Find the row associated with maximum date after groupby in Pandas
up vote
2
down vote
favorite
I have a pandas DataFrame with 3 columns containing a PERSON_ID, MOVING_DATE AND PLACE as follows:
df = pandas.DataFrame(
[[1,datetime.datetime(2018, 1, 1), 'New York'],
[1, datetime.datetime(2018, 1, 20), 'Rio de Janeiro'],
[1, datetime.datetime(2018, 2, 13), 'London'],
[2, datetime.datetime(2017, 6, 12), 'Seatle'],
[2, datetime.datetime(2016, 10, 10), 'New Mexico'],
[3, datetime.datetime(2017, 9, 19), 'Sao Paulo'],
[3, datetime.datetime(2015, 12, 11), 'Bangladesh']]],
columns=['PERSON ID', 'MOVING DATE', 'PLACE']
)
PERSON ID MOVING DATE PLACE
0 1 2018-01-01 New York
1 1 2018-01-20 Rio de Janeiro
2 1 2018-02-13 London
3 2 2017-06-12 Seatle
4 2 2016-10-10 New Mexico
5 3 2017-09-19 Sao Paulo
6 3 2015-12-11 Bangladesh
I would like to find the place where the person is based on its last movement date (MOVEMENT_DATE).
Is it possible to get the result with the groupby method?
So far, I've tried:
df = df.sort_values(['PERSON ID', 'MOVING DATE'])
df.groupby(['PERSON ID', 'MOVING DATE']).agg(
{'MOVING DATE': max, 'PLACE': 'last'}
)
but it didn't work out.
Any help would be appreciated.
Thanks in advance,
Rhenan
python pandas pandas-groupby
asked Nov 9 at 19:37
Rhenan Bartels
131110
add a comment |
up vote
2
down vote
favorite
I have a pandas DataFrame with 3 columns containing a PERSON_ID, MOVING_DATE AND PLACE as follows:
df = pandas.DataFrame(
[[1,datetime.datetime(2018, 1, 1), 'New York'],
[1, datetime.datetime(2018, 1, 20), 'Rio de Janeiro'],
[1, datetime.datetime(2018, 2, 13), 'London'],
[2, datetime.datetime(2017, 6, 12), 'Seatle'],
[2, datetime.datetime(2016, 10, 10), 'New Mexico'],
[3, datetime.datetime(2017, 9, 19), 'Sao Paulo'],
[3, datetime.datetime(2015, 12, 11), 'Bangladesh']]],
columns=['PERSON ID', 'MOVING DATE', 'PLACE']
)
PERSON ID MOVING DATE PLACE
0 1 2018-01-01 New York
1 1 2018-01-20 Rio de Janeiro
2 1 2018-02-13 London
3 2 2017-06-12 Seatle
4 2 2016-10-10 New Mexico
5 3 2017-09-19 Sao Paulo
6 3 2015-12-11 Bangladesh
I would like to find the place where the person is based on its last movement date (MOVEMENT_DATE).
Is it possible to get the result with the groupby method?
So far, I've tried:
df = df.sort_values(['PERSON ID', 'MOVING DATE'])
df.groupby(['PERSON ID', 'MOVING DATE']).agg(
{'MOVING DATE': max, 'PLACE': 'last'}
)
but it didn't work out.
Any help would be appreciated.
Thanks in advance,
Rhenan
python pandas pandas-groupby
asked Nov 9 at 19:37
Rhenan Bartels
131110
add a comment |
up vote
2
down vote
favorite
up vote
2
down vote
favorite
I have a pandas DataFrame with 3 columns containing a PERSON_ID, MOVING_DATE AND PLACE as follows:
df = pandas.DataFrame(
[[1,datetime.datetime(2018, 1, 1), 'New York'],
[1, datetime.datetime(2018, 1, 20), 'Rio de Janeiro'],
[1, datetime.datetime(2018, 2, 13), 'London'],
[2, datetime.datetime(2017, 6, 12), 'Seatle'],
[2, datetime.datetime(2016, 10, 10), 'New Mexico'],
[3, datetime.datetime(2017, 9, 19), 'Sao Paulo'],
[3, datetime.datetime(2015, 12, 11), 'Bangladesh']]],
columns=['PERSON ID', 'MOVING DATE', 'PLACE']
)
PERSON ID MOVING DATE PLACE
0 1 2018-01-01 New York
1 1 2018-01-20 Rio de Janeiro
2 1 2018-02-13 London
3 2 2017-06-12 Seatle
4 2 2016-10-10 New Mexico
5 3 2017-09-19 Sao Paulo
6 3 2015-12-11 Bangladesh
I would like to find the place where the person is based on its last movement date (MOVEMENT_DATE).
Is it possible to get the result with the groupby method?
So far, I've tried:
df = df.sort_values(['PERSON ID', 'MOVING DATE'])
df.groupby(['PERSON ID', 'MOVING DATE']).agg(
{'MOVING DATE': max, 'PLACE': 'last'}
)
but it didn't work out.
Any help would be appreciated.
