I've got a DataFrame
storing daily-based data which is as below:
Date Open High Low Close Volume
2010-01-04 38.660000 39.299999 38.509998 39.279999 1293400
2010-01-05 39.389999 39.520000 39.029999 39.430000 1261400
2010-01-06 39.549999 40.700001 39.020000 40.250000 1879800
2010-01-07 40.090000 40.349998 39.910000 40.090000 836400
2010-01-08 40.139999 40.310001 39.720001 40.290001 654600
2010-01-11 40.209999 40.520000 40.040001 40.290001 963600
2010-01-12 40.160000 40.340000 39.279999 39.980000 1012800
2010-01-13 39.930000 40.669998 39.709999 40.560001 1773400
2010-01-14 40.490002 40.970001 40.189999 40.520000 1240600
2010-01-15 40.570000 40.939999 40.099998 40.450001 1244200
What I intend to do is to merge it into weekly-based data. After grouping:
which should look like this:
Date Open High Low Close Volume
2010-01-04 38.660000 40.700001 38.509998 40.290001 5925600
2010-01-11 40.209999 40.970001 39.279999 40.450001 6234600
Currently, my code snippet is as below, which function should I use to mapping daily-based data to the expected weekly-based data? Many thanks!
import pandas_datareader.data as web
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2016, 12, 31)
f = web.DataReader("MNST", "yahoo", start, end, session=session)
print f
You can resample
(to weekly), offset
(shift), and apply
aggregation rules as follows:
logic = {'Open' : 'first',
'High' : 'max',
'Low' : 'min',
'Close' : 'last',
'Volume': 'sum'}
offset = pd.offsets.timedelta(days=-6)
f = pd.read_clipboard(parse_dates=['Date'], index_col=['Date'])
f.resample('W', loffset=offset).apply(logic)
to get:
Open High Low Close Volume
Date
2010-01-04 38.660000 40.700001 38.509998 40.290001 5925600
2010-01-11 40.209999 40.970001 39.279999 40.450001 6234600