Pandas: issues with groupby. Error: 'ValueError: Grouper for not 1-dimensional'












0















I've been looking for this error here, but I only found this solution 1 (which doesn't work in my case). Does anybody can guide me on how to solve it?.



My dataset (df2) looks like this: 



      id_cl  id_sup total_t cl_ind  cl_city  sup_ind  sup_city  same_city

0   1000135 1797029  414.85  I5610  11308.0    G4711   10901.0   no

1   1000135 1798069  19.76   I5610  11308.0    G4719   10901.0   no

2   1000135 1923186  302.73  I5610  11308.0    G4630   10901.0   no

3   1000135 2502927  1262.86 I5610  11308.0    G4630   11308.0   yes

4   1000135 2504288  155.04  I5610  11308.0    G4711   11308.0   yes


I need to group this dataset as follows:



df_sup = df2.groupby(['cl_city','cl_ind','same_city']).agg({'id_sup':'nunique', 'total_t':'sum'})


But when performing this, I'm getting this error!:



ValueError: Grouper for 'cl_city' not 1-dimensional


As a result I need something like this: 



                                 id_sup      total_t

cl_city     cl_ind  same_city       

  10701      A0112         no         2    21964.22

                          yes        31     3530.40

             A0122         no      2374 23328061.47

                          yes      1228  2684408.12

             A0127         no        11    19962.68

                          yes         7      915.44

             A0163         no       357   574827.97

                          yes       140     60385.7





python pandas







share|improve this question

























  • What is the output of df2.columns?

    – Peter Leimbigler
    Nov 21 '18 at 22:47











  • as an output I need to know: how many unique 'id_sup' and the sum of 'total_t' of every item of cl_city, cl_ind and same_city. Basically a table with columns: cl_city | cl_ind | same_city | count of unique id_sup | sum of total_T

    – PAstudilloE
    Nov 21 '18 at 22:54











  • Possible duplicate of stackoverflow.com/questions/43298192/…

    – Kevin Fang
    Nov 21 '18 at 22:58








  • 1





    @PAstudilloE, thanks! This does reveal the immediate cause of the error: for some terrible reason, the columns of df2 are a highly nested MultiIndex, where instead of each string being a label, each string is actually the name of a separate *index level*(!) The fix for this comes from stackoverflow.com/q/14507794, and is this: df.columns = [' '.join(col).strip() for col in df.columns.values]. You mentioned df2 is the result of many merges; I suspect those merges are not written optimally, leading to this pathological MultiIndex situation.

    – Peter Leimbigler
    Nov 21 '18 at 23:21






  • 1





    Thanks @PeterLeimbigler!! That's exactly what happened. I solved the issue as you recommended.

    – PAstudilloE
    Nov 21 '18 at 23:26
















0















I've been looking for this error here, but I only found this solution 1 (which doesn't work in my case). Does anybody can guide me on how to solve it?.



My dataset (df2) looks like this: 



      id_cl  id_sup total_t cl_ind  cl_city  sup_ind  sup_city  same_city

0   1000135 1797029  414.85  I5610  11308.0    G4711   10901.0   no

1   1000135 1798069  19.76   I5610  11308.0    G4719   10901.0   no

2   1000135 1923186  302.73  I5610  11308.0    G4630   10901.0   no

3   1000135 2502927  1262.86 I5610  11308.0    G4630   11308.0   yes

4   1000135 2504288  155.04  I5610  11308.0    G4711   11308.0   yes


I need to group this dataset as follows:



df_sup = df2.groupby(['cl_city','cl_ind','same_city']).agg({'id_sup':'nunique', 'total_t':'sum'})


But when performing this, I'm getting this error!:



ValueError: Grouper for 'cl_city' not 1-dimensional


As a result I need something like this: 



                                 id_sup      total_t

cl_city     cl_ind  same_city       

  10701      A0112         no         2    21964.22

                          yes        31     3530.40

             A0122         no      2374 23328061.47

                          yes      1228  2684408.12

             A0127         no        11    19962.68

                          yes         7      915.44

             A0163         no       357   574827.97

                          yes       140     60385.7





python pandas







share|improve this question

























  • What is the output of df2.columns?

    – Peter Leimbigler
    Nov 21 '18 at 22:47











  • as an output I need to know: how many unique 'id_sup' and the sum of 'total_t' of every item of cl_city, cl_ind and same_city. Basically a table with columns: cl_city | cl_ind | same_city | count of unique id_sup | sum of total_T

    – PAstudilloE
    Nov 21 '18 at 22:54











  • Possible duplicate of stackoverflow.com/questions/43298192/…

    – Kevin Fang
    Nov 21 '18 at 22:58








  • 1





    @PAstudilloE, thanks! This does reveal the immediate cause of the error: for some terrible reason, the columns of df2 are a highly nested MultiIndex, where instead of each string being a label, each string is actually the name of a separate *index level*(!) The fix for this comes from stackoverflow.com/q/14507794, and is this: df.columns = [' '.join(col).strip() for col in df.columns.values]. You mentioned df2 is the result of many merges; I suspect those merges are not written optimally, leading to this pathological MultiIndex situation.

