I understand that OHLC re-sampling of time series data in Pandas, using one column of data, will work perfectly, for example on the following dataframe:
>>df
ctime openbid
1443654000 1.11700
1443654060 1.11700
...
df['ctime'] = pd.to_datetime(df['ctime'], unit='s')
df = df.set_index('ctime')
df.resample('1H', how='ohlc', axis=0, fill_method='bfill')
>>>
open high low close
ctime
2015-09-30 23:00:00 1.11700 1.11700 1.11687 1.11697
2015-09-30 24:00:00 1.11700 1.11712 1.11697 1.11697
...
But what do I do if the data is already in an OHLC format? From what I can gather the OHLC method of the API calculates an OHLC slice for every column, hence if my data is in the format:
ctime openbid highbid lowbid closebid
0 1443654000 1.11700 1.11700 1.11687 1.11697
1 1443654060 1.11700 1.11712 1.11697 1.11697
2 1443654120 1.11701 1.11708 1.11699 1.11708
When I try to re-sample I get an OHLC for each of the columns, like so:
openbid highbid \
open high low close open high
ctime
2015-09-30 23:00:00 1.11700 1.11700 1.11700 1.11700 1.11700 1.11712
2015-09-30 23:01:00 1.11701 1.11701 1.11701 1.11701 1.11708 1.11708
...
lowbid \
low close open high low close
ctime
2015-09-30 23:00:00 1.11700 1.11712 1.11687 1.11697 1.11687 1.11697
2015-09-30 23:01:00 1.11708 1.11708 1.11699 1.11699 1.11699 1.11699
...
closebid
open high low close
ctime
2015-09-30 23:00:00 1.11697 1.11697 1.11697 1.11697
2015-09-30 23:01:00 1.11708 1.11708 1.11708 1.11708
Is there a quick(ish) workaround for this that someone is willing to share please, without me having to get knee-deep in pandas manual?
Thanks.
ps, there is this answer - Converting OHLC stock data into a different timeframe with python and pandas - but it was 4 years ago, so I am hoping there has been some progress.
This is similar to the answer you linked, but it a little cleaner, and faster, because it uses the optimized aggregations, rather than lambdas.
Note that the resample(...).agg(...)
syntax requires pandas version 0.18.0
.
In [101]: df.resample('1H').agg({'openbid': 'first',
'highbid': 'max',
'lowbid': 'min',
'closebid': 'last'})
Out[101]:
lowbid highbid closebid openbid
ctime
2015-09-30 23:00:00 1.11687 1.11712 1.11708 1.117