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How to choose specific time in a dataframe
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I want to choose a range of time from my data, but I can't find the approach to choose a range of time in this code.
How do I fix my code?
Thanks!!!
I saw this code
data = pd.DataFrame({'EUROSTOXX': es['SX5E'][es.index > dt.datetime(1999, 1, 1)]})
from my textbook.
The time I want to correct is from (1999, 1, 1) to (2016, 1, 1)
I tried several codes to change the time, for example:
data = pd.DataFrame({'EUROSTOXX': es['SX5E'][dt.datetime(2016, 1, 1)> es.index > dt.datetime(1999, 1, 1)]})
but it failed. Is there anyone could save me?
python pandas datetime
edited Jan 3 at 15:13
sacuL
30.9k42044
asked Jan 3 at 15:12
Scott ShenScott Shen
1
add a comment |
I want to choose a range of time from my data, but I can't find the approach to choose a range of time in this code.
How do I fix my code?
Thanks!!!
I saw this code
data = pd.DataFrame({'EUROSTOXX': es['SX5E'][es.index > dt.datetime(1999, 1, 1)]})
from my textbook.
The time I want to correct is from (1999, 1, 1) to (2016, 1, 1)
I tried several codes to change the time, for example:
data = pd.DataFrame({'EUROSTOXX': es['SX5E'][dt.datetime(2016, 1, 1)> es.index > dt.datetime(1999, 1, 1)]})
but it failed. Is there anyone could save me?
python pandas datetime
edited Jan 3 at 15:13
sacuL
30.9k42044
asked Jan 3 at 15:12
Scott ShenScott Shen
1
1
Possible duplicate of How to slice a Pandas Time Series using a logical expression involving dates
– Yuca
Jan 3 at 15:16
Its always advisable to provide the minimal code which can ve reproduce in order to get and provide explicit and more feasible answer, Would you be able to show us few line of your dataframe?
– pygo
Jan 3 at 16:10
add a comment |
I want to choose a range of time from my data, but I can't find the approach to choose a range of time in this code.
How do I fix my code?
Thanks!!!
I saw this code
data = pd.DataFrame({'EUROSTOXX': es['SX5E'][es.index > dt.datetime(1999, 1, 1)]})
from my textbook.
The time I want to correct is from (1999, 1, 1) to (2016, 1, 1)
I tried several codes to change the time, for example:
data = pd.DataFrame({'EUROSTOXX': es['SX5E'][dt.datetime(2016, 1, 1)> es.index > dt.datetime(1999, 1, 1)]})
but it failed. Is there anyone could save me?
python pandas datetime
edited Jan 3 at 15:13
sacuL
30.9k42044
asked Jan 3 at 15:12
Scott ShenScott Shen
1
I want to choose a range of time from my data, but I can't find the approach to choose a range of time in this code.
How do I fix my code?
Thanks!!!
I saw this code
data = pd.DataFrame({'EUROSTOXX': es['SX5E'][es.index > dt.datetime(1999, 1, 1)]})
from my textbook.
The time I want to correct is from (1999, 1, 1) to (2016, 1, 1)
I tried several codes to change the time, for example:
data = pd.DataFrame({'EUROSTOXX': es['SX5E'][dt.datetime(2016, 1, 1)> es.index > dt.datetime(1999, 1, 1)]})
but it failed. Is there anyone could save me?
python pandas datetime
python pandas datetime
edited Jan 3 at 15:13
sacuL
30.9k42044
asked Jan 3 at 15:12
Scott ShenScott Shen
1
edited Jan 3 at 15:13
sacuL
30.9k42044
asked Jan 3 at 15:12
Scott ShenScott Shen
1
edited Jan 3 at 15:13
sacuL
30.9k42044
edited Jan 3 at 15:13
sacuL
30.9k42044
edited Jan 3 at 15:13
sacuL
30.9k42044
30.9k42044
asked Jan 3 at 15:12
Scott ShenScott Shen
1
asked Jan 3 at 15:12
Scott ShenScott Shen
1
asked Jan 3 at 15:12
Scott ShenScott Shen
1
1
1
Possible duplicate of How to slice a Pandas Time Series using a logical expression involving dates
– Yuca
Jan 3 at 15:16
Its always advisable to provide the minimal code which can ve reproduce in order to get and provide explicit and more feasible answer, Would you be able to show us few line of your dataframe?
