Create Attribute of Comma Seperated Values [duplicate]
This question already has an answer here:
How to use groupby to concatenate strings in python pandas?
1 answer
Pandas groupby: How to get a union of strings
6 answers
I have a loans dataset and each row represents a loan plan and has a customer ID associated with it.
Loan_ID Customer_ID
1 A
2 B
3 A
4 C
5 A
6 C
I want to create a seperate table which will be grouped by Customer_ID and will contain an attribute that is a comma seperated list of the Loan_ID.
Customer_ID Loan_ID_List
A 1,3,5
B 2
C 4,6
python pandas
asked Nov 14 '18 at 18:14
Briain O DonnellBriain O Donnell
1339
marked as duplicate by user3483203 python
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Nov 14 '18 at 18:16
This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.
add a comment |
This question already has an answer here:
How to use groupby to concatenate strings in python pandas?
1 answer
Pandas groupby: How to get a union of strings
6 answers
I have a loans dataset and each row represents a loan plan and has a customer ID associated with it.
Loan_ID Customer_ID
1 A
2 B
3 A
4 C
5 A
6 C
I want to create a seperate table which will be grouped by Customer_ID and will contain an attribute that is a comma seperated list of the Loan_ID.
Customer_ID Loan_ID_List
A 1,3,5
B 2
C 4,6
python pandas
asked Nov 14 '18 at 18:14
Briain O DonnellBriain O Donnell
1339
marked as duplicate by user3483203 python
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Nov 14 '18 at 18:16
This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.
df.astype(str).groupby('Customer_ID')['Loan_ID'].agg(','.join)
– user3483203
Nov 14 '18 at 18:18
add a comment |
This question already has an answer here:
How to use groupby to concatenate strings in python pandas?
1 answer
Pandas groupby: How to get a union of strings
6 answers
I have a loans dataset and each row represents a loan plan and has a customer ID associated with it.
Loan_ID Customer_ID
1 A
2 B
3 A
4 C
5 A
6 C
I want to create a seperate table which will be grouped by Customer_ID and will contain an attribute that is a comma seperated list of the Loan_ID.
Customer_ID Loan_ID_List
A 1,3,5
B 2
C 4,6
python pandas
asked Nov 14 '18 at 18:14
Briain O DonnellBriain O Donnell
1339
This question already has an answer here:
How to use groupby to concatenate strings in python pandas?
1 answer
Pandas groupby: How to get a union of strings
6 answers
I have a loans dataset and each row represents a loan plan and has a customer ID associated with it.
Loan_ID Customer_ID
1 A
2 B
3 A
4 C
5 A
6 C
I want to create a seperate table which will be grouped by Customer_ID and will contain an attribute that is a comma seperated list of the Loan_ID.
Customer_ID Loan_ID_List
A 1,3,5
B 2
C 4,6
This question already has an answer here:
How to use groupby to concatenate strings in python pandas?
1 answer
Pandas groupby: How to get a union of strings
6 answers
python pandas
python pandas
asked Nov 14 '18 at 18:14
Briain O DonnellBriain O Donnell
1339
asked Nov 14 '18 at 18:14
Briain O DonnellBriain O Donnell
1339
asked Nov 14 '18 at 18:14
Briain O DonnellBriain O Donnell
1339
asked Nov 14 '18 at 18:14
Briain O DonnellBriain O Donnell
1339
asked Nov 14 '18 at 18:14
Briain O DonnellBriain O Donnell
1339
1339
marked as duplicate by user3483203 python
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marked as duplicate by user3483203 python
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This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.
df.astype(str).groupby('Customer_ID')['Loan_ID'].agg(','.join)
– user3483203
Nov 14 '18 at 18:18
add a comment |
df.astype(str).groupby('Customer_ID')['Loan_ID'].agg(','.join)
– user3483203
Nov 14 '18 at 18:18
df.astype(str).groupby('Customer_ID')['Loan_ID'].agg(','.join)– user3483203
Nov 14 '18 at 18:18
df.astype(str).groupby('Customer_ID')['Loan_ID'].agg(','.join)– user3483203
Nov 14 '18 at 18:18
add a comment |
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df.astype(str).groupby('Customer_ID')['Loan_ID'].agg(','.join)– user3483203
Nov 14 '18 at 18:18