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pandas group by multiple columns and remove rows based on multiple conditions
Add one row to pandas DataFrameSelecting multiple columns in a pandas dataframeRenaming columns in pandasAdding new column to existing DataFrame in Python pandasDelete column from pandas DataFrame by column nameHow to drop rows of Pandas DataFrame whose value in certain columns is NaNHow to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandasDeleting DataFrame row in Pandas based on column valueGet list from pandas DataFrame column headers
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;
I have a dataframe which is as follows:
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. From this I want to group by imagename and brandname. Wherever the values in xdiff and ydiff is less than 10 then remove the second line.
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.
I could do this quickly in R using dplyr group by, lag and lead functions. However, I am not sure how to combine different functions in python to achieve this. This is what I have tried so far:
df[df.groupby(['imagename','brandname']).xdiff.transform() <= 10]
Not sure what function should I call within transform and how to include ydiff too.
The expected output is as follows:
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
python pandas
asked Mar 23 at 6:21
ApricotApricot
1,03311135
add a comment |
I have a dataframe which is as follows:
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. From this I want to group by imagename and brandname. Wherever the values in xdiff and ydiff is less than 10 then remove the second line.
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.
I could do this quickly in R using dplyr group by, lag and lead functions. However, I am not sure how to combine different functions in python to achieve this. This is what I have tried so far:
df[df.groupby(['imagename','brandname']).xdiff.transform() <= 10]
Not sure what function should I call within transform and how to include ydiff too.
The expected output is as follows:
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
python pandas
asked Mar 23 at 6:21
ApricotApricot
1,03311135
can you show the expected output for above dataframe
– Naga Kiran
Mar 23 at 6:36
@NagaKiran added the expected output
– Apricot
Mar 23 at 6:38
add a comment |
I have a dataframe which is as follows:
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. From this I want to group by imagename and brandname. Wherever the values in xdiff and ydiff is less than 10 then remove the second line.
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.
I could do this quickly in R using dplyr group by, lag and lead functions. However, I am not sure how to combine different functions in python to achieve this. This is what I have tried so far:
df[df.groupby(['imagename','brandname']).xdiff.transform() <= 10]
Not sure what function should I call within transform and how to include ydiff too.
The expected output is as follows:
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
python pandas
asked Mar 23 at 6:21
ApricotApricot
1,03311135
I have a dataframe which is as follows:
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. From this I want to group by imagename and brandname. Wherever the values in xdiff and ydiff is less than 10 then remove the second line.
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.
I could do this quickly in R using dplyr group by, lag and lead functions. However, I am not sure how to combine different functions in python to achieve this. This is what I have tried so far:
df[df.groupby(['imagename','brandname']).xdiff.transform() <= 10]
Not sure what function should I call within transform and how to include ydiff too.
The expected output is as follows:
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
python pandas
python pandas
asked Mar 23 at 6:21
ApricotApricot
1,03311135
asked Mar 23 at 6:21
ApricotApricot
1,03311135
edited Mar 23 at 6:37
Apricot
asked Mar 23 at 6:21
ApricotApricot
1,03311135
asked Mar 23 at 6:21
ApricotApricot
1,03311135
asked Mar 23 at 6:21
ApricotApricot
1,03311135
1,03311135
can you show the expected output for above dataframe
– Naga Kiran
Mar 23 at 6:36
@NagaKiran added the expected output
– Apricot
Mar 23 at 6:38
add a comment |
can you show the expected output for above dataframe
– Naga Kiran
Mar 23 at 6:36
@NagaKiran added the expected output
– Apricot
Mar 23 at 6:38
can you show the expected output for above dataframe
– Naga Kiran
Mar 23 at 6:36
can you show the expected output for above dataframe
– Naga Kiran
Mar 23 at 6:36
@NagaKiran added the expected output
– Apricot
Mar 23 at 6:38
@NagaKiran added the expected output
– Apricot
Mar 23 at 6:38
add a comment |
1 Answer
1
active
oldest
votes
You can take individual groupby frames and apply the conditions through apply function
#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:
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
answered Mar 23 at 6:46
Naga KiranNaga Kiran
2,5491617
Thank you. I want the ydiff also included in the code
– Apricot
Mar 23 at 6:56
incorporated the change @Apricot
– Naga Kiran
Mar 23 at 7:00
many thanks...works perfectly
– Apricot
Mar 23 at 8:26
1
actually i could not get any insight from that, condition is fine ,x.iloc[range(0,len(x),2)] in this condition we are actually reducing the data, please try of assigning one group data to x explicitly and check the shape and conditions outside of apply function by for reduced dataframe, mostly it will work.
