“Python” Get particular rows of an hour from data frame
0
I want to get all rows of each hour or day do some calculations for them.
So i mean how to iterate over data frame and filter rows of an hour some, do calculations and then move to the next hour
Question: How to get the rows of each hour/or day?
df
Date TimeStamp col1
20150102 20:00:00 SomeData
20150102 20:01:00 SomeData
20150102 20:02:00 SomeData
20150102 20:03:00 SomeData
20150102 20:04:00 SomeData
20150102 20:05:00 SomeData
for index, row in df.iterrows():
#grouping these rows of a min/an hour and do some calculations based on these rows
python python-3.x pandas dataframe
asked Nov 21 '18 at 1:00
RouteRoute
61
add a comment |
0
I want to get all rows of each hour or day do some calculations for them.
So i mean how to iterate over data frame and filter rows of an hour some, do calculations and then move to the next hour
Question: How to get the rows of each hour/or day?
df
Date TimeStamp col1
20150102 20:00:00 SomeData
20150102 20:01:00 SomeData
20150102 20:02:00 SomeData
20150102 20:03:00 SomeData
20150102 20:04:00 SomeData
20150102 20:05:00 SomeData
for index, row in df.iterrows():
#grouping these rows of a min/an hour and do some calculations based on these rows
python python-3.x pandas dataframe
asked Nov 21 '18 at 1:00
RouteRoute
61
Can you be more specific about your expected output? It's possible that you can achieve your goal with vectorized operations, which would be way faster and more readable than explicit looping.
– Peter Leimbigler
Nov 21 '18 at 1:01
I expect to get the rows of each hour (e.g. 20:00:00-20:59:00). I used to do such stuff with iteration but any other way is also accepted.
– Route
Nov 21 '18 at 1:08
what "stuff" specifically are you intending to do? What calculated value(s) should accompany the rows of each hour?
– Peter Leimbigler
Nov 21 '18 at 1:29
I will go column by column and use the values in each row of this column to compare many thing such as Momentum, Rate of change, P/L, etc.
– Route
Nov 21 '18 at 1:40
without concrete example data and a specific description of what you intend to do with it, all I can offer is to echo the posted answer, which is as vague as the question: look intodf.groupby. Also check out how to create a minimal, complete, verifiable example: stackoverflow.com/help/mcve
– Peter Leimbigler
Nov 21 '18 at 1:46
add a comment |
0
0
0
I want to get all rows of each hour or day do some calculations for them.
So i mean how to iterate over data frame and filter rows of an hour some, do calculations and then move to the next hour
Question: How to get the rows of each hour/or day?
df
Date TimeStamp col1
20150102 20:00:00 SomeData
20150102 20:01:00 SomeData
20150102 20:02:00 SomeData
20150102 20:03:00 SomeData
20150102 20:04:00 SomeData
20150102 20:05:00 SomeData
for index, row in df.iterrows():
#grouping these rows of a min/an hour and do some calculations based on these rows
python python-3.x pandas dataframe
asked Nov 21 '18 at 1:00
RouteRoute
61
I want to get all rows of each hour or day do some calculations for them.
So i mean how to iterate over data frame and filter rows of an hour some, do calculations and then move to the next hour
Question: How to get the rows of each hour/or day?
df
Date TimeStamp col1
20150102 20:00:00 SomeData
20150102 20:01:00 SomeData
20150102 20:02:00 SomeData
20150102 20:03:00 SomeData
20150102 20:04:00 SomeData
20150102 20:05:00 SomeData
for index, row in df.iterrows():
#grouping these rows of a min/an hour and do some calculations based on these rows
python python-3.x pandas dataframe
python python-3.x pandas dataframe
asked Nov 21 '18 at 1:00
RouteRoute
61
asked Nov 21 '18 at 1:00
RouteRoute
61
edited Nov 21 '18 at 1:01
Route
asked Nov 21 '18 at 1:00
RouteRoute
61
asked Nov 21 '18 at 1:00
RouteRoute
61
asked Nov 21 '18 at 1:00
RouteRoute
61
61
Can you be more specific about your expected output? It's possible that you can achieve your goal with vectorized operations, which would be way faster and more readable than explicit looping.
– Peter Leimbigler
Nov 21 '18 at 1:01
I expect to get the rows of each hour (e.g. 20:00:00-20:59:00). I used to do such stuff with iteration but any other way is also accepted.
– Route
Nov 21 '18 at 1:08
what "stuff" specifically are you intending to do? What calculated value(s) should accompany the rows of each hour?
– Peter Leimbigler
Nov 21 '18 at 1:29
I will go column by column and use the values in each row of this column to compare many thing such as Momentum, Rate of change, P/L, etc.
– Route
Nov 21 '18 at 1:40
without concrete example data and a specific description of what you intend to do with it, all I can offer is to echo the posted answer, which is as vague as the question: look intodf.groupby. Also check out how to create a minimal, complete, verifiable example: stackoverflow.com/help/mcve
– Peter Leimbigler
Nov 21 '18 at 1:46
add a comment |
Can you be more specific about your expected output? It's possible that you can achieve your goal with vectorized operations, which would be way faster and more readable than explicit looping.
– Peter Leimbigler
Nov 21 '18 at 1:01
I expect to get the rows of each hour (e.g. 20:00:00-20:59:00). I used to do such stuff with iteration but any other way is also accepted.
– Route
Nov 21 '18 at 1:08
what "stuff" specifically are you intending to do? What calculated value(s) should accompany the rows of each hour?
– Peter Leimbigler
Nov 21 '18 at 1:29
I will go column by column and use the values in each row of this column to compare many thing such as Momentum, Rate of change, P/L, etc.
