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开源软件名称(OpenSource Name):altair-viz/altair开源软件地址(OpenSource Url):https://github.com/altair-viz/altair开源编程语言(OpenSource Language):Python 99.9%开源软件介绍(OpenSource Introduction):AltairThe Vega-Altair open source project is not affiliated with Altair Engineering, Inc. 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 JSON specification. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code. Altair was originally developed by Jake Vanderplas and Brian Granger in close collaboration with the UW Interactive Data Lab. Altair DocumentationSee Altair's Documentation Site, as well as Altair's Tutorial Notebooks. ExampleHere is an example using Altair to quickly visualize and display a dataset with the native Vega-Lite renderer in the JupyterLab: 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',
) 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. 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_params(
brush
)
bars = alt.Chart(source).mark_bar().encode(
y='Origin',
color='Origin',
x='count(Origin)'
).transform_filter(
brush
)
points & bars Getting your Questions AnsweredIf you have a question that is not addressed in the documentation, there are several ways to ask:
We'll do our best to get your question answered A Python API for statistical visualizationsAltair provides a Python API for building statistical visualizations in a declarative manner. By statistical visualization we mean:
The Altair API contains no actual visualization rendering code but instead emits JSON data structures following the Vega-Lite specification. The resulting Vega-Lite JSON data can be rendered in the following user-interfaces:
Features
InstallationTo use Altair for visualization, you need to install two sets of tools
Altair can be installed with either Example and tutorial notebooksWe 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 or Colab, click on one of the following badges: Project philosophyMany excellent plotting libraries exist in Python, including the main ones: Each library does a particular set of things well. User challengesHowever, 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 and the Interactive Data Lab's Polestar and Voyager are excellent examples of such UIs. Design approach and solutionWe 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:
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 installAltair requires the following dependencies: If you have cloned the repository, run the following command from the root of the repository:
If you do not wish to clone the repository, you can install using:
TestingTo run the test suite you must have py.test installed. To run the tests, use
(you can omit the Feedback and ContributionSee Citing AltairIf you use Altair in academic work, please consider citing https://joss.theoj.org/papers/10.21105/joss.01057 as @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 project, which Altair is based on: https://dl.acm.org/doi/10.1109/TVCG.2016.2599030 @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 in the constellation Aquila, and along with Deneb and Vega forms the northern-hemisphere asterism known as the Summer Triangle. |
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