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+Metadata-Version: 2.1
+Name: altair
+Version: 4.2.2
+Summary: Altair: A declarative statistical visualization library for Python.
+Home-page: http://altair-viz.github.io
+Download-URL: http://github.com/altair-viz/altair/
+Author: Brian E. Granger / Jake VanderPlas
+Author-email: jakevdp@gmail.com
+License: BSD 3-clause
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Environment :: Console
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Requires-Python: >=3.7
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: entrypoints
+Requires-Dist: jinja2
+Requires-Dist: jsonschema (>=3.0)
+Requires-Dist: numpy
+Requires-Dist: pandas (>=0.18)
+Requires-Dist: toolz
+Provides-Extra: dev
+Requires-Dist: black ; extra == 'dev'
+Requires-Dist: docutils ; extra == 'dev'
+Requires-Dist: ipython ; extra == 'dev'
+Requires-Dist: flake8 ; extra == 'dev'
+Requires-Dist: pytest ; extra == 'dev'
+Requires-Dist: sphinx ; extra == 'dev'
+Requires-Dist: mistune (<2.0.0) ; extra == 'dev'
+Requires-Dist: m2r ; extra == 'dev'
+Requires-Dist: vega-datasets ; extra == 'dev'
+Requires-Dist: recommonmark ; extra == 'dev'
+
+# Altair <a href="https://altair-viz.github.io/"><img align="right" src="https://altair-viz.github.io/_static/altair-logo-light.png" height="50"></img></a>
+
+[![build status](https://img.shields.io/travis/altair-viz/altair/master.svg?style=flat)](https://travis-ci.org/altair-viz/altair)
+[![github actions](https://github.com/altair-viz/altair/workflows/build/badge.svg)](https://github.com/altair-viz/altair/actions?query=workflow%3Abuild)
+[![code style black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+[![JOSS Paper](https://joss.theoj.org/papers/10.21105/joss.01057/status.svg)](https://joss.theoj.org/papers/10.21105/joss.01057)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/altair)](https://pypi.org/project/altair)
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/altair-viz/altair_notebooks/master?urlpath=lab/tree/notebooks/Index.ipynb)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/altair-viz/altair_notebooks/blob/master/notebooks/Index.ipynb)
+
+[https://altair-viz.github.io](https://altair-viz.github.io)
+
+**Altair** is a declarative statistical visualization library for Python. With Altair, you can spend more time understanding your data and its meaning. Altair's
+API is simple, friendly and consistent and built on top of the powerful
+[Vega-Lite](https://github.com/vega/vega-lite) JSON specification. This elegant
+simplicity produces beautiful and effective visualizations with a minimal amount of code. *Altair is developed by [Jake Vanderplas](https://github.com/jakevdp) and [Brian
+Granger](https://github.com/ellisonbg) in close collaboration with the [UW
+Interactive Data Lab](https://idl.cs.washington.edu/).*
+
+## Altair Documentation
+
+See [Altair's Documentation Site](https://altair-viz.github.io),
+as well as Altair's [Tutorial Notebooks](https://github.com/altair-viz/altair_notebooks).
+
+## Example
+
+Here is an example using Altair to quickly visualize and display a dataset with the native Vega-Lite renderer in the JupyterLab:
+
+```python
+import altair as alt
+
+# load a simple dataset as a pandas DataFrame
+from vega_datasets import data
+cars = data.cars()
+
+alt.Chart(cars).mark_point().encode(
+    x='Horsepower',
+    y='Miles_per_Gallon',
+    color='Origin',
+)
+```
+
+![Altair Visualization](https://raw.githubusercontent.com/altair-viz/altair/master/images/cars.png)
+
+One of the unique features of Altair, inherited from Vega-Lite, is a declarative grammar of not just visualization, but _interaction_. 
+With a few modifications to the example above we can create a linked histogram that is filtered based on a selection of the scatter plot.
