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        "author": "Jake VanderPlas",
        "author_email": "jakevdp@gmail.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
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        "description": "# vega_datasets\n\n[![build status](http://img.shields.io/travis/altair-viz/vega_datasets/master.svg?style=flat)](https://travis-ci.org/altair-viz/vega_datasets)\n\nA Python package for offline access to [vega datasets](https://github.com/vega/vega-datasets).\n\nThis package has several goals:\n\n- Provide straightforward access in Python to the datasets made available at [vega-datasets](https://github.com/vega/vega-datasets).\n- return the results in the form of a Pandas dataframe.\n- 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.\n\nCurrently the package bundles a half-dozen datasets, and falls back to using HTTP requests for the others.\n\n## Installation\n\n```\n$ pip install vega_datasets\n```\n\n## Usage\n\nThe main object in this library is ``data``:\n\n```python\n>>> from vega_datasets import data\n```\n\nIt contains attributes that access all available datasets, locally if\navailable. For example, here is the well-known iris dataset:\n\n```python\n>>> df = data.iris()\n>>> df.head()\n   petalLength  petalWidth  sepalLength  sepalWidth species\n0          1.4         0.2          5.1         3.5  setosa\n1          1.4         0.2          4.9         3.0  setosa\n2          1.3         0.2          4.7         3.2  setosa\n3          1.5         0.2          4.6         3.1  setosa\n4          1.4         0.2          5.0         3.6  setosa\n```\n\nIf you're curious about the source data, you can access the URL for any of the available datasets:\n\n```python\n>>> data.iris.url\n'https://vega.github.io/vega-datasets/data/iris.json'\n```\n\nFor datasets bundled with the package, you can also find their location on disk:\n\n```python\n>>> data.iris.filepath\n'/lib/python3.6/site-packages/vega_datasets/data/iris.json'\n```\n\n## Available Datasets\n\nTo list all the available datsets, use ``list_datasets``:\n\n```python\n>>> data.list_datasets()\n['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']\n```\n\nTo 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:\n\n```python\n>>> from vega_datasets import local_data\n>>> local_data.list_datasets()\n\n['airports', 'anscombe', 'barley', 'burtin', 'cars', 'crimea', 'driving', 'iowa-electricity', 'iris', 'seattle-temps', 'seattle-weather', 'sf-temps', 'stocks', 'us-employment', \"wheat\"]\n```\n\nWe 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.\n\n## Dataset Information\n\nIf you want more information about any dataset, you can use the ``description`` property:\n\n```python\n>>> data.iris.description\n'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]_.'\n```\n\nThis information is also part of the ``data.iris`` doc string.\nDescriptions are not yet included for all the datasets in the package; we hope to add more information on this in the future.\n\n\n",
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