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|
%global _empty_manifest_terminate_build 0
Name: python-UpSetPlot
Version: 0.8.0
Release: 1
Summary: Draw Lex et al.'s UpSet plots with Pandas and Matplotlib
License: BSD-3-Clause
URL: https://upsetplot.readthedocs.io
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/25/62/f9ab73c23da63d77e8498253b043d03c65c259f4d0358309b37f56cdf5cd/UpSetPlot-0.8.0.tar.gz
BuildArch: noarch
%description
|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.
There are many ways to represent the categorisation of data, as covered in
our `Data Format Guide <https://upsetplot.readthedocs.io/en/stable/formats.html>`_.
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
following::
>>> 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
Then::
>>> from upsetplot import plot
>>> plot(example) # doctest: +SKIP
>>> from matplotlib import pyplot
>>> pyplot.show() # doctest: +SKIP
makes:
And you can save the image in various formats::
>>> pyplot.savefig("/path/to/myplot.pdf") # doctest: +SKIP
>>> pyplot.savefig("/path/to/myplot.png") # doctest: +SKIP
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 ``cat1``, ``cat2`` and ``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.
Rotation
We call the above plot style "horizontal" because the category intersections
are presented from left to right. `Vertical plots
<http://upsetplot.readthedocs.io/en/latest/auto_examples/plot_vertical.html>`__
are also supported!
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.
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 category
membership::
>>> from upsetplot import from_memberships
>>> example = from_memberships(
>>> 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
See also `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
missing value).
%package -n python3-UpSetPlot
Summary: Draw Lex et al.'s UpSet plots with Pandas and Matplotlib
Provides: python-UpSetPlot
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-UpSetPlot
|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.
There are many ways to represent the categorisation of data, as covered in
our `Data Format Guide <https://upsetplot.readthedocs.io/en/stable/formats.html>`_.
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
following::
>>> 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
Then::
>>> from upsetplot import plot
>>> plot(example) # doctest: +SKIP
>>> from matplotlib import pyplot
>>> pyplot.show() # doctest: +SKIP
makes:
And you can save the image in various formats::
>>> pyplot.savefig("/path/to/myplot.pdf") # doctest: +SKIP
>>> pyplot.savefig("/path/to/myplot.png") # doctest: +SKIP
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 ``cat1``, ``cat2`` and ``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.
Rotation
We call the above plot style "horizontal" because the category intersections
are presented from left to right. `Vertical plots
<http://upsetplot.readthedocs.io/en/latest/auto_examples/plot_vertical.html>`__
are also supported!
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.
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 category
membership::
>>> from upsetplot import from_memberships
>>> example = from_memberships(
>>> 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
See also `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
missing value).
%package help
Summary: Development documents and examples for UpSetPlot
Provides: python3-UpSetPlot-doc
%description help
|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.
There are many ways to represent the categorisation of data, as covered in
our `Data Format Guide <https://upsetplot.readthedocs.io/en/stable/formats.html>`_.
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
following::
>>> 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
Then::
>>> from upsetplot import plot
>>> plot(example) # doctest: +SKIP
>>> from matplotlib import pyplot
>>> pyplot.show() # doctest: +SKIP
makes:
And you can save the image in various formats::
>>> pyplot.savefig("/path/to/myplot.pdf") # doctest: +SKIP
>>> pyplot.savefig("/path/to/myplot.png") # doctest: +SKIP
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 ``cat1``, ``cat2`` and ``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.
Rotation
We call the above plot style "horizontal" because the category intersections
are presented from left to right. `Vertical plots
<http://upsetplot.readthedocs.io/en/latest/auto_examples/plot_vertical.html>`__
are also supported!
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.
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 category
membership::
>>> from upsetplot import from_memberships
>>> example = from_memberships(
>>> 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
See also `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
missing value).
%prep
%autosetup -n UpSetPlot-0.8.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-UpSetPlot -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.8.0-1
- Package Spec generated
|
