本文实例讲述了python3.5 pandas模块之series用法。分享给大家供大家参考，具体如下：
1、pandas模块引入与基本数据结构
2、series的创建
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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
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#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
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#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
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#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
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#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
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#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：
amy 20
jack 18
lili 23
susan 15
dtype: int64
=======设置name属性后=======
姓名
amy 20
jack 18
lili 23
susan 15
name: 年龄, dtype: int64
希望本文所述对大家python程序设计有所帮助。
原文链接：https://blog.csdn.net/loveliuzz/article/details/78498077