    – Peter Leimbigler
    Nov 21 '18 at 23:21






  • 1





    Thanks @PeterLeimbigler!! That's exactly what happened. I solved the issue as you recommended.

    – PAstudilloE
    Nov 21 '18 at 23:26














0












0








0








I've been looking for this error here, but I only found this solution 1 (which doesn't work in my case). Does anybody can guide me on how to solve it?.



My dataset (df2) looks like this: 



      id_cl  id_sup total_t cl_ind  cl_city  sup_ind  sup_city  same_city

0   1000135 1797029  414.85  I5610  11308.0    G4711   10901.0   no

1   1000135 1798069  19.76   I5610  11308.0    G4719   10901.0   no

2   1000135 1923186  302.73  I5610  11308.0    G4630   10901.0   no

3   1000135 2502927  1262.86 I5610  11308.0    G4630   11308.0   yes

4   1000135 2504288  155.04  I5610  11308.0    G4711   11308.0   yes


I need to group this dataset as follows:



df_sup = df2.groupby(['cl_city','cl_ind','same_city']).agg({'id_sup':'nunique', 'total_t':'sum'})


But when performing this, I'm getting this error!:



ValueError: Grouper for 'cl_city' not 1-dimensional


As a result I need something like this: 



                                 id_sup      total_t

cl_city     cl_ind  same_city       

  10701      A0112         no         2    21964.22

                          yes        31     3530.40

             A0122         no      2374 23328061.47

                          yes      1228  2684408.12

             A0127         no        11    19962.68

                          yes         7      915.44

             A0163         no       357   574827.97

                          yes       140     60385.7





python pandas







share|improve this question
















I've been looking for this error here, but I only found this solution 1 (which doesn't work in my case). Does anybody can guide me on how to solve it?.



My dataset (df2) looks like this: 



      id_cl  id_sup total_t cl_ind  cl_city  sup_ind  sup_city  same_city

0   1000135 1797029  414.85  I5610  11308.0    G4711   10901.0   no

1   1000135 1798069  19.76   I5610  11308.0    G4719   10901.0   no

2   1000135 1923186  302.73  I5610  11308.0    G4630   10901.0   no

3   1000135 2502927  1262.86 I5610  11308.0    G4630   11308.0   yes

4   1000135 2504288  155.04  I5610  11308.0    G4711   11308.0   yes


I need to group this dataset as follows:



df_sup = df2.groupby(['cl_city','cl_ind','same_city']).agg({'id_sup':'nunique', 'total_t':'sum'})


But when performing this, I'm getting this error!:



ValueError: Grouper for 'cl_city' not 1-dimensional


As a result I need something like this: 



                                 id_sup      total_t

cl_city     cl_ind  same_city       

  10701      A0112         no         2    21964.22

                          yes        31     3530.40

             A0122         no      2374 23328061.47

                          yes      1228  2684408.12

             A0127         no        11    19962.68

                          yes         7      915.44

             A0163         no       357   574827.97

                          yes       140     60385.7





python pandas




python pandas






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 21 '18 at 23:03







PAstudilloE

















asked Nov 21 '18 at 22:37









PAstudilloEPAstudilloE

142111




142111













  • What is the output of df2.columns?

    – Peter Leimbigler
    Nov 21 '18 at 22:47











  • as an output I need to know: how many unique 'id_sup' and the sum of 'total_t' of every item of cl_city, cl_ind and same_city. Basically a table with columns: cl_city | cl_ind | same_city | count of unique id_sup | sum of total_T

    – PAstudilloE
    Nov 21 '18 at 22:54











  • Possible duplicate of stackoverflow.com/questions/43298192/…

    – Kevin Fang
    Nov 21 '18 at 22:58








  • 1





    @PAstudilloE, thanks! This does reveal the immediate cause of the error: for some terrible reason, the columns of df2 are a highly nested MultiIndex, where instead of each string being a label, each string is actually the name of a separate *index level*(!) The fix for this comes from stackoverflow.com/q/14507794, and is this: df.columns = [' '.join(col).strip() for col in df.columns.values]. You mentioned df2 is the result of many merges; I suspect those merges are not written optimally, leading to this pathological MultiIndex situation.

    – Peter Leimbigler
    Nov 21 '18 at 23:21






  • 1





    Thanks @PeterLeimbigler!! That's exactly what happened. I solved the issue as you recommended.

    – PAstudilloE
    Nov 21 '18 at 23:26



















  • What is the output of df2.columns?

