# concat

import numpy as npimport pandas as pd from pandas import Series,DataFramedf1 = DataFrame(data=np.random.randint(0,100,size=(3,3)),index=['a','b','c'],columns=['A','B','C'])df2 = DataFrame(data=np.random.randint(0,100,size=(3,3)),index=['a','e','c'],columns=['A','E','C'])pd.concat((df1,df1),axis=0,join='inner') #列 outer# concat 匹配级联 不匹配级联pd.concat((df1,df2),axis=0,join='inner') #outer 用的多 df1.append(df2) #在后面追加  默认在列上 级联
     <表表横纵的拼接>
      /合并merge的区别
      <数据的合并>
     

# merge

一对一 合并import numpy as npfrom pandas import DataFrame,Seriesimport pandas as pddf1 = DataFrame({
   'employee':['Bob','Jake','Lisa'],                'group':['Accounting','Engineering','Engineering'],                })df2 = DataFrame({
   'employee':['Lisa','Bob','Jake'],                'hire_date':[2004,2008,2012],                })pd.merge(df2,df1)  #默认inner  outer显示全数据#left, right, how='inner', on条件, left_on=None, right_on=None, left_index=False, right_index=False

多对一合并df3 = DataFrame({    'employee':['Lisa','Jake'],    'group':['Accounting','Engineering'],    'hire_date':[2004,2016]})df4 = DataFrame({
   'group':['Accounting','Engineering','Engineering'],                       'supervisor':['Carly','Guido','Steve']                })pd.merge(df3,df4,'outer')

多对多合并df1 = DataFrame({
   'employee':['Bob','Jake','Lisa'],                 'group':['Accounting','Engineering','Engineering']})df5 = DataFrame({
   'group':['Engineering','Engineering','HR'],                'supervisor':['Carly','Guido','Steve']                })pd.merge(df1,df5,'right')   #on条件     suffixes=('_x', '_y')指定冲突列名

#inner outer左右表数据的完整性 #left right 区别

加载excl数据: pd.read_excel('excl_path',sheetname=1)

key的规范化当列冲突时，即有多个列名称相同时，需要使用on=来指定哪一个列作为key，配合suffixes指定冲突列名df1 = DataFrame({
   'employee':['Jack',"Summer","Steve"],                 'group':['Accounting','Finance','Marketing']})df2 = DataFrame({
   'employee':['Jack','Bob',"Jake"],                 'hire_date':[2003,2009,2012],                'group':['Accounting','sell','ceo']})pd.merge(df1,df2,'outer','group',suffixes=('_1', '_2'))当两张表没有可进行连接的列时，可使用left_on和right_on手动指定merge中左右两边的哪一列列作为连接的列pd.merge(df1,df5,'outer',left_on='employee',right_on='name',) #有NaNpd.merge(df1,df5,'inner',left_on='employee',right_on='name',) # 内合并inner交集  只保留两者都有的key(默认模式) 外合并outer并集  how='outer' 补NaN