Count appearances of a value until it changes to another value











up vote
7
down vote

favorite
1












I have the following DataFrame:



df = pd.DataFrame([10, 10, 23, 23, 9, 9, 9, 10, 10, 10, 10, 12], columns=['values'])


I want to calculate the frequency of each value, but not an overall count - the count of each value until it changes to another value.



I tried:



df['values'].value_counts()


but it gives me



10    6
9 3
23 2
12 1


The desired output is



10:2 
23:2
9:3
10:4
12:1


How can I do this?










share|improve this question
























  • You might want to have a look at "run-length encoding", since that's basically what you want to be doing.
    – Buhb
    Nov 29 at 21:36















up vote
7
down vote

favorite
1












I have the following DataFrame:



df = pd.DataFrame([10, 10, 23, 23, 9, 9, 9, 10, 10, 10, 10, 12], columns=['values'])


I want to calculate the frequency of each value, but not an overall count - the count of each value until it changes to another value.



I tried:



df['values'].value_counts()


but it gives me



10    6
9 3
23 2
12 1


The desired output is



10:2 
23:2
9:3
10:4
12:1


How can I do this?










share|improve this question
























  • You might want to have a look at "run-length encoding", since that's basically what you want to be doing.
    – Buhb
    Nov 29 at 21:36













up vote
7
down vote

favorite
1









up vote
7
down vote

favorite
1






1





I have the following DataFrame:



df = pd.DataFrame([10, 10, 23, 23, 9, 9, 9, 10, 10, 10, 10, 12], columns=['values'])


I want to calculate the frequency of each value, but not an overall count - the count of each value until it changes to another value.



I tried:



df['values'].value_counts()


but it gives me



10    6
9 3
23 2
12 1


The desired output is



10:2 
23:2
9:3
10:4
12:1


How can I do this?










share|improve this question















I have the following DataFrame:



df = pd.DataFrame([10, 10, 23, 23, 9, 9, 9, 10, 10, 10, 10, 12], columns=['values'])


I want to calculate the frequency of each value, but not an overall count - the count of each value until it changes to another value.



I tried:



df['values'].value_counts()


but it gives me



10    6
9 3
23 2
12 1


The desired output is



10:2 
23:2
9:3
10:4
12:1


How can I do this?







python pandas count frequency






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 29 at 20:01









Alex Riley

75.6k21155159




75.6k21155159










asked Nov 29 at 15:43









Mischa

666




666












  • You might want to have a look at "run-length encoding", since that's basically what you want to be doing.
    – Buhb
    Nov 29 at 21:36


















  • You might want to have a look at "run-length encoding", since that's basically what you want to be doing.
    – Buhb
    Nov 29 at 21:36
















You might want to have a look at "run-length encoding", since that's basically what you want to be doing.
– Buhb
Nov 29 at 21:36




You might want to have a look at "run-length encoding", since that's basically what you want to be doing.
– Buhb
Nov 29 at 21:36












5 Answers
5






active

oldest

votes

















up vote
12
down vote













Use:



df = df.groupby(df['values'].ne(df['values'].shift()).cumsum())['values'].value_counts()


Or:



df = df.groupby([df['values'].ne(df['values'].shift()).cumsum(), 'values']).size()




print (df)
values values
1 10 2
2 23 2
3 9 3
4 10 4
5 12 1
Name: values, dtype: int64


Last for remove first level:



df = df.reset_index(level=0, drop=True)
print (df)
values
10 2
23 2
9 3
10 4
12 1
dtype: int64


Explanation:



Compare original column by shifted with not equal ne and then add cumsum for helper Series:



print (pd.concat([df['values'], a, b, c], 
keys=('orig','shifted', 'not_equal', 'cumsum'), axis=1))
orig shifted not_equal cumsum
0 10 NaN True 1
1 10 10.0 False 1
2 23 10.0 True 2
3 23 23.0 False 2
4 9 23.0 True 3
5 9 9.0 False 3
6 9 9.0 False 3
7 10 9.0 True 4
8 10 10.0 False 4
9 10 10.0 False 4
10 10 10.0 False 4
11 12 10.0 True 5





share|improve this answer























  • i got an error : Duplicated level name: "values", assigned to level 1, is already used for level 0.
    – Mischa
    Nov 29 at 15:52