– Naga Kiran
Mar 23 at 8:52
1
sorry...it works correctly...i made some silly mistakes
– Apricot
Mar 23 at 9:29
|
show 2 more comments
Your Answer
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You can take individual groupby frames and apply the conditions through apply function
#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:
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
answered Mar 23 at 6:46
Naga KiranNaga Kiran
2,5491617
Thank you. I want the ydiff also included in the code
– Apricot
Mar 23 at 6:56
incorporated the change @Apricot
– Naga Kiran
Mar 23 at 7:00
many thanks...works perfectly
– Apricot
Mar 23 at 8:26
1
actually i could not get any insight from that, condition is fine ,x.iloc[range(0,len(x),2)] in this condition we are actually reducing the data, please try of assigning one group data to x explicitly and check the shape and conditions outside of apply function by for reduced dataframe, mostly it will work.
– Naga Kiran
Mar 23 at 8:52
1
sorry...it works correctly...i made some silly mistakes
– Apricot
Mar 23 at 9:29
|
show 2 more comments
You can take individual groupby frames and apply the conditions through apply function
#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:
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
answered Mar 23 at 6:46
Naga KiranNaga Kiran
2,5491617
Thank you. I want the ydiff also included in the code
– Apricot
Mar 23 at 6:56
incorporated the change @Apricot
– Naga Kiran
Mar 23 at 7:00
many thanks...works perfectly
– Apricot
Mar 23 at 8:26
1
actually i could not get any insight from that, condition is fine ,x.iloc[range(0,len(x),2)] in this condition we are actually reducing the data, please try of assigning one group data to x explicitly and check the shape and conditions outside of apply function by for reduced dataframe, mostly it will work.
– Naga Kiran
Mar 23 at 8:52
1
sorry...it works correctly...i made some silly mistakes
– Apricot
Mar 23 at 9:29
|
show 2 more comments
You can take individual groupby frames and apply the conditions through apply function
#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:
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
answered Mar 23 at 6:46
Naga KiranNaga Kiran
2,5491617
You can take individual groupby frames and apply the conditions through apply function
#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:
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
answered Mar 23 at 6:46
Naga KiranNaga Kiran
2,5491617
edited Mar 23 at 7:00
answered Mar 23 at 6:46
Naga KiranNaga Kiran
2,5491617
answered Mar 23 at 6:46
Naga KiranNaga Kiran
2,5491617
answered Mar 23 at 6:46
Naga KiranNaga Kiran
2,5491617
2,5491617
Thank you. I want the ydiff also included in the code
– Apricot
Mar 23 at 6:56
incorporated the change @Apricot
– Naga Kiran
Mar 23 at 7:00
many thanks...works perfectly
– Apricot
Mar 23 at 8:26
1
actually i could not get any insight from that, condition is fine ,x.iloc[range(0,len(x),2)] in this condition we are actually reducing the data, please try of assigning one group data to x explicitly and check the shape and conditions outside of apply function by for reduced dataframe, mostly it will work.
– Naga Kiran
Mar 23 at 8:52
1
sorry...it works correctly...i made some silly mistakes
– Apricot
Mar 23 at 9:29
|
show 2 more comments
Thank you. I want the ydiff also included in the code
– Apricot
Mar 23 at 6:56
incorporated the change @Apricot
– Naga Kiran
Mar 23 at 7:00
many thanks...works perfectly
– Apricot
Mar 23 at 8:26
1
actually i could not get any insight from that, condition is fine ,x.iloc[range(0,len(x),2)] in this condition we are actually reducing the data, please try of assigning one group data to x explicitly and check the shape and conditions outside of apply function by for reduced dataframe, mostly it will work.
– Naga Kiran
Mar 23 at 8:52
1
sorry...it works correctly...i made some silly mistakes
– Apricot
Mar 23 at 9:29
Thank you. I want the ydiff also included in the code
– Apricot
Mar 23 at 6:56
Thank you. I want the ydiff also included in the code
– Apricot
Mar 23 at 6:56
incorporated the change @Apricot
– Naga Kiran
Mar 23 at 7:00
incorporated the change @Apricot
– Naga Kiran
Mar 23 at 7:00
many thanks...works perfectly
– Apricot
Mar 23 at 8:26
many thanks...works perfectly
– Apricot
Mar 23 at 8:26
1
1
actually i could not get any insight from that, condition is fine ,x.iloc[range(0,len(x),2)] in this condition we are actually reducing the data, please try of assigning one group data to x explicitly and check the shape and conditions outside of apply function by for reduced dataframe, mostly it will work.
– Naga Kiran
Mar 23 at 8:52
actually i could not get any insight from that, condition is fine ,x.iloc[range(0,len(x),2)] in this condition we are actually reducing the data, please try of assigning one group data to x explicitly and check the shape and conditions outside of apply function by for reduced dataframe, mostly it will work.
– Naga Kiran
Mar 23 at 8:52
1
1
sorry...it works correctly...i made some silly mistakes
– Apricot
Mar 23 at 9:29
sorry...it works correctly...i made some silly mistakes
– Apricot
Mar 23 at 9:29
|
show 2 more comments
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can you show the expected output for above dataframe
– Naga Kiran
Mar 23 at 6:36
@NagaKiran added the expected output
– Apricot
Mar 23 at 6:38