Summarise based on binary values










0














How can I summarize this dataframe.



OCT NOV DEC JAN Feb Mar Apr May Jun Jul Aug Sep Total Sales
0 0 0 0 0 1 0 0 0 0 0 0 0 4825379.818
1 0 0 0 0 1 0 0 0 0 0 0 0 5522382.297
2 0 0 0 0 1 0 0 0 0 0 0 0 5721183.289
3 0 0 0 0 1 0 0 0 0 0 0 0 5374047.083
4 0 0 0 0 0 1 0 0 0 0 0 0 4917456.971


I want it to be in form



Month Sales
JAN 1235
FEB 5454
.. ..


..










share|improve this question


























    0














    How can I summarize this dataframe.



    OCT NOV DEC JAN Feb Mar Apr May Jun Jul Aug Sep Total Sales
    0 0 0 0 0 1 0 0 0 0 0 0 0 4825379.818
    1 0 0 0 0 1 0 0 0 0 0 0 0 5522382.297
    2 0 0 0 0 1 0 0 0 0 0 0 0 5721183.289
    3 0 0 0 0 1 0 0 0 0 0 0 0 5374047.083
    4 0 0 0 0 0 1 0 0 0 0 0 0 4917456.971


    I want it to be in form



    Month Sales
    JAN 1235
    FEB 5454
    .. ..


    ..










    share|improve this question
























      0












      0








      0


      1





      How can I summarize this dataframe.



      OCT NOV DEC JAN Feb Mar Apr May Jun Jul Aug Sep Total Sales
      0 0 0 0 0 1 0 0 0 0 0 0 0 4825379.818
      1 0 0 0 0 1 0 0 0 0 0 0 0 5522382.297
      2 0 0 0 0 1 0 0 0 0 0 0 0 5721183.289
      3 0 0 0 0 1 0 0 0 0 0 0 0 5374047.083
      4 0 0 0 0 0 1 0 0 0 0 0 0 4917456.971


      I want it to be in form



      Month Sales
      JAN 1235
      FEB 5454
      .. ..


      ..










      share|improve this question













      How can I summarize this dataframe.



      OCT NOV DEC JAN Feb Mar Apr May Jun Jul Aug Sep Total Sales
      0 0 0 0 0 1 0 0 0 0 0 0 0 4825379.818
      1 0 0 0 0 1 0 0 0 0 0 0 0 5522382.297
      2 0 0 0 0 1 0 0 0 0 0 0 0 5721183.289
      3 0 0 0 0 1 0 0 0 0 0 0 0 5374047.083
      4 0 0 0 0 0 1 0 0 0 0 0 0 4917456.971


      I want it to be in form



      Month Sales
      JAN 1235
      FEB 5454
      .. ..


      ..







      python pandas






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 11 at 10:28









      Ayman Alawin

      417




      417






















          1 Answer
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          oldest

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          0














          You can multiple by mul extracted column by pop first, then sum rows for Series, rename for uppercase months names, add reindex for correct order, rename_axis for nw column name with reset_index:



          cats = ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL',
          'AUG', 'SEP', 'OCT', 'NOV', 'DEC']

          df1 = (df.mul(df.pop('Total Sales'), axis=0)
          .sum()
          .rename(str.upper)
          .reindex(cats)
          .rename_axis('Month')
          .reset_index(name='Sales'))
          print (df1)
          Month Sales
          0 JAN 0.000000e+00
          1 FEB 2.144299e+07
          2 MAR 4.917457e+06
          3 APR 0.000000e+00
          4 MAY 0.000000e+00
          5 JUN 0.000000e+00
          6 JUL 0.000000e+00
          7 AUG 0.000000e+00
          8 SEP 0.000000e+00
          9 OCT 0.000000e+00
          10 NOV 0.000000e+00
          11 DEC 0.000000e+00





          share|improve this answer


















          • 1




            Downvoter, if there's something wrong with my answer, please let me know, so I can correct it. Thanks.
            – jezrael
            Nov 11 at 10:34






          • 1




            I upvoted the answer
            – Ayman Alawin
            Nov 11 at 10:38










          • I need them as separate columns
            – Ayman Alawin
            Nov 11 at 10:40






          • 1




            Give me a sec pelase.
            – jezrael
            Nov 11 at 10:40











          Your Answer






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          1 Answer
          1






          active

          oldest

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          1 Answer
          1






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          0














          You can multiple by mul extracted column by pop first, then sum rows for Series, rename for uppercase months names, add reindex for correct order, rename_axis for nw column name with reset_index:



          cats = ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL',
          'AUG', 'SEP', 'OCT', 'NOV', 'DEC']

          df1 = (df.mul(df.pop('Total Sales'), axis=0)
          .sum()
          .rename(str.upper)
          .reindex(cats)
          .rename_axis('Month')
          .reset_index(name='Sales'))
          print (df1)
          Month Sales
          0 JAN 0.000000e+00
          1 FEB 2.144299e+07
          2 MAR 4.917457e+06
          3 APR 0.000000e+00
          4 MAY 0.000000e+00
          5 JUN 0.000000e+00
          6 JUL 0.000000e+00
          7 AUG 0.000000e+00
          8 SEP 0.000000e+00
          9 OCT 0.000000e+00
          10 NOV 0.000000e+00
          11 DEC 0.000000e+00





          share|improve this answer


















          • 1




            Downvoter, if there's something wrong with my answer, please let me know, so I can correct it. Thanks.
            – jezrael
            Nov 11 at 10:34






