Pandas groupby rank date time
1
i got an issue over ranking of date times. Lets say i have following table.
ID TIME
01 2018-07-11 11:12:20
01 2018-07-12 12:00:23
01 2018-07-13 12:00:00
02 2019-09-11 11:00:00
02 2019-09-12 12:00:00
and i want to add another column to rank the table by time for each id and group. I used
df['RANK'] = data.groupby('ID')['TIME'].rank(ascending=True)
but get an error:
'NoneType' object is not callable
If i replace datetime to numbers, it works.... any solutions?
python pandas date rank
asked Nov 12 at 15:33
kxell2001
144
add a comment |
1
i got an issue over ranking of date times. Lets say i have following table.
ID TIME
01 2018-07-11 11:12:20
01 2018-07-12 12:00:23
01 2018-07-13 12:00:00
02 2019-09-11 11:00:00
02 2019-09-12 12:00:00
and i want to add another column to rank the table by time for each id and group. I used
df['RANK'] = data.groupby('ID')['TIME'].rank(ascending=True)
but get an error:
'NoneType' object is not callable
If i replace datetime to numbers, it works.... any solutions?
python pandas date rank
asked Nov 12 at 15:33
kxell2001
144
add a comment |
1
1
1
i got an issue over ranking of date times. Lets say i have following table.
ID TIME
01 2018-07-11 11:12:20
01 2018-07-12 12:00:23
01 2018-07-13 12:00:00
02 2019-09-11 11:00:00
02 2019-09-12 12:00:00
and i want to add another column to rank the table by time for each id and group. I used
df['RANK'] = data.groupby('ID')['TIME'].rank(ascending=True)
but get an error:
'NoneType' object is not callable
If i replace datetime to numbers, it works.... any solutions?
python pandas date rank
asked Nov 12 at 15:33
kxell2001
144
i got an issue over ranking of date times. Lets say i have following table.
ID TIME
01 2018-07-11 11:12:20
01 2018-07-12 12:00:23
01 2018-07-13 12:00:00
02 2019-09-11 11:00:00
02 2019-09-12 12:00:00
and i want to add another column to rank the table by time for each id and group. I used
df['RANK'] = data.groupby('ID')['TIME'].rank(ascending=True)
but get an error:
'NoneType' object is not callable
If i replace datetime to numbers, it works.... any solutions?
python pandas date rank
python pandas date rank
asked Nov 12 at 15:33
kxell2001
144
asked Nov 12 at 15:33
kxell2001
144
asked Nov 12 at 15:33
kxell2001
144
asked Nov 12 at 15:33
kxell2001
144
asked Nov 12 at 15:33
kxell2001
144
144
add a comment |
add a comment |
1 Answer
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1
For me working convert column to datetimes for avoid your error.
data['TIME'] = pd.to_datetime(data['TIME'])
data['RANK'] = data.groupby('ID')['TIME'].rank(ascending=True)
print (data)
ID TIME RANK
0 1 2018-07-11 11:12:20 1.0
1 1 2018-07-12 12:00:23 2.0
2 1 2018-07-13 12:00:00 3.0
3 2 2019-09-11 11:00:00 1.0
4 2 2019-09-12 12:00:00 2.0
answered Nov 12 at 15:35
jezrael
319k22258337
1
Silly me..... Ty for advice. Works fine
– kxell2001
Nov 12 at 15:39
@kxell2001 - error is really confused, so I am not curious about your question. Good luck!
– jezrael
Nov 12 at 15:39
add a comment |
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1 Answer
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1 Answer
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active
oldest
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1
For me working convert column to datetimes for avoid your error.
data['TIME'] = pd.to_datetime(data['TIME'])
data['RANK'] = data.groupby('ID')['TIME'].rank(ascending=True)
print (data)
ID TIME RANK
0 1 2018-07-11 11:12:20 1.0
1 1 2018-07-12 12:00:23 2.0
2 1 2018-07-13 12:00:00 3.0
3 2 2019-09-11 11:00:00 1.0
4 2 2019-09-12 12:00:00 2.0
answered Nov 12 at 15:35
jezrael
319k22258337
1
Silly me..... Ty for advice. Works fine
– kxell2001
Nov 12 at 15:39
@kxell2001 - error is really confused, so I am not curious about your question. Good luck!
– jezrael
Nov 12 at 15:39
add a comment |
1
For me working convert column to datetimes for avoid your error.
data['TIME'] = pd.to_datetime(data['TIME'])
data['RANK'] = data.groupby('ID')['TIME'].rank(ascending=True)
print (data)
ID TIME RANK
0 1 2018-07-11 11:12:20 1.0
1 1 2018-07-12 12:00:23 2.0
2 1 2018-07-13 12:00:00 3.0
3 2 2019-09-11 11:00:00 1.0
4 2 2019-09-12 12:00:00 2.0
answered Nov 12 at 15:35
jezrael
319k22258337
1
Silly me..... Ty for advice. Works fine
– kxell2001
Nov 12 at 15:39
@kxell2001 - error is really confused, so I am not curious about your question. Good luck!
– jezrael
Nov 12 at 15:39
add a comment |
1
1
1
For me working convert column to datetimes for avoid your error.
data['TIME'] = pd.to_datetime(data['TIME'])
data['RANK'] = data.groupby('ID')['TIME'].rank(ascending=True)
print (data)
ID TIME RANK
0 1 2018-07-11 11:12:20 1.0
1 1 2018-07-12 12:00:23 2.0
2 1 2018-07-13 12:00:00 3.0
3 2 2019-09-11 11:00:00 1.0
4 2 2019-09-12 12:00:00 2.0
answered Nov 12 at 15:35
jezrael
319k22258337
For me working convert column to datetimes for avoid your error.
data['TIME'] = pd.to_datetime(data['TIME'])
data['RANK'] = data.groupby('ID')['TIME'].rank(ascending=True)
print (data)
ID TIME RANK
0 1 2018-07-11 11:12:20 1.0
1 1 2018-07-12 12:00:23 2.0
2 1 2018-07-13 12:00:00 3.0
3 2 2019-09-11 11:00:00 1.0
4 2 2019-09-12 12:00:00 2.0
answered Nov 12 at 15:35
jezrael
319k22258337
answered Nov 12 at 15:35
jezrael
319k22258337
answered Nov 12 at 15:35
jezrael
319k22258337
answered Nov 12 at 15:35
jezrael
319k22258337
319k22258337
1
Silly me..... Ty for advice. Works fine
– kxell2001
Nov 12 at 15:39
@kxell2001 - error is really confused, so I am not curious about your question. Good luck!
– jezrael
Nov 12 at 15:39
add a comment |
1
Silly me..... Ty for advice. Works fine
– kxell2001
Nov 12 at 15:39
@kxell2001 - error is really confused, so I am not curious about your question. Good luck!
– jezrael
Nov 12 at 15:39
1
1
Silly me..... Ty for advice. Works fine
– kxell2001
Nov 12 at 15:39
Silly me..... Ty for advice. Works fine
– kxell2001
Nov 12 at 15:39
@kxell2001 - error is really confused, so I am not curious about your question. Good luck!
– jezrael
Nov 12 at 15:39
@kxell2001 - error is really confused, so I am not curious about your question. Good luck!
– jezrael
Nov 12 at 15:39
add a comment |
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