Repository: pyviz/pyviz.org Branch: master Commit: 927b110bca20 Files: 29 Total size: 110.3 KB Directory structure: gitextract_479j_ta9/ ├── .github/ │ └── workflows/ │ └── docs.yml ├── .gitignore ├── LICENSE.txt ├── README.md ├── anaconda-project-lock.yml ├── anaconda-project.yml ├── doc/ │ ├── _static/ │ │ └── custom.css │ ├── conf.py │ ├── dashboarding/ │ │ ├── index.md │ │ └── index.rst │ ├── high-level/ │ │ ├── index.md │ │ └── index.rst │ ├── index.md │ ├── index.rst │ ├── overviews/ │ │ ├── index.md │ │ └── index.rst │ ├── scivis/ │ │ ├── index.md │ │ └── index.rst │ ├── tools.md │ └── tutorials/ │ ├── index.md │ └── index.rst └── tools/ ├── README.md ├── build.py ├── build_cache.py ├── builtons.yml ├── conda_downloads.py ├── sponsors.yml ├── template.html └── tools.yml ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/workflows/docs.yml ================================================ name: docs on: push: branches: - master pull_request: branches: - '*' schedule: - cron: "0 8 * 1-12 1" # Every monday at 8 am workflow_dispatch: permissions: # To allow the workflow to push to the origin, when actions/checkout is used. contents: write jobs: pre_ci: runs-on: 'ubuntu-latest' steps: - uses: actions/checkout@v4 with: # required for PRs fetch-depth: 2 - name: Get commit message id: get_commit_message run: | if [[ '${{ github.event_name }}' == 'push' ]]; then echo "commit_message=$(git log --format=%B -n 1 HEAD)" >> $GITHUB_OUTPUT elif [[ '${{ github.event_name }}' == 'pull_request' ]]; then echo "commit_message=$(git log --format=%B -n 1 HEAD^2)" >> $GITHUB_OUTPUT fi outputs: commit_message: echo "${{ steps.get_commit_message.outputs.commit_message }}" build_docs: name: Documentation runs-on: 'ubuntu-latest' needs: pre_ci if: "contains(needs.pre_ci.outputs.commit_message, 'website_dev') || github.ref == 'refs/heads/master'" timeout-minutes: 120 defaults: run: shell: bash -l {0} steps: - uses: actions/checkout@v3 - uses: conda-incubator/setup-miniconda@v2 with: auto-activate-base: true activate-environment: "" miniconda-version: "latest" - name: conda setup run: | conda install anaconda-project anaconda-project prepare - name: Build cache run: | anaconda-project run build_cache git config user.name github-actions git config user.email github-actions@github.com mv ./doc/_static/cache ./tmp git fetch origin cache git checkout cache mv ./tmp/* ./doc/_static/cache git add -f ./doc/_static/cache ls ./doc/_static/cache git commit -m "adding cached badges" git push -f origin HEAD:cache - uses: actions/checkout@v3 with: clean: false - name: Build website run: | git checkout -b deploy-tmp git fetch origin cache # all cached badges are in this branch git checkout origin/cache -- ./doc/_static/cache anaconda-project run build_website - name: git status run: | git status git diff - name: Deploy main if: ${{ github.ref == 'refs/heads/master' }} uses: peaceiris/actions-gh-pages@v3 with: publish_dir: ./builtdocs cname: pyviz.org github_token: ${{ secrets.GITHUB_TOKEN }} force_orphan: true ================================================ FILE: .gitignore ================================================ # Byte-compiled / DLL / optimized files... __pycache__/ # OSX .DS_STORE # Jupyter notebook *.ipynb_checkpoints/ # nbsite output builtdocs/ doc/tools.rst doc/_static/cache/ # Site building temp output tools/pypi_invalid_badges.txt # pytest .pytest_cache # anaconda-project envs *~ ================================================ FILE: LICENSE.txt ================================================ Creative Commons Attribution 4.0 International Public License (CC-BY) By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution 4.0 International Public License ("Public License"). 