Python3.5 Pandas模块之Series用法实例分析

Z时代
2024-01-10
分类：综合

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


#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author:ZhengzhengLiu
#模块引入
import numpy as np
import pandas as pd
from 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 np
import pandas as pd
from 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 np
import pandas as pd
from 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 np
import pandas as pd
from 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 np
import pandas as pd
from 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 np
import pandas as pd
from 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