# -*- coding: utf-8 -*- import pandas as pd from pandas import Series,DataFrame import numpy as np ''' Series 是一个简单的一维数组对象,带索引 ''' obj=Series([1,2,3,4]) print(obj) # 0 1 # 1 2 # 2 3 # 3 4 # dtype: int64 #Series 特点左边索引又边值 print(obj.values)#[1 2 3 4] print(obj.index)#RangeIndex(start=0, stop=4, step=1) #自己标注索引 obj=Series([1,2,3,4],index=['a','b','c','d']) print(obj) # a 1 # b 2 # c 3 # d 4 # dtype: int64 print(obj.index)#Index(['a', 'b', 'c', 'd'], dtype='object') #和字典一样,通过索引找值获取这数组 a=obj['a'] print(a)#1 #多值 b=obj[['a','b','c']] print(b) # a 1 # b 2 # c 3 # dtype: int64 ''' 数组的运算索引值不会变 ''' print(obj[obj>2]) # c 3 # d 4 # dtype: int64 print(obj*2) # a 2 # b 4 # c 6 # d 8 # dtype: int64 print(np.exp(obj)) # a 2.718282 # b # 7.389056 # c 20.085537 # d 54.598150 # dtype: float64 #判断索引是否在数组中 print('a' in obj)#True print('e' in obj)#False #如果数组存放在python字典里,可以直接通过这个字典创建Series dict={'hhb':24.3,'zjx':23.0,'zsb':88.0} a=Series(dict) print(a) # hhb 24 # zjx 23 # zsb 88 # dtype: int64 #如果只传入一个字典 states=['hhb','zjx','ssb'] obj2=Series(dict,index=states) print(obj2) # hhb 24.0 # zjx 23.0 # ssb NaN # dtype: float64 #有索引的就会自动加载,没有的值不显示,多出来的索引,值为NaN,这就时所谓的缺失数据 print(obj2.isnull()) # hhb False # zjx False # ssb True # dtype: bool pd.isnull(obj2) pd.notnull(obj2) #对于多数运算而言。Series算数运算会自动补齐不同的索引数据 print(obj2+a) # hhb 48.6 # ssb NaN # zjx 46.0 # zsb NaN # dtype: float64 #Series本身有一个name属性 a.name='shit' a.index.name='shit_name' print(a) # shit_name # hhb 24.3 # zjx 23.0 # zsb 88.0 # Name: shit, dtype: float64 #Series索因可以通过赋值的方式,就改 a.index=['aaa','bbbb','ccc'] print(a) # aaa 24.3 # bbbb 23.0 # ccc 88.0 # Name: shit, dtype: float64
python数据分析三:pandas的Series模块
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