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    "info": {
        "author": "Jake VanderPlas",
        "author_email": "jakevdp@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.7"
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        "description": "# altair-transform\n\nPython evaluation of Altair/Vega-Lite transforms.\n\n[![build status](http://img.shields.io/travis/altair-viz/altair-transform/master.svg?style=flat)](https://travis-ci.org/altair-viz/altair-transform)\n\n## Example\n\nThe Vega-Lite specification includes the ability to apply a\nwide range of transformations to input data within the chart\nspecification. As an example, here is a sliding window average\nof a Gaussian random walk, implemented in Altair:\n\n```python\nimport altair as alt\nimport numpy as np\nimport pandas as pd\n\nrand = np.random.RandomState(12345)\n\ndf = pd.DataFrame({\n    'x': np.arange(200),\n    'y': rand.randn(200).cumsum()\n})\n\npoints = alt.Chart(df).mark_point().encode(\n    x='x:Q',\n    y='y:Q'\n)\n\nline = alt.Chart(df).transform_window(\n    ymean='mean(y)',\n    sort=[alt.SortField('x')],\n    frame=[5, 5]\n).mark_line(color='red').encode(\n    x='x:Q',\n    y='ymean:Q'\n)\n\npoints + line\n```\n![Altair Visualization](images/random_walk.png)\n\nBecause the transform is encoded within the renderer, however, it\nis not easy from Altair to access the computed values.\n\nThis is where ``altair_transform`` comes in. It includes a (nearly)\ncomplete Python implementation of Vega-Lite's transform layer, so\nthat you can easily extract a pandas dataframe with the computed\nvalues shown in the chart:\n\n```python\nfrom altair_transform import extract_data\ndata = extract_data(line)\ndata.head()\n```\n<table border=\"0\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>x</th>\n      <th>y</th>\n      <th>ymean</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>-0.204708</td>\n      <td>0.457749</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0.274236</td>\n      <td>0.771093</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>-0.245203</td>\n      <td>1.041320</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>-0.800933</td>\n      <td>1.336943</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>1.164847</td>\n      <td>1.698085</td>\n    </tr>\n  </tbody>\n</table>\n\nFrom here, you can work with the transformed data directly\nin Python.\n\n\n",
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        "name": "altair-transform",
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