pandas, python - how to select specific times in t

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

In upcoming pandas 0.8.0, you'll be able to write

hour = ts.index.hour
selector = ((10 <= hour) & (hour <= 13)) | ((20 <= hour) & (hour <= 23))
data = ts[selector]

Answer 2

As it looks messy in my comment above, I decided to provide another answer which is a syntax update for pandas 0.10.0 on Marc's answer, combined with Wes' hint:

import pandas as pd
from datetime import datetime

dr = pd.date_range(datetime(2009,1,1),datetime(2010,12,31),freq='H')
dt = pd.DataFrame(rand(len(dr),2),dr)
hour = dt.index.hour
selector = ((10 <= hour) & (hour <= 13)) | ((20<=hour) & (hour<=23))
data = dt[selector]

Answer 3

Pandas DataFrame has a built-in function pandas.DataFrame.between_time

df = pd.DataFrame(np.random.randn(1000, 2),
                  index=pd.date_range(start='2017-01-01', freq='10min', periods=1000))

Create 2 data frames for each period of time:

df1 = df.between_time(start_time='10:00', end_time='13:00') 
df2 = df.between_time(start_time='20:00', end_time='23:00')

Data frame you want is merged and sorted df1 and df2:

pd.concat([df1, df2], axis=0).sort_index()

Answer 4

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)