Unique values count in a dataframe










2















I have data like this:



Date | Location | Item
-----------------------
1 | x | a
1 | x | b
1 | x | a
2 | b | a


and I'd like to extract unique values of rows and add a column for number of occurence



I tried this command but it failed:



p=df3.groupby(['date', 'location', 'item']).count()


Result=



Date | Location | Item | Occurences
------------------------------------
1 | x | a | 2
1 | x | b | 1
2 | b | a | 1









share|improve this question
























  • perhaps .size() instead of .count() ? (also, as a suggestion, including a small executable/code example would make it easier to follow what you've tried and what you're seeing.)

    – Garrett
    Nov 14 '18 at 13:45















2















I have data like this:



Date | Location | Item
-----------------------
1 | x | a
1 | x | b
1 | x | a
2 | b | a


and I'd like to extract unique values of rows and add a column for number of occurence



I tried this command but it failed:



p=df3.groupby(['date', 'location', 'item']).count()


Result=



Date | Location | Item | Occurences
------------------------------------
1 | x | a | 2
1 | x | b | 1
2 | b | a | 1









share|improve this question
























  • perhaps .size() instead of .count() ? (also, as a suggestion, including a small executable/code example would make it easier to follow what you've tried and what you're seeing.)

    – Garrett
    Nov 14 '18 at 13:45













2












2








2








I have data like this:



Date | Location | Item
-----------------------
1 | x | a
1 | x | b
1 | x | a
2 | b | a


and I'd like to extract unique values of rows and add a column for number of occurence



I tried this command but it failed:



p=df3.groupby(['date', 'location', 'item']).count()


Result=



Date | Location | Item | Occurences
------------------------------------
1 | x | a | 2
1 | x | b | 1
2 | b | a | 1









share|improve this question
















I have data like this:



Date | Location | Item
-----------------------
1 | x | a
1 | x | b
1 | x | a
2 | b | a


and I'd like to extract unique values of rows and add a column for number of occurence



I tried this command but it failed:



p=df3.groupby(['date', 'location', 'item']).count()


Result=



Date | Location | Item | Occurences
------------------------------------
1 | x | a | 2
1 | x | b | 1
2 | b | a | 1






python dataframe pandas-groupby






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share|improve this question













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edited Nov 15 '18 at 10:38









Vadim Kotov

4,73163549




4,73163549










asked Nov 14 '18 at 11:57









Dr. knowDr. know

241




241












  • perhaps .size() instead of .count() ? (also, as a suggestion, including a small executable/code example would make it easier to follow what you've tried and what you're seeing.)

    – Garrett
    Nov 14 '18 at 13:45

















  • perhaps .size() instead of .count() ? (also, as a suggestion, including a small executable/code example would make it easier to follow what you've tried and what you're seeing.)

    – Garrett
    Nov 14 '18 at 13:45
















perhaps .size() instead of .count() ? (also, as a suggestion, including a small executable/code example would make it easier to follow what you've tried and what you're seeing.)

– Garrett
Nov 14 '18 at 13:45





perhaps .size() instead of .count() ? (also, as a suggestion, including a small executable/code example would make it easier to follow what you've tried and what you're seeing.)

– Garrett
Nov 14 '18 at 13:45












1 Answer
1






active

oldest

votes


















0














Use .size():



df.groupby(['Date', 'Location', 'Item']).size().rename('Ocurrences').to_frame().reset_index()

Date Location Item Ocurrences
0 1 x a 2
1 1 x b 1
2 2 b a 1





share|improve this answer






















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    1 Answer
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    active

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    1 Answer
    1






    active

    oldest

    votes









    active

    oldest

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    active

    oldest

    votes









    0














    Use .size():



    df.groupby(['Date', 'Location', 'Item']).size().rename('Ocurrences').to_frame().reset_index()

    Date Location Item Ocurrences
    0 1 x a 2
    1 1 x b 1
    2 2 b a 1





    share|improve this answer



























      0














      Use .size():



      df.groupby(['Date', 'Location', 'Item']).size().rename('Ocurrences').to_frame().reset_index()

      Date Location Item Ocurrences
      0 1 x a 2
      1 1 x b 1
      2 2 b a 1





      share|improve this answer

























        0












        0








        0







        Use .size():



        df.groupby(['Date', 'Location', 'Item']).size().rename('Ocurrences').to_frame().reset_index()

        Date Location Item Ocurrences
        0 1 x a 2
        1 1 x b 1
        2 2 b a 1





        share|improve this answer













        Use .size():



        df.groupby(['Date', 'Location', 'Item']).size().rename('Ocurrences').to_frame().reset_index()

        Date Location Item Ocurrences
        0 1 x a 2
        1 1 x b 1
        2 2 b a 1






        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Nov 14 '18 at 15:29









        Franco PiccoloFranco Piccolo

        1,591716




        1,591716





























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