    – Peter Leimbigler
    Nov 21 '18 at 22:47











  • as an output I need to know: how many unique 'id_sup' and the sum of 'total_t' of every item of cl_city, cl_ind and same_city. Basically a table with columns: cl_city | cl_ind | same_city | count of unique id_sup | sum of total_T

    – PAstudilloE
    Nov 21 '18 at 22:54











  • Possible duplicate of stackoverflow.com/questions/43298192/…

    – Kevin Fang
    Nov 21 '18 at 22:58








  • 1





    @PAstudilloE, thanks! This does reveal the immediate cause of the error: for some terrible reason, the columns of df2 are a highly nested MultiIndex, where instead of each string being a label, each string is actually the name of a separate *index level*(!) The fix for this comes from stackoverflow.com/q/14507794, and is this: df.columns = [' '.join(col).strip() for col in df.columns.values]. You mentioned df2 is the result of many merges; I suspect those merges are not written optimally, leading to this pathological MultiIndex situation.

    – Peter Leimbigler
    Nov 21 '18 at 23:21






  • 1





    Thanks @PeterLeimbigler!! That's exactly what happened. I solved the issue as you recommended.

    – PAstudilloE
    Nov 21 '18 at 23:26

















What is the output of df2.columns?

– Peter Leimbigler
Nov 21 '18 at 22:47





What is the output of df2.columns?

– Peter Leimbigler
Nov 21 '18 at 22:47













as an output I need to know: how many unique 'id_sup' and the sum of 'total_t' of every item of cl_city, cl_ind and same_city. Basically a table with columns: cl_city | cl_ind | same_city | count of unique id_sup | sum of total_T

– PAstudilloE
Nov 21 '18 at 22:54





as an output I need to know: how many unique 'id_sup' and the sum of 'total_t' of every item of cl_city, cl_ind and same_city. Basically a table with columns: cl_city | cl_ind | same_city | count of unique id_sup | sum of total_T

– PAstudilloE
Nov 21 '18 at 22:54













Possible duplicate of stackoverflow.com/questions/43298192/…

– Kevin Fang
Nov 21 '18 at 22:58







Possible duplicate of stackoverflow.com/questions/43298192/…

– Kevin Fang
Nov 21 '18 at 22:58






1




1





@PAstudilloE, thanks! This does reveal the immediate cause of the error: for some terrible reason, the columns of df2 are a highly nested MultiIndex, where instead of each string being a label, each string is actually the name of a separate *index level*(!) The fix for this comes from stackoverflow.com/q/14507794, and is this: df.columns = [' '.join(col).strip() for col in df.columns.values]. You mentioned df2 is the result of many merges; I suspect those merges are not written optimally, leading to this pathological MultiIndex situation.

– Peter Leimbigler
Nov 21 '18 at 23:21





@PAstudilloE, thanks! This does reveal the immediate cause of the error: for some terrible reason, the columns of df2 are a highly nested MultiIndex, where instead of each string being a label, each string is actually the name of a separate *index level*(!) The fix for this comes from stackoverflow.com/q/14507794, and is this: df.columns = [' '.join(col).strip() for col in df.columns.values]. You mentioned df2 is the result of many merges; I suspect those merges are not written optimally, leading to this pathological MultiIndex situation.

– Peter Leimbigler
Nov 21 '18 at 23:21




1




1





Thanks @PeterLeimbigler!! That's exactly what happened. I solved the issue as you recommended.

– PAstudilloE
Nov 21 '18 at 23:26





Thanks @PeterLeimbigler!! That's exactly what happened. I solved the issue as you recommended.

– PAstudilloE
Nov 21 '18 at 23:26












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I don't know why python is showing me that error, df2 is the result of merging several previous datasets and it does not have any duplicate columns.



I solve this issue in a silly way but it worked. I converted df2 to a CSV file and then I load it again. After that, everything is working fine. [But I can't figure out, why python is showing that error]. Hope it helps.






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    I don't know why python is showing me that error, df2 is the result of merging several previous datasets and it does not have any duplicate columns.



    I solve this issue in a silly way but it worked. I converted df2 to a CSV file and then I load it again. After that, everything is working fine. [But I can't figure out, why python is showing that error]. Hope it helps.






    share|improve this answer




























      0














      I don't know why python is showing me that error, df2 is the result of merging several previous datasets and it does not have any duplicate columns.



      I solve this issue in a silly way but it worked. I converted df2 to a CSV file and then I load it again. After that, everything is working fine. [But I can't figure out, why python is showing that error]. Hope it helps.






      share|improve this answer


























        0












        0








        0







        I don't know why python is showing me that error, df2 is the result of merging several previous datasets and it does not have any duplicate columns.



        I solve this issue in a silly way but it worked. I converted df2 to a CSV file and then I load it again. After that, everything is working fine. [But I can't figure out, why python is showing that error]. Hope it helps.






        share|improve this answer













        I don't know why python is showing me that error, df2 is the result of merging several previous datasets and it does not have any duplicate columns.



        I solve this issue in a silly way but it worked. I converted df2 to a CSV file and then I load it again. After that, everything is working fine. [But I can't figure out, why python is showing that error]. Hope it helps.







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Nov 21 '18 at 23:09









        PAstudilloEPAstudilloE

        142111




        142111
































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