import numpy as np

import pandas as pd

from pandas import Series,DataFrame

df1 = 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>

一对一 合并

import numpy as np

from pandas import DataFrame,Series

import pandas as pd

df1 = 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

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NaN

pd.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