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#!/usr/bin/env Python# -*- coding:utf-8 -*-# Author:ZhengzhengLiu#模块引入import numpy as npimport pandas as pdfrom pandas import Series,DataFrame#1.Series通过numpy一维数组创建print("=========Series通过numpy一维数组创建==========")arr = np.array([1,2,3,4,5])s1 = pd.Series(arr)print(s1)print(s1.index)print(s1.values)#2.Series直接通过一维数组创建print("=========Series直接通过一维数组创建==========")s2 = pd.Series([10.5,20,38,40])print(s2)#修改索引值s2.index = ['a','b','c','d']print(s2)#Series通过一维数组创建，可以在创建的同时自定义索引值，# 也可以之后通过赋值的形式去修改print("=========Series创建的同时自定义索引值和数据类型==========")s3 = pd.Series(data=[89,78,90,87],dtype=np.float64,        index=['语文','数学','英语','科学'])print(s3)#3.Series通过字典创建,字典的键对应索引，值对应数据print("=========Series通过字典创建==========")dict = {'a':1,'b':2,"c":3,"d":4}s4 = pd.Series(dict)print(s4)

运行结果：

=========Series通过numpy一维数组创建==========
0 1
1 2
2 3
3 4
4 5
dtype: int32
RangeIndex(start=0, stop=5, step=1)
[1 2 3 4 5]
=========Series直接通过一维数组创建==========
0 10.5
1 20.0
2 38.0
3 40.0
dtype: float64
a 10.5
b 20.0
c 38.0
d 40.0
dtype: float64
=========Series创建的同时自定义索引值和数据类型==========
语文 89.0
数学 78.0
英语 90.0
科学 87.0
dtype: float64
=========Series通过字典创建==========
a 1
b 2
c 3
d 4
dtype: int64

3、Series值的获取

#模块引入import numpy as npimport pandas as pdfrom pandas import Series,DataFrame#4.Series值的获取print("=========Series值的获取==========")s2 = pd.Series([10.5,20,38,40])#修改索引值s2.index = ['a','b','c','d']print(s2)print(s2[0])    #方括号+下标值的形式获取Series值print(s2["a"])   #方括号+索引的形式获取Series值

运行结果：

=========Series值的获取==========
a 10.5
b 20.0
c 38.0
d 40.0
dtype: float64
10.5
10.5

4、Series运算

#模块引入import numpy as npimport pandas as pdfrom pandas import Series,DataFrame#5.Series值的运算#Series中元素级别的运算结果，包含索引值并且键值关系保持不变print("=========Series值的运算==========")s6 = pd.Series({'a':1,'b':2,"c":3,"d":4})print(s6)print("=========打印Series大于2的值==========")print(s6[s6>2])print("=========打印Series的值除以2==========")print(s6/2)#numpy中的通用函数在Series中也支持s7= pd.Series([1,2,-3,-4])print(np.exp(s7))

运行结果：

=========Series值的运算==========
a 1
b 2
c 3
d 4
dtype: int64
=========打印Series大于2的值==========
c 3
d 4
dtype: int64
=========打印Series的值除以2==========
a 0.5
b 1.0
c 1.5
d 2.0
dtype: float64
0 2.718282
1 7.389056
2 0.049787
3 0.018316
dtype: float64

5、Series缺失值检验

#模块引入import numpy as npimport pandas as pdfrom pandas import Series,DataFrame#6.Series缺失值检验scores = Series({"a":88,"b":79,"c":98,"d":100})print(scores)new = ["a","b","e","c","d"]scores = Series(scores,index=new)print(scores)print("======过滤出为缺失值的项=======")print(scores.isnull())       #NAN值返回True#print(pd.isnull(scores))      #与上面一句等价print("======过滤出为非缺失值的项=======")print(pd.notnull(scores))      #非NAN值返回True

运行结果：

a 88
b 79
c 98
d 100
dtype: int64
a 88.0
b 79.0
e NaN
c 98.0
d 100.0
dtype: float64
======过滤出为缺失值的项=======
a False
b False
e True
c False
d False
dtype: bool
======过滤出为非缺失值的项=======
a True
b True
e False
c True
d True
dtype: bool

6、Series自动对齐

#模块引入import numpy as npimport pandas as pdfrom pandas import Series,DataFrame#7.Series自动对齐s8 = Series([12,28,46],index=["p1","p2","p3"])s9 = Series([2,4,6,8],index=["p2","p3","p4","p5"])print("=======s8=======")print(s8)print("=======s9=======")print(s9)print("=======s8+s9=======")print(s8+s9)

运行结果：

=======s8=======
p1 12
p2 28
p3 46
dtype: int64
=======s9=======
p2 2
p3 4
p4 6
p5 8
dtype: int64
=======s8+s9=======
p1 NaN
p2 30.0
p3 50.0
p4 NaN
p5 NaN
dtype: float64

7、Series及其索引的name属性

#模块引入import numpy as npimport pandas as pdfrom pandas import Series,DataFrame#8.Series及其name属性s10 = Series({"jack":18,"amy":20,"lili":23,"susan":15})print(s10)print("=======设置name属性后=======")s10.name = "年龄"    #数据名称标签s10.index.name = "姓名"    #索引名称标签print(s10)

运行结果：