PyPI 官网下载 | altair-1.0.0rc4.zip

# Altair [](https://travis-ci.org/ellisonbg/altair) Altair is a declarative statistical visualization library for Python. [Altair Documentation](altair/notebooks/01-Index.ipynb) *Altair is developed by [Brian Granger](https://github.com/ellisonbg) and [Jake Vanderplas](https://github.com/jakevdp) in close collaboration with the [UW Interactive Data Lab](http://idl.cs.washington.edu/).* 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. Here is an example using Altair to quickly visualize and display a dataset with the native Vega-Lite renderer in the Jupyter Notebook: ```python from altair import Chart, load_dataset # load data as a pandas DataFrame cars = load_dataset('cars') Chart(cars).mark_point().encode( x='Horsepower', y='Miles_per_Gallon', color='Origin', ) ```  ## 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](http://vita.had.co.nz/papers/tidy-data.pdf) where the rows correspond to samples and the columns correspond the observed variables. * The data is mapped to the **visual properties** (position, color, size, shape, faceting, etc.) using the group-by operation of Pandas and SQL. 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. For convenience, Altair can optionally use [ipyvega](https://github.com/vega/ipyvega) to display client-side renderings seamlessly in the Jupyter notebook. ## 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, on GitHub and [nbviewer](http://nbviewer.jupyter.org/). * Export visualizations to PNG images, stand-alone HTML pages and the [Online Vega-Lite Editor](https://vega.github.io/vega-editor/?mode=vega-lite). * Serialize visualizations as JSON files. * Explore Altair with 40 example datasets and over 70 examples. ## Installation Altair can be installed with the following commands: ``` pip install altair jupyter nbextension install --sys-prefix --py vega ``` Or using conda: ``` conda install altair ``` ## Examples and tutorial For more information and examples of Altair's API, see the [Altair Documentation](altair/notebooks/01-Index.ipynb). To immediately download the Altair Documentation as runnable Jupyter notebooks, run the following code from a Jupyter Notebook: ```python from altair import tutorial tutorial() ``` ## Project philosophy Many excellent plotting libraries exist in Python, including the main ones: * [Matplotlib](http://matplotlib.org/) * [Bokeh](http://bokeh.pydata.org/en/latest/) * [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/#) * [Lightning](http://lightning-viz.org/) * [Plotly](https://plot.ly/) * [Pandas built-in plotting](http://pandas.pydata.org/pandas-docs/stable/visualization.html) * [HoloViews](http://ioam.github.io/holoviews/) * [VisPy](http://vispy.org/) 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 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 a 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](http://www.tableau.com/) and the [Interactive Data Lab's](http://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](http://pandas.pydata.org/) * [traitlets](https://github.com/ipython/traitlets) * [IPython](https://github.com/ipython/ipython) For visualization in the IPython/Jupyter notebook using the Vega-Lite renderer, Altair additionally requires * [Jupyter Notebook](https://jupyter.readthedocs.io/en/latest/install.html) * [ipyvega](https://github.com/vega/ipyvega) If you have cloned the repository, run the following command from the root of the repository: ``` pip install -e . ``` If you do not wish to clone the repository, you can install using: ``` pip install git+https://github.com/ellisonbg/altair ``` ## Testing To run the test suite you must have [py.test](http://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 We welcome any input, feedback, bug reports, and contributions via [Altair's GitHub Repository](http://github.com/ellisonbg/altair/). In particular, we welcome companion efforts from other visualization libraries to render the Vega-Lite specifications output by Altair. We see this portion of the effort as much bigger than Altair itself: the Vega and Vega-Lite specifications are perhaps the best existing candidates for a principled *lingua franca* of data visualization. ## Whence Altair? Altair is the [brightest star](https://en.wikipedia.org/wiki/Altair) in the constellation Aquil