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# Python tools for data visualization | | | | --- | --- | | Build Status | [![Build Status](https://github.com/pyviz/pyviz.org/actions/workflows/docs.yml/badge.svg)](https://github.com/pyviz/pyviz.org/actions) | | Website | [![gh-pages](https://img.shields.io/github/last-commit/pyviz/pyviz.org/gh-pages.svg)](https://github.com/pyviz/pyviz.org/tree/gh-pages) [![site](https://img.shields.io/website-up-down-green-red/https/pyviz.org.svg)](https://pyviz.org) | Source material to build [pyviz.org](https://pyviz.org). This site is owned by [NumFocus](https://numfocus.org) and is currently managed by Anaconda, Inc. for the community, but is open to everyone involved in Python data visualization; see [#2](https://github.com/pyviz/website/issues/2). ## Building pyviz.org Whenever a PR is merged, or a commit is pushed to master, a Github Actions job is triggered that builds pyviz.org. ## Building dev site To build the [dev site](https://pyviz-dev.github.io/pyviz.org), just push a commit containing the string: `website_dev`. This will start a job on Github Actions that when complete will deploy to the dev site. **NOTE:** This will work on any branch, so it is recommended that you use it to test builds on PRs, just try not to trample on other people's toes. ## Building website locally Install anaconda-project: ```bash conda install anaconda-project ``` Build the cached badges: ```bash anaconda-project build_cache ``` Build the website: ```bash anaconda-project build_website ``` View the website locally: ```bash python -m http.server 8000 ``` ## Adding a tool to the "All Tools" page See the [README](tools/README.md) in the tools directory for instructions on adding a tool to the "All Tools" page. ================================================ FILE: anaconda-project-lock.yml ================================================ # This is an Anaconda project lock file. # The lock file locks down exact versions of all your dependencies. # # In most cases, this file is automatically maintained by the `anaconda-project` command or GUI tools. # It's best to keep this file in revision control (such as git or svn). # The file is in YAML format, please see http://www.yaml.org/start.html for more. # # # Set to false to ignore locked versions. # locking_enabled: true # # A key goes in here for each env spec. # env_specs: default: locked: true env_spec_hash: 978f894d8eec02b2a98d51e5ac316d783e8c9e0e platforms: - linux-64 - osx-64 packages: unix: - aiobotocore=1.4.0=pyhd8ed1ab_0 - aioitertools=0.10.0=pyhd8ed1ab_0 - aiosignal=1.2.0=pyhd8ed1ab_0 - alabaster=0.7.12=py_0 - appdirs=1.4.4=pyh9f0ad1d_0 - argon2-cffi=21.3.0=pyhd8ed1ab_0 - async-timeout=4.0.2=pyhd8ed1ab_0 - asynctest=0.13.0=py_0 - attrs=21.4.0=pyhd8ed1ab_0 - babel=2.9.1=pyh44b312d_0 - backcall=0.2.0=pyh9f0ad1d_0 - backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0 - backports=1.1=pyhd3eb1b0_0 - beautifulsoup4=4.11.1=pyha770c72_0 - blas=2.114=openblas - bleach=5.0.0=pyhd8ed1ab_0 - botocore=1.20.106=pyhd8ed1ab_0 - charset-normalizer=2.0.12=pyhd8ed1ab_0 - cloudpickle=2.0.0=pyhd8ed1ab_0 - colorama=0.4.4=pyh9f0ad1d_0 - colorcet=2.0.6=py_0 - dask-core=2021.10.0=pyhd3eb1b0_0 - dask=2021.10.0=pyhd8ed1ab_0 - decorator=5.1.1=pyhd8ed1ab_0 - defusedxml=0.7.1=pyhd8ed1ab_0 - entrypoints=0.4=pyhd8ed1ab_0 - flit-core=3.7.1=pyhd8ed1ab_0 - fsspec=2021.7.0=pyhd8ed1ab_0 - heapdict=1.0.1=py_0 - idna=3.3=pyhd8ed1ab_0 - imagesize=1.3.0=pyhd8ed1ab_0 - importlib_resources=5.7.1=pyhd8ed1ab_0 - intake-parquet=0.2.3=py_0 - intake=0.6.3=pyhd8ed1ab_0 - ipython_genutils=0.2.0=py_1 - jinja2=3.0.1=pyhd8ed1ab_0 - jmespath=0.10.0=pyh9f0ad1d_0 - jsonschema=4.4.0=pyhd8ed1ab_0 - jupyter_client=7.1.2=pyhd8ed1ab_0 - jupyterlab_pygments=0.2.2=pyhd8ed1ab_0 - m2r2=0.3.1=pyhd8ed1ab_1 - markdown=3.3.4=pyhd8ed1ab_0 - matplotlib-inline=0.1.3=pyhd8ed1ab_0 - nbclient=0.6.0=pyhd8ed1ab_0 - nbconvert-core=6.5.0=pyhd8ed1ab_0 - nbconvert-pandoc=6.5.0=pyhd8ed1ab_0 - nbconvert=6.5.0=pyhd8ed1ab_0 - nbformat=5.3.0=pyhd8ed1ab_0 - nbsite=0.6.7=py_0 - nest-asyncio=1.5.5=pyhd8ed1ab_0 - packaging=21.3=pyhd8ed1ab_0 - pandocfilters=1.5.0=pyhd8ed1ab_0 - param=1.12.1=py_0 - parquet-cpp=1.5.1=2 - parso=0.8.3=pyhd8ed1ab_0 - partd=1.2.0=pyhd8ed1ab_0 - pexpect=4.8.0=pyh9f0ad1d_2 - pickleshare=0.7.5=py_1003 - pip=22.0.4=pyhd8ed1ab_0 - prometheus_client=0.14.1=pyhd8ed1ab_0 - prompt-toolkit=3.0.29=pyha770c72_0 - ptyprocess=0.7.0=pyhd3deb0d_0 - pycparser=2.21=pyhd8ed1ab_0 - pyct-core=0.4.8=py_0 - pyct=0.4.8=py_0 - pygments=2.11.2=pyhd8ed1ab_0 - pyopenssl=22.0.0=pyhd8ed1ab_0 - pyparsing=3.0.8=pyhd8ed1ab_0 - python-dateutil=2.8.2=pyhd8ed1ab_0 - python-fastjsonschema=2.15.3=pyhd8ed1ab_0 - pytz=2022.1=pyhd8ed1ab_0 - pyviz_comms=2.2.0=py_0 - requests=2.26.0=pyhd8ed1ab_1 - s3fs=2021.8.0=pyhd8ed1ab_0 - send2trash=1.8.0=pyhd8ed1ab_0 - six=1.16.0=pyh6c4a22f_0 - snowballstemmer=2.2.0=pyhd8ed1ab_0 - sortedcontainers=2.4.0=pyhd8ed1ab_0 - soupsieve=2.3.1=pyhd8ed1ab_0 - sphinx=4.5.0=pyh6c4a22f_0 - sphinxcontrib-applehelp=1.0.2=py_0 - sphinxcontrib-devhelp=1.0.2=py_0 - sphinxcontrib-htmlhelp=2.0.0=pyhd8ed1ab_0 - sphinxcontrib-jsmath=1.0.1=py_0 - sphinxcontrib-qthelp=1.0.3=py_0 - sphinxcontrib-serializinghtml=1.1.5=pyhd8ed1ab_2 - tblib=1.7.0=pyhd8ed1ab_0 - tinycss2=1.1.1=pyhd8ed1ab_0 - toolz=0.11.2=pyhd8ed1ab_0 - traitlets=5.1.1=pyhd8ed1ab_0 - typing-extensions=4.2.0=hd8ed1ab_0 - typing_extensions=4.2.0=pyha770c72_0 - urllib3=1.26.9=pyhd8ed1ab_0 - wcwidth=0.2.5=pyh9f0ad1d_2 - webencodings=0.5.1=py_1 - wheel=0.37.1=pyhd8ed1ab_0 - zict=2.1.0=pyhd8ed1ab_0 - zipp=3.8.0=pyhd8ed1ab_0 linux-64: - _libgcc_mutex=0.1=conda_forge - _openmp_mutex=4.5=1_llvm - abseil-cpp=20210324.2=h9c3ff4c_0 - aiohttp=3.8.1=py37h540881e_1 - argon2-cffi-bindings=21.2.0=py37h540881e_2 - arrow-cpp=6.0.1=py37hbd77c41_5_cpu - aws-c-auth=0.6.8=hadad3cd_1 - aws-c-cal=0.5.12=h70efedd_7 - aws-c-common=0.6.17=h7f98852_0 - aws-c-compression=0.2.14=h7c7754b_7 - aws-c-event-stream=0.2.7=hd2be095_32 - aws-c-http=0.6.10=h416565a_3 - aws-c-io=0.10.14=he836878_0 - aws-c-mqtt=0.7.10=h885097b_0 - aws-c-s3=0.1.29=h8d70ed6_0 - aws-c-sdkutils=0.1.1=h7c7754b_4 - aws-checksums=0.1.12=h7c7754b_6 - aws-crt-cpp=0.17.10=h6ab17b9_5 - aws-sdk-cpp=1.9.160=h36ff4c5_0 - blas-devel=3.9.0=14_linux64_openblas - bokeh=2.4.2=py37h89c1867_1 - bottleneck=1.3.4=py37hda87dfa_1 - brotlipy=0.7.0=py37h540881e_1004 - bzip2=1.0.8=h7f98852_4 - c-ares=1.18.1=h7f98852_0 - ca-certificates=2022.3.29=h06a4308_0 - certifi=2021.10.8=py37h89c1867_2 - cffi=1.15.0=py37hd667e15_1 - click=8.1.2=py37h89c1867_0 - cramjam=2.5.0=py37hfd0a3e1_0 - 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libgfortran=5.0.0=9_3_0_h6c81a4c_23 - liblapack=3.9.0=14_osx64_openblas - liblapacke=3.9.0=14_osx64_openblas - libnghttp2=1.47.0=h942079c_0 - libopenblas=0.3.20=openmp_hb3cd9ec_0 - libpng=1.6.37=h7cec526_2 - libprotobuf=3.19.4=hcf210ce_0 - libsodium=1.0.18=hbcb3906_1 - libssh2=1.10.0=h52ee1ee_2 - libthrift=0.15.0=h054ceb0_0 - libtiff=4.3.0=h17f2ce3_3 - libutf8proc=2.7.0=h0d85af4_0 - libwebp-base=1.2.2=h0d85af4_1 - libwebp=1.2.2=h28dabe5_0 - libxcb=1.13=h0d85af4_1004 - libzlib=1.2.11=h6c3fc93_1014 - llvm-openmp=13.0.1=hcb1a161_1 - locket=0.2.1=py37hecd8cb5_2 - lz4-c=1.9.3=he49afe7_1 - markupsafe=2.1.1=py37h69ee0a8_1 - mistune=0.8.4=py37h271585c_1005 - msgpack-python=1.0.3=py37h18621fa_1 - multidict=6.0.2=py37h69ee0a8_1 - ncurses=6.3=h96cf925_1 - notebook=6.4.0=py37hecd8cb5_0 - numexpr=2.8.1=py37h9c3cb84_0 - numpy=1.21.6=py37h345d48f_0 - openblas=0.3.20=openmp_h5ad848b_0 - openjpeg=2.4.0=h6e7aa92_1 - openssl=1.1.1n=h6c3fc93_0 - orc=1.7.1=h84518c8_1 - pandas=1.3.5=py37h743cdd8_0 - pandoc=2.18=h694c41f_0 - pillow=9.1.0=py37h2540ef4_2 - psutil=5.9.0=py37h69ee0a8_1 - pthread-stubs=0.4=hc929b4f_1001 - pyarrow=6.0.1=py37hd1ae41a_5_cpu - pyrsistent=0.18.1=py37h69ee0a8_1 - pysocks=1.7.1=py37hf985489_5 - python-snappy=0.6.0=py37h1f5a272_2 - python=3.7.11=h88f2d9e_0 - python_abi=3.7=2_cp37m - pyyaml=5.4.1=py37h271585c_1 - pyzmq=22.3.0=py37h8f778e5_1 - re2=2021.11.01=he49afe7_0 - readline=8.1.2=hca72f7f_1 - setuptools=62.1.0=py37hf985489_0 - snappy=1.1.9=he9d5cce_0 - sqlite=3.38.2=hb516253_0 - terminado=0.13.3=py37hf985489_1 - thrift=0.16.0=py37h0582d14_1 - tk=8.6.12=h5dbffcc_0 - tornado=5.1.1=py37h1de35cc_1000 - wrapt=1.14.0=py37h69ee0a8_1 - xorg-libxau=1.0.9=h35c211d_0 - xorg-libxdmcp=1.1.3=h35c211d_0 - xz=5.2.5=haf1e3a3_1 - yaml=0.2.5=h0d85af4_2 - yarl=1.7.2=py37h69ee0a8_2 - zeromq=4.3.4=he49afe7_1 - zlib=1.2.11=h6c3fc93_1014 - zstd=1.5.2=h582d3a0_0 ================================================ FILE: anaconda-project.yml ================================================ name: pyviz.org description: pyviz.org commands: build_cache: unix: | python tools/conda_downloads.py BADGE=stars python tools/build_cache.py BADGE=contributors python tools/build_cache.py BADGE=license python tools/build_cache.py BADGE=pypi_downloads python tools/build_cache.py build_website: unix: | python tools/build.py mv tools/index.rst doc/tools.rst nbsite generate-rst --org pyviz --project-name pyviz nbsite build --what=html --output=builtdocs channels: - defaults - pyviz - conda-forge packages: - python==3.7.11 - jinja2==3.0.1 - markdown==3.3.4 - nbsite==0.6.7 - pyyaml==5.4.1 - requests==2.26.0 - tornado==5.1.1 - m2r2==0.3.1 - colorcet==2.0.6 - fastparquet==0.7.1 - intake==0.6.3 - intake-parquet==0.2.3 - s3fs==2021.8.0 - python-snappy==0.6.0 platforms: - linux-64 - osx-64 ================================================ FILE: doc/_static/custom.css ================================================ div.body { max-width: 2000px; } iframe { -moz-transform: scale(0.25, 0.25); -webkit-transform: scale(0.25, 0.25); -o-transform: scale(0.25, 0.25); -ms-transform: scale(0.25, 0.25); transform: scale(0.25, 0.25); -moz-transform-origin: top left; -webkit-transform-origin: top left; -o-transform-origin: top left; -ms-transform-origin: top left; transform-origin: top left; width: 3100px; margin-right: -2500px; height: 1600px; margin-bottom: -1200px; } #tools-wrapper a { text-decoration:none; } #tools-wrapper .sponsor-logo, #tools-wrapper .builton-logo { max-height: 20px; } #tools-wrapper .empty-cell { text-align: center; } #tools-wrapper table img { max-width: fit-content; } ================================================ FILE: doc/conf.py ================================================ # noqa from nbsite.shared_conf import * project = u'PyViz' authors = u'PyViz authors' copyright = u' 2019, ' + authors description = 'How to solve visualization problems with Python tools.' version = release = '0.0.1' extensions.extend(['m2r2']) html_static_path += ['_static'] html_favicon = '_static/favicon.ico' html_theme_options = { 'logo': 'logo.png', 'logo_name': False, 'page_width': '90%', 'font_family': "Ubuntu, sans-serif", 'font_size': '0.9em', 'link': '#347ab4', 'link_hover': '#1c4669', 