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%global _empty_manifest_terminate_build 0
Name: python-siuba
Version: 0.4.2
Release: 1
Summary: A package for quick, scrappy analyses with pandas and SQL
License: MIT
URL: https://github.com/machow/siuba
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/85/ac/6e5c4407971bd1a01846334d0a8a2bb103e24b0ac5955fea3abdc6c834a4/siuba-0.4.2.tar.gz
BuildArch: noarch
Requires: python3-pandas
Requires: python3-numpy
Requires: python3-SQLAlchemy
Requires: python3-PyYAML
Requires: python3-plotnine
Requires: python3-jupyter
Requires: python3-nbval
Requires: python3-sphinx
Requires: python3-nbsphinx
Requires: python3-jupytext
Requires: python3-gapminder
Requires: python3-pytest
Requires: python3-hypothesis
%description
*scrappy data analysis, with seamless support for pandas and SQL*
[](https://github.com/machow/siuba/actions?query=workflow%3ACI+branch%3Amain)
[](https://siuba.org)
[](https://mybinder.org/v2/gh/machow/siuba/master)
<img width="30%" align="right" src="./docs/siuba_small.svg">
siuba ([小巴](http://www.cantonese.sheik.co.uk/dictionary/words/9139/)) is a port of [dplyr](https://github.com/tidyverse/dplyr) and other R libraries. It supports a tabular data analysis workflow centered on 5 common actions:
* `select()` - keep certain columns of data.
* `filter()` - keep certain rows of data.
* `mutate()` - create or modify an existing column of data.
* `summarize()` - reduce one or more columns down to a single number.
* `arrange()` - reorder the rows of data.
These actions can be preceded by a `group_by()`, which causes them to be applied individually to grouped rows of data. Moreover, many SQL concepts, such as `distinct()`, `count()`, and joins are implemented.
Inputs to these functions can be a pandas `DataFrame` or SQL connection (currently postgres, redshift, or sqlite).
For more on the rationale behind tools like dplyr, see this [tidyverse paper](https://tidyverse.tidyverse.org/articles/paper.html).
For examples of siuba in action, see the [siuba guide](https://siuba.org/guide).
%package -n python3-siuba
Summary: A package for quick, scrappy analyses with pandas and SQL
Provides: python-siuba
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-siuba
*scrappy data analysis, with seamless support for pandas and SQL*
[](https://github.com/machow/siuba/actions?query=workflow%3ACI+branch%3Amain)
[](https://siuba.org)
[](https://mybinder.org/v2/gh/machow/siuba/master)
<img width="30%" align="right" src="./docs/siuba_small.svg">
siuba ([小巴](http://www.cantonese.sheik.co.uk/dictionary/words/9139/)) is a port of [dplyr](https://github.com/tidyverse/dplyr) and other R libraries. It supports a tabular data analysis workflow centered on 5 common actions:
* `select()` - keep certain columns of data.
* `filter()` - keep certain rows of data.
* `mutate()` - create or modify an existing column of data.
* `summarize()` - reduce one or more columns down to a single number.
* `arrange()` - reorder the rows of data.
These actions can be preceded by a `group_by()`, which causes them to be applied individually to grouped rows of data. Moreover, many SQL concepts, such as `distinct()`, `count()`, and joins are implemented.
Inputs to these functions can be a pandas `DataFrame` or SQL connection (currently postgres, redshift, or sqlite).
For more on the rationale behind tools like dplyr, see this [tidyverse paper](https://tidyverse.tidyverse.org/articles/paper.html).
For examples of siuba in action, see the [siuba guide](https://siuba.org/guide).
%package help
Summary: Development documents and examples for siuba
Provides: python3-siuba-doc
%description help
*scrappy data analysis, with seamless support for pandas and SQL*
[](https://github.com/machow/siuba/actions?query=workflow%3ACI+branch%3Amain)
[](https://siuba.org)
[](https://mybinder.org/v2/gh/machow/siuba/master)
<img width="30%" align="right" src="./docs/siuba_small.svg">
siuba ([小巴](http://www.cantonese.sheik.co.uk/dictionary/words/9139/)) is a port of [dplyr](https://github.com/tidyverse/dplyr) and other R libraries. It supports a tabular data analysis workflow centered on 5 common actions:
* `select()` - keep certain columns of data.
* `filter()` - keep certain rows of data.
* `mutate()` - create or modify an existing column of data.
* `summarize()` - reduce one or more columns down to a single number.
* `arrange()` - reorder the rows of data.
These actions can be preceded by a `group_by()`, which causes them to be applied individually to grouped rows of data. Moreover, many SQL concepts, such as `distinct()`, `count()`, and joins are implemented.
Inputs to these functions can be a pandas `DataFrame` or SQL connection (currently postgres, redshift, or sqlite).
For more on the rationale behind tools like dplyr, see this [tidyverse paper](https://tidyverse.tidyverse.org/articles/paper.html).
For examples of siuba in action, see the [siuba guide](https://siuba.org/guide).
%prep
%autosetup -n siuba-0.4.2
%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-siuba -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.2-1
- Package Spec generated
|
