bqplot:IPython/Jupyter notebooks绘图库
from GitHub - bqplot/bqplot: Plotting library for IPython/Jupyter notebooks
bqplot
is a 2-D visualization system for Jupyter, based on the constructs of the Grammar of Graphics.
Usage
In bqplot, every component of a plot is an interactive widget. This allows the user to integrate visualizations with other Jupyter interactive widgets to create integrated GUIs with a few lines of Python code.
Goals
- Provide a unified framework for 2-D visualizations with a pythonic API
- Provide a sensible API for adding user interactions (panning, zooming, selection, etc)
Two APIs are provided
Object Model
, which is inspired by the constructs of the Grammar of Graphics (figure, marks, axes, scales). This API is verbose but is fully customizablepyplot
, which is a context-based API similar to Matplotlib's pyplot.pyplot
provides sensible default choices for most parameters
Trying it online
To try out bqplot interactively in your web browser, just click on the binder link:
Dependencies
This package depends on the following packages:
ipywidgets
(version >=7.0.0, <8.0)traitlets
(version >=4.3.0, <5.0)traittypes
(Version >=0.2.1, <0.3)numpy
pandas
Installation
Using pip:
$ pip install bqplot
Using conda
$ conda install -c conda-forge bqplot
If you are using JupyterLab <=2:
$ jupyter labextension install @jupyter-widgets/jupyterlab-manager bqplot
Development installation
For a development installation (requires JupyterLab (version >= 3) and yarn):
$ git clone https://github.com/bqplot/bqplot.git
$ cd bqplot
$ pip install -e .
$ jupyter nbextension install --py --overwrite --symlink --sys-prefix bqplot
$ jupyter nbextension enable --py --sys-prefix bqplot
Note for developers: the --symlink
argument on Linux or OS X allows one to modify the JavaScript code in-place. This feature is not available with Windows.
For the experimental JupyterLab extension, install the Python package, make sure the Jupyter widgets extension is installed, and install the bqplot extension:
$ pip install "ipywidgets>=7.6"
$ jupyter labextension develop . --overwrite
Whenever you make a change of the JavaScript code, you will need to rebuild:
cd js
yarn run build
Then refreshing the JupyterLab/Jupyter Notebook is enough to reload the changes.
Running tests
You can install the dependencies necessary to run the tests with:
conda env update -f test-environment.yml
And run it with for Python tests:
pytest
And cd js
to run the JS tests with:
yarn run test
Every time you make a change on your tests it's necessary to rebuild the JS side:
yarn run build
Examples
Using the pyplot
API
Using the Object Model
API
Documentation
Full documentation is available at https://bqplot.readthedocs.io/