  • 1




    @Mischa - Then add .rename like df['values'].ne(df['values'].shift()).cumsum().rename('val1')
    – jezrael
    Nov 29 at 15:53










  • @jezrael, ++ve for nice code sir, could you please explain it by dividing it into parts df = df.groupby([df['values'].ne(df['values'].shift()).cumsum(), 'values']).size() as it is not clear, will be grateful to you.
    – RavinderSingh13
    Nov 30 at 12:34


















up vote
5
down vote













You can keep track of where the changes in df['values'] occur:



changes = df['values'].diff().ne(0).cumsum()
print(changes)

0 1
1 1
2 2
3 2
4 3
5 3
6 3
7 4
8 4
9 4
10 4
11 5


And groupby the changes and also df['values'] (to keep them as index) computing the size of each group



df.groupby([changes,'values']).size().reset_index(level=0, drop=True)

values
10 2
23 2
9 3
10 4
12 1
dtype: int64





share|improve this answer






























    up vote
    5
    down vote













    itertools.groupby



    from itertools import groupby

    pd.Series(*zip(*[[len([*v]), k] for k, v in groupby(df['values'])]))

    10 2
    23 2
    9 3
    10 4
    12 1
    dtype: int64




    It's a generator



    def f(x):
    count = 1
    for this, that in zip(x, x[1:]):
    if this == that:
    count += 1
    else:
    yield count, this
    count = 1
    yield count, [*x][-1]

    pd.Series(*zip(*f(df['values'])))

    10 2
    23 2
    9 3
    10 4
    12 1
    dtype: int64





    share|improve this answer






























      up vote
      4
      down vote













      Using crosstab



      df['key']=df['values'].diff().ne(0).cumsum()
      pd.crosstab(df['key'],df['values'])
      Out[353]:
      values 9 10 12 23
      key
      1 0 2 0 0
      2 0 0 0 2
      3 3 0 0 0
      4 0 4 0 0
      5 0 0 1 0


      Slightly modify the result above



      pd.crosstab(df['key'],df['values']).stack().loc[lambda x:x.ne(0)]
      Out[355]:
      key values
      1 10 2
      2 23 2
      3 9 3
      4 10 4
      5 12 1
      dtype: int64




      Base on python groupby



      from itertools import groupby

      [ (k,len(list(g))) for k,g in groupby(df['values'].tolist())]
      Out[366]: [(10, 2), (23, 2), (9, 3), (10, 4), (12, 1)]





      share|improve this answer






























        up vote
        0
        down vote













        This is far from the most time/memory efficient method that in this thread but here's an iterative approach that is pretty straightforward. Please feel encouraged to suggest improvements on this method.



        import pandas as pd

        df = pd.DataFrame([10, 10, 23, 23, 9, 9, 9, 10, 10, 10, 10, 12], columns=['values'])

        dict_count = {}
        for v in df['values'].unique():
        dict_count[v] = 0

        curr_val = df.iloc[0]['values']
        count = 1
        for i in range(1, len(df)):
        if df.iloc[i]['values'] == curr_val:
        count += 1
        else:
        if count > dict_count[curr_val]:
        dict_count[curr_val] = count
        curr_val = df.iloc[i]['values']
        count = 1
        if count > dict_count[curr_val]:
        dict_count[curr_val] = count

        df_count = pd.DataFrame(dict_count, index=[0])
        print(df_count)





        share|improve this answer





















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          5 Answers
          5






          active

          oldest

          votes








          5 Answers
          5






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes








          up vote
          12
          down vote













          Use:



          df = df.groupby(df['values'].ne(df['values'].shift()).cumsum())['values'].value_counts()


          Or:



          df = df.groupby([df['values'].ne(df['values'].shift()).cumsum(), 'values']).size()




          print (df)
          values values
          1 10 2
          2 23 2
          3 9 3
          4 10 4
          5 12 1
          Name: values, dtype: int64


          Last for remove first level:



          df = df.reset_index(level=0, drop=True)
          print (df)
          values
          10 2
          23 2
          9 3
          10 4
          12 1
          dtype: int64


          Explanation:



