%global _empty_manifest_terminate_build 0 Name: python-vega-datasets Version: 0.9.0 Release: 1 Summary: A Python package for offline access to Vega datasets License: MIT URL: http://github.com/altair-viz/vega_datasets Source0: https://mirrors.nju.edu.cn/pypi/web/packages/8f/a0/ce608d9a5b82fce2ebaa2311136b1e1d1dc2807f501bbdfa56bd174fff76/vega_datasets-0.9.0.tar.gz BuildArch: noarch Requires: python3-pandas %description # vega_datasets [![build status](http://img.shields.io/travis/altair-viz/vega_datasets/master.svg?style=flat)](https://travis-ci.org/altair-viz/vega_datasets) [![github actions](https://github.com/altair-viz/vega_datasets/workflows/build/badge.svg)](https://github.com/altair-viz/vega_datasets/actions?query=workflow%3Abuild) [![github actions](https://github.com/altair-viz/vega_datasets/workflows/lint/badge.svg)](https://github.com/altair-viz/vega_datasets/actions?query=workflow%3Alint) [![code style black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) A Python package for offline access to [vega datasets](https://github.com/vega/vega-datasets). This package has several goals: - Provide straightforward access in Python to the datasets made available at [vega-datasets](https://github.com/vega/vega-datasets). - return the results in the form of a Pandas dataframe. - wherever dataset size and/or license constraints make it possible, bundle the dataset with the package so that datasets can be loaded in the absence of a web connection. Currently the package bundles a half-dozen datasets, and falls back to using HTTP requests for the others. ## Installation ``vega_datasets`` is compatible with Python 3.5 or newer. Install with: ``` $ pip install vega_datasets ``` ## Usage The main object in this library is ``data``: ```python >>> from vega_datasets import data ``` It contains attributes that access all available datasets, locally if available. For example, here is the well-known iris dataset: ```python >>> df = data.iris() >>> df.head() petalLength petalWidth sepalLength sepalWidth species 0 1.4 0.2 5.1 3.5 setosa 1 1.4 0.2 4.9 3.0 setosa 2 1.3 0.2 4.7 3.2 setosa 3 1.5 0.2 4.6 3.1 setosa 4 1.4 0.2 5.0 3.6 setosa ``` If you're curious about the source data, you can access the URL for any of the available datasets: ```python >>> data.iris.url 'https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/iris.json' ``` For datasets bundled with the package, you can also find their location on disk: ```python >>> data.iris.filepath '/lib/python3.6/site-packages/vega_datasets/data/iris.json' ``` ## Available Datasets To list all the available datsets, use ``list_datasets``: ```python >>> data.list_datasets() ['7zip', 'airports', 'anscombe', 'barley', 'birdstrikes', 'budget', 'budgets', 'burtin', 'cars', 'climate', 'co2-concentration', 'countries', 'crimea', 'disasters', 'driving', 'earthquakes', 'ffox', 'flare', 'flare-dependencies', 'flights-10k', 'flights-200k', 'flights-20k', 'flights-2k', 'flights-3m', 'flights-5k', 'flights-airport', 'gapminder', 'gapminder-health-income', 'gimp', 'github', 'graticule', 'income', 'iris', 'jobs', 'londonBoroughs', 'londonCentroids', 'londonTubeLines', 'lookup_groups', 'lookup_people', 'miserables', 'monarchs', 'movies', 'normal-2d', 'obesity', 'points', 'population', 'population_engineers_hurricanes', 'seattle-temps', 'seattle-weather', 'sf-temps', 'sp500', 'stocks', 'udistrict', 'unemployment', 'unemployment-across-industries', 'us-10m', 'us-employment', 'us-state-capitals', 'weather', 'weball26', 'wheat', 'world-110m', 'zipcodes'] ``` To list local datasets (i.e. those that are bundled with the package and can be used without a web connection), use the ``local_data`` object instead: ```python >>> from vega_datasets import local_data >>> local_data.list_datasets() ['airports', 'anscombe', 'barley', 'burtin', 'cars', 'crimea', 'driving', 'iowa-electricity', 'iris', 'seattle-temps', 'seattle-weather', 'sf-temps', 'stocks', 'us-employment', "wheat"] ``` We plan to add more local datasets in the future, subject to size and licensing constraints. See the [local datasets issue](https://github.com/altair-viz/vega_datasets/issues/1) if you would like to help with this. ## Dataset Information If you want more information about any dataset, you can use the ``description`` property: ```python >>> data.iris.description 'This classic dataset contains lengths and widths of petals and sepals for 150 iris flowers, drawn from three species. It was introduced by R.A. Fisher in 1936 [1]_.' ``` This information is also part of the ``data.iris`` doc string. Descriptions are not yet