A data analysis package based on modelling and manipulation of mathematical step functions. Strongly aligned with pandas.
The staircase package enables data analysis through mathematical step functions. Step functions can be used to represent continuous time series - think changes in state over time, queue size over time, utilisation over time, success rates over time etc.
The package is built upon
pandas, with a deliberate, stylistic alignment to the latter in order to integrate seamlessly into the pandas ecosystem.
The staircase package makes converting raw, temporal data into time series easy and readable. Furthermore there is a rich variety of arithmetic operations, relational operations, logical operations, statistical operations, to enable analysis, in addition to functions for univariate analysis, aggregations and compatibility with datetimes.
In this example, we consider data corresponding to site views for a website in October 2021. The start and end times have been logged for each session, in addition to one of three countries codes (AU, UK, US). These times are recorded with
pandas.Timestamp and any time which falls outside of October is logged as
>>> data start end country 0 NaT 2021-10-01 00:00:50 AU 1 NaT 2021-10-01 00:07:45 AU 2 NaT 2021-10-01 00:05:58 AU 3 NaT 2021-10-01 00:08:48 AU 4 NaT 2021-10-01 00:05:26 AU ... ... ... ... 425728 2021-10-31 23:57:16 NaT US 425729 2021-10-31 23:57:25 NaT US 425730 2021-10-31 23:58:59 NaT US 425731 2021-10-31 23:59:45 NaT US 425732 2021-10-31 23:59:59 NaT US
Note that the number of users viewing the site over time can be modelled as a step function. The value of the function increases by 1 every time a user arrives at the site, and decreases by 1 every time a user leaves the site. This step function can be thought of as the sum of three step functions: AU users + UK users + US users. Creating a step function for AU users, for example, is simple. To achieve it we use the Stairs class, which represents a step function:
>>> import staircase as sc >>> views_AU = sc.Stairs(data.query("country == 'AU'"), "start", "end") >>> views_AU <staircase.Stairs, id=1609972469384>
We can visualise the function with the plot function:
Rather than creating a separate variable for each country, we can create a
pandas.Series to hold a step function for each country. We can even give this Series a "Stairs" type.
>>> october = (pd.Timestamp("2021-10"), pd.Timestamp("2021-11")) >>> series_stepfunctions = ( ... data.groupby("country") ... .apply(sc.Stairs, "start", "end") ... .apply(sc.Stairs.clip, october) # set step functions to be undefined outside of October ... .astype("Stairs") ... ) >>> series_stepfunctions country AU <staircase.Stairs, id=2516367680328> UK <staircase.Stairs, id=2516362550664> US <staircase.Stairs, id=2516363585928> dtype: Stairs
The plotting backend to
staircase is provided by
>>> import matplotlib.pyplot as plt >>> _, ax = plt.subplots(figsize=(15,4)) >>> series_stepfunctions.sc.plot(ax, alpha=0.7) >>> ax.legend()
Now plotting step functions is useful, but the real fun starts when we go beyond this:
staircase can be installed from PyPI:
python -m pip install staircase
or also with conda:
conda install -c conda-forge staircase
The complete guide to using staircase can be found at staircase.dev
There are many ways in which contributions can be made - the first and foremost being using staircase and giving feedback.
Bug reports, feature requests and ideas can be submitted via the Github issue tracker.
Additionally, bug fixes. enhancements, and improvements to the code and documentation are also appreciated and can be done via pull requests. Take a look at the current issues and if there is one you would like to work on please leave a comment to that effect.
This project is licensed under the MIT License - see the LICENSE file for details
The seeds of staircase began developing at the Hunter Valley Coal Chain Coordinator, where it finds strong application in analysing simulated data. Thanks for the support!
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