          • 1




            I upvoted the answer
            – Ayman Alawin
            Nov 11 at 10:38










          • I need them as separate columns
            – Ayman Alawin
            Nov 11 at 10:40






          • 1




            Give me a sec pelase.
            – jezrael
            Nov 11 at 10:40
















          0














          You can multiple by mul extracted column by pop first, then sum rows for Series, rename for uppercase months names, add reindex for correct order, rename_axis for nw column name with reset_index:



          cats = ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL',
          'AUG', 'SEP', 'OCT', 'NOV', 'DEC']

          df1 = (df.mul(df.pop('Total Sales'), axis=0)
          .sum()
          .rename(str.upper)
          .reindex(cats)
          .rename_axis('Month')
          .reset_index(name='Sales'))
          print (df1)
          Month Sales
          0 JAN 0.000000e+00
          1 FEB 2.144299e+07
          2 MAR 4.917457e+06
          3 APR 0.000000e+00
          4 MAY 0.000000e+00
          5 JUN 0.000000e+00
          6 JUL 0.000000e+00
          7 AUG 0.000000e+00
          8 SEP 0.000000e+00
          9 OCT 0.000000e+00
          10 NOV 0.000000e+00
          11 DEC 0.000000e+00





          share|improve this answer


















          • 1




            Downvoter, if there's something wrong with my answer, please let me know, so I can correct it. Thanks.
            – jezrael
            Nov 11 at 10:34






          • 1




            I upvoted the answer
            – Ayman Alawin
            Nov 11 at 10:38










          • I need them as separate columns
            – Ayman Alawin
            Nov 11 at 10:40






          • 1




            Give me a sec pelase.
            – jezrael
            Nov 11 at 10:40














          0












          0








          0






          You can multiple by mul extracted column by pop first, then sum rows for Series, rename for uppercase months names, add reindex for correct order, rename_axis for nw column name with reset_index:



          cats = ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL',
          'AUG', 'SEP', 'OCT', 'NOV', 'DEC']

          df1 = (df.mul(df.pop('Total Sales'), axis=0)
          .sum()
          .rename(str.upper)
          .reindex(cats)
          .rename_axis('Month')
          .reset_index(name='Sales'))
          print (df1)
          Month Sales
          0 JAN 0.000000e+00
          1 FEB 2.144299e+07
          2 MAR 4.917457e+06
          3 APR 0.000000e+00
          4 MAY 0.000000e+00
          5 JUN 0.000000e+00
          6 JUL 0.000000e+00
          7 AUG 0.000000e+00
          8 SEP 0.000000e+00
          9 OCT 0.000000e+00
          10 NOV 0.000000e+00
          11 DEC 0.000000e+00





          share|improve this answer














          You can multiple by mul extracted column by pop first, then sum rows for Series, rename for uppercase months names, add reindex for correct order, rename_axis for nw column name with reset_index:



          cats = ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL',
          'AUG', 'SEP', 'OCT', 'NOV', 'DEC']

          df1 = (df.mul(df.pop('Total Sales'), axis=0)
          .sum()
          .rename(str.upper)
          .reindex(cats)
          .rename_axis('Month')
          .reset_index(name='Sales'))
          print (df1)
          Month Sales
          0 JAN 0.000000e+00
          1 FEB 2.144299e+07
          2 MAR 4.917457e+06
          3 APR 0.000000e+00
          4 MAY 0.000000e+00
          5 JUN 0.000000e+00
          6 JUL 0.000000e+00
          7 AUG 0.000000e+00
          8 SEP 0.000000e+00
          9 OCT 0.000000e+00
          10 NOV 0.000000e+00
          11 DEC 0.000000e+00






          share|improve this answer














          share|improve this answer



          share|improve this answer








          edited Nov 11 at 10:41

























          answered Nov 11 at 10:33









          jezrael

          319k22258336




          319k22258336







          • 1




            Downvoter, if there's something wrong with my answer, please let me know, so I can correct it. Thanks.
            – jezrael
            Nov 11 at 10:34






          • 1




            I upvoted the answer
            – Ayman Alawin
            Nov 11 at 10:38










          • I need them as separate columns
            – Ayman Alawin
            Nov 11 at 10:40






          • 1




            Give me a sec pelase.
            – jezrael
            Nov 11 at 10:40













          • 1




            Downvoter, if there's something wrong with my answer, please let me know, so I can correct it. Thanks.
            – jezrael
            Nov 11 at 10:34






          • 1




            I upvoted the answer
            – Ayman Alawin
            Nov 11 at 10:38










          • I need them as separate columns
            – Ayman Alawin
            Nov 11 at 10:40






          • 1




            Give me a sec pelase.
            – jezrael
            Nov 11 at 10:40








          1




          1




          Downvoter, if there's something wrong with my answer, please let me know, so I can correct it. Thanks.
          – jezrael
          Nov 11 at 10:34




          Downvoter, if there's something wrong with my answer, please let me know, so I can correct it. Thanks.
          – jezrael
          Nov 11 at 10:34




          1




          1




          I upvoted the answer
          – Ayman Alawin
          Nov 11 at 10:38




          I upvoted the answer
          – Ayman Alawin
          Nov 11 at 10:38












          I need them as separate columns
          – Ayman Alawin
          Nov 11 at 10:40




          I need them as separate columns
          – Ayman Alawin
          Nov 11 at 10:40




          1




          1




          Give me a sec pelase.
          – jezrael
          Nov 11 at 10:40





          Give me a sec pelase.
          – jezrael
          Nov 11 at 10:40


















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