如下所示:
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left1 = pd.DataFrame({‘key ':[‘a' , 'b' , 'a' , 'a' , 'b' , 'c' ], 'value' : range ( 6 )}) right1 = pd.DataFrame({‘group_val ':[3.5,7]},index = [‘a' , 'b' ]) print (left1) print (right1) result = pd.merge(left1,right1,left_on = 'key' ,right_index = True ) print (result) |
层次化数据的索引
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lefth = pd.DataFrame({‘key1 ':[‘Ohio' , 'Ohio' , 'Ohio' , 'Nevada' , 'Nevada' ], ‘key2':[ 2000 , 2001 , 2002 , 2001 , 2002 ], ‘data':np.arange( 5 )}) print (lefth) righth = pd.DataFrame(np.arange( 12 ).reshape( 6 , 2 ),index = [[‘Nevada ',' Nevada ',' Ohio ',' Ohio ',' Ohio ',' Ohio'], [ 2001 , 2000 , 2000 , 200 , 2001 , 2002 ]]) print (righth) result = pd.merge(lefth,righth,left_on = [‘key1 ',' key2'],right_index = True ) print (result) |
以上代码如果想改为外部连接 how = ‘outer' 就可以了
同时合并双方索引
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left2 = pd.DataFrame([[ 1 , 2 ],[ 3 , 4 ],[ 5 , 6 ]],index = [‘a ',' c ',' e '],columns=[‘Ohio' , 'Neveda' ]) right2 = pd.DataFrame([[ 7 , 8 ],[ 9 , 10 ],[ 11 , 12 ],[ 13 , 14 ]],index = [‘b ',' c ',' d ',' e '],columns=[‘Missouri' , 'Alabma' ]) print (left2) print (right2) result = pd.merge(left2,right2,how = 'outer' ,left_index = True ,right_index = True ) print (result) |
以上这篇pandas表连接 索引上的合并方法就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持服务器之家。
原文链接:https://blog.csdn.net/cuihuijun1hao/article/details/78244900