          Compare original column by shifted with not equal ne and then add cumsum for helper Series:



          print (pd.concat([df['values'], a, b, c], 
          keys=('orig','shifted', 'not_equal', 'cumsum'), axis=1))
          orig shifted not_equal cumsum
          0 10 NaN True 1
          1 10 10.0 False 1
          2 23 10.0 True 2
          3 23 23.0 False 2
          4 9 23.0 True 3
          5 9 9.0 False 3
          6 9 9.0 False 3
          7 10 9.0 True 4
          8 10 10.0 False 4
          9 10 10.0 False 4
          10 10 10.0 False 4
          11 12 10.0 True 5





          share|improve this answer























          • i got an error : Duplicated level name: "values", assigned to level 1, is already used for level 0.
            – Mischa
            Nov 29 at 15:52






          • 1




            @Mischa - Then add .rename like df['values'].ne(df['values'].shift()).cumsum().rename('val1')
            – jezrael
            Nov 29 at 15:53










          • @jezrael, ++ve for nice code sir, could you please explain it by dividing it into parts df = df.groupby([df['values'].ne(df['values'].shift()).cumsum(), 'values']).size() as it is not clear, will be grateful to you.
            – RavinderSingh13
            Nov 30 at 12:34















          up vote
          12
          down vote













          Use:



          df = df.groupby(df['values'].ne(df['values'].shift()).cumsum())['values'].value_counts()


          Or:



          df = df.groupby([df['values'].ne(df['values'].shift()).cumsum(), 'values']).size()




          print (df)
          values values
          1 10 2
          2 23 2
          3 9 3
          4 10 4
          5 12 1
          Name: values, dtype: int64


          Last for remove first level:



          df = df.reset_index(level=0, drop=True)
          print (df)
          values
          10 2
          23 2
          9 3
          10 4
          12 1
          dtype: int64


          Explanation:



          Compare original column by shifted with not equal ne and then add cumsum for helper Series:



          print (pd.concat([df['values'], a, b, c], 
          keys=('orig','shifted', 'not_equal', 'cumsum'), axis=1))
          orig shifted not_equal cumsum
          0 10 NaN True 1
          1 10 10.0 False 1
          2 23 10.0 True 2
          3 23 23.0 False 2
          4 9 23.0 True 3
          5 9 9.0 False 3
          6 9 9.0 False 3
          7 10 9.0 True 4
          8 10 10.0 False 4
          9 10 10.0 False 4
          10 10 10.0 False 4
          11 12 10.0 True 5





          share|improve this answer























          • i got an error : Duplicated level name: "values", assigned to level 1, is already used for level 0.
            – Mischa
            Nov 29 at 15:52






          • 1




            @Mischa - Then add .rename like df['values'].ne(df['values'].shift()).cumsum().rename('val1')
            – jezrael
            Nov 29 at 15:53










          • @jezrael, ++ve for nice code sir, could you please explain it by dividing it into parts df = df.groupby([df['values'].ne(df['values'].shift()).cumsum(), 'values']).size() as it is not clear, will be grateful to you.
            – RavinderSingh13
            Nov 30 at 12:34













          up vote
          12
          down vote










          up vote
          12
          down vote









          Use:



          df = df.groupby(df['values'].ne(df['values'].shift()).cumsum())['values'].value_counts()


          Or:



          df = df.groupby([df['values'].ne(df['values'].shift()).cumsum(), 'values']).size()




          print (df)
          values values
          1 10 2
          2 23 2
          3 9 3
          4 10 4
          5 12 1
          Name: values, dtype: int64


          Last for remove first level:



          df = df.reset_index(level=0, drop=True)
          print (df)
          values
          10 2
          23 2
          9 3
          10 4
          12 1
          dtype: int64


          Explanation:



          Compare original column by shifted with not equal ne and then add cumsum for helper Series:



          print (pd.concat([df['values'], a, b, c], 
          keys=('orig','shifted', 'not_equal', 'cumsum'), axis=1))
          orig shifted not_equal cumsum
          0 10 NaN True 1
          1 10 10.0 False 1
          2 23 10.0 True 2
          3 23 23.0 False 2
          4 9 23.0 True 3
          5 9 9.0 False 3
          6 9 9.0 False 3
          7 10 9.0 True 4
          8 10 10.0 False 4
          9 10 10.0 False 4
          10 10 10.0 False 4
          11 12 10.0 True 5





          share|improve this answer














          Use:



          df = df.groupby(df['values'].ne(df['values'].shift()).cumsum())['values'].value_counts()


