摘要：import pandas as pds1 = pd.Series([0,1,2],index=['A','B','C'])s2 = pd.Series([3,4],index=['D','E'])s3 = pd.concat([s1,s2]) # 默认行连接
concat
import pandas as pds1 = pd.Series([0,1,2],index=['A','B','C'])s2 = pd.Series([3,4],index=['D','E'])s3 = pd.concat([s1,s2]) # 默认行连接s4 = pd.concat([s1,s2],axis=1)print(s1)print('*'*10)print(s2)print('-'*10)print(s3) print('='*10)print(s4)相同字段的表（列名一样）首尾相接
import pandas as pd# 相同字段的表（列名一样）首尾相接df1 = pd.DataFrame([[1,2],[3,4]],columns=['a','b'])df2 = pd.DataFrame({'a':[5,6],'b':[7,8]},index=[2,3])s1 = pd.Series([9,10],index=[4,5],name='a')s2 = pd.Series([11,12],index=[4,5],name='b')df3 = pd.DataFrame({s1.name:s1,s2.name:s2})df = [df1,df2,df3]c1 = pd.concat(df)print(df1)print("* "*10)print(df2)print('- '*10)print(df3)print('~ '*10)print(c1)print('+ '*10)# 相接的时候识别数据源自于哪张表，可以增加keys参数c2 = pd.concat([df1,df2,df3],keys=['x','y','z'])print(c2)# 也可通过字典来增加分组d1 = {'X':df1,'Y':df2,'Z':df3}c3 = pd.concat(d1)print("* "*10)print(c3)横向表拼接（行对齐），axis = 1
import pandas as pd# 横向表拼接（行对齐）df1 = pd.DataFrame([[1,2],[3,4],[5,6]],columns=['a','b'])df2 = pd.DataFrame({'A':[11,12,13],'B':[14,15,16]},index=[1,2,3])# 当axis = 1的时候，concat就是行对齐，然后将不同列名称的两张表合并c1 = pd.concat([df1,df2],axis=1)print(df1)print("* "*6)print(df2)print('- '*6)print(c1)# join参数，'inner'：两表的交集；'outer'：两表的并集c2 = pd.concat([df1,df2],axis=1,join='inner')c3 = pd.concat([df1,df2],axis=1,join='outer')print('= '*6)print(c2)print('+ '*6)print(c3)无视index的concat
如果两个表的index没有实际含义，使用ignore_index参数，置True，合并的两个表就是根据列字段对齐，然后合并，最后重新整理一个新的index.
import pandas as pd# ignore_index参数df1 = pd.DataFrame({'A':[1,2,3],'B':[4,5,6],'C':[7,8,9]})df2 = pd.DataFrame({'A':[11,12,13],'C':[17,18,19],'D':[20,21,22]},index=[1,2,3])c1 = pd.concat([df1,df2],ignore_index=True)print(df1)print("* "*6)print(df2)print('- '*6)print(c1)来源：思淼教育