Thanks in advance,
Rhenan
python pandas pandas-groupby
asked Nov 9 at 19:37
Rhenan Bartels
131110
I have a pandas DataFrame with 3 columns containing a PERSON_ID, MOVING_DATE AND PLACE as follows:
df = pandas.DataFrame(
[[1,datetime.datetime(2018, 1, 1), 'New York'],
[1, datetime.datetime(2018, 1, 20), 'Rio de Janeiro'],
[1, datetime.datetime(2018, 2, 13), 'London'],
[2, datetime.datetime(2017, 6, 12), 'Seatle'],
[2, datetime.datetime(2016, 10, 10), 'New Mexico'],
[3, datetime.datetime(2017, 9, 19), 'Sao Paulo'],
[3, datetime.datetime(2015, 12, 11), 'Bangladesh']]],
columns=['PERSON ID', 'MOVING DATE', 'PLACE']
)
PERSON ID MOVING DATE PLACE
0 1 2018-01-01 New York
1 1 2018-01-20 Rio de Janeiro
2 1 2018-02-13 London
3 2 2017-06-12 Seatle
4 2 2016-10-10 New Mexico
5 3 2017-09-19 Sao Paulo
6 3 2015-12-11 Bangladesh
I would like to find the place where the person is based on its last movement date (MOVEMENT_DATE).
Is it possible to get the result with the groupby method?
So far, I've tried:
df = df.sort_values(['PERSON ID', 'MOVING DATE'])
df.groupby(['PERSON ID', 'MOVING DATE']).agg(
{'MOVING DATE': max, 'PLACE': 'last'}
)
but it didn't work out.
Any help would be appreciated.
Thanks in advance,
Rhenan
python pandas pandas-groupby
python pandas pandas-groupby
asked Nov 9 at 19:37
Rhenan Bartels
131110
asked Nov 9 at 19:37
Rhenan Bartels
131110
edited Nov 9 at 20:10
asked Nov 9 at 19:37
Rhenan Bartels
131110
asked Nov 9 at 19:37
Rhenan Bartels
131110
asked Nov 9 at 19:37
Rhenan Bartels
131110
131110
add a comment |
add a comment |
3 Answers
3
active
oldest
votes
up vote
5
down vote
accepted
A one-liner using DataFrame.groupby and Grouper.last:
df.sort_values('MOVING DATE').groupby('PERSON ID').last()
output:
MOVING DATE PLACE
PERSON ID
1 2018-02-13 London
2 2017-06-12 Seatle
3 2017-09-19 Sao Paulo
answered Nov 9 at 19:45
Yuca
2,5542525
1
Great answer @Yuca. Thanks!!
– Rhenan Bartels
Nov 9 at 20:07
add a comment |
up vote
2
down vote
A sort is overkill here, that's O(nlogn) time complexity, when you can do this with loc and idxmax:
df.loc[df.groupby('PERSON ID')['MOVING DATE'].idxmax()]
PERSON ID MOVING DATE PLACE
2 1 2018-02-13 London
3 2 2017-06-12 Seatle
5 3 2017-09-19 Sao Paulo
answered Nov 9 at 20:50
user3483203
29.9k82354
add a comment |
up vote
1
down vote
To add to @Yuca and the answer provided, you could also utilize the .max() function provided within the pandas lib in the same way .last() was added.
more: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.max.html
answered Nov 9 at 19:57
Tyler Filko
436
1
Hi @Tyler Filko, thanks for the answer, but the .max() method will use the letters of the place's name to select the maximum place, won't it?
– Rhenan Bartels
Nov 9 at 20:12
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
5
down vote
accepted
A one-liner using DataFrame.groupby and Grouper.last:
df.sort_values('MOVING DATE').groupby('PERSON ID').last()
output:
MOVING DATE PLACE
PERSON ID
1 2018-02-13 London
2 2017-06-12 Seatle
3 2017-09-19 Sao Paulo
answered Nov 9 at 19:45
Yuca
2,5542525
1
Great answer @Yuca. Thanks!!
– Rhenan Bartels
Nov 9 at 20:07
add a comment |
up vote
5
down vote
accepted
A one-liner using DataFrame.groupby and Grouper.last:
df.sort_values('MOVING DATE').groupby('PERSON ID').last()
output:
MOVING DATE PLACE
PERSON ID
1 2018-02-13 London
2 2017-06-12 Seatle
3 2017-09-19 Sao Paulo
answered Nov 9 at 19:45
Yuca
2,5542525
1
Great answer @Yuca. Thanks!!
– Rhenan Bartels
Nov 9 at 20:07
add a comment |
up vote
5
down vote
accepted
up vote
5
down vote
accepted
A one-liner using DataFrame.groupby and Grouper.last:
df.sort_values('MOVING DATE').groupby('PERSON ID').last()
output:
MOVING DATE PLACE
PERSON ID
1 2018-02-13 London
2 2017-06-12 Seatle
3 2017-09-19 Sao Paulo
answered Nov 9 at 19:45
Yuca
2,5542525
A one-liner using DataFrame.groupby and Grouper.last:
df.sort_values('MOVING DATE').groupby('PERSON ID').last()
output:
MOVING DATE PLACE
PERSON ID
1 2018-02-13 London
2 2017-06-12 Seatle
3 2017-09-19 Sao Paulo
answered Nov 9 at 19:45
Yuca
2,5542525
edited Nov 9 at 19:57
answered Nov 9 at 19:45
Yuca
2,5542525
answered Nov 9 at 19:45
Yuca
2,5542525
answered Nov 9 at 19:45
Yuca
2,5542525
2,5542525
1
Great answer @Yuca. Thanks!!