          Or:



          df = df.groupby([df['values'].ne(df['values'].shift()).cumsum(), 'values']).size()




          print (df)
          values values
          1 10 2
          2 23 2
          3 9 3
          4 10 4
          5 12 1
          Name: values, dtype: int64


          Last for remove first level:



          df = df.reset_index(level=0, drop=True)
          print (df)
          values
          10 2
          23 2
          9 3
          10 4
          12 1
          dtype: int64


          Explanation:



          Compare original column by shifted with not equal ne and then add cumsum for helper Series:



          print (pd.concat([df['values'], a, b, c], 
          keys=('orig','shifted', 'not_equal', 'cumsum'), axis=1))
          orig shifted not_equal cumsum
          0 10 NaN True 1
          1 10 10.0 False 1
          2 23 10.0 True 2
          3 23 23.0 False 2
          4 9 23.0 True 3
          5 9 9.0 False 3
          6 9 9.0 False 3
          7 10 9.0 True 4
          8 10 10.0 False 4
          9 10 10.0 False 4
          10 10 10.0 False 4
          11 12 10.0 True 5






          share|improve this answer














          share|improve this answer



          share|improve this answer








          edited Nov 29 at 15:51

























          answered Nov 29 at 15:45









          jezrael

          312k21247323




          312k21247323












          • i got an error : Duplicated level name: "values", assigned to level 1, is already used for level 0.
            – Mischa
            Nov 29 at 15:52






          • 1




            @Mischa - Then add .rename like df['values'].ne(df['values'].shift()).cumsum().rename('val1')
            – jezrael
            Nov 29 at 15:53










          • @jezrael, ++ve for nice code sir, could you please explain it by dividing it into parts df = df.groupby([df['values'].ne(df['values'].shift()).cumsum(), 'values']).size() as it is not clear, will be grateful to you.
            – RavinderSingh13
            Nov 30 at 12:34


















          • i got an error : Duplicated level name: "values", assigned to level 1, is already used for level 0.
            – Mischa
            Nov 29 at 15:52






          • 1




            @Mischa - Then add .rename like df['values'].ne(df['values'].shift()).cumsum().rename('val1')
            – jezrael
            Nov 29 at 15:53










          • @jezrael, ++ve for nice code sir, could you please explain it by dividing it into parts df = df.groupby([df['values'].ne(df['values'].shift()).cumsum(), 'values']).size() as it is not clear, will be grateful to you.
            – RavinderSingh13
            Nov 30 at 12:34
















          i got an error : Duplicated level name: "values", assigned to level 1, is already used for level 0.
          – Mischa
          Nov 29 at 15:52




          i got an error : Duplicated level name: "values", assigned to level 1, is already used for level 0.
          – Mischa
          Nov 29 at 15:52




          1




          1




          @Mischa - Then add .rename like df['values'].ne(df['values'].shift()).cumsum().rename('val1')
          – jezrael
          Nov 29 at 15:53




          @Mischa - Then add .rename like df['values'].ne(df['values'].shift()).cumsum().rename('val1')
          – jezrael
          Nov 29 at 15:53












          @jezrael, ++ve for nice code sir, could you please explain it by dividing it into parts df = df.groupby([df['values'].ne(df['values'].shift()).cumsum(), 'values']).size() as it is not clear, will be grateful to you.
          – RavinderSingh13
          Nov 30 at 12:34




          @jezrael, ++ve for nice code sir, could you please explain it by dividing it into parts df = df.groupby([df['values'].ne(df['values'].shift()).cumsum(), 'values']).size() as it is not clear, will be grateful to you.
          – RavinderSingh13
          Nov 30 at 12:34












          up vote
          5
          down vote













          You can keep track of where the changes in df['values'] occur:



          changes = df['values'].diff().ne(0).cumsum()
          print(changes)

          0 1
          1 1
          2 2
          3 2
          4 3
          5 3
          6 3
          7 4
          8 4
          9 4
          10 4
          11 5


          And groupby the changes and also df['values'] (to keep them as index) computing the size of each group



          df.groupby([changes,'values']).size().reset_index(level=0, drop=True)

          values
          10 2
          23 2
          9 3
          10 4
          12 1
          dtype: int64





          share|improve this answer



























            up vote
            5
            down vote













            You can keep track of where the changes in df['values'] occur:



            changes = df['values'].diff().ne(0).cumsum()
            print(changes)