+
+```python 
+import altair as alt
+from vega_datasets import data
+
+source = data.cars()
+
+brush = alt.selection(type='interval')
+
+points = alt.Chart(source).mark_point().encode(
+    x='Horsepower',
+    y='Miles_per_Gallon',
+    color=alt.condition(brush, 'Origin', alt.value('lightgray'))
+).add_selection(
+    brush
+)
+
+bars = alt.Chart(source).mark_bar().encode(
+    y='Origin',
+    color='Origin',
+    x='count(Origin)'
+).transform_filter(
+    brush
+)
+
+points & bars
+```
+
+![Altair Visualization Gif](https://raw.githubusercontent.com/altair-viz/altair/master/images/cars_scatter_bar.gif)
+
+
+## Getting your Questions Answered
+
+If you have a question that is not addressed in the documentation, there are several ways to ask:
+
+- open a [Github Issue](https://github.com/altair-viz/altair/issues)
+- post a [StackOverflow Question](https://stackoverflow.com/questions/tagged/altair) (be sure to use the `altair` tag)
+- ask on the [Altair Google Group](https://groups.google.com/forum/#!forum/altair-viz)
+
+We'll do our best to get your question answered
+
+## A Python API for statistical visualizations
+
+Altair provides a Python API for building statistical visualizations in a declarative
+manner. By statistical visualization we mean:
+
+* The **data source** is a `DataFrame` that consists of columns of different data types (quantitative, ordinal, nominal and date/time).
+* The `DataFrame` is in a [tidy format](https://vita.had.co.nz/papers/tidy-data.pdf)
+  where the rows correspond to samples and the columns correspond to the observed variables.
+* The data is mapped to the **visual properties** (position, color, size, shape,
+  faceting, etc.) using the group-by data transformation.
+
+The Altair API contains no actual visualization rendering code but instead
+emits JSON data structures following the
+[Vega-Lite](https://github.com/vega/vega-lite) specification. The resulting
+Vega-Lite JSON data can be rendered in the following user-interfaces:
+
+* [Jupyter Notebook](https://github.com/jupyter/notebook) (by installing [ipyvega](https://github.com/vega/ipyvega)).
+* [JupyterLab](https://github.com/jupyterlab/jupyterlab) (no additional dependencies needed).
+* [nteract](https://github.com/nteract/nteract) (no additional dependencies needed).
+
+## Features
+
+* Carefully-designed, declarative Python API based on
+  [traitlets](https://github.com/ipython/traitlets).
+* Auto-generated internal Python API that guarantees visualizations are type-checked and
+  in full conformance with the [Vega-Lite](https://github.com/vega/vega-lite)
+  specification.
+* Auto-generate Altair Python code from a Vega-Lite JSON spec.
+* Display visualizations in the live Jupyter Notebook, JupyterLab, nteract, on GitHub and
+  [nbviewer](https://nbviewer.jupyter.org/).
+* Export visualizations to PNG/SVG images, stand-alone HTML pages and the
+[Online Vega-Lite Editor](https://vega.github.io/editor/#/).
+* Serialize visualizations as JSON files.
+* Explore Altair with dozens of examples in the [Example Gallery](https://altair-viz.github.io/gallery/index.html)
+
+## Installation
+
+To use Altair for visualization, you need to install two sets of tools
+
+1. The core Altair Package and its dependencies
+
+2. The renderer for the frontend you wish to use (i.e. `Jupyter Notebook`,
+   `JupyterLab`, or `nteract`)
+
+Altair can be installed with either ``pip`` or with ``conda``.
+For full installation instructions, please see
+https://altair-viz.github.io/getting_started/installation.html
+
+## Example and tutorial notebooks
+
+We maintain a separate Github repository of Jupyter Notebooks that contain an
+interactive tutorial and examples:
+
+https://github.com/altair-viz/altair_notebooks
+
+To launch a live notebook server with those notebook using [binder](https://mybinder.org/) or
+[Colab](https://colab.research.google.com), click on one of the following badges:
+
+[![Binder](https://beta.mybinder.org/badge.svg)](https://beta.mybinder.org/v2/gh/altair-viz/altair_notebooks/master)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/altair-viz/altair_notebooks/blob/master/notebooks/Index.ipynb)
+
+## Project philosophy
+
+Many excellent plotting libraries exist in Python, including the main ones:
+
+* [Matplotlib](https://matplotlib.org/)
+* [Bokeh](https://bokeh.pydata.org/en/latest/)
+* [Seaborn](https://seaborn.pydata.org/)
+* [Lightning](https://github.com/lightning-viz/lightning)
+* [Plotly](https://plot.ly/)
+* [Pandas built-in plotting](https://pandas.pydata.org/pandas-docs/stable/visualization.html)
+* [HoloViews](https://holoviews.org)
+* [VisPy](https://vispy.org/)
+* [pygg](https://www.github.com/sirrice/pygg)
+
+Each library does a particular set of things well.
+
+### User challenges
+
+However, such a proliferation of options creates great difficulty for users
+as they have to wade through all of these APIs to find which of them is the
+best for the task at hand. None of these libraries are optimized for
+high-level statistical visualization, so users have to assemble their own
+using a mishmash of APIs. For individuals just learning data science, this
+forces them to focus on learning APIs rather than exploring their data.
