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author | noptuno <repollo.marrero@gmail.com> | 2023-04-28 02:29:30 +0200 |
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committer | noptuno <repollo.marrero@gmail.com> | 2023-04-28 02:29:30 +0200 |
commit | 355dee533bb34a571b9367820a63cccb668cf866 (patch) | |
tree | 838af886b4fec07320aeb10f0d1e74ba79e79b5c /venv/lib/python3.9/site-packages/altair-4.2.2.dist-info | |
parent | added pyproject.toml file (diff) | |
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diff --git a/venv/lib/python3.9/site-packages/altair-4.2.2.dist-info/INSTALLER b/venv/lib/python3.9/site-packages/altair-4.2.2.dist-info/INSTALLER new file mode 100644 index 00000000..a1b589e3 --- /dev/null +++ b/venv/lib/python3.9/site-packages/altair-4.2.2.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/venv/lib/python3.9/site-packages/altair-4.2.2.dist-info/LICENSE b/venv/lib/python3.9/site-packages/altair-4.2.2.dist-info/LICENSE new file mode 100644 index 00000000..d4706753 --- /dev/null +++ b/venv/lib/python3.9/site-packages/altair-4.2.2.dist-info/LICENSE @@ -0,0 +1,28 @@ +Copyright (c) 2015, Brian E. Granger and Jake Vanderplas +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of altair nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + diff --git a/venv/lib/python3.9/site-packages/altair-4.2.2.dist-info/METADATA b/venv/lib/python3.9/site-packages/altair-4.2.2.dist-info/METADATA new file mode 100644 index 00000000..e6798bf5 --- /dev/null +++ b/venv/lib/python3.9/site-packages/altair-4.2.2.dist-info/METADATA @@ -0,0 +1,320 @@ +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). diff --git a/venv/lib/python3.9/site-packages/altair-4.2.2.dist-info/RECORD b/venv/lib/python3.9/site-packages/altair-4.2.2.dist-info/RECORD new file mode 100644 index 00000000..fab971d5 --- /dev/null +++ b/venv/lib/python3.9/site-packages/altair-4.2.2.dist-info/RECORD @@ -0,0 +1,507 @@ +altair-4.2.2.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
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