            0 1
            1 1
            2 2
            3 2
            4 3
            5 3
            6 3
            7 4
            8 4
            9 4
            10 4
            11 5


            And groupby the changes and also df['values'] (to keep them as index) computing the size of each group



            df.groupby([changes,'values']).size().reset_index(level=0, drop=True)

            values
            10 2
            23 2
            9 3
            10 4
            12 1
            dtype: int64





            share|improve this answer

























              up vote
              5
              down vote










              up vote
              5
              down vote









              You can keep track of where the changes in df['values'] occur:



              changes = df['values'].diff().ne(0).cumsum()
              print(changes)

              0 1
              1 1
              2 2
              3 2
              4 3
              5 3
              6 3
              7 4
              8 4
              9 4
              10 4
              11 5


              And groupby the changes and also df['values'] (to keep them as index) computing the size of each group



              df.groupby([changes,'values']).size().reset_index(level=0, drop=True)

              values
              10 2
              23 2
              9 3
              10 4
              12 1
              dtype: int64





              share|improve this answer














              You can keep track of where the changes in df['values'] occur:



              changes = df['values'].diff().ne(0).cumsum()
              print(changes)

              0 1
              1 1
              2 2
              3 2
              4 3
              5 3
              6 3
              7 4
              8 4
              9 4
              10 4
              11 5


              And groupby the changes and also df['values'] (to keep them as index) computing the size of each group



              df.groupby([changes,'values']).size().reset_index(level=0, drop=True)

              values
              10 2
              23 2
              9 3
              10 4
              12 1
              dtype: int64






              share|improve this answer














              share|improve this answer



              share|improve this answer








              edited Nov 29 at 16:01

























              answered Nov 29 at 15:55









              nixon

              1,50016




              1,50016






















                  up vote
                  5
                  down vote













                  itertools.groupby



                  from itertools import groupby

                  pd.Series(*zip(*[[len([*v]), k] for k, v in groupby(df['values'])]))

                  10 2
                  23 2
                  9 3
                  10 4
                  12 1
                  dtype: int64




                  It's a generator



                  def f(x):
                  count = 1
                  for this, that in zip(x, x[1:]):
                  if this == that:
                  count += 1
                  else:
                  yield count, this
                  count = 1
                  yield count, [*x][-1]

                  pd.Series(*zip(*f(df['values'])))

                  10 2
                  23 2
                  9 3
                  10 4
                  12 1
                  dtype: int64





                  share|improve this answer



























                    up vote
                    5
                    down vote













                    itertools.groupby



                    from itertools import groupby

                    pd.Series(*zip(*[[len([*v]), k] for k, v in groupby(df['values'])]))

                    10 2
                    23 2
                    9 3
                    10 4
                    12 1
                    dtype: int64




                    It's a generator



                    def f(x):
                    count = 1
                    for this, that in zip(x, x[1:]):
                    if this == that:
                    count += 1
                    else:
                    yield count, this
                    count = 1
                    yield count, [*x][-1]

                    pd.Series(*zip(*f(df['values'])))

                    10 2
                    23 2
                    9 3
                    10 4
                    12 1
                    dtype: int64





                    share|improve this answer

























                      up vote
                      5
                      down vote










                      up vote
                      5
                      down vote









                      itertools.groupby



                      from itertools import groupby

                      pd.Series(*zip(*[[len([*v]), k] for k, v in groupby(df['values'])]))

                      10 2
                      23 2
                      9 3
                      10 4
                      12 1
                      dtype: int64




                      It's a generator



                      def f(x):
                      count = 1
                      for this, that in zip(x, x[1:]):
                      if this == that:
                      count += 1
                      else:
                      yield count, this
                      count = 1
                      yield count, [*x][-1]

                      pd.Series(*zip(*f(df['values'])))

                      10 2
                      23 2
                      9 3
                      10 4
                      12 1
                      dtype: int64





                      share|improve this answer














                      itertools.groupby



                      from itertools import groupby

                      pd.Series(*zip(*[[len([*v]), k] for k, v in groupby(df['values'])]))

                      10 2
                      23 2
                      9 3
                      10 4
                      12 1
                      dtype: int64




                      It's a generator



                      def f(x):
                      count = 1
                      for this, that in zip(x, x[1:]):
                      if this == that:
                      count += 1
                      else:
                      yield count, this
                      count = 1
                      yield count, [*x][-1]

                      pd.Series(*zip(*f(df['values'])))

