According to Wikipedia, EWMA works well with stationary data, but it does not work as expected in the presence of trends, or seasonality. In those cases you should use a second or third order EWMA method, respectively. I decided to look at the pandas ewma function to see how it handled trends, and this is … Read more
With pandas 0.18 the resample API changed (see the docs). So for pandas >= 0.18 the answer is: In [31]: frame.resample(‘1H’).agg({‘radiation’: np.sum, ‘tamb’: np.mean}) Out[31]: tamb radiation 2012-04-05 08:00:00 5.161235 279.507182 2012-04-05 09:00:00 4.968145 290.941073 2012-04-05 10:00:00 4.478531 317.678285 2012-04-05 11:00:00 4.706206 335.258633 2012-04-05 12:00:00 2.457873 8.655838 Old Answer: I am answering my question to … Read more
Matplotlib uses an internal floating point format for dates. You just need to convert your date to that format (using matplotlib.dates.date2num or matplotlib.dates.datestr2num) and then use annotate as usual. As a somewhat excessively fancy example: import datetime as dt import matplotlib.pyplot as plt import matplotlib.dates as mdates x = [dt.datetime(2009, 05, 01), dt.datetime(2010, 06, 01), … Read more
The solution offered by fuglede is great but if your data is very noisy (like the one in the picture) you will end up with lots of misleading local extremes. I suggest that you use scipy.signal.argrelextrema() method. The .argrelextrema() method has its own limitations but it has a useful feature where you can specify the … Read more
Here are some ideas if you want to create your own simple algorithm: Use xor of the current value with the previous value to obtain a set of bits describing the difference. Divide this difference into two parts: one part is the “mantissa bits” and one part is the “exponent bits”. Use variable length encoding … Read more
I’ve been having a similar issue and am trying to find the best path forward. Try moving your data into a Pandas DataFrame and then call StatsModels tsa.seasonal_decompose. See the following example: import statsmodels.api as sm dta = sm.datasets.co2.load_pandas().data # deal with missing values. see issue dta.co2.interpolate(inplace=True) res = sm.tsa.seasonal_decompose(dta.co2) resplot = res.plot() You can … 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