'extra_nav_links': { 'Github': 'https://github.com/pyviz/website', }, 'show_powered_by': False, } html_context.update({ 'PROJECT': project, 'DESCRIPTION': description, 'AUTHOR': authors, # WEBSITE_SERVER is optional for tests and local builds, but allows defining a canonical URL for search engines 'WEBSITE_SERVER': 'https://pyviz.org', }) ================================================ FILE: doc/dashboarding/index.md ================================================ # Dashboarding tools Just about any Python library can be used to create a "static" PNG, SVG, HTML, or other output that can be pasted into a presentation, sent in an email, published as a figure in a paper, and so on. Many people also want or need to create "live" Python-backed applications or dashboards that a user can interact with to explore or analyze some data. Python offers several libraries for this purpose. When PyViz.org was created, the four main tools designed specifically for web-based dashboarding in Python were: - [Dash](https://plot.ly/products/dash) (from [Plotly](https://plot.ly)); see the [blog post](https://medium.com/@plotlygraphs/introducing-dash-5ecf7191b503) - [Panel](https://panel.pyviz.org) (from [Anaconda](http://anaconda.com)); see the [blog post](https://medium.com/@philipp.jfr/panel-announcement-2107c2b15f52) - [Voila](https://github.com/QuantStack/voila) (from [QuantStack](http://quantstack.net)); see the [blog post](https://blog.jupyter.org/and-voil%C3%A0-f6a2c08a4a93); used with separate layout tools like [jupyter-flex](https://github.com/danielfrg/jupyter-flex) or templates like [voila-vuetify](https://github.com/voila-dashboards/voila-vuetify). - [Streamlit](https://www.streamlit.io); see the [blog post](https://towardsdatascience.com/coding-ml-tools-like-you-code-ml-models-ddba3357eace) Since then, dozens of other libraries have been created, all of which are listed at [pyviz.org/tools#dashboarding](https://pyviz.org/tools#dashboarding). Some of them are compared in these overview articles: - [A Survey of Python Frameworks](https://ploomber.io/blog/survey-python-frameworks/), 25 Sep 2024: Ellie Ko. Comparing Streamlit, Shiny for Python, Panel, Flask, Chainlit, Dash, Voila, and Gradio. - [Streamlit vs Dash vs Voilà vs Panel — Battle of The Python Dashboarding Giants](https://medium.datadriveninvestor.com/streamlit-vs-dash-vs-voil%C3%A0-vs-panel-battle-of-the-python-dashboarding-giants-177c40b9ea57) 30 Mar 2021: Stephen Kilcommins. Comparing Streamlit, Dash, Voilà, and Panel for dashboarding. Links to more detailed explorations for each library individually. - [Are Dashboards for Me?](https://towardsdatascience.com/are-dashboards-for-me-7f66502986b1) 7 Jul 2020: Dan Lester. Overview of Python and R dashboard tools, including Voila, ipywidgets, binder, Shiny, Dash, Streamlit, Bokeh, and Panel. There are also other tools that can be used for some aspects of dashboarding as well as many other tasks: - [Bokeh](http://bokeh.org) is a plotting library, a widget and app library, and a server for both plots and dashboards. [Panel](https://panel.pyviz.org) is built on Bokeh, providing a higher-level toolkit specifically focused on app and dashboard creation and supporting multiple plotting libraries (not just Bokeh). - [ipywidgets](https://ipywidgets.readthedocs.io) provides a wide array of Jupyter-compatible widgets and an interface supported by many Python libraries, but sharing as a dashboard requires a separate deployable server like [Voila](https://github.com/QuantStack/voila). - [matplotlib](http://matplotlib.org) supports many different backends, including several native GUI toolkit interfaces