目录
前言
继续上一节的内容。往期内容如下：
一、DataFrame
行也可以通过位置或特殊属性的 loc 进行选取，列的引用直接说明列的名称即可
1. 列的选取
DataFrame中的一列，可以按照字典型标记或属性那样检索为series：
示例：
import pandas as pd
import numpy as np
data = {'state': ['Astrilia', 'Mexico', 'China', 'Japan'],
'years': [2000, 2001, 2002, 2003],
'pop': [1.5, 3.6, 2.4, 5.1]}
frame = pd.DataFrame(data, columns = ['years', 'state', 'pop'])
val = pd.Series([-1.2, -1.5, -1.7])
frame['debt'] = val
val_1 = pd.Series([100, 200, 300], index = [0, 1, 3])
frame['pofit'] = val_1
print(frame)
frame_1 = frame['state']
print(frame_1)
frame_2 = frame.state
print(frame_2)
#可以说，frame['state']和frame.state是等价的结果：
years state pop debt pofit
0 2000 Astrilia 1.5 -1.2 100.0
1 2001 Mexico 3.6 -1.5 200.0
2 2002 China 2.4 -1.7 NaN
3 2003 Japan 5.1 NaN 300.0
0 Astrilia
1 Mexico
2 China
3 Japan
Name: state, dtype: object
0 Astrilia
1 Mexico
2 China
3 Japan
Name: state, dtype: object2. 行的选取
通过特殊属性loc进行选取：
示例：
import pandas as pd
import numpy as np
data = {'state': ['Astrilia', 'Mexico', 'China', 'Japan'],
'years': [2000, 2001, 2002, 2003],
'pop': [1.5, 3.6, 2.4, 5.1]}
frame = pd.DataFrame(data, columns = ['years', 'state', 'pop'])
val = pd.Series([-1.2, -1.5, -1.7])
frame['debt'] = val
val_1 = pd.Series([100, 200, 300], index = [0, 1, 3])
frame['pofit'] = val_1
print(frame)
#当行为默认的索引标签时
frame_1row = frame.loc[1]
print(frame_1row)
#当行有自己设定的索引标签时
frame_label = pd.DataFrame(data, columns = ['years', 'state', 'pop'], index = ['one', 'two', 'three', 'four'])
print(frame_label)
frame_label_row = frame_label.loc['two']
print(frame_label_row)结果：
years state pop debt pofit
0 2000 Astrilia 1.5 -1.2 100.0
1 2001 Mexico 3.6 -1.5 200.0
2 2002 China 2.4 -1.7 NaN
3 2003 Japan 5.1 NaN 300.0
years 2001
state Mexico
pop 3.6
debt -1.5
pofit 200.0
Name: 1, dtype: object
years state pop
one 2000 Astrilia 1.5
two 2001 Mexico 3.6
three 2002 China 2.4
four 2003 Japan 5.1
years 2001
state Mexico
pop 3.6
Name: two, dtype: object3. 列的修改
列的引用是可以修改的。例如空的‘debt’列可以赋值为标量值或值数组。
示例：
import pandas as pd
import numpy as np
data = {'state': ['Astrilia', 'Mexico', 'China'],
'years': [2000, 2001, 2002],
'pop': [1.5, 3.6, 2.4]}
frame = pd.DataFrame(data, columns = ['years', 'state', 'pop'])
print(frame)
frame['debt'] = 16.2
print(frame)
frame['pofit'] = np.random.randint(100, 200, size = 3)
print(frame)结果：
years state pop
0 2000 Astrilia 1.5
1 2001 Mexico 3.6
2 2002 China 2.4
years state pop debt
0 2000 Astrilia 1.5 16.2
1 2001 Mexico 3.6 16.2
2 2002 China 2.4 16.2
years state pop debt pofit
0 2000 Astrilia 1.5 16.2 192
1 2001 Mexico 3.6 16.2 138
2 2002 China 2.4 16.2 140当将列表或数组赋值给一个列时，值的长度必须和DataFrame的长度相匹配。
示例：
import pandas as pd
import numpy as np
data = {'state': ['Astrilia', 'Mexico', 'China', 'Japan'],
'years': [2000, 2001, 2002, 2003],
'pop': [1.5, 3.6, 2.4, 5.1]}
frame = pd.DataFrame(data, columns = ['years', 'state', 'pop'])
print(frame)
val = pd.Series([-1.2, -1.5, -1.7])
frame['debt'] = val
print(frame)
val_1 = pd.Series([100, 200, 300], index = [0, 1, 3])
frame['pofit'] = val_1
print(frame)结果：
years state pop
0 2000 Astrilia 1.5
1 2001 Mexico 3.6
2 2002 China 2.4
3 2003 Japan 5.1
years state pop debt
0 2000 Astrilia 1.5 -1.2
1 2001 Mexico 3.6 -1.5
2 2002 China 2.4 -1.7
3 2003 Japan 5.1 NaN
years state pop debt pofit
0 2000 Astrilia 1.5 -1.2 100.0
1 2001 Mexico 3.6 -1.5 200.0
2 2002 China 2.4 -1.7 NaN
3 2003 Japan 5.1 NaN 300.0如果被复制的列不存在，则会生成一个新的列：
示例：
import pandas as pd
import numpy as np
data = {'state': ['Astrilia', 'Mexico', 'China', 'Japan'],
'years': [2000, 2001, 2002, 2003],
'pop': [1.5, 3.6, 2.4, 5.1]}
frame = pd.DataFrame(data, columns = ['years', 'state', 'pop'])
val = pd.Series([-1.2, -1.5, -1.7])
frame['debt'] = val
val_1 = pd.Series([100, 200, 300], index = [0, 1, 3])
frame['pofit'] = val_1
print(frame)
#给新的一列赋值
frame['date'] = np.random.randint(1, 10, size = 4)
print(frame)结果：
years state pop debt pofit
0 2000 Astrilia 1.5 -1.2 100.0
1 2001 Mexico 3.6 -1.5 200.0
2 2002 China 2.4 -1.7 NaN
3 2003 Japan 5.1 NaN 300.0
years state pop debt pofit date
0 2000 Astrilia 1.5 -1.2 100.0 7
1 2001 Mexico 3.6 -1.5 200.0 1
2 2002 China 2.4 -1.7 NaN 8
3 2003 Japan 5.1 NaN 300.0 44. 列的删除
用del函数
示例：先增添一个由布尔值组成的列：
import pandas as pd
import numpy as np
data = {'state': ['Astrilia', 'Mexico', 'China', 'Mexico'],
'years': [2000, 2001, 2002, 2003],
'pop': [1.5, 3.6, 2.4, 5.1]}
frame = pd.DataFrame(data, columns = ['years', 'state', 'pop'])
val = pd.Series([-1.2, -1.5, -1.7])
frame['debt'] = val
val_1 = pd.Series([100, 200, 300], index = [0, 1, 3])
frame['pofit'] = val_1
print(frame)
'''现在构建一个布尔值组成的数组，如果state == Mexico，则在FT列输出T，否则为F'''
#方法1
frame['TF'] = frame.state == 'Mexico'
print(frame)
print(frame.TF[0])
#方法2
Buer = []
for i in range(4):
Buer.append(frame.state[i] == 'Mexico')
frame['tf'] = Buer
print(frame)结果：
years state pop debt pofit
0 2000 Astrilia 1.5 -1.2 100.0
1 2001 Mexico 3.6 -1.5 200.0
2 2002 China 2.4 -1.7 NaN
3 2003 Mexico 5.1 NaN 300.0
years state pop debt pofit TF
0 2000 Astrilia 1.5 -1.2 100.0 False
1 2001 Mexico 3.6 -1.5 200.0 True
2 2002 China 2.4 -1.7 NaN False
3 2003 Mexico 5.1 NaN 300.0 True
False
years state pop debt pofit TF tf
0 2000 Astrilia 1.5 -1.2 100.0 False False
1 2001 Mexico 3.6 -1.5 200.0 True True
2 2002 China 2.4 -1.7 NaN False False
3 2003 Mexico 5.1 NaN 300.0 True True
进程已结束,退出代码0
然后删除TF列：
import pandas as pd
import numpy as np
data = {'state': ['Astrilia', 'Mexico', 'China', 'Mexico'],
'years': [2000, 2001, 2002, 2003],
'pop': [1.5, 3.6, 2.4, 5.1]}
frame = pd.DataFrame(data, columns = ['years', 'state', 'pop'])
val = pd.Series([-1.2, -1.5, -1.7])
frame['debt'] = val
val_1 = pd.Series([100, 200, 300], index = [0, 1, 3])
frame['pofit'] = val_1
print(frame)
'''现在构建一个布尔值组成的数组，如果state == Mexico，则在FT列输出T，否则为F'''
#构建一个新的列
frame['TF'] = frame.state == 'Mexico'
print(frame)
#删除该列
del frame['TF']
print(frame)结果：
years state pop debt pofit
0 2000 Astrilia 1.5 -1.2 100.0
1 2001 Mexico 3.6 -1.5 200.0
2 2002 China 2.4 -1.7 NaN
3 2003 Mexico 5.1 NaN 300.0
years state pop debt pofit TF
0 2000 Astrilia 1.5 -1.2 100.0 False
1 2001 Mexico 3.6 -1.5 200.0 True
2 2002 China 2.4 -1.7 NaN False
3 2003 Mexico 5.1 NaN 300.0 True
years state pop debt pofit
0 2000 Astrilia 1.5 -1.2 100.0
1 2001 Mexico 3.6 -1.5 200.0
2 2002 China 2.4 -1.7 NaN
3 2003 Mexico 5.1 NaN 300.0
进程已结束,退出代码0
注意这里：
必须要这样写，才可以正常跑通
如果写成这样：
这两种写法都会报错！！！
5. 嵌套字典赋给DataFrame
如果嵌套字典被赋值给DataFrame，pandas会将字典的键作为列，内部字典的键作为行索引：
示例：
import pandas as pd
import numpy as np
pop = {'MZY': {2001: 2.4, 2002: 2.9},
'DRX': {2000: 1.5, 2001: 1.7, 2002: 3.6}}
frame = pd.DataFrame(pop)
print(frame)结果：
MZY DRX
2001 2.4 1.7
2002 2.9 3.6
2000 NaN 1.5可以使用类似于numpy的语法对其进行转置：
示例：
import pandas as pd
import numpy as np
pop = {'MZY': {2001: 2.4, 2002: 2.9},
'DRX': {2000: 1.5, 2001: 1.7, 2002: 3.6}}
frame = pd.DataFrame(pop)
print(frame)
#转置
print(frame.T)结果：
MZY DRX
2001 2.4 1.7
2002 2.9 3.6
2000 NaN 1.5
2001 2002 2000
MZY 2.4 2.9 NaN
DRX 1.7 3.6 1.5如果显示指明索引的话，内部字典的键不会被排序：
示例：
import pandas as pd
import numpy as np
pop = {'MZY': {2001: 2.4, 2002: 2.9},
'DRX': {2000: 1.5, 2001: 1.7, 2002: 3.6}}
frame = pd.DataFrame(pop)
print(frame)
frame1 = pd.DataFrame(pop, index=[2000, 2002, 2001, 2003])
print(frame1)结果：
MZY DRX
2001 2.4 1.7
2002 2.9 3.6
2000 NaN 1.5
MZY DRX
2000 NaN 1.5
2002 2.9 3.6
2001 2.4 1.7
2003 NaN NaN总结
尽管Series和DataFrame不能解决所有问题，但是它们为大多数应用提供了一个有效、易用的基础。