                      10 2
                      23 2
                      9 3
                      10 4
                      12 1
                      dtype: int64






                      share|improve this answer














                      share|improve this answer



                      share|improve this answer








                      edited Nov 29 at 16:38

























                      answered Nov 29 at 15:59









                      piRSquared

                      150k21135279




                      150k21135279






















                          up vote
                          4
                          down vote













                          Using crosstab



                          df['key']=df['values'].diff().ne(0).cumsum()
                          pd.crosstab(df['key'],df['values'])
                          Out[353]:
                          values 9 10 12 23
                          key
                          1 0 2 0 0
                          2 0 0 0 2
                          3 3 0 0 0
                          4 0 4 0 0
                          5 0 0 1 0


                          Slightly modify the result above



                          pd.crosstab(df['key'],df['values']).stack().loc[lambda x:x.ne(0)]
                          Out[355]:
                          key values
                          1 10 2
                          2 23 2
                          3 9 3
                          4 10 4
                          5 12 1
                          dtype: int64




                          Base on python groupby



                          from itertools import groupby

                          [ (k,len(list(g))) for k,g in groupby(df['values'].tolist())]
                          Out[366]: [(10, 2), (23, 2), (9, 3), (10, 4), (12, 1)]





                          share|improve this answer



























                            up vote
                            4
                            down vote













                            Using crosstab



                            df['key']=df['values'].diff().ne(0).cumsum()
                            pd.crosstab(df['key'],df['values'])
                            Out[353]:
                            values 9 10 12 23
                            key
                            1 0 2 0 0
                            2 0 0 0 2
                            3 3 0 0 0
                            4 0 4 0 0
                            5 0 0 1 0


                            Slightly modify the result above



                            pd.crosstab(df['key'],df['values']).stack().loc[lambda x:x.ne(0)]
                            Out[355]:
                            key values
                            1 10 2
                            2 23 2
                            3 9 3
                            4 10 4
                            5 12 1
                            dtype: int64




                            Base on python groupby



                            from itertools import groupby

                            [ (k,len(list(g))) for k,g in groupby(df['values'].tolist())]
                            Out[366]: [(10, 2), (23, 2), (9, 3), (10, 4), (12, 1)]





                            share|improve this answer

























                              up vote
                              4
                              down vote










                              up vote
                              4
                              down vote









                              Using crosstab



                              df['key']=df['values'].diff().ne(0).cumsum()
                              pd.crosstab(df['key'],df['values'])
                              Out[353]:
                              values 9 10 12 23
                              key
                              1 0 2 0 0
                              2 0 0 0 2
                              3 3 0 0 0
                              4 0 4 0 0
                              5 0 0 1 0


                              Slightly modify the result above



                              pd.crosstab(df['key'],df['values']).stack().loc[lambda x:x.ne(0)]
                              Out[355]:
                              key values
                              1 10 2
                              2 23 2
                              3 9 3
                              4 10 4
                              5 12 1
                              dtype: int64




                              Base on python groupby



                              from itertools import groupby

                              [ (k,len(list(g))) for k,g in groupby(df['values'].tolist())]
                              Out[366]: [(10, 2), (23, 2), (9, 3), (10, 4), (12, 1)]





                              share|improve this answer














                              Using crosstab



                              df['key']=df['values'].diff().ne(0).cumsum()
                              pd.crosstab(df['key'],df['values'])
                              Out[353]:
                              values 9 10 12 23
                              key
                              1 0 2 0 0
                              2 0 0 0 2
                              3 3 0 0 0
                              4 0 4 0 0
                              5 0 0 1 0


                              Slightly modify the result above



                              pd.crosstab(df['key'],df['values']).stack().loc[lambda x:x.ne(0)]
                              Out[355]:
                              key values
                              1 10 2
                              2 23 2
                              3 9 3
                              4 10 4
                              5 12 1
                              dtype: int64




                              Base on python groupby



                              from itertools import groupby

                              [ (k,len(list(g))) for k,g in groupby(df['values'].tolist())]
                              Out[366]: [(10, 2), (23, 2), (9, 3), (10, 4), (12, 1)]






                              share|improve this answer














                              share|improve this answer



                              share|improve this answer








                              edited Nov 29 at 15:59

























                              answered Nov 29 at 15:48









                              W-B

                              95.5k72961




                              95.5k72961






















                                  up vote
                                  0
                                  down vote













                                  This is far from the most time/memory efficient method that in this thread but here's an iterative approach that is pretty straightforward. Please feel encouraged to suggest improvements on this method.



                                  import pandas as pd

                                  df = pd.DataFrame([10, 10, 23, 23, 9, 9, 9, 10, 10, 10, 10, 12], columns=['values'])

                                  dict_count = {}
                                  for v in df['values'].unique():
                                  dict_count[v] = 0

                                  curr_val = df.iloc[0]['values']
                                  count = 1
                                  for i in range(1, len(df)):
                                  if df.iloc[i]['values'] == curr_val:
                                  count += 1
                                  else:
                                  if count > dict_count[curr_val]:
                                  dict_count[curr_val] = count
                                  curr_val = df.iloc[i]['values']
                                  count = 1
                                  if count > dict_count[curr_val]:
                                  dict_count[curr_val] = count

                                  df_count = pd.DataFrame(dict_count, index=[0])
                                  print(df_count)





                                  share|improve this answer

























                                    up vote
                                    0
                                    down vote













                                    This is far from the most time/memory efficient method that in this thread but here's an iterative approach that is pretty straightforward. Please feel encouraged to suggest improvements on this method.



                                    import pandas as pd

                                    df = pd.DataFrame([10, 10, 23, 23, 9, 9, 9, 10, 10, 10, 10, 12], columns=['values'])

                                    dict_count = {}
                                    for v in df['values'].unique():
                                    dict_count[v] = 0

                                    curr_val = df.iloc[0]['values']
                                    count = 1
                                    for i in range(1, len(df)):
                                    if df.iloc[i]['values'] == curr_val:
                                    count += 1
                                    else:
                                    if count > dict_count[curr_val]:
                                    dict_count[curr_val] = count
                                    curr_val = df.iloc[i]['values']
                                    count = 1
                                    if count > dict_count[curr_val]:
                                    dict_count[curr_val] = count

                                    df_count = pd.DataFrame(dict_count, index=[0])
                                    print(df_count)





                                    share|improve this answer























                                      up vote
                                      0
                                      down vote










                                      up vote
                                      0
                                      down vote









                                      This is far from the most time/memory efficient method that in this thread but here's an iterative approach that is pretty straightforward. Please feel encouraged to suggest improvements on this method.



                                      import pandas as pd

                                      df = pd.DataFrame([10, 10, 23, 23, 9, 9, 9, 10, 10, 10, 10, 12], columns=['values'])

                                      dict_count = {}
                                      for v in df['values'].unique():
                                      dict_count[v] = 0

                                      curr_val = df.iloc[0]['values']
                                      count = 1
                                      for i in range(1, len(df)):
                                      if df.iloc[i]['values'] == curr_val:
                                      count += 1
                                      else:
                                      if count > dict_count[curr_val]:
                                      dict_count[curr_val] = count
                                      curr_val = df.iloc[i]['values']
                                      count = 1
                                      if count > dict_count[curr_val]:
                                      dict_count[curr_val] = count

                                      df_count = pd.DataFrame(dict_count, index=[0])
                                      print(df_count)





                                      share|improve this answer












                                      This is far from the most time/memory efficient method that in this thread but here's an iterative approach that is pretty straightforward. Please feel encouraged to suggest improvements on this method.



                                      import pandas as pd

                                      df = pd.DataFrame([10, 10, 23, 23, 9, 9, 9, 10, 10, 10, 10, 12], columns=['values'])

                                      dict_count = {}
                                      for v in df['values'].unique():
                                      dict_count[v] = 0

                                      curr_val = df.iloc[0]['values']
                                      count = 1
                                      for i in range(1, len(df)):
                                      if df.iloc[i]['values'] == curr_val:
                                      count += 1
                                      else:
                                      if count > dict_count[curr_val]:
                                      dict_count[curr_val] = count
                                      curr_val = df.iloc[i]['values']
                                      count = 1
                                      if count > dict_count[curr_val]:
                                      dict_count[curr_val] = count

                                      df_count = pd.DataFrame(dict_count, index=[0])
                                      print(df_count)






                                      share|improve this answer












                                      share|improve this answer



                                      share|improve this answer










                                      answered Nov 30 at 19:22









                                      UBears

                                      104111




                                      104111






























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