included for all the datasets in the package; we hope to add more information on this in the future. %package -n python3-vega-datasets Summary: A Python package for offline access to Vega datasets Provides: python-vega-datasets BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-vega-datasets # vega_datasets [![build status](http://img.shields.io/travis/altair-viz/vega_datasets/master.svg?style=flat)](https://travis-ci.org/altair-viz/vega_datasets) [![github actions](https://github.com/altair-viz/vega_datasets/workflows/build/badge.svg)](https://github.com/altair-viz/vega_datasets/actions?query=workflow%3Abuild) [![github actions](https://github.com/altair-viz/vega_datasets/workflows/lint/badge.svg)](https://github.com/altair-viz/vega_datasets/actions?query=workflow%3Alint) [![code style black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) A Python package for offline access to [vega datasets](https://github.com/vega/vega-datasets). This package has several goals: - Provide straightforward access in Python to the datasets made available at [vega-datasets](https://github.com/vega/vega-datasets). - return the results in the form of a Pandas dataframe. - wherever dataset size and/or license constraints make it possible, bundle the dataset with the package so that datasets can be loaded in the absence of a web connection. Currently the package bundles a half-dozen datasets, and falls back to using HTTP requests for the others. ## Installation ``vega_datasets`` is compatible with Python 3.5 or newer. Install with: ``` $ pip install vega_datasets ``` ## Usage The main object in this library is ``data``: ```python >>> from vega_datasets import data ``` It contains attributes that access all available datasets, locally if available. For example, here is the well-known iris dataset: ```python >>> df = data.iris() >>> df.head() petalLength petalWidth sepalLength sepalWidth species 0 1.4 0.2 5.1 3.5 setosa 1 1.4 0.2 4.9 3.0 setosa 2 1.3 0.2 4.7 3.2 setosa 3 1.5 0.2 4.6 3.1 setosa 4 1.4 0.2 5.0 3.6 setosa ``` If you're curious about the source data, you can access the URL for any of the available datasets: ```python >>> data.iris.url 'https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/iris.json' ``` For datasets bundled with the package, you can also find their location on disk: ```python >>> data.iris.filepath '/lib/python3.6/site-packages/vega_datasets/data/iris.json' ``` ## Available Datasets To list all the available datsets, use ``list_datasets``: ```python >>> data.list_datasets() ['7zip', 'airports', 'anscombe', 'barley', 'birdstrikes', 'budget', 'budgets', 'burtin', 'cars', 'climate', 'co2-concentration', 'countries', 'crimea', 'disasters', 'driving', 'earthquakes', 'ffox', 'flare', 'flare-dependencies', 'flights-10k', 'flights-200k', 'flights-20k', 'flights-2k', 'flights-3m', 'flights-5k', 'flights-airport', 'gapminder', 'gapminder-health-income', 'gimp', 'github', 'graticule', 'income', 'iris', 'jobs', 'londonBoroughs', 'londonCentroids', 'londonTubeLines', 'lookup_groups', 'lookup_people', 'miserables', 'monarchs', 'movies', 'normal-2d', 'obesity', 'points', 'population', 'population_engineers_hurricanes', 'seattle-temps', 'seattle-weather', 'sf-temps', 'sp500', 'stocks', 'udistrict', 'unemployment', 'unemployment-across-industries', 'us-10m', 'us-employment', 'us-state-capitals', 'weather', 'weball26', 'wheat', 'world-110m', 'zipcodes'] ``` To list local datasets (i.e. those that are bundled with the package and can be used without a web connection), use the ``local_data`` object instead: ```python >>> from vega_datasets import local_data >>> local_data.list_datasets() ['airports', 'anscombe', 'barley', 'burtin', 'cars', 'crimea', 'driving', 'iowa-electricity', 'iris', 'seattle-temps', 'seattle-weather', 'sf-temps', 'stocks', 'us-employment', "wheat"] ``` We plan to add more local datasets in the future, subject to size and licensing constraints. See the [local datasets issue](https://github.com/altair-viz/vega_datasets/issues/1) if you would like to help with this. ## Dataset Information If you want more information about any dataset, you can use the ``description`` property: ```python >>> data.iris.description 'This classic dataset contains lengths and widths of petals and sepals for 150 iris flowers, drawn from three species. It was introduced by R.A. Fisher in 1936 [1]_.' ``` This information is also part of the ``data.iris`` doc string. Descriptions are not yet included for all the datasets in the package; we hope to add more information on this in the future. %package help Summary: Development