– pygo
Jan 3 at 16:10
add a comment |
1
Possible duplicate of How to slice a Pandas Time Series using a logical expression involving dates
– Yuca
Jan 3 at 15:16
Its always advisable to provide the minimal code which can ve reproduce in order to get and provide explicit and more feasible answer, Would you be able to show us few line of your dataframe?
– pygo
Jan 3 at 16:10
1
1
Possible duplicate of How to slice a Pandas Time Series using a logical expression involving dates
– Yuca
Jan 3 at 15:16
Possible duplicate of How to slice a Pandas Time Series using a logical expression involving dates
– Yuca
Jan 3 at 15:16
Its always advisable to provide the minimal code which can ve reproduce in order to get and provide explicit and more feasible answer, Would you be able to show us few line of your dataframe?
– pygo
Jan 3 at 16:10
Its always advisable to provide the minimal code which can ve reproduce in order to get and provide explicit and more feasible answer, Would you be able to show us few line of your dataframe?
– pygo
Jan 3 at 16:10
add a comment |
2 Answers
2
active
oldest
votes
This syntax should work :
data = pd.DataFrame({'EUROSTOXX': es['SX5E'][(es.index > datetime(1999, 1, 1)) & (es.index < dt.datetime(2016, 1, 1))]})
answered Jan 3 at 16:03
dmdipdmdip
849711
add a comment |
Just an example for illustration to Select Time Range:
DataFrame Example:
>>> df
date
0 2001-01-01 00:00:00
1 2001-01-01 01:00:00
2 2001-01-01 02:00:00
3 2001-01-01 03:00:00
4 2001-01-01 04:00:00
5 2001-01-01 05:00:00
6 2001-01-01 06:00:00
7 2001-01-01 07:00:00
8 2001-01-01 08:00:00
9 2001-01-01 09:00:00
One way you can get it as follows:
>>> df[(df['date'] > '2001-01-01 00:00:00') & (df['date'] <= '2001-01-01 03:00:00')]
date
1 2001-01-01 01:00:00
2 2001-01-01 02:00:00
3 2001-01-01 03:00:00
Secondly, setting the date column as an Index and then applying loc method:
>>> df = df.set_index(df['date'])
>>> df
date
date
2001-01-01 00:00:00 2001-01-01 00:00:00
2001-01-01 01:00:00 2001-01-01 01:00:00
2001-01-01 02:00:00 2001-01-01 02:00:00
2001-01-01 03:00:00 2001-01-01 03:00:00
2001-01-01 04:00:00 2001-01-01 04:00:00
2001-01-01 05:00:00 2001-01-01 05:00:00
2001-01-01 06:00:00 2001-01-01 06:00:00
2001-01-01 07:00:00 2001-01-01 07:00:00
2001-01-01 08:00:00 2001-01-01 08:00:00
2001-01-01 09:00:00 2001-01-01 09:00:00
Now using loc :
>>> df.loc['2001-01-01 00:00:00':'2001-01-01 03:00:00']
date
date
2001-01-01 00:00:00 2001-01-01 00:00:00
2001-01-01 01:00:00 2001-01-01 01:00:00
2001-01-01 02:00:00 2001-01-01 02:00:00
2001-01-01 03:00:00 2001-01-01 03:00:00
Hope it will help.
answered Jan 3 at 16:23
pygopygo
3,2171721
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
This syntax should work :
data = pd.DataFrame({'EUROSTOXX': es['SX5E'][(es.index > datetime(1999, 1, 1)) & (es.index < dt.datetime(2016, 1, 1))]})
answered Jan 3 at 16:03
dmdipdmdip
849711
add a comment |
This syntax should work :
data = pd.DataFrame({'EUROSTOXX': es['SX5E'][(es.index > datetime(1999, 1, 1)) & (es.index < dt.datetime(2016, 1, 1))]})
answered Jan 3 at 16:03
dmdipdmdip
849711
add a comment |
This syntax should work :
data = pd.DataFrame({'EUROSTOXX': es['SX5E'][(es.index > datetime(1999, 1, 1)) & (es.index < dt.datetime(2016, 1, 1))]})
answered Jan 3 at 16:03
dmdipdmdip
849711
This syntax should work :
data = pd.DataFrame({'EUROSTOXX': es['SX5E'][(es.index > datetime(1999, 1, 1)) & (es.index < dt.datetime(2016, 1, 1))]})
answered Jan 3 at 16:03
dmdipdmdip
849711
answered Jan 3 at 16:03
dmdipdmdip
849711
answered Jan 3 at 16:03
dmdipdmdip
849711
answered Jan 3 at 16:03
dmdipdmdip
849711
849711
add a comment |
add a comment |
Just an example for illustration to Select Time Range:
DataFrame Example:
>>> df
date
0 2001-01-01 00:00:00
1 2001-01-01 01:00:00
2 2001-01-01 02:00:00
3 2001-01-01 03:00:00
4 2001-01-01 04:00:00
5 2001-01-01 05:00:00
6 2001-01-01 06:00:00
7 2001-01-01 07:00:00
8 2001-01-01 08:00:00
9 2001-01-01 09:00:00
One way you can get it as follows:
>>> df[(df['date'] > '2001-01-01 00:00:00') & (df['date'] <= '2001-01-01 03:00:00')]
date
1 2001-01-01 01:00:00
2 2001-01-01 02:00:00
3 2001-01-01 03:00:00
Secondly, setting the date column as an Index and then applying loc method:
>>> df = df.set_index(df['date'])
>>> df
date
date
2001-01-01 00:00:00 2001-01-01 00:00:00
2001-01-01 01:00:00 2001-01-01 01:00:00
2001-01-01 02:00:00 2001-01-01 02:00:00
2001-01-01 03:00:00 2001-01-01 03:00:00
2001-01-01 04:00:00 2001-01-01 04:00:00
2001-01-01 05:00:00 2001-01-01 05:00:00
2001-01-01 06:00:00 2001-01-01 06:00:00
2001-01-01 07:00:00 2001-01-01 07:00:00
2001-01-01 08:00:00 2001-01-01 08:00:00
2001-01-01 09:00:00 2001-01-01 09:00:00
Now using loc :
>>> df.loc['2001-01-01 00:00:00':'2001-01-01 03:00:00']
date
date
2001-01-01 00:00:00 2001-01-01 00:00:00
2001-01-01 01:00:00 2001-01-01 01:00:00
2001-01-01 02:00:00 2001-01-01 02:00:00
2001-01-01 03:00:00 2001-01-01 03:00:00
Hope it will help.
answered Jan 3 at 16:23
pygopygo
3,2171721
add a comment |
Just an example for illustration to Select Time Range:
DataFrame Example:
>>> df
date
0 2001-01-01 00:00:00
1 2001-01-01 01:00:00
2 2001-01-01 02:00:00
3 2001-01-01 03:00:00
4 2001-01-01 04:00:00
5 2001-01-01 05:00:00
6 2001-01-01 06:00:00
7 2001-01-01 07:00:00
8 2001-01-01 08:00:00
9 2001-01-01 09:00:00
One way you can get it as follows:
>>> df[(df['date'] > '2001-01-01 00:00:00') & (df['date'] <= '2001-01-01 03:00:00')]
date
1 2001-01-01 01:00:00
2 2001-01-01 02:00:00
3 2001-01-01 03:00:00
Secondly, setting the date column as an Index and then applying loc method:
>>> df = df.set_index(df['date'])
>>> df
date
date
2001-01-01 00:00:00 2001-01-01 00:00:00
2001-01-01 01:00:00 2001-01-01 01:00:00
2001-01-01 02:00:00 2001-01-01 02:00:00
2001-01-01 03:00:00 2001-01-01 03:00:00
2001-01-01 04:00:00 2001-01-01 04:00:00
2001-01-01 05:00:00 2001-01-01 05:00:00
2001-01-01 06:00:00 2001-01-01 06:00:00
2001-01-01 07:00:00 2001-01-01 07:00:00
2001-01-01 08:00:00 2001-01-01 08:00:00
2001-01-01 09:00:00 2001-01-01 09:00:00
Now using loc :
>>> df.loc['2001-01-01 00:00:00':'2001-01-01 03:00:00']
date
date
2001-01-01 00:00:00 2001-01-01 00:00:00
2001-01-01 01:00:00 2001-01-01 01:00:00
2001-01-01 02:00:00 2001-01-01 02:00:00
2001-01-01 03:00:00 2001-01-01 03:00:00
Hope it will help.
answered Jan 3 at 16:23
pygopygo
3,2171721
add a comment |
Just an example for illustration to Select Time Range:
DataFrame Example:
>>> df
date
0 2001-01-01 00:00:00
1 2001-01-01 01:00:00
2 2001-01-01 02:00:00
3 2001-01-01 03:00:00
4 2001-01-01 04:00:00
5 2001-01-01 05:00:00
6 2001-01-01 06:00:00
7 2001-01-01 07:00:00
8 2001-01-01 08:00:00
9 2001-01-01 09:00:00
One way you can get it as follows:
>>> df[(df['date'] > '2001-01-01 00:00:00') & (df['date'] <= '2001-01-01 03:00:00')]
date
1 2001-01-01 01:00:00
2 2001-01-01 02:00:00
3 2001-01-01 03:00:00
Secondly, setting the date column as an Index and then applying loc method:
>>> df = df.set_index(df['date'])
>>> df
date
date
2001-01-01 00:00:00 2001-01-01 00:00:00
2001-01-01 01:00:00 2001-01-01 01:00:00
2001-01-01 02:00:00 2001-01-01 02:00:00
2001-01-01 03:00:00 2001-01-01 03:00:00
2001-01-01 04:00:00 2001-01-01 04:00:00
2001-01-01 05:00:00 2001-01-01 05:00:00
2001-01-01 06:00:00 2001-01-01 06:00:00
2001-01-01 07:00:00 2001-01-01 07:00:00
2001-01-01 08:00:00 2001-01-01 08:00:00
2001-01-01 09:00:00 2001-01-01 09:00:00
Now using loc :
>>> df.loc['2001-01-01 00:00:00':'2001-01-01 03:00:00']
date
date
2001-01-01 00:00:00 2001-01-01 00:00:00
2001-01-01 01:00:00 2001-01-01 01:00:00
2001-01-01 02:00:00 2001-01-01 02:00:00
2001-01-01 03:00:00 2001-01-01 03:00:00
Hope it will help.
answered Jan 3 at 16:23
pygopygo
3,2171721
Just an example for illustration to Select Time Range:
DataFrame Example:
>>> df
date
0 2001-01-01 00:00:00
1 2001-01-01 01:00:00
2 2001-01-01 02:00:00
3 2001-01-01 03:00:00
4 2001-01-01 04:00:00
5 2001-01-01 05:00:00
6 2001-01-01 06:00:00
7 2001-01-01 07:00:00
8 2001-01-01 08:00:00
9 2001-01-01 09:00:00
One way you can get it as follows:
>>> df[(df['date'] > '2001-01-01 00:00:00') & (df['date'] <= '2001-01-01 03:00:00')]
date
1 2001-01-01 01:00:00
2 2001-01-01 02:00:00
3 2001-01-01 03:00:00
Secondly, setting the date column as an Index and then applying loc method:
>>> df = df.set_index(df['date'])
>>> df
date
date
2001-01-01 00:00:00 2001-01-01 00:00:00
2001-01-01 01:00:00 2001-01-01 01:00:00
2001-01-01 02:00:00 2001-01-01 02:00:00
2001-01-01 03:00:00 2001-01-01 03:00:00
2001-01-01 04:00:00 2001-01-01 04:00:00
2001-01-01 05:00:00 2001-01-01 05:00:00
2001-01-01 06:00:00 2001-01-01 06:00:00
2001-01-01 07:00:00 2001-01-01 07:00:00
2001-01-01 08:00:00 2001-01-01 08:00:00
2001-01-01 09:00:00 2001-01-01 09:00:00
Now using loc :
>>> df.loc['2001-01-01 00:00:00':'2001-01-01 03:00:00']
date
date
2001-01-01 00:00:00 2001-01-01 00:00:00
2001-01-01 01:00:00 2001-01-01 01:00:00
2001-01-01 02:00:00 2001-01-01 02:00:00
2001-01-01 03:00:00 2001-01-01 03:00:00
Hope it will help.
answered Jan 3 at 16:23
pygopygo
3,2171721
answered Jan 3 at 16:23
pygopygo
3,2171721
answered Jan 3 at 16:23
pygopygo
3,2171721
answered Jan 3 at 16:23
pygopygo
3,2171721
3,2171721
add a comment |
add a comment |
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1
Possible duplicate of How to slice a Pandas Time Series using a logical expression involving dates
– Yuca
Jan 3 at 15:16
Its always advisable to provide the minimal code which can ve reproduce in order to get and provide explicit and more feasible answer, Would you be able to show us few line of your dataframe?
– pygo
Jan 3 at 16:10