+
+Another challenge is current plotting APIs require the user to write code,
+even for incidental details of a visualization. This results in an unfortunate
+and unnecessary cognitive burden as the visualization type (histogram,
+scatterplot, etc.) can often be inferred using basic information such as the
+columns of interest and the data types of those columns.
+
+For example, if you are interested in the visualization of two numerical
+columns, a scatterplot is almost certainly a good starting point. If you add
+a categorical column to that, you probably want to encode that column using
+colors or facets. If inferring the visualization proves difficult at times, a
+simple user interface can construct a visualization without any coding.
+[Tableau](https://www.tableau.com/) and the [Interactive Data
+Lab's](https://idl.cs.washington.edu/)
+[Polestar](https://github.com/vega/polestar) and
+[Voyager](https://github.com/vega/voyager) are excellent examples of such UIs.
+
+### Design approach and solution
+
+We believe that these challenges can be addressed without the creation of yet
+another visualization library that has a programmatic API and built-in
+rendering. Altair's approach to building visualizations uses a layered design
+that leverages the full capabilities of existing visualization libraries:
+
+1. Create a constrained, simple Python API (Altair) that is purely declarative
+2. Use the API (Altair) to emit JSON output that follows the Vega-Lite spec
+3. Render that spec using existing visualization libraries
+
+This approach enables users to perform exploratory visualizations with a much
+simpler API initially, pick an appropriate renderer for their usage case, and
+then leverage the full capabilities of that renderer for more advanced plot
+customization.
+
+We realize that a declarative API will necessarily be limited compared to the
+full programmatic APIs of Matplotlib, Bokeh, etc. That is a deliberate design
+choice we feel is needed to simplify the user experience of exploratory
+visualization.
+
+## Development install
+
+Altair requires the following dependencies:
+
+* [pandas](https://pandas.pydata.org/)
+* [traitlets](https://github.com/ipython/traitlets)
+* [IPython](https://github.com/ipython/ipython)
+
+If you have cloned the repository, run the following command from the root of the repository:
+
+```
+pip install -e .[dev]
+```
+
+If you do not wish to clone the repository, you can install using:
+
+```
+pip install git+https://github.com/altair-viz/altair
+```
+
+## Testing
+
+To run the test suite you must have [py.test](https://pytest.org/latest/) installed.
+To run the tests, use
+
+```
+py.test --pyargs altair
+```
+(you can omit the `--pyargs` flag if you are running the tests from a source checkout).
+
+## Feedback and Contribution
+
+See [`CONTRIBUTING.md`](https://github.com/altair-viz/altair/blob/master/CONTRIBUTING.md)
+
+## Citing Altair
+
+[![JOSS Paper](https://joss.theoj.org/papers/10.21105/joss.01057/status.svg)](https://joss.theoj.org/papers/10.21105/joss.01057)
+
+If you use Altair in academic work, please consider citing https://joss.theoj.org/papers/10.21105/joss.01057 as
+
+```bib
+@article{VanderPlas2018,
+    doi = {10.21105/joss.01057},
+    url = {https://doi.org/10.21105/joss.01057},
+    year = {2018},
+    publisher = {The Open Journal},
+    volume = {3},
+    number = {32},
+    pages = {1057},
+    author = {Jacob VanderPlas and Brian Granger and Jeffrey Heer and Dominik Moritz and Kanit Wongsuphasawat and Arvind Satyanarayan and Eitan Lees and Ilia Timofeev and Ben Welsh and Scott Sievert},
+    title = {Altair: Interactive Statistical Visualizations for Python},
+    journal = {Journal of Open Source Software}
+}
+```
+Please additionally consider citing the [vega-lite](https://vega.github.io/vega-lite/) project, which Altair is based on: https://dl.acm.org/doi/10.1109/TVCG.2016.2599030
+```bib
+@article{Satyanarayan2017,
+    author={Satyanarayan, Arvind and Moritz, Dominik and Wongsuphasawat, Kanit and Heer, Jeffrey},
+    title={Vega-Lite: A Grammar of Interactive Graphics},
+    journal={IEEE transactions on visualization and computer graphics},
+    year={2017},
+    volume={23},
+    number={1},
+    pages={341-350},
+    publisher={IEEE}
+} 
+```
+
+## Whence Altair?
+
+Altair is the [brightest star](https://en.wikipedia.org/wiki/Altair) in the constellation Aquila, and along with Deneb and Vega forms the northern-hemisphere asterism known as the [Summer Triangle](https://en.wikipedia.org/wiki/Summer_Triangle).