Count of values grouped per month, year - Pandas Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern) Data science time! April 2019 and salary with experience The Ask Question Wizard is Live!MySQL Query GROUP BY day / month / yearHow do I get the row count of a pandas DataFrame?Select rows from a DataFrame based on values in a column in pandasGet statistics for each group (such as count, mean, etc) using pandas GroupBy?Count unique dates in pandas dataframeCounting values using pandas groupbysplitting of date column to day, month, year in python 2.7Count Unique Days Groupby Month/YearCounting events per contributor per dayHow to count the number of dropoffs per month for dataframe column

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Count of values grouped per month, year - Pandas



Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
Data science time! April 2019 and salary with experience
The Ask Question Wizard is Live!MySQL Query GROUP BY day / month / yearHow do I get the row count of a pandas DataFrame?Select rows from a DataFrame based on values in a column in pandasGet statistics for each group (such as count, mean, etc) using pandas GroupBy?Count unique dates in pandas dataframeCounting values using pandas groupbysplitting of date column to day, month, year in python 2.7Count Unique Days Groupby Month/YearCounting events per contributor per dayHow to count the number of dropoffs per month for dataframe column



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6















I am trying to groupby counts of dates per month and year in a specific output. I can do it per day but can't get the same output per month/year.



d = (
'Date' : ['1/1/18','1/1/18','2/1/18','3/1/18','1/2/18','1/3/18','2/1/19','3/1/19'],
'Val' : ['A','B','C','D','A','B','C','D'],
)

df = pd.DataFrame(data = d)

df['Date'] = pd.to_datetime(df['Date'], format= '%d/%m/%y')

df['Count_d'] = df.Date.map(df.groupby('Date').size())


This is the output I want:



 Date Val Count_d
0 2018-01-01 A 2
1 2018-01-01 B 2
2 2018-01-02 C 1
3 2018-01-03 D 1
4 2018-02-01 A 1
5 2018-03-01 B 1
6 2019-01-02 C 1
7 2019-01-03 D 1


When I attempt to do similar but per month and year I use the following:



df1 = df.groupby([df['Date'].dt.year.rename('year'), df['Date'].dt.month.rename('month')]).agg('count')
print(df)


But the output is:



 Date Val
count count
year month
2018 1 4 4
2 1 1
3 1 1
2019 1 2 2


Intended Output:



 Date Val Count_d Count_m Count_y
0 2018-01-01 A 2 4 6
1 2018-01-01 B 2 4 6
2 2018-01-02 C 1 4 6
3 2018-01-03 D 1 4 6
4 2018-02-01 A 1 1 6
5 2018-03-01 B 1 1 6
6 2019-01-02 C 1 2 2
7 2019-01-03 D 1 2 2









share|improve this question






























    6















    I am trying to groupby counts of dates per month and year in a specific output. I can do it per day but can't get the same output per month/year.



    d = (
    'Date' : ['1/1/18','1/1/18','2/1/18','3/1/18','1/2/18','1/3/18','2/1/19','3/1/19'],
    'Val' : ['A','B','C','D','A','B','C','D'],
    )

    df = pd.DataFrame(data = d)

    df['Date'] = pd.to_datetime(df['Date'], format= '%d/%m/%y')

    df['Count_d'] = df.Date.map(df.groupby('Date').size())


    This is the output I want:



     Date Val Count_d
    0 2018-01-01 A 2
    1 2018-01-01 B 2
    2 2018-01-02 C 1
    3 2018-01-03 D 1
    4 2018-02-01 A 1
    5 2018-03-01 B 1
    6 2019-01-02 C 1
    7 2019-01-03 D 1


    When I attempt to do similar but per month and year I use the following:



    df1 = df.groupby([df['Date'].dt.year.rename('year'), df['Date'].dt.month.rename('month')]).agg('count')
    print(df)


    But the output is:



     Date Val
    count count
    year month
    2018 1 4 4
    2 1 1
    3 1 1
    2019 1 2 2


    Intended Output:



     Date Val Count_d Count_m Count_y
    0 2018-01-01 A 2 4 6
    1 2018-01-01 B 2 4 6
    2 2018-01-02 C 1 4 6
    3 2018-01-03 D 1 4 6
    4 2018-02-01 A 1 1 6
    5 2018-03-01 B 1 1 6
    6 2019-01-02 C 1 2 2
    7 2019-01-03 D 1 2 2









    share|improve this question


























      6












      6








      6








      I am trying to groupby counts of dates per month and year in a specific output. I can do it per day but can't get the same output per month/year.



      d = (
      'Date' : ['1/1/18','1/1/18','2/1/18','3/1/18','1/2/18','1/3/18','2/1/19','3/1/19'],
      'Val' : ['A','B','C','D','A','B','C','D'],
      )

      df = pd.DataFrame(data = d)

      df['Date'] = pd.to_datetime(df['Date'], format= '%d/%m/%y')

      df['Count_d'] = df.Date.map(df.groupby('Date').size())


      This is the output I want:



       Date Val Count_d
      0 2018-01-01 A 2
      1 2018-01-01 B 2
      2 2018-01-02 C 1
      3 2018-01-03 D 1
      4 2018-02-01 A 1
      5 2018-03-01 B 1
      6 2019-01-02 C 1
      7 2019-01-03 D 1


      When I attempt to do similar but per month and year I use the following:



      df1 = df.groupby([df['Date'].dt.year.rename('year'), df['Date'].dt.month.rename('month')]).agg('count')
      print(df)


      But the output is:



       Date Val
      count count
      year month
      2018 1 4 4
      2 1 1
      3 1 1
      2019 1 2 2


      Intended Output:



       Date Val Count_d Count_m Count_y
      0 2018-01-01 A 2 4 6
      1 2018-01-01 B 2 4 6
      2 2018-01-02 C 1 4 6
      3 2018-01-03 D 1 4 6
      4 2018-02-01 A 1 1 6
      5 2018-03-01 B 1 1 6
      6 2019-01-02 C 1 2 2
      7 2019-01-03 D 1 2 2









      share|improve this question
















      I am trying to groupby counts of dates per month and year in a specific output. I can do it per day but can't get the same output per month/year.



      d = (
      'Date' : ['1/1/18','1/1/18','2/1/18','3/1/18','1/2/18','1/3/18','2/1/19','3/1/19'],
      'Val' : ['A','B','C','D','A','B','C','D'],
      )

      df = pd.DataFrame(data = d)

      df['Date'] = pd.to_datetime(df['Date'], format= '%d/%m/%y')

      df['Count_d'] = df.Date.map(df.groupby('Date').size())


      This is the output I want:



       Date Val Count_d
      0 2018-01-01 A 2
      1 2018-01-01 B 2
      2 2018-01-02 C 1
      3 2018-01-03 D 1
      4 2018-02-01 A 1
      5 2018-03-01 B 1
      6 2019-01-02 C 1
      7 2019-01-03 D 1


      When I attempt to do similar but per month and year I use the following:



      df1 = df.groupby([df['Date'].dt.year.rename('year'), df['Date'].dt.month.rename('month')]).agg('count')
      print(df)


      But the output is:



       Date Val
      count count
      year month
      2018 1 4 4
      2 1 1
      3 1 1
      2019 1 2 2


      Intended Output:



       Date Val Count_d Count_m Count_y
      0 2018-01-01 A 2 4 6
      1 2018-01-01 B 2 4 6
      2 2018-01-02 C 1 4 6
      3 2018-01-03 D 1 4 6
      4 2018-02-01 A 1 1 6
      5 2018-03-01 B 1 1 6
      6 2019-01-02 C 1 2 2
      7 2019-01-03 D 1 2 2






      python pandas group-by count transform






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Apr 19 at 3:08







      jonboy

















      asked Apr 17 at 11:05









      jonboyjonboy

      45115




      45115






















          2 Answers
          2






          active

          oldest

          votes


















          6














          Use GroupBy.transform for columns with same size like original DataFrame:



          df['Date'] = pd.to_datetime(df['Date'], format= '%d/%m/%y')
          y = df['Date'].dt.year
          m = df['Date'].dt.month

          df['Count_d'] = df.groupby('Date')['Date'].transform('size')
          df['Count_m'] = df.groupby([y, m])['Date'].transform('size')
          df['Count_y'] = df.groupby(y)['Date'].transform('size')

          print(df)
          Date Val Count_d Count_m Count_y
          0 2018-01-01 A 2 4 6
          1 2018-01-01 B 2 4 6
          2 2018-01-02 C 1 4 6
          3 2018-01-03 D 1 4 6
          4 2018-02-01 A 1 1 6
          5 2018-03-01 B 1 1 6
          6 2019-01-02 C 1 2 2
          7 2019-01-03 D 1 2 2





          share|improve this answer

























          • just found that they are removing agg with dict. any idea why?

            – anky_91
            Apr 17 at 11:14











          • @anky_91 - because same size columns like original df.

            – jezrael
            Apr 17 at 11:15











          • where did you see that @anky_91

            – Erfan
            Apr 17 at 11:15












          • @Erfan got a future warning. i was implementing wrongly i guess, jez made that clear

            – anky_91
            Apr 17 at 11:16



















          1














          You can do this with pd.Grouper



          df['Count_d'] = df.groupby([pd.Grouper(key='Date', freq='D')])['Date'].transform('size').astype(int)
          df['Count_m'] = df.groupby([pd.Grouper(key='Date', freq='M')])['Date'].transform('size').astype(int)
          df['Count_y'] = df.groupby([pd.Grouper(key='Date', freq='Y')])['Date'].transform('size').astype(int)


          Which will give



           Date Val Count_d Count_m Count_y
          0 2018-01-01 A 2 4 6
          1 2018-01-01 B 2 4 6
          2 2018-01-02 C 1 4 6
          3 2018-01-03 D 1 4 6
          4 2018-02-01 A 1 1 6
          5 2018-03-01 B 1 1 6
          6 2019-01-02 C 1 2 2
          7 2019-01-03 D 1 2 2


          You can groupby various different frequencies with this, see the documentation on DateOffsets






          share|improve this answer























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            2 Answers
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            2 Answers
            2






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            6














            Use GroupBy.transform for columns with same size like original DataFrame:



            df['Date'] = pd.to_datetime(df['Date'], format= '%d/%m/%y')
            y = df['Date'].dt.year
            m = df['Date'].dt.month

            df['Count_d'] = df.groupby('Date')['Date'].transform('size')
            df['Count_m'] = df.groupby([y, m])['Date'].transform('size')
            df['Count_y'] = df.groupby(y)['Date'].transform('size')

            print(df)
            Date Val Count_d Count_m Count_y
            0 2018-01-01 A 2 4 6
            1 2018-01-01 B 2 4 6
            2 2018-01-02 C 1 4 6
            3 2018-01-03 D 1 4 6
            4 2018-02-01 A 1 1 6
            5 2018-03-01 B 1 1 6
            6 2019-01-02 C 1 2 2
            7 2019-01-03 D 1 2 2





            share|improve this answer

























            • just found that they are removing agg with dict. any idea why?

              – anky_91
              Apr 17 at 11:14











            • @anky_91 - because same size columns like original df.

              – jezrael
              Apr 17 at 11:15











            • where did you see that @anky_91

              – Erfan
              Apr 17 at 11:15












            • @Erfan got a future warning. i was implementing wrongly i guess, jez made that clear

              – anky_91
              Apr 17 at 11:16
















            6














            Use GroupBy.transform for columns with same size like original DataFrame:



            df['Date'] = pd.to_datetime(df['Date'], format= '%d/%m/%y')
            y = df['Date'].dt.year
            m = df['Date'].dt.month

            df['Count_d'] = df.groupby('Date')['Date'].transform('size')
            df['Count_m'] = df.groupby([y, m])['Date'].transform('size')
            df['Count_y'] = df.groupby(y)['Date'].transform('size')

            print(df)
            Date Val Count_d Count_m Count_y
            0 2018-01-01 A 2 4 6
            1 2018-01-01 B 2 4 6
            2 2018-01-02 C 1 4 6
            3 2018-01-03 D 1 4 6
            4 2018-02-01 A 1 1 6
            5 2018-03-01 B 1 1 6
            6 2019-01-02 C 1 2 2
            7 2019-01-03 D 1 2 2





            share|improve this answer

























            • just found that they are removing agg with dict. any idea why?

              – anky_91
              Apr 17 at 11:14











            • @anky_91 - because same size columns like original df.

              – jezrael
              Apr 17 at 11:15











            • where did you see that @anky_91

              – Erfan
              Apr 17 at 11:15












            • @Erfan got a future warning. i was implementing wrongly i guess, jez made that clear

              – anky_91
              Apr 17 at 11:16














            6












            6








            6







            Use GroupBy.transform for columns with same size like original DataFrame:



            df['Date'] = pd.to_datetime(df['Date'], format= '%d/%m/%y')
            y = df['Date'].dt.year
            m = df['Date'].dt.month

            df['Count_d'] = df.groupby('Date')['Date'].transform('size')
            df['Count_m'] = df.groupby([y, m])['Date'].transform('size')
            df['Count_y'] = df.groupby(y)['Date'].transform('size')

            print(df)
            Date Val Count_d Count_m Count_y
            0 2018-01-01 A 2 4 6
            1 2018-01-01 B 2 4 6
            2 2018-01-02 C 1 4 6
            3 2018-01-03 D 1 4 6
            4 2018-02-01 A 1 1 6
            5 2018-03-01 B 1 1 6
            6 2019-01-02 C 1 2 2
            7 2019-01-03 D 1 2 2





            share|improve this answer















            Use GroupBy.transform for columns with same size like original DataFrame:



            df['Date'] = pd.to_datetime(df['Date'], format= '%d/%m/%y')
            y = df['Date'].dt.year
            m = df['Date'].dt.month

            df['Count_d'] = df.groupby('Date')['Date'].transform('size')
            df['Count_m'] = df.groupby([y, m])['Date'].transform('size')
            df['Count_y'] = df.groupby(y)['Date'].transform('size')

            print(df)
            Date Val Count_d Count_m Count_y
            0 2018-01-01 A 2 4 6
            1 2018-01-01 B 2 4 6
            2 2018-01-02 C 1 4 6
            3 2018-01-03 D 1 4 6
            4 2018-02-01 A 1 1 6
            5 2018-03-01 B 1 1 6
            6 2019-01-02 C 1 2 2
            7 2019-01-03 D 1 2 2






            share|improve this answer














            share|improve this answer



            share|improve this answer








            edited Apr 17 at 11:16

























            answered Apr 17 at 11:11









            jezraeljezrael

            361k26327408




            361k26327408












            • just found that they are removing agg with dict. any idea why?

              – anky_91
              Apr 17 at 11:14











            • @anky_91 - because same size columns like original df.

              – jezrael
              Apr 17 at 11:15











            • where did you see that @anky_91

              – Erfan
              Apr 17 at 11:15












            • @Erfan got a future warning. i was implementing wrongly i guess, jez made that clear

              – anky_91
              Apr 17 at 11:16


















            • just found that they are removing agg with dict. any idea why?

              – anky_91
              Apr 17 at 11:14











            • @anky_91 - because same size columns like original df.

              – jezrael
              Apr 17 at 11:15











            • where did you see that @anky_91

              – Erfan
              Apr 17 at 11:15












            • @Erfan got a future warning. i was implementing wrongly i guess, jez made that clear

              – anky_91
              Apr 17 at 11:16

















            just found that they are removing agg with dict. any idea why?

            – anky_91
            Apr 17 at 11:14





            just found that they are removing agg with dict. any idea why?

            – anky_91
            Apr 17 at 11:14













            @anky_91 - because same size columns like original df.

            – jezrael
            Apr 17 at 11:15





            @anky_91 - because same size columns like original df.

            – jezrael
            Apr 17 at 11:15













            where did you see that @anky_91

            – Erfan
            Apr 17 at 11:15






            where did you see that @anky_91

            – Erfan
            Apr 17 at 11:15














            @Erfan got a future warning. i was implementing wrongly i guess, jez made that clear

            – anky_91
            Apr 17 at 11:16






            @Erfan got a future warning. i was implementing wrongly i guess, jez made that clear

            – anky_91
            Apr 17 at 11:16














            1














            You can do this with pd.Grouper



            df['Count_d'] = df.groupby([pd.Grouper(key='Date', freq='D')])['Date'].transform('size').astype(int)
            df['Count_m'] = df.groupby([pd.Grouper(key='Date', freq='M')])['Date'].transform('size').astype(int)
            df['Count_y'] = df.groupby([pd.Grouper(key='Date', freq='Y')])['Date'].transform('size').astype(int)


            Which will give



             Date Val Count_d Count_m Count_y
            0 2018-01-01 A 2 4 6
            1 2018-01-01 B 2 4 6
            2 2018-01-02 C 1 4 6
            3 2018-01-03 D 1 4 6
            4 2018-02-01 A 1 1 6
            5 2018-03-01 B 1 1 6
            6 2019-01-02 C 1 2 2
            7 2019-01-03 D 1 2 2


            You can groupby various different frequencies with this, see the documentation on DateOffsets






            share|improve this answer



























              1














              You can do this with pd.Grouper



              df['Count_d'] = df.groupby([pd.Grouper(key='Date', freq='D')])['Date'].transform('size').astype(int)
              df['Count_m'] = df.groupby([pd.Grouper(key='Date', freq='M')])['Date'].transform('size').astype(int)
              df['Count_y'] = df.groupby([pd.Grouper(key='Date', freq='Y')])['Date'].transform('size').astype(int)


              Which will give



               Date Val Count_d Count_m Count_y
              0 2018-01-01 A 2 4 6
              1 2018-01-01 B 2 4 6
              2 2018-01-02 C 1 4 6
              3 2018-01-03 D 1 4 6
              4 2018-02-01 A 1 1 6
              5 2018-03-01 B 1 1 6
              6 2019-01-02 C 1 2 2
              7 2019-01-03 D 1 2 2


              You can groupby various different frequencies with this, see the documentation on DateOffsets






              share|improve this answer

























                1












                1








                1







                You can do this with pd.Grouper



                df['Count_d'] = df.groupby([pd.Grouper(key='Date', freq='D')])['Date'].transform('size').astype(int)
                df['Count_m'] = df.groupby([pd.Grouper(key='Date', freq='M')])['Date'].transform('size').astype(int)
                df['Count_y'] = df.groupby([pd.Grouper(key='Date', freq='Y')])['Date'].transform('size').astype(int)


                Which will give



                 Date Val Count_d Count_m Count_y
                0 2018-01-01 A 2 4 6
                1 2018-01-01 B 2 4 6
                2 2018-01-02 C 1 4 6
                3 2018-01-03 D 1 4 6
                4 2018-02-01 A 1 1 6
                5 2018-03-01 B 1 1 6
                6 2019-01-02 C 1 2 2
                7 2019-01-03 D 1 2 2


                You can groupby various different frequencies with this, see the documentation on DateOffsets






                share|improve this answer













                You can do this with pd.Grouper



                df['Count_d'] = df.groupby([pd.Grouper(key='Date', freq='D')])['Date'].transform('size').astype(int)
                df['Count_m'] = df.groupby([pd.Grouper(key='Date', freq='M')])['Date'].transform('size').astype(int)
                df['Count_y'] = df.groupby([pd.Grouper(key='Date', freq='Y')])['Date'].transform('size').astype(int)


                Which will give



                 Date Val Count_d Count_m Count_y
                0 2018-01-01 A 2 4 6
                1 2018-01-01 B 2 4 6
                2 2018-01-02 C 1 4 6
                3 2018-01-03 D 1 4 6
                4 2018-02-01 A 1 1 6
                5 2018-03-01 B 1 1 6
                6 2019-01-02 C 1 2 2
                7 2019-01-03 D 1 2 2


                You can groupby various different frequencies with this, see the documentation on DateOffsets







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Apr 17 at 11:11









                Ken SymeKen Syme

                2,13611116




                2,13611116



























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