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How to count tickets with status change filtered byYear-To-Date in Pandas?


How do you change the size of figures drawn with matplotlib?How can I count the occurrences of a list item?How to change the order of DataFrame columns?How to drop rows of Pandas DataFrame whose value in a certain column is NaNChange data type of columns in PandasHow do I get the row count of a pandas DataFrame?How to iterate over rows in a DataFrame in Pandas?How to deal with SettingWithCopyWarning in Pandas?How to count the NaN values in a column in pandas DataFrameHow to check if any value is NaN in a Pandas DataFrame






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0















I have 2 snapshots of data set stored in 2 dataframes that contains JIRA tickets, there is a column called UpdatedDate which tells me when the snapshot was taken.
I want to calculate number of tickets which still remain open filter by Year-to-Date which basically means: how many tickets in total (combined snapshots) are still open util tomorrow (eg.2019-03-29).



But the problem is the both of my dataframes can contain the same JIRA issue, but the status of the ticket might or might not change.



# this df1 (Snapshot 1)
Issue key Project name Status UpdatedDate
111 Proj1 Analysis 2019-03-18
222 Proj1 Open 2019-03-18

# this df2 (Snapshot 2)
Issue key Project name Status UpdatedDate
111 Proj1 Done 2019-03-28
222 Proj1 Open 2019-03-28


So as the table indicated above, issue111's status has changed to Done on snapshot 2 where as issue222's status is still Open.
So if my Year-to-Date filter is set on 2019-03-29. it will show me 2 ticket with Status Open, but one of them will be a duplication.



How can I count number of ticket that are still open but without duplicates?










share|improve this question






























    0















    I have 2 snapshots of data set stored in 2 dataframes that contains JIRA tickets, there is a column called UpdatedDate which tells me when the snapshot was taken.
    I want to calculate number of tickets which still remain open filter by Year-to-Date which basically means: how many tickets in total (combined snapshots) are still open util tomorrow (eg.2019-03-29).



    But the problem is the both of my dataframes can contain the same JIRA issue, but the status of the ticket might or might not change.



    # this df1 (Snapshot 1)
    Issue key Project name Status UpdatedDate
    111 Proj1 Analysis 2019-03-18
    222 Proj1 Open 2019-03-18

    # this df2 (Snapshot 2)
    Issue key Project name Status UpdatedDate
    111 Proj1 Done 2019-03-28
    222 Proj1 Open 2019-03-28


    So as the table indicated above, issue111's status has changed to Done on snapshot 2 where as issue222's status is still Open.
    So if my Year-to-Date filter is set on 2019-03-29. it will show me 2 ticket with Status Open, but one of them will be a duplication.



    How can I count number of ticket that are still open but without duplicates?










    share|improve this question


























      0












      0








      0








      I have 2 snapshots of data set stored in 2 dataframes that contains JIRA tickets, there is a column called UpdatedDate which tells me when the snapshot was taken.
      I want to calculate number of tickets which still remain open filter by Year-to-Date which basically means: how many tickets in total (combined snapshots) are still open util tomorrow (eg.2019-03-29).



      But the problem is the both of my dataframes can contain the same JIRA issue, but the status of the ticket might or might not change.



      # this df1 (Snapshot 1)
      Issue key Project name Status UpdatedDate
      111 Proj1 Analysis 2019-03-18
      222 Proj1 Open 2019-03-18

      # this df2 (Snapshot 2)
      Issue key Project name Status UpdatedDate
      111 Proj1 Done 2019-03-28
      222 Proj1 Open 2019-03-28


      So as the table indicated above, issue111's status has changed to Done on snapshot 2 where as issue222's status is still Open.
      So if my Year-to-Date filter is set on 2019-03-29. it will show me 2 ticket with Status Open, but one of them will be a duplication.



      How can I count number of ticket that are still open but without duplicates?










      share|improve this question














      I have 2 snapshots of data set stored in 2 dataframes that contains JIRA tickets, there is a column called UpdatedDate which tells me when the snapshot was taken.
      I want to calculate number of tickets which still remain open filter by Year-to-Date which basically means: how many tickets in total (combined snapshots) are still open util tomorrow (eg.2019-03-29).



      But the problem is the both of my dataframes can contain the same JIRA issue, but the status of the ticket might or might not change.



      # this df1 (Snapshot 1)
      Issue key Project name Status UpdatedDate
      111 Proj1 Analysis 2019-03-18
      222 Proj1 Open 2019-03-18

      # this df2 (Snapshot 2)
      Issue key Project name Status UpdatedDate
      111 Proj1 Done 2019-03-28
      222 Proj1 Open 2019-03-28


      So as the table indicated above, issue111's status has changed to Done on snapshot 2 where as issue222's status is still Open.
      So if my Year-to-Date filter is set on 2019-03-29. it will show me 2 ticket with Status Open, but one of them will be a duplication.



      How can I count number of ticket that are still open but without duplicates?







      python pandas dataframe






      share|improve this question













      share|improve this question











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      asked Mar 28 at 17:38









      bossangelobossangelo

      273 bronze badges




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          you can sort_values() and then drop_duplicates():



          pd.concat([df1, df2]) 
          .sort_values(['UpdatedDate'], ascending=[False])
          .drop_duplicates(['Issue key'], keep='first')
          .loc[lambda x: x.Status == 'Open']





          share|improve this answer



























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            1 Answer
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            you can sort_values() and then drop_duplicates():



            pd.concat([df1, df2]) 
            .sort_values(['UpdatedDate'], ascending=[False])
            .drop_duplicates(['Issue key'], keep='first')
            .loc[lambda x: x.Status == 'Open']





            share|improve this answer





























              0
















              you can sort_values() and then drop_duplicates():



              pd.concat([df1, df2]) 
              .sort_values(['UpdatedDate'], ascending=[False])
              .drop_duplicates(['Issue key'], keep='first')
              .loc[lambda x: x.Status == 'Open']





              share|improve this answer



























                0














                0










                0









                you can sort_values() and then drop_duplicates():



                pd.concat([df1, df2]) 
                .sort_values(['UpdatedDate'], ascending=[False])
                .drop_duplicates(['Issue key'], keep='first')
                .loc[lambda x: x.Status == 'Open']





                share|improve this answer













                you can sort_values() and then drop_duplicates():



                pd.concat([df1, df2]) 
                .sort_values(['UpdatedDate'], ascending=[False])
                .drop_duplicates(['Issue key'], keep='first')
                .loc[lambda x: x.Status == 'Open']






                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Mar 28 at 18:16









                jxcjxc

                2,9602 gold badges4 silver badges17 bronze badges




                2,9602 gold badges4 silver badges17 bronze badges

































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