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
If you want to keep the memory usage constant as you get more and more data, then you’re going to have to resample that data somehow. That implies that you must apply some sort of rebinning scheme. You can wait until you acquire a certain amount of raw inputs before beginning the rebinning, but you … Read more
Convert the integer timestamps in the index to a DatetimeIndex: data.index = pd.to_datetime(data.index, unit=”s”) This interprets the integers as seconds since the Epoch. For example, given data = pd.DataFrame( {‘Timestamp’:[1313331280, 1313334917, 1313334917, 1313340309, 1313340309], ‘Price’: [10.4]*3 + [10.5]*2, ‘Volume’: [0.779, 0.101, 0.316, 0.150, 1.8]}) data = data.set_index([‘Timestamp’]) # Price Volume # Timestamp # 1313331280 10.4 … Read more
I’d use iloc, which takes a row/column slice, both based on integer position and following normal python syntax. If you want every 5th row: df.iloc[::5, :]