such as Qt that can be used for building arbitrarily complex native applications that can be used instead of a web-based dashboard like those above. - [Bowtie](https://github.com/jwkvam/bowtie) (from Jacques Kvam) allows users to build dashboards in pure Python. - [flask](http://flask.pocoo.org/) is a Python-backed web server that can be used to build arbitrary web sites, including those with Python plots that then function as [flask dashboards](https://pusher.com/tutorials/live-dashboard-python), but is not specifically set up to make dashboarding easier. ================================================ FILE: doc/dashboarding/index.rst ================================================ .. mdinclude:: index.md .. toctree:: :titlesonly: :hidden: :maxdepth: 2 Dash Panel Voila ================================================ FILE: doc/high-level/index.md ================================================ # High-level tools The full list of [Python viz tools](../tools.html) is very long and covers a wide range of functionality. Many users share similar needs, and can get very far using a high-level tool that covers the most common tasks succinctly and conveniently, typically by providing a simpler API on top of an existing plotting tool. ## Pandas .plot() API The longest-established of these tools is the [Pandas .plot() API](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html). This basic plotting interface uses [Matplotlib](http://matplotlib.org) to render static PNGs in a Jupyter notebook or for exporting from Python, with a command that can be as simple as `df.plot()` for a DataFrame with two columns. The Pandas .plot() API has emerged as a de-facto standard for high-level plotting APIs in Python, and is now supported by many different libraries that use other underlying plotting engines to provide additional power and flexibility. Thus learning this API allows you to access capabilities provided by a wide variety of underlying tools, with relatively little additional effort. The libraries currently supporting this API include: - [Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html) -- Matplotlib-based API included with Pandas. Static PNG output in Jupyter notebooks. - [xarray](https://xarray.pydata.org/en/stable/plotting.html) -- Matplotlib-based API included with xarray, based on pandas .plot API. Static PNG output in Jupyter notebooks. - [hvPlot](https://hvplot.pyviz.org) -- HoloViews and Bokeh-based interactive plots for Pandas, GeoPandas, xarray, Dask, Intake, and Streamz data. - [Pandas Bokeh](https://github.com/PatrikHlobil/Pandas-Bokeh) -- Bokeh-based interactive plots, for Pandas, GeoPandas, and PySpark data. - [Cufflinks](https://github.com/santosjorge/cufflinks) -- Plotly-based interactive plots for Pandas data. - [PdVega](https://altair-viz.github.io/pdvega) -- Vega-lite-based, JSON-encoded interactive plots for Pandas data. ## Other high-level APIs - [Seaborn](https://seaborn.pydata.org) -- Matplotlib-based high-level interface for drawing statistical graphics. - [Altair](https://altair-viz.github.io/) -- Declarative Vega-lite-based interactive plots. - [HoloViews](https://holoviews.org) -- Declarative Bokeh, Matplotlib, or Plotly-based interactive plots for tidy data. - [Chartify](https://github.com/spotify/chartify) -- Bokeh-based interactive plots for tidy data. - [Plotly Express](https://www.plotly.express/) -- Plotly-based interactive plots. ================================================ FILE: doc/high-level/index.rst ================================================ .. mdinclude:: index.md .. toctree:: :titlesonly: :hidden: :maxdepth: 2 Pandas .plot xarray .plot hvPlot Pandas Bokeh Cufflinks PdVega Seaborn Altair HoloViews Chartify Plotly Express ================================================ FILE: doc/index.md ================================================ # Python tools for data visualization Welcome to PyViz! The PyViz.org website is an open platform for helping users decide on the best open-source (OSS) Python data visualization tools for their purposes, with links, overviews, comparisons, and examples. Contents: - [Overviews](overviews/index.html) of the OSS visualization packages available in Python, how they relate to each other, and the core concepts that underlie them. - [High-level tools](high-level/index.html) for getting started with Python viz, creating powerful plots in just a few lines of code. - [All tools](tools.html) available for doing viz in Python OSS, as a live table for comparing maturity, popularity, and support. - [Dashboarding](dashboarding/index.html) tools for sharing live Python-backed visualizations. - [SciVis](scivis/index.html) tools for rendering data embedded in three-dimensional space. - [Tutorials](tutorials/index.html) showing how to use the available tools to accomplish various categories of tasks. - [Topic examples](https://examples.pyviz.org) of using Python viz tools to analyze or describe specific datasets in a particular domain or field of research. ## This site If you are a part of the Python data visualization landscape, then this is _your_ site! All content has been contributed by individual library authors and users, and you could be next! Please open an issue or PR on [this GitHub repo](https://github.com/pyviz/website) to start a conversation. The goal is to make everyone doing viz in Python more productive, have more power, and make a greater impact from their work. **NOTE:** The contents of PyViz.org from June 2019 or earlier, focusing on Datashader, HoloViews, GeoViews, Panel, Param, and hvPlot, are now at [HoloViz.org](https://holoviz.org). PyViz.org is now a fully open guide to all Python visualization tools. If you are looking for Brian Thomas's PyViz smart-home visualization tool, check out his `paper `_. ================================================ FILE: doc/index.rst ================================================ .. mdinclude:: index.md .. toctree:: :titlesonly: :hidden: :maxdepth: 2 Home Overviews High-level tools All tools Dashboarding SciVis Tutorials ================================================ FILE: doc/overviews/index.md ================================================ # Overviews The Python visualization landscape can seem daunting at first. These overviews attempt to shine light on common patterns and use cases, comparing or discussing multiple plotting libraries. Note that some of the projects discussed in the overviews are no longer maintained, so be sure to check the list of [dormant projects](../tools.html#dormant-projects) before choosing that library. Adaptation of Jake VanderPlas' graphic about the Python visualization landscape, by Nicolas P. Rougier - [PyViz: Data Visualization in Python](https://docs.google.com/presentation/d/19PWM-9oKkcKlVHycVg2kBHK84UEA55Df2yi5lFQ_7PQ/), 18 Apr 2026: James A. Bednar. Brief survey of PyViz.org as slides, with examples of each category. - [7 Python Libraries That Make Visualization Beautiful](https://medium.com/@abdur.rahman12/7-python-libraries-that-make-visualization-beautiful-3d2ffb308611), 22 Sep 2025: Abdur Rahman. Brief overview of PyWaffle, Plotnine, Datashader, JoyPy, Sankeyview, PyCirclize, and Weave. - [10 Python Libraries That Build Dashboards in Minutes](https://medium.com/@abdur.rahman12/10-python-libraries-that-build-dashboards-in-minutes-f1b6724946fa), 25 Dec 2025: Abdur Rahman. Brief overview of Streamlit, Dash, Panel, Plotly, Bokeh, Voilà, Gradio, Altair, NiceGUI, and Flask + HTMX. - [I built the same dashboard 8 times | Which Python framework was best?](https://www.youtube.com/watch?v=k_fWYqCBUCE), 24 Aug 2025: Fanilo Andrianasolo. 