pandas.Series

class pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)

One-dimensional ndarray with axis labels (including time series).

Labels need not be unique but must be any hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Statistical methods from ndarray have been overridden to automatically exclude missing data (currently represented as NaN)

Operations between Series (+, -, /, , *) align values based on their associated index values– they need not be the same length. The result index will be the sorted union of the two indexes.

Parameters :

data : array-like, dict, or scalar value Contains data stored in Series index : array-like or Index (1d) Values must be unique and hashable, same length as data. Index object (or other iterable of same length as data) Will default to np.arange(len(data)) if not provided. If both a dict and index sequence are used, the index will override the keys found in the dict. dtype : numpy.dtype or None If None, dtype will be inferred copy : boolean, default False Copy input data

Series 类似数组，但是它有标签(label) 或者索引(index).

1. 从最简单的series开始看。

from pandas import Series, DataFrameimport pandas as pd  ser1 = Series([1,2,3,4])print(ser1)#0    1#1    2#2    3#3    4#dtype: int64

此时因为没有设置index,所以用默认

2. 加上索引

ser2 = Series(range(4),index=['a','b','c','d'])print(ser2)#a    0#b    1#c    2#d    3#dtype: int64

3. dictionnary 作为输入

dict1 = {
   'ohio':35000,'Texas':71000,'Oregon':1600,'Utah':500}ser3 = Series(dict1)#Oregon     1600#Texas     71000#Utah        500#ohio      35000#dtype: int64

key：默认设置为index

dict1 = {
   'ohio':35000,'Texas':71000,'Oregon':1600,'Utah':500}ser3 = Series(dict1)#Oregon     1600#Texas     71000#Utah        500#ohio      35000#dtype: int64print(ser3)states = ['California', 'Ohio', 'Oregon', 'Texas']ser4 = Series(dict1,index = states)print(ser4)#California        NaN#Ohio              NaN#Oregon         1600.0#Texas         71000.0#dtype: float64

用了dictionary时候，也是可以特定的制定index的，当没有map到value的时候，给NaN.

print(pd.isnull(ser4))#California     True#Ohio           True#Oregon        False#Texas         False#dtype: bool

函数isnull判断是否为null

print(pd.isnull(ser4))#California     True#Ohio           True#Oregon        False#Texas         False#dtype: bool

 

函数notnull判断是否为非null

print(pd.notnull(ser4))#California    False#Ohio          False#Oregon         True#Texas          True#dtype: bool

 

4. 访问元素和索引用法

print (ser2['a']) #0#print (ser2['a','c']) errorprint (ser2[['a','c']]) #a    0#c    2#dtype: int64print(ser2.values) #[0 1 2 3]print(ser2.index) #Index(['a', 'b', 'c', 'd'], dtype='object')

5. 运算， pandas的series保留Numpy的数组操作

print(ser2[ser2>2])#d    3#dtype: int64print(ser2*2)#a    0#b    2#c    4#d    6#dtype: int64print(np.exp(ser2))#a     1.000000#b     2.718282#c     7.389056#d    20.085537#dtype: float64

6. series 的自动匹配，这个有点类似sql中的full join，会基于索引键链接，没有的设置为null

print (ser3+ser4)#California         NaN#Ohio               NaN#Oregon          3200.0#Texas         142000.0#Utah               NaN#ohio               NaN#dtype: float64

7. series对象和索引都有一个name属性

ser4.index.name = 'state'ser4.name = 'population count'print(ser4)#state#California        NaN#Ohio              NaN#Oregon         1600.0#Texas         71000.0#Name: population count, dtype: float64

 8.预览数据

print(ser4.head(2))print(ser4.tail(2))#state#California   NaN#Ohio         NaN#Name: population count, dtype: float64#state#Oregon     1600.0#Texas     71000.0#Name: population count, dtype: float64