Saving the duplicate rows in a seperate dataframe
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
asked Nov 16 '18 at 7:25
themasterthemaster
15415
add a comment |
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
asked Nov 16 '18 at 7:25
themasterthemaster
15415
1
All values in columnhios_plan_identifierare 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
add a comment |
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
asked Nov 16 '18 at 7:25
themasterthemaster
15415
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
asked Nov 16 '18 at 7:25
themasterthemaster
15415
asked Nov 16 '18 at 7:25
themasterthemaster
15415
edited Nov 16 '18 at 7:31
themaster
asked Nov 16 '18 at 7:25
themasterthemaster
15415
asked Nov 16 '18 at 7:25
themasterthemaster
15415
asked Nov 16 '18 at 7:25
themasterthemaster
15415
15415
1
All values in columnhios_plan_identifierare 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
add a comment |
1
All values in columnhios_plan_identifierare 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
add a comment |
1 Answer
1
active
oldest
votes
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
answered Nov 16 '18 at 7:34
Sandeep KadapaSandeep Kadapa
7,398831
@themaster Glad to help.
– Sandeep Kadapa
Nov 16 '18 at 10:22
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
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
answered Nov 16 '18 at 7:34
Sandeep KadapaSandeep Kadapa
7,398831
@themaster Glad to help.
– Sandeep Kadapa
Nov 16 '18 at 10:22
add a comment |
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
answered Nov 16 '18 at 7:34
Sandeep KadapaSandeep Kadapa
7,398831
@themaster Glad to help.
– Sandeep Kadapa
Nov 16 '18 at 10:22
add a comment |
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
answered Nov 16 '18 at 7:34
Sandeep KadapaSandeep Kadapa
7,398831
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
answered Nov 16 '18 at 7:34
Sandeep KadapaSandeep Kadapa
7,398831
answered Nov 16 '18 at 7:34
Sandeep KadapaSandeep Kadapa
7,398831
answered Nov 16 '18 at 7:34
Sandeep KadapaSandeep Kadapa
7,398831
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
add a comment |
@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
add a comment |
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1
All values in column
hios_plan_identifierare 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