1-hour video comparing Streamlit, Gradio, Panel, Dash, Shiny, Solara, NiceGUI and Reflex for building web applications in Python. - [Practical Python Dashboards: The Best 5 Frameworks For Interactive Maps](https://medium.com/data-science-collective/practical-python-dashboards-the-best-5-frameworks-for-interactive-maps-0834ca7f0637), 12 Jun 2025: John Loewen. Comparing Streamlit, Dash, Shiny, Voila, and Panel for plotting choropleth maps. - [The Best Python Dashboard Tools: Comparative Analysis With Practical Examples](https://medium.com/data-storytelling-corner/the-best-python-dashboard-tools-comparative-analysis-with-practical-examples-759636cc48ef), 11 Jun 2025: John Loewen. Comparing Streamlit, Dash, Shiny, Voila, and Panel. - [Python Packages for Data Visualization in 2025](https://python.plainenglish.io/python-packages-for-data-visualization-in-2025-9cb2132c9a7e), 27 January 2025: Zlatan B. Comparing Matplotlib, Datashader, Seaborn, Plotnine, Altair, hvPlot, HoloViews, Bokeh, Plotly, and PyVista. - [Matplotlib Alternatives That Actually Save You Time](https://nathanrosidi.medium.com/matplotlib-alternatives-that-actually-save-you-time-75631616cc4e), 13 May 2025: Nathan Rosidi. Comparing Plotly, Seaborn, Bokeh, Altair, and Plotnine as Matplotlib alternatives. - [Which Python Dashboard Is Better? Dash, Panel And Streamlit Showdown](https://pub.towardsai.net/which-python-dashboard-is-better-dash-panel-and-streamlit-showdown-8d4f8bf744f9), 5 Feb 2025: John Loewen. Comparing Plotly, Dash, and Streamlit generation from LLMs. - [The Essential Guide to R and Python Libraries for Data Visualization](https://towardsdatascience.com/the-essential-guide-to-r-and-python-libraries-for-data-visualization-33be8511c976), 16 Dec 2024: Sarah Lea. Comparing Matplotlib, Seaborn, Plotly, Pandas .plot(), Bokeh, Altair, HoloViews, and Folium. - [A Survey of Python Frameworks](https://ploomber.io/blog/survey-python-frameworks/), 25 Sep 2024: Ellie Ko. Comparing Streamlit, Shiny for Python, Panel, Flask, Chainlit, Dash, Voila, and Gradio. - I bet you didn’t use these Python visualization libraries!, 10-14 Sep 2024: Abhinaba Banerjee. Comparing [Altair and Plotnine (part 1)](https://python.plainenglish.io/i-bet-you-didnt-use-these-python-visualization-libraries-9da9531a1855), [Datashader, Pygal, and Geoplot (part 2)](https://python.plainenglish.io/i-bet-you-didnt-use-these-python-visualization-libraries-part-2-1f201a0a0547), VisPy, and typlot (part 3). - [The Power of Pandas Plots: Backends](https://towardsdatascience.com/the-power-of-pandas-plots-backends-6a08d52071d2), 29 Aug 2024: Pierre-Etienne Toulemonde. Comparing matplotlib, plotly, and hvPlot for plotting with Pandas. - [7 Best Python Libraries For Data Visualisation](https://medium.com/@inverita/7-best-python-libraries-for-data-visualisation-517020f725a4), 25 Jan 2024: inVerita. Comparing Matplotlib, Seaborn, Plotly, Bokeh, Altair, and HoloViews. - [Top-5 Python Frontend Libraries for Data Science, part 2](https://python.plainenglish.io/top-5-python-frontend-libraries-for-data-science-part-2-4d07a48d2fde), 31 Mar 2024: Artem Shelamanov. Comparing Voila, PyWebIO, Gradio, Panel, and Dash. - [Top-5 Python Frontend Libraries for Data Science, part 1](https://python.plainenglish.io/top-5-python-