Skip to main content

Computing descriptive statistics over streaming data

Project description


Python module to compute running statistics over data, like when measuring timings from a stream.

Properties: - count - min,max,mean - std and variance - kurtosis and skewness # allows to measure the “normality of the dataset”

The main class is LiveStat to which data can be appended with append(x). For incremental values the DeltaLiveStat provides an easy to use helper.


from livestat import LiveStat,DeltaLiveStat

x = LiveStat(“optionalname”) x.append(10) x.append(20) print x # count is 2

x = DeltaLiveStat(“dt”) x.append(10) x.append(20) print x # count is 1 containing the difference

#also from array x.extend([10,20,30,40,50])

Extra Features:

# the LiveStat objects can be combined for example when performing over different data Windows or in a multiprocessing environment x.merge(y) # now x contains the merge of the statistics

# the LiveStat object can be multipled by scalar or translated, for the objective of performing some unit transformation. All the measures are transformed appropriately x + 5 x * 5

In progress: - numpy support - normality test

Package Repository

This project is maintained here:

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
livestat-0.1.1.macosx-10.9-intel.exe (72.6 kB) Copy SHA256 hash SHA256 Windows Installer any
livestat-0.1.1.tar.gz (8.1 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page