API Reference

Plotting

upsetplot.plot(data, fig=None, **kwargs)[source]

Make an UpSet plot of data on fig

Parameters:
data : pandas.Series or pandas.DataFrame

Values for each set to plot. Should have multi-index where each level is binary, corresponding to set membership. If a DataFrame, sum_over must be a string or False.

fig : matplotlib.figure.Figure, optional

Defaults to a new figure.

kwargs

Other arguments for UpSet

Returns:
subplots : dict of matplotlib.axes.Axes

Keys are ‘matrix’, ‘intersections’, ‘totals’, ‘shading’

class upsetplot.UpSet(data, orientation='horizontal', sort_by='degree', sort_categories_by='cardinality', subset_size='legacy', sum_over=None, facecolor='black', with_lines=True, element_size=32, intersection_plot_elements=6, totals_plot_elements=2, show_counts='', sort_sets_by='deprecated')[source]

Manage the data and drawing for a basic UpSet plot

Primary public method is plot().

Parameters:
data : pandas.Series or pandas.DataFrame

Elements associated with categories (a DataFrame), or the size of each subset of categories (a Series). Should have MultiIndex where each level is binary, corresponding to category membership. If a DataFrame, sum_over must be a string or False.

orientation : {‘horizontal’ (default), ‘vertical’}

If horizontal, intersections are listed from left to right.

sort_by : {‘cardinality’, ‘degree’}

If ‘cardinality’, subset are listed from largest to smallest. If ‘degree’, they are listed in order of the number of categories intersected.

sort_categories_by : {‘cardinality’, None}

Whether to sort the categories by total cardinality, or leave them in the provided order.

subset_size : {‘auto’, ‘count’, ‘sum’}

Configures how to calculate the size of a subset. Choices are:

‘auto’

If data is a DataFrame, count the number of rows in each group, unless sum_over is specified. If data is a Series with at most one row for each group, use the value of the Series. If data is a Series with more than one row per group, raise a ValueError.

‘count’

Count the number of rows in each group.

‘sum’

Sum the value of the data Series, or the DataFrame field specified by sum_over.

Until version 0.4, the default is ‘legacy’ which uses sum_over to control this behaviour. From version 0.4, ‘auto’ will be default.

sum_over : str or None

If subset_size='sum' or 'auto', then the intersection size is the sum of the specified field in the data DataFrame. If a Series, only None is supported and its value is summed.

If subset_size='legacy', sum_over must be specified when data is a DataFrame. If False, the intersection plot will show the count of each subset. Otherwise, it shows the sum of the specified field.

facecolor : str

Color for bar charts and dots.

with_lines : bool

Whether to show lines joining dots in the matrix, to mark multiple categories being intersected.

element_size : float or None

Side length in pt. If None, size is estimated to fit figure

intersection_plot_elements : int

The intersections plot should be large enough to fit this many matrix elements. Set to 0 to disable intersection size bars.

totals_plot_elements : int

The totals plot should be large enough to fit this many matrix elements.

show_counts : bool or str, default=False

Whether to label the intersection size bars with the cardinality of the intersection. When a string, this formats the number. For example, ‘%d’ is equivalent to True.

sort_sets_by

Methods

add_catplot(self, kind[, value, elements]) Add a seaborn catplot over subsets when plot() is called.
make_grid(self[, fig]) Get a SubplotSpec for each Axes, accounting for label text width
plot(self[, fig]) Draw all parts of the plot onto fig or a new figure
plot_intersections(self, ax) Plot bars indicating intersection size
plot_matrix(self, ax) Plot the matrix of intersection indicators onto ax
plot_totals(self, ax) Plot bars indicating total set size
plot_shading  
add_catplot(self, kind, value=None, elements=3, **kw)[source]

Add a seaborn catplot over subsets when plot() is called.

Parameters:
kind : str

One of {“point”, “bar”, “strip”, “swarm”, “box”, “violin”, “boxen”}

value : str, optional

Column name for the value to plot (i.e. y if orientation=’horizontal’), required if data is a DataFrame.

elements : int, default=3

Size of the axes counted in number of matrix elements.

**kw : dict

Additional keywords to pass to seaborn.catplot().

Our implementation automatically determines ‘ax’, ‘data’, ‘x’, ‘y’ and ‘orient’, so these are prohibited keys in kw.

Returns:
None
make_grid(self, fig=None)[source]

Get a SubplotSpec for each Axes, accounting for label text width

plot(self, fig=None)[source]

Draw all parts of the plot onto fig or a new figure

Parameters:
fig : matplotlib.figure.Figure, optional

Defaults to a new figure.