– Route
Nov 21 '18 at 1:40
without concrete example data and a specific description of what you intend to do with it, all I can offer is to echo the posted answer, which is as vague as the question: look intodf.groupby. Also check out how to create a minimal, complete, verifiable example: stackoverflow.com/help/mcve
– Peter Leimbigler
Nov 21 '18 at 1:46
Can you be more specific about your expected output? It's possible that you can achieve your goal with vectorized operations, which would be way faster and more readable than explicit looping.
– Peter Leimbigler
Nov 21 '18 at 1:01
Can you be more specific about your expected output? It's possible that you can achieve your goal with vectorized operations, which would be way faster and more readable than explicit looping.
– Peter Leimbigler
Nov 21 '18 at 1:01
I expect to get the rows of each hour (e.g. 20:00:00-20:59:00). I used to do such stuff with iteration but any other way is also accepted.
– Route
Nov 21 '18 at 1:08
I expect to get the rows of each hour (e.g. 20:00:00-20:59:00). I used to do such stuff with iteration but any other way is also accepted.
– Route
Nov 21 '18 at 1:08
what "stuff" specifically are you intending to do? What calculated value(s) should accompany the rows of each hour?
– Peter Leimbigler
Nov 21 '18 at 1:29
what "stuff" specifically are you intending to do? What calculated value(s) should accompany the rows of each hour?
– Peter Leimbigler
Nov 21 '18 at 1:29
I will go column by column and use the values in each row of this column to compare many thing such as Momentum, Rate of change, P/L, etc.
– Route
Nov 21 '18 at 1:40
I will go column by column and use the values in each row of this column to compare many thing such as Momentum, Rate of change, P/L, etc.
– Route
Nov 21 '18 at 1:40
without concrete example data and a specific description of what you intend to do with it, all I can offer is to echo the posted answer, which is as vague as the question: look into
df.groupby. Also check out how to create a minimal, complete, verifiable example: stackoverflow.com/help/mcve– Peter Leimbigler
Nov 21 '18 at 1:46
without concrete example data and a specific description of what you intend to do with it, all I can offer is to echo the posted answer, which is as vague as the question: look into
df.groupby. Also check out how to create a minimal, complete, verifiable example: stackoverflow.com/help/mcve– Peter Leimbigler
Nov 21 '18 at 1:46
add a comment |
1 Answer
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0
You should use pandas groupby to group the rows by hour/day then you can perform calculations on the other columns.
Extracting the hours/days into another column would look something like:
df['Hour'] = df['TimeStamp'].dt.hour
df['Day'] = df['Date'].dt.day
After which you would groupby with:
df.groupby('Hour')...
df.groupby('Day')...
answered Nov 21 '18 at 1:14
zrelovazrelova
7181522
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
You should use pandas groupby to group the rows by hour/day then you can perform calculations on the other columns.
Extracting the hours/days into another column would look something like:
df['Hour'] = df['TimeStamp'].dt.hour
df['Day'] = df['Date'].dt.day
After which you would groupby with:
df.groupby('Hour')...
df.groupby('Day')...
answered Nov 21 '18 at 1:14
zrelovazrelova
7181522
add a comment |
0
You should use pandas groupby to group the rows by hour/day then you can perform calculations on the other columns.
Extracting the hours/days into another column would look something like:
df['Hour'] = df['TimeStamp'].dt.hour
df['Day'] = df['Date'].dt.day
After which you would groupby with:
df.groupby('Hour')...
df.groupby('Day')...
answered Nov 21 '18 at 1:14
zrelovazrelova
7181522
add a comment |
0
0
0
You should use pandas groupby to group the rows by hour/day then you can perform calculations on the other columns.
Extracting the hours/days into another column would look something like:
df['Hour'] = df['TimeStamp'].dt.hour
df['Day'] = df['Date'].dt.day
After which you would groupby with:
df.groupby('Hour')...
df.groupby('Day')...
answered Nov 21 '18 at 1:14
zrelovazrelova
7181522
You should use pandas groupby to group the rows by hour/day then you can perform calculations on the other columns.
Extracting the hours/days into another column would look something like:
df['Hour'] = df['TimeStamp'].dt.hour
df['Day'] = df['Date'].dt.day
After which you would groupby with:
df.groupby('Hour')...
df.groupby('Day')...
answered Nov 21 '18 at 1:14
zrelovazrelova
7181522
answered Nov 21 '18 at 1:14
zrelovazrelova
7181522
answered Nov 21 '18 at 1:14
zrelovazrelova
7181522
answered Nov 21 '18 at 1:14
zrelovazrelova
7181522
7181522
add a comment |
add a comment |
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Can you be more specific about your expected output? It's possible that you can achieve your goal with vectorized operations, which would be way faster and more readable than explicit looping.
– Peter Leimbigler
Nov 21 '18 at 1:01
I expect to get the rows of each hour (e.g. 20:00:00-20:59:00). I used to do such stuff with iteration but any other way is also accepted.
– Route
Nov 21 '18 at 1:08
what "stuff" specifically are you intending to do? What calculated value(s) should accompany the rows of each hour?
– Peter Leimbigler
Nov 21 '18 at 1:29
I will go column by column and use the values in each row of this column to compare many thing such as Momentum, Rate of change, P/L, etc.
– Route
Nov 21 '18 at 1:40
without concrete example data and a specific description of what you intend to do with it, all I can offer is to echo the posted answer, which is as vague as the question: look into
df.groupby. Also check out how to create a minimal, complete, verifiable example: stackoverflow.com/help/mcve– Peter Leimbigler
Nov 21 '18 at 1:46