Skip to main content

Draw Lex et al.'s UpSet plots with Pandas and Matplotlib

Project description

UpSetPlot documentation
============================

|version| |licence| |py-versions|

|issues| |build| |docs| |coverage|

This is another Python implementation of UpSet plots by Lex et al. [Lex2014]_.
UpSet plots are used to visualise set overlaps; like Venn diagrams but
more readable. Documentation is at https://upsetplot.readthedocs.io.

This ``upsetplot`` library tries to provide a simple interface backed by an
extensible, object-oriented design.

The basic input format is a `pandas.Series` containing counts
corresponding to set intersection sizes. The index indicates which rows
pertain to which sets, by having multiple boolean indices, like ``example``
in the following::

>>> from upsetplot import generate_data
>>> example = generate_data(aggregated=True)
>>> example # doctest: +NORMALIZE_WHITESPACE
set0 set1 set2
False False False 56
True 283
True False 1279
True 5882
True False False 24
True 90
True False 429
True 1957
Name: value, dtype: int64

Then::

>>> from upsetplot import plot
>>> plot(example) # doctest: +SKIP
>>> from matplotlib import pyplot
>>> pyplot.show() # doctest: +SKIP

makes:

.. image:: http://upsetplot.readthedocs.io/en/latest/_images/sphx_glr_plot_generated_001.png
:target: ../auto_examples/plot_generated.html

This plot shows the cardinality of every set combination seen in our data. The
leftmost column counts items absent from any set. The next three columns count
items only in ``set1``, ``set2`` and ``set3``` respectively, with following
columns showing cardinalities for items in each combination of exactly two
named sets. The rightmost column counts items in all three sets.

Rotation
........

We call the above plot style "horizontal" because the set intersections are
presented from left to right. `Vertical plots
<http://upsetplot.readthedocs.io/en/latest/auto_examples/plot_vertical.html>`__
are also supported!

.. image:: http://upsetplot.readthedocs.io/en/latest/_images/sphx_glr_plot_vertical_001.png
:target: http://upsetplot.readthedocs.io/en/latest/auto_examples/plot_vertical.html

Distributions
.............

Providing a DataFrame rather than a Series as input allows us to expressively
`plot the distribution of variables
<http://upsetplot.readthedocs.io/en/latest/auto_examples/plot_boston.html>`__
in each subset.

.. image:: http://upsetplot.readthedocs.io/en/latest/_images/sphx_glr_plot_boston_001.png
:target: http://upsetplot.readthedocs.io/en/latest/auto_examples/plot_boston.html


Loading datasets
................

While the dataset above is randomly generated, you can prepare your own dataset
for input to upsetplot. A helpful tool is `from_memberships`, which allows
us to reconstruct the example above by indicating each data point's set
membership::

>>> from upsetplot import from_memberships
>>> example = from_memberships(
... [[],
... ['set2'],
... ['set1'],
... ['set1', 'set2'],
... ['set0'],
... ['set0', 'set2'],
... ['set0', 'set1'],
... ['set0', 'set1', 'set2'],
... ],
... data=[56, 283, 1279, 5882, 24, 90, 429, 1957]
... )
>>> example # doctest: +NORMALIZE_WHITESPACE
0
set0 set1 set2
False False False 56
True 283
True False 1279
True 5882
True False False 24
True 90
True False 429
True 1957

Installation
------------

To install the library, you can use `pip`::

$ pip install upsetplot

Installation requires:

* pandas
* matplotlib >= 2.0
* seaborn to use `UpSet.add_catplot`

It should then be possible to::

>>> import upsetplot

in Python.

Why an alternative to py-upset?
-------------------------------

Probably for petty reasons. It appeared `py-upset
<https://github.com/ImSoErgodic/py-upset>`_ was not being maintained. Its
input format was undocumented, inefficient and, IMO, inappropriate. It did not
facilitate showing plots of each set intersection distribution as in Lex et
al's work introducing UpSet plots. Nor did it include the horizontal bar plots
illustrated there. It did not support Python 2. I decided it would be easier to
construct a cleaner version than to fix it.

References
----------

.. [Lex2014] Alexander Lex, Nils Gehlenborg, Hendrik Strobelt, Romain Vuillemot, Hanspeter Pfister,
*UpSet: Visualization of Intersecting Sets*,
IEEE Transactions on Visualization and Computer Graphics (InfoVis '14), vol. 20, no. 12, pp. 1983–1992, 2014.
doi: `doi.org/10.1109/TVCG.2014.2346248 <https://doi.org/10.1109/TVCG.2014.2346248>`_


.. |py-versions| image:: https://img.shields.io/pypi/pyversions/upsetplot.svg
:alt: Python versions supported

.. |version| image:: https://badge.fury.io/py/upsetplot.svg
:alt: Latest version on PyPi
:target: https://badge.fury.io/py/upsetplot

.. |build| image:: https://travis-ci.org/jnothman/UpSetPlot.svg?branch=master
:alt: Travis CI build status
:scale: 100%
:target: https://travis-ci.org/jnothman/UpSetPlot

.. |issues| image:: https://img.shields.io/github/issues/jnothman/UpSetPlot.svg
:alt: Issue tracker
:target: https://github.com/jnothman/UpSetPlot

.. |coverage| image:: https://coveralls.io/repos/github/jnothman/UpSetPlot/badge.svg
:alt: Test coverage
:target: https://coveralls.io/github/jnothman/UpSetPlot

.. |docs| image:: https://readthedocs.org/projects/upsetplot/badge/?version=latest
:alt: Documentation Status
:scale: 100%
:target: https://upsetplot.readthedocs.io/en/latest/?badge=latest

.. |licence| image:: https://img.shields.io/badge/Licence-BSD-blue.svg
:target: https://opensource.org/licenses/BSD-3-Clause

Project details


Download files

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

Source Distribution

UpSetPlot-0.2.0.tar.gz (12.6 kB view details)

Uploaded Source

File details

Details for the file UpSetPlot-0.2.0.tar.gz.

File metadata

  • Download URL: UpSetPlot-0.2.0.tar.gz
  • Upload date:
  • Size: 12.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.29.0 CPython/3.6.7

File hashes

Hashes for UpSetPlot-0.2.0.tar.gz
Algorithm Hash digest
SHA256 b24ef42f652e138db0dbb3754abd92f812bb605c98c300ba47582f5f6caf957f
MD5 5c4d02859ce8bc42fb02589e39996fd4
BLAKE2b-256 3c8bd662b91f3b0b08c351601dcd308e0f418029c196b247010f9e395f86be0f

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page