resampling

I'm looking for a pandas equivalent of the resample method for a dataframe whose isn't a DatetimeIndex but an array of integers, or maybe even floats. I know that for some cases ( this one , for example) the resample method can be substituted easily by a reindex and interpolation, but for some cases (I think) it can't. For example, if I have df = pd.DataFrame(np.random.randn(10,2)) withdates = df.set_index(pd.date_range('2012-01-01', periods=10)) withdates.resample('5D', np.std) this gives me 0 1 2012-01-01 1.184582 0.492113 2012-01-06 0.533134 0.982562 but I can't produce the same result with

I have a 1-d numpy array which I would like to downsample. Any of the following methods are acceptable if the downsampling raster doesn't perfectly fit the data: overlap downsample intervals convert whatever number of values remains at the end to a separate downsampled value interpolate to fit raster basically if I have 1 2 6 2 1 and I am downsampling by a factor of 3, all of the following are ok: 3 3 3 1.5 or whatever an interpolation would give me here. I'm just looking for the fastest/easiest way to do this. I found scipy.signal.decimate , but that sounds like it decimates the values (takes

Dataframe.resample() works only with timeseries data. I cannot find a way of getting every nth row from non-timeseries data. What is the best method? chrisb I'd use iloc , which takes a row/column slice, both based on integer position and following normal python syntax. df.iloc[::5, :] Though @chrisb's accepted answer does answer the question, I would like to add to it the following. A simple method I use to get the nth data or drop the nth row is the following: df1 = df[df.index % 3 != 0] # Excludes every 3rd row starting from 0 df2 = df[df.index % 3 == 0] # Selects every 3rd raw starting

I am looking for an algorithm that determines percentiles for live data capture. For example, consider the development of a server application. The server might have response times as follows: 17 ms 33 ms 52 ms 60 ms 55 ms etc. It is useful to report the 90th percentile response time, 80th percentile response time, etc. The naive algorithm is to insert each response time into a list. When statistics are requested, sort the list and get the values at the proper positions. Memory usages scales linearly with the number of requests. Is there an algorithm that yields "approximate" percentile

I'm writing some code that plays back WAV files at different speeds, so that the wave is either slower and lower-pitched, or faster and higher-pitched. I'm currently using simple linear interpolation, like so: int newlength = (int)Math.Round(rawdata.Length * lengthMultiplier); float[] output = new float[newlength]; for (int i = 0; i < newlength; i++) { float realPos = i / lengthMultiplier; int iLow = (int)realPos; int iHigh = iLow + 1; float remainder = realPos - (float)iLow; float lowval = 0; float highval = 0; if ((iLow >= 0) && (iLow < rawdata.Length)) { lowval = rawdata[iLow]; } if ((iHigh