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.
upsetplot library tries to provide a simple interface backed by an
extensible, object-oriented design.
There are many ways to represent the categorisation of data, as covered in our Data Format Guide.
Our internal input format uses a
pandas.Series containing counts
corresponding to subset sizes, where each subset is an intersection of named
categories. The index of the Series indicates which rows pertain to which
categories, by having multiple boolean indices, like
example in the
>>> from upsetplot import generate_counts >>> example = generate_counts() >>> example cat0 cat1 cat2 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
>>> from upsetplot import plot >>> plot(example) >>> from matplotlib import pyplot >>> pyplot.show()
And you can save the image in various formats:
>>> pyplot.savefig("/path/to/myplot.pdf") >>> pyplot.savefig("/path/to/myplot.png")
This plot shows the cardinality of every category combination seen in our data.
The leftmost column counts items absent from any category. The next three
columns count items only in
cat3 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.
We call the above plot style “horizontal” because the category intersections are presented from left to right. Vertical plots are also supported!
Providing a DataFrame rather than a Series as input allows us to expressively plot the distribution of variables in each subset.
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 category
>>> from upsetplot import from_memberships >>> example = from_memberships( ... [, ... ['cat2'], ... ['cat1'], ... ['cat1', 'cat2'], ... ['cat0'], ... ['cat0', 'cat2'], ... ['cat0', 'cat1'], ... ['cat0', 'cat1', 'cat2'], ... ], ... data=[56, 283, 1279, 5882, 24, 90, 429, 1957] ... ) >>> example cat0 cat1 cat2 False False False 56 True 283 True False 1279 True 5882 True False False 24 True 90 True False 429 True 1957 dtype: int64
from_contents, another way to describe categorised data, and
from_indicators which allows each category to be indicated by a column in
the data frame (or a function of the column’s data such as whether it is a
To install the library, you can use
$ pip install upsetplot
- matplotlib >= 2.0
- seaborn to use
It should then be possible to:
>>> import upsetplot
Why an alternative to py-upset?¶
Probably for petty reasons. It appeared py-upset was not being maintained. Its input format was undocumented, inefficient and, IMO, inappropriate. It did not facilitate showing plots of each subset’s 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.
|[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|
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