Saving the duplicate rows in a seperate dataframe












0















I am able to delete the duplicate rows using pandas,



df.drop_duplicates(subset=['issuer_id', 'hios_plan_identifier', 'group_or_individual_plan_type']) .
For I know , it drops all the duplicates keeping the first occurrence which is the default functionality.



My requirement is that I want to save the dropped data to a another dataframe checking on a subsets of columns.



I have my dataframe df,



  issuer_id hios_plan_identifier  plan_year group_or_individual_plan_type

0        484      99806CAAUSJ-TMP       2018                         Group

1        484      99806CAAUSJ-TMP       2018                         Group

2        484      99806CAAUSJ-TMP       2018                         Group

3        484      99806CAAUSJ-TMP       2018                         Group


I want to drop the duplicates from df(will have only 1 row) and save the rest in another dataframe df1 (will have 3 rows).






python pandas dataframe







share|improve this question




















  • 1





    All values in column hios_plan_identifier are unique, so no duplicates in the example. Please check it.

    – Sandeep Kadapa
    Nov 16 '18 at 7:29











  • My bad.. updated

    – themaster
    Nov 16 '18 at 7:32
















0















I am able to delete the duplicate rows using pandas,



df.drop_duplicates(subset=['issuer_id', 'hios_plan_identifier', 'group_or_individual_plan_type']) .
For I know , it drops all the duplicates keeping the first occurrence which is the default functionality.



My requirement is that I want to save the dropped data to a another dataframe checking on a subsets of columns.



I have my dataframe df,



  issuer_id hios_plan_identifier  plan_year group_or_individual_plan_type

0        484      99806CAAUSJ-TMP       2018                         Group

1        484      99806CAAUSJ-TMP       2018                         Group

2        484      99806CAAUSJ-TMP       2018                         Group

3        484      99806CAAUSJ-TMP       2018                         Group


I want to drop the duplicates from df(will have only 1 row) and save the rest in another dataframe df1 (will have 3 rows).






python pandas dataframe







share|improve this question




















  • 1





    All values in column hios_plan_identifier are unique, so no duplicates in the example. Please check it.

    – Sandeep Kadapa
    Nov 16 '18 at 7:29











  • My bad.. updated

    – themaster
    Nov 16 '18 at 7:32














0












0








0








I am able to delete the duplicate rows using pandas,



df.drop_duplicates(subset=['issuer_id', 'hios_plan_identifier', 'group_or_individual_plan_type']) .
For I know , it drops all the duplicates keeping the first occurrence which is the default functionality.



My requirement is that I want to save the dropped data to a another dataframe checking on a subsets of columns.



I have my dataframe df,



  issuer_id hios_plan_identifier  plan_year group_or_individual_plan_type

0        484      99806CAAUSJ-TMP       2018                         Group

1        484      99806CAAUSJ-TMP       2018                         Group

2        484      99806CAAUSJ-TMP       2018                         Group

3        484      99806CAAUSJ-TMP       2018                         Group


I want to drop the duplicates from df(will have only 1 row) and save the rest in another dataframe df1 (will have 3 rows).






python pandas dataframe







share|improve this question
















I am able to delete the duplicate rows using pandas,



df.drop_duplicates(subset=['issuer_id', 'hios_plan_identifier', 'group_or_individual_plan_type']) .
For I know , it drops all the duplicates keeping the first occurrence which is the default functionality.



My requirement is that I want to save the dropped data to a another dataframe checking on a subsets of columns.



I have my dataframe df,



  issuer_id hios_plan_identifier  plan_year group_or_individual_plan_type

0        484      99806CAAUSJ-TMP       2018                         Group

1        484      99806CAAUSJ-TMP       2018                         Group

2        484      99806CAAUSJ-TMP       2018                         Group

3        484      99806CAAUSJ-TMP       2018                         Group


I want to drop the duplicates from df(will have only 1 row) and save the rest in another dataframe df1 (will have 3 rows).






python pandas dataframe




python pandas dataframe






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 16 '18 at 7:31







themaster

















asked Nov 16 '18 at 7:25









themasterthemaster

15415




15415








  • 1





    All values in column hios_plan_identifier are unique, so no duplicates in the example. Please check it.

    – Sandeep Kadapa
    Nov 16 '18 at 7:29











  • My bad.. updated

    – themaster
    Nov 16 '18 at 7:32














  • 1





    All values in column hios_plan_identifier are unique, so no duplicates in the example. Please check it.

    – Sandeep Kadapa
    Nov 16 '18 at 7:29











  • My bad.. updated

    – themaster
    Nov 16 '18 at 7:32








1




1





All values in column hios_plan_identifier are unique, so no duplicates in the example. Please check it.

– Sandeep Kadapa
Nov 16 '18 at 7:29





All values in column hios_plan_identifier are unique, so no duplicates in the example. Please check it.

