

使用`for`循环创建 Pandas 列