熊猫按多列分组并根据多个条件删除行
[英]pandas group by multiple columns and remove rows based on multiple conditions
问题描述
I have a dataframe which is as follows:
我有一个数据框，如下所示：
imagename,locationName,brandname,x,y,w,h,xdiff,ydiff
95-20180407-215120-235505-00050.jpg,Shirt,SAMSUNG,0,490,177,82,0,0
95-20180407-215120-235505-00050.jpg,Shirt,SAMSUNG,1,491,182,78,1,1
95-20180407-215120-235505-00050.jpg,Shirt,DHFL,3,450,94,45,2,-41
95-20180407-215120-235505-00050.jpg,Shirt,DHFL,5,451,95,48,2,1
95-20180407-215120-235505-00050.jpg,DUGOUT,VIVO,167,319,36,38,162,-132
95-20180407-215120-235505-00050.jpg,Shirt,DHFL,446,349,99,90,279,30
95-20180407-215120-235505-00050.jpg,Shirt,DHFL,455,342,84,93,9,-7
95-20180407-215120-235505-00050.jpg,Shirt,GOIBIBO,559,212,70,106,104,-130
Its a csv dump.它是一个 csv 转储。 From this I want to group by imagename and brandname.由此我想按图像名称和品牌名称进行分组。 Wherever the values in xdiff and ydiff is less than 10 then remove the second line.只要 xdiff 和 ydiff 中的值小于 10，就删除第二行。
For example, from the first two lines I want to delete the second line, similarly from lines 3 and 4 I want to delete line 4.例如，从前两行我想删除第二行，类似地从第 3 行和第 4 行我想删除第 4 行。
I could do this quickly in R using dplyr group by, lag and lead functions.我可以在 R 中使用 dplyr group by、lag 和 lead 函数快速完成此操作。 However, I am not sure how to combine different functions in python to achieve this.但是，我不确定如何在 python 中组合不同的函数来实现这一点。 This is what I have tried so far:这是我迄今为止尝试过的：
df[df.groupby(['imagename','brandname']).xdiff.transform() <= 10]
Not sure what function should I call within transform and how to include ydiff too.不确定我应该在转换中调用什么函数以及如何包含ydiff 。
The expected output is as follows:预期输出如下：
imagename,locationName,brandname,x,y,w,h,xdiff,ydiff
95-20180407-215120-235505-00050.jpg,Shirt,SAMSUNG,0,490,177,82,0,0
95-20180407-215120-235505-00050.jpg,Shirt,DHFL,3,450,94,45,2,-41
95-20180407-215120-235505-00050.jpg,DUGOUT,VIVO,167,319,36,38,162,-132
95-20180407-215120-235505-00050.jpg,Shirt,DHFL,446,349,99,90,279,30
95-20180407-215120-235505-00050.jpg,Shirt,GOIBIBO,559,212,70,106,104,-130
1 个解决方案
解决方案1
1 已采纳 2019-03-23 06:46:16
You can take individual groupby frames and apply the conditions through apply function您可以采用单独的 groupby 帧并通过apply函数应用条件
#df.groupby(['imagename','brandname'],group_keys=False).apply(lambda x: x.iloc[range(0,len(x),2)] if x['xdiff'].lt(10).any() else x)
df.groupby(['imagename','brandname'],group_keys=False).apply(lambda x: x.iloc[range(0,len(x),2)] if (x['xdiff'].lt(10).any() and x['ydiff'].lt(10).any()) else x)
Out:出去：
imagename locationName brandname x y w h xdiff ydiff
2 95-20180407-215120-235505-00050.jpg Shirt DHFL 3 450 94 45 2 -41
5 95-20180407-215120-235505-00050.jpg Shirt DHFL 446 349 99 90 279 30
7 95-20180407-215120-235505-00050.jpg Shirt GOIBIBO 559 212 70 106 104 -130
0 95-20180407-215120-235505-00050.jpg Shirt SAMSUNG 0 490 177 82 0 0
4 95-20180407-215120-235505-00050.jpg DUGOUT VIVO 167 319 36 38 162 -132
声明:本站的技术帖子网页，遵循CC BY-SA 4.0协议，如果您需要转载，请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.