Returns:
subplots : dict of matplotlib.axes.Axes

Keys are ‘matrix’, ‘intersections’, ‘totals’, ‘shading’

plot_intersections(self, ax)[source]

Plot bars indicating intersection size

plot_matrix(self, ax)[source]

Plot the matrix of intersection indicators onto ax

plot_totals(self, ax)[source]

Plot bars indicating total set size

Dataset loading and generation

upsetplot.from_contents(contents, data=None, id_column='id')[source]

Build data from category listings

Parameters:
contents : Mapping (or iterable over pairs) of strings to sets

Keys are category names, values are sets of identifiers (int or string).

data : DataFrame, optional

If provided, this should be indexed by the identifiers used in Python Documentation contents.

id_column : str, default=’id’

The column name to use for the identifiers in the output.

Returns:
DataFrame

data is returned with its index indicating category membership, including a column named according to id_column. If data is not given, the order of rows is not assured.

Notes

The order of categories in the output DataFrame is determined from Python Documentation contents, which may have non-deterministic iteration order.

Examples

>>> from upsetplot import from_contents
>>> contents = {'cat1': ['a', 'b', 'c'],
...             'cat2': ['b', 'd'],
...             'cat3': ['e']}
>>> from_contents(contents)  # doctest: +NORMALIZE_WHITESPACE
                  id
cat1  cat2  cat3
True  False False  a
      True  False  b
      False False  c
False True  False  d
      False True   e
>>> import pandas as pd
>>> contents = {'cat1': [0, 1, 2],
...             'cat2': [1, 3],
...             'cat3': [4]}
>>> data = pd.DataFrame({'favourite': ['green', 'red', 'red',
...                                    'yellow', 'blue']})
>>> from_contents(contents, data=data)  # doctest: +NORMALIZE_WHITESPACE
                   id favourite
cat1  cat2  cat3
True  False False   0     green
      True  False   1       red
      False False   2       red
False True  False   3    yellow
      False True    4      blue
upsetplot.from_memberships(memberships, data=None)[source]

Load data where each sample has a collection of category names

The output should be suitable for passing to UpSet or plot.

Parameters:
memberships : sequence of collections of strings

Each element corresponds to a data point, indicating the sets it is a member of. Each category is named by a string.

data : Series-like or DataFrame-like, optional

If given, the index of category memberships is attached to this data. It must have the same length as memberships. If not given, the series will contain the value 1.

Returns:
DataFrame or Series

data is returned with its index indicating category membership. It will be a Series if data is a Series or 1d numeric array. The index will have levels ordered by category names.

Examples

>>> from upsetplot import from_memberships
>>> from_memberships([
...     ['cat1', 'cat3'],
...     ['cat2', 'cat3'],
...     ['cat1'],
...     []
... ])  # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
cat1   cat2   cat3
True   False  True     1
False  True   True     1
True   False  False    1
False  False  False    1
Name: ones, dtype: ...
>>> # now with data:
>>> import numpy as np
>>> from_memberships([
...     ['cat1', 'cat3'],
...     ['cat2', 'cat3'],
...     ['cat1'],
...     []
... ], data=np.arange(12).reshape(4, 3))  # doctest: +NORMALIZE_WHITESPACE
                   0   1   2
cat1  cat2  cat3
True  False True   0   1   2
False True  True   3   4   5
True  False False  6   7   8
False False False  9  10  11
upsetplot.generate_counts(seed=0, n_samples=10000, n_categories=3)[source]

Generate artificial counts corresponding to set intersections

Parameters:
seed : int

A seed for randomisation

n_samples : int

Number of samples to generate statistics over

n_categories : int

Number of categories (named “cat0”, “cat1”, …) to generate

Returns:
Series

Counts indexed by boolean indicator mask for each category.

See also

generate_samples
Generates a DataFrame of samples that these counts are derived from.
upsetplot.generate_samples(seed=0, n_samples=10000, n_categories=3)[source]

Generate artificial samples assigned to set intersections

Parameters:
seed : int

A seed for randomisation

n_samples : int

Number of samples to generate

n_categories : int

Number of categories (named “cat0”, “cat1”, …) to generate

Returns:
DataFrame

Field ‘value’ is a weight or score for each element. Field ‘index’ is a unique id for each element. Index includes a boolean indicator mask for each category.

Note: Further fields may be added in future versions.

See also

generate_counts
Generates the counts for each subset of categories corresponding to these samples.