Series的基本特征：
1、类似一维数组的对象
2、由数据和索引组成
import pandas as pd
>>> aSer=pd.Series([1,2.0,'a'])
>>> aSer
0 1
1 2
2 a
dtype: object
bSer=pd.Series(['apple','peach','lemon'],index=[1,2,3])
>>> bSer
1 apple
2 peach
3 lemon
dtype: object
>>> bSer.index
Int64Index([1, 2, 3], dtype='int64')
>>> bSer.values
array(['apple', 'peach', 'lemon'], dtype=object)
Series的基本运算：
from pandas import Series
>>> aSer=Series([3,5,7],index=['a','b','c'])
>>>
>>> aSer['b']
5
aSer*2
a 6
b 10
c 14
dtype: int64
>>> import numpy as np
>>> np.exp(aSer)
a 20.085537
b 148.413159
c 1096.633158
dtype: float64
Series的数据对齐：
import pandas as pd
>>> data={'AXP':'86.40','CSCO':'122.64','BA':'99.44'}
>>> sindex=['AXP','CSCO','BA','AAPL']
>>> aSer=pd.Series(data,index=sindex)
>>> aSer
AXP 86.40
CSCO 122.64
BA 99.44
AAPL NaN
dtype: object
>>> pd.isnull(aSer)
AXP False
CSCO False
BA False
AAPL True
dtype: bool
重要功能：在算术运算中自动对齐不同索引的数据。
aSer=pd.Series(data,index=sindex)
>>> aSer
AXP 86.40
CSCO 122.64
BA 99.44
AAPL NaN
dtype: object
>>> bSer={'AXP':'86.40','CSCO':'122.64','CVX':'23.78'}
cSer=pd.Series(bSer)
>>> aSer+cSer
AAPL NaN
AXP 86.4086.40
BA NaN
CSCO 122.64122.64
CVX NaN
dtype: object
Series的name属性：
1、Series对象本身及其索引均有一个name属性
2、Series的name属性与其他功能关系密切
import pandas as pd
>>> data={'AXP':'86.40','CSCO':'122.64','BA':'99.44'}
>>> sindex=['AXP','CSCO','BA','AAPL']
>>> aSer=pd.Series(data,index=sindex)
>>> aSer.name='cnames'
>>> aSer.index.name='volume'
>>> aSer
volume
AXP 86.40
CSCO 122.64
BA 99.44
AAPL NaN
Name: cnames, dtype: object