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The scikits.timeseries module provides classes and functions for manipulating, reporting, and plotting time series of various frequencies. The focus is on convenient data access and manipulation while leveraging the existing mathematical functionality in numpy and scipy.

If the following scenarios sound familiar to you, then you will likely find the scikits.timeseries module useful:

  • Compare many time series with different ranges of data (eg. stock prices);
  • Create time series plots with intelligently spaced axis labels;
  • Convert a daily time series to monthly by taking the average value during each month;
  • Work with data that has missing values;
  • Determine the last business day of the previous month/quarter/year for reporting purposes;
  • Compute a moving standard deviation efficiently.

These are just some of the scenarios that are made very simple with the scikits.timeseries module.


The scikits.timeseries module was originally developed by Matt Knox to manipulate financial and economic data of weekday (Monday-Friday), monthly, and quarterly frequencies and to compare data series of differing frequencies. Matt created a large number of frequency conversion algorithms (implemented in C for extra speed) for reshaping the series. The initial version was released winter 2006 as a module in the (now defunct) SciPy sandbox.

Pierre Gerard-Marchant rewrote the original prototype late December 2006 and adapted it to be based on the class for handling missing data in order to work with environmental time series.