documents and examples for vega-datasets Provides: python3-vega-datasets-doc %description help # vega_datasets [![build status](http://img.shields.io/travis/altair-viz/vega_datasets/master.svg?style=flat)](https://travis-ci.org/altair-viz/vega_datasets) [![github actions](https://github.com/altair-viz/vega_datasets/workflows/build/badge.svg)](https://github.com/altair-viz/vega_datasets/actions?query=workflow%3Abuild) [![github actions](https://github.com/altair-viz/vega_datasets/workflows/lint/badge.svg)](https://github.com/altair-viz/vega_datasets/actions?query=workflow%3Alint) [![code style black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) A Python package for offline access to [vega datasets](https://github.com/vega/vega-datasets). This package has several goals: - Provide straightforward access in Python to the datasets made available at [vega-datasets](https://github.com/vega/vega-datasets). - return the results in the form of a Pandas dataframe. - wherever dataset size and/or license constraints make it possible, bundle the dataset with the package so that datasets can be loaded in the absence of a web connection. Currently the package bundles a half-dozen datasets, and falls back to using HTTP requests for the others. ## Installation ``vega_datasets`` is compatible with Python 3.5 or newer. Install with: ``` $ pip install vega_datasets ``` ## Usage The main object in this library is ``data``: ```python >>> from vega_datasets import data ``` It contains attributes that access all available datasets, locally if available. For example, here is the well-known iris dataset: ```python >>> df = data.iris() >>> df.head() petalLength petalWidth sepalLength sepalWidth species 0 1.4 0.2 5.1 3.5 setosa 1 1.4 0.2 4.9 3.0 setosa 2 1.3 0.2 4.7 3.2 setosa 3 1.5 0.2 4.6 3.1 setosa 4 1.4 0.2 5.0 3.6 setosa ``` If you're curious about the source data, you can access the URL for any of the available datasets: ```python >>> data.iris.url 'https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/iris.json' ``` For datasets bundled with the package, you can also find their location on disk: ```python >>> data.iris.filepath '/lib/python3.6/site-packages/vega_datasets/data/iris.json' ``` ## Available Datasets To list all the available datsets, use ``list_datasets``: ```python >>> data.list_datasets() ['7zip', 'airports', 'anscombe', 'barley', 'birdstrikes', 'budget', 'budgets', 'burtin', 'cars', 'climate', 'co2-concentration', 'countries', 'crimea', 'disasters', 'driving', 'earthquakes', 'ffox', 'flare', 'flare-dependencies', 'flights-10k', 'flights-200k', 'flights-20k', 'flights-2k', 'flights-3m', 'flights-5k', 'flights-airport', 'gapminder', 'gapminder-health-income', 'gimp', 'github', 'graticule', 'income', 'iris', 'jobs', 'londonBoroughs', 'londonCentroids', 'londonTubeLines', 'lookup_groups', 'lookup_people', 'miserables', 'monarchs', 'movies', 'normal-2d', 'obesity', 'points', 'population', 'population_engineers_hurricanes', 'seattle-temps', 'seattle-weather', 'sf-temps', 'sp500', 'stocks', 'udistrict', 'unemployment', 'unemployment-across-industries', 'us-10m', 'us-employment', 'us-state-capitals', 'weather', 'weball26', 'wheat', 'world-110m', 'zipcodes'] ``` To list local datasets (i.e. those that are bundled with the package and can be used without a web connection), use the ``local_data`` object instead: ```python >>> from vega_datasets import local_data >>> local_data.list_datasets() ['airports', 'anscombe', 'barley', 'burtin', 'cars', 'crimea', 'driving', 'iowa-electricity', 'iris', 'seattle-temps', 'seattle-weather', 'sf-temps', 'stocks', 'us-employment', "wheat"] ``` We plan to add more local datasets in the future, subject to size and licensing constraints. See the [local datasets issue](https://github.com/altair-viz/vega_datasets/issues/1) if you would like to help with this. ## Dataset Information If you want more information about any dataset, you can use the ``description`` property: ```python >>> data.iris.description 'This classic dataset contains lengths and widths of petals and sepals for 150 iris flowers, drawn from three species. It was introduced by R.A. Fisher in 1936 [1]_.' ``` This information is also part of the ``data.iris`` doc string. Descriptions are not yet included for all the datasets in the package; we hope to add more information on this in the future. %prep %autosetup -n vega-datasets-0.9.0 %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-vega-datasets -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Sun Apr 23 2023 Python_Bot - 0.9.0-1 - Package Spec generated