Question

I worked now for quite some time using python and pandas for analysing a set of hourly data and find it quite nice (Coming from Matlab.)

Now I am kind of stuck. I created my DataFrame like that:

SamplingRateMinutes=60
index = DateRange(initialTime,finalTime, offset=datetools.Minute(SamplingRateMinutes))
ts=DataFrame(data, index=index)

What I want to do now is to select the Data for all days at the hours 10 to 13 and 20-23 to use the data for further calculations.
So far I sliced the data using

 selectedData=ts[begin:end]

And I am sure to get some kind of dirty looping to select the data needed. But there must be a more elegant way to index exacly what I want. I am sure this is a common problem and the solution in pseudocode should look somewhat like that:

myIndex=ts.index[10<=ts.index.hour<=13 or 20<=ts.index.hour<=23]
selectedData=ts[myIndex]

To mention I am an engineer and no programer 🙂 … yet

Answer 1

Here’s an example that does what you want:

In [32]: from datetime import datetime as dt

In [33]: dr = p.DateRange(dt(2009,1,1),dt(2010,12,31), offset=p.datetools.Hour())

In [34]: hr = dr.map(lambda x: x.hour)

In [35]: dt = p.DataFrame(rand(len(dr),2), dr)

In [36]: dt 

Out[36]: 
<class 'pandas.core.frame.DataFrame'>
DateRange: 17497 entries, 2009-01-01 00:00:00 to 2010-12-31 00:00:00
offset: <1 Hour>
Data columns:
0    17497  non-null values
1    17497  non-null values
dtypes: float64(2)

In [37]: dt[(hr >= 10) & (hr <=16)]

Out[37]: 
<class 'pandas.core.frame.DataFrame'>
Index: 5103 entries, 2009-01-01 10:00:00 to 2010-12-30 16:00:00
Data columns:
0    5103  non-null values
1    5103  non-null values
dtypes: float64(2)