– Rhenan Bartels
Nov 9 at 20:07
add a comment |
1
Great answer @Yuca. Thanks!!
– Rhenan Bartels
Nov 9 at 20:07
1
1
Great answer @Yuca. Thanks!!
– Rhenan Bartels
Nov 9 at 20:07
Great answer @Yuca. Thanks!!
– Rhenan Bartels
Nov 9 at 20:07
add a comment |
up vote
2
down vote
A sort is overkill here, that's O(nlogn) time complexity, when you can do this with loc and idxmax:
df.loc[df.groupby('PERSON ID')['MOVING DATE'].idxmax()]
PERSON ID MOVING DATE PLACE
2 1 2018-02-13 London
3 2 2017-06-12 Seatle
5 3 2017-09-19 Sao Paulo
answered Nov 9 at 20:50
user3483203
29.9k82354
add a comment |
up vote
2
down vote
A sort is overkill here, that's O(nlogn) time complexity, when you can do this with loc and idxmax:
df.loc[df.groupby('PERSON ID')['MOVING DATE'].idxmax()]
PERSON ID MOVING DATE PLACE
2 1 2018-02-13 London
3 2 2017-06-12 Seatle
5 3 2017-09-19 Sao Paulo
answered Nov 9 at 20:50
user3483203
29.9k82354
add a comment |
up vote
2
down vote
up vote
2
down vote
A sort is overkill here, that's O(nlogn) time complexity, when you can do this with loc and idxmax:
df.loc[df.groupby('PERSON ID')['MOVING DATE'].idxmax()]
PERSON ID MOVING DATE PLACE
2 1 2018-02-13 London
3 2 2017-06-12 Seatle
5 3 2017-09-19 Sao Paulo
answered Nov 9 at 20:50
user3483203
29.9k82354
A sort is overkill here, that's O(nlogn) time complexity, when you can do this with loc and idxmax:
df.loc[df.groupby('PERSON ID')['MOVING DATE'].idxmax()]
PERSON ID MOVING DATE PLACE
2 1 2018-02-13 London
3 2 2017-06-12 Seatle
5 3 2017-09-19 Sao Paulo
answered Nov 9 at 20:50
user3483203
29.9k82354
answered Nov 9 at 20:50
user3483203
29.9k82354
answered Nov 9 at 20:50
user3483203
29.9k82354
answered Nov 9 at 20:50
user3483203
29.9k82354
29.9k82354
add a comment |
add a comment |
up vote
1
down vote
To add to @Yuca and the answer provided, you could also utilize the .max() function provided within the pandas lib in the same way .last() was added.
more: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.max.html
answered Nov 9 at 19:57
Tyler Filko
436
1
Hi @Tyler Filko, thanks for the answer, but the .max() method will use the letters of the place's name to select the maximum place, won't it?
– Rhenan Bartels
Nov 9 at 20:12
add a comment |
up vote
1
down vote
To add to @Yuca and the answer provided, you could also utilize the .max() function provided within the pandas lib in the same way .last() was added.
more: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.max.html
answered Nov 9 at 19:57
Tyler Filko
436
1
Hi @Tyler Filko, thanks for the answer, but the .max() method will use the letters of the place's name to select the maximum place, won't it?
– Rhenan Bartels
Nov 9 at 20:12
add a comment |
up vote
1
down vote
up vote
1
down vote
To add to @Yuca and the answer provided, you could also utilize the .max() function provided within the pandas lib in the same way .last() was added.
more: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.max.html
answered Nov 9 at 19:57
Tyler Filko
436
To add to @Yuca and the answer provided, you could also utilize the .max() function provided within the pandas lib in the same way .last() was added.
more: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.max.html
answered Nov 9 at 19:57
Tyler Filko
436
answered Nov 9 at 19:57
Tyler Filko
436
answered Nov 9 at 19:57
Tyler Filko
436
answered Nov 9 at 19:57
Tyler Filko
436
436
1
Hi @Tyler Filko, thanks for the answer, but the .max() method will use the letters of the place's name to select the maximum place, won't it?
– Rhenan Bartels
Nov 9 at 20:12
add a comment |
1
Hi @Tyler Filko, thanks for the answer, but the .max() method will use the letters of the place's name to select the maximum place, won't it?
– Rhenan Bartels
Nov 9 at 20:12
1
1
Hi @Tyler Filko, thanks for the answer, but the .max() method will use the letters of the place's name to select the maximum place, won't it?
– Rhenan Bartels
Nov 9 at 20:12
Hi @Tyler Filko, thanks for the answer, but the .max() method will use the letters of the place's name to select the maximum place, won't it?
– Rhenan Bartels
Nov 9 at 20:12
add a comment |
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