– Sandeep Kadapa
Nov 16 '18 at 7:29













My bad.. updated

– themaster
Nov 16 '18 at 7:32





My bad.. updated

– themaster
Nov 16 '18 at 7:32












1 Answer
1






active

oldest

votes


















1














Use duplicated and assign the values to df1 and then drop_duplicates on df:



subset_col = ['issuer_id', 'hios_plan_identifier', 'group_or_individual_plan_type']

df1 = df.loc[df.duplicated(subset=subset_col),:]

df = df.drop_duplicates(subset=subset_col)



print(df)

   issuer_id hios_plan_identifier  plan_year group_or_individual_plan_type

0        484      99806CAAUSJ-TMP       2018                         Group



print(df1)

   issuer_id hios_plan_identifier  plan_year group_or_individual_plan_type

1        484      99806CAAUSJ-TMP       2018                         Group

2        484      99806CAAUSJ-TMP       2018                         Group

3        484      99806CAAUSJ-TMP       2018                         Group





share|improve this answer
























  • @themaster Glad to help.

    – Sandeep Kadapa
    Nov 16 '18 at 10:22











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1 Answer
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active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









1














Use duplicated and assign the values to df1 and then drop_duplicates on df:



subset_col = ['issuer_id', 'hios_plan_identifier', 'group_or_individual_plan_type']

df1 = df.loc[df.duplicated(subset=subset_col),:]

df = df.drop_duplicates(subset=subset_col)



print(df)

   issuer_id hios_plan_identifier  plan_year group_or_individual_plan_type

0        484      99806CAAUSJ-TMP       2018                         Group



print(df1)

   issuer_id hios_plan_identifier  plan_year group_or_individual_plan_type

1        484      99806CAAUSJ-TMP       2018                         Group

2        484      99806CAAUSJ-TMP       2018                         Group

3        484      99806CAAUSJ-TMP       2018                         Group





share|improve this answer
























  • @themaster Glad to help.

    – Sandeep Kadapa
    Nov 16 '18 at 10:22
















1














Use duplicated and assign the values to df1 and then drop_duplicates on df:



subset_col = ['issuer_id', 'hios_plan_identifier', 'group_or_individual_plan_type']

df1 = df.loc[df.duplicated(subset=subset_col),:]

df = df.drop_duplicates(subset=subset_col)



print(df)

   issuer_id hios_plan_identifier  plan_year group_or_individual_plan_type

0        484      99806CAAUSJ-TMP       2018                         Group



print(df1)

   issuer_id hios_plan_identifier  plan_year group_or_individual_plan_type

1        484      99806CAAUSJ-TMP       2018                         Group

2        484      99806CAAUSJ-TMP       2018                         Group

3        484      99806CAAUSJ-TMP       2018                         Group





share|improve this answer
























  • @themaster Glad to help.

    – Sandeep Kadapa
    Nov 16 '18 at 10:22














1












1








1







Use duplicated and assign the values to df1 and then drop_duplicates on df:



subset_col = ['issuer_id', 'hios_plan_identifier', 'group_or_individual_plan_type']

df1 = df.loc[df.duplicated(subset=subset_col),:]

df = df.drop_duplicates(subset=subset_col)



print(df)

   issuer_id hios_plan_identifier  plan_year group_or_individual_plan_type

0        484      99806CAAUSJ-TMP       2018                         Group



print(df1)

   issuer_id hios_plan_identifier  plan_year group_or_individual_plan_type

1        484      99806CAAUSJ-TMP       2018                         Group

2        484      99806CAAUSJ-TMP       2018                         Group

3        484      99806CAAUSJ-TMP       2018                         Group





share|improve this answer













Use duplicated and assign the values to df1 and then drop_duplicates on df:



subset_col = ['issuer_id', 'hios_plan_identifier', 'group_or_individual_plan_type']

df1 = df.loc[df.duplicated(subset=subset_col),:]

df = df.drop_duplicates(subset=subset_col)



print(df)

   issuer_id hios_plan_identifier  plan_year group_or_individual_plan_type

0        484      99806CAAUSJ-TMP       2018                         Group



print(df1)

   issuer_id hios_plan_identifier  plan_year group_or_individual_plan_type

1        484      99806CAAUSJ-TMP       2018                         Group

2        484      99806CAAUSJ-TMP       2018                         Group

3        484      99806CAAUSJ-TMP       2018                         Group






share|improve this answer












share|improve this answer



share|improve this answer










answered Nov 16 '18 at 7:34









Sandeep KadapaSandeep Kadapa

7,398831




7,398831













  • @themaster Glad to help.

    – Sandeep Kadapa
    Nov 16 '18 at 10:22



















  • @themaster Glad to help.

    – Sandeep Kadapa
    Nov 16 '18 at 10:22

















@themaster Glad to help.

– Sandeep Kadapa
Nov 16 '18 at 10:22





@themaster Glad to help.

– Sandeep Kadapa
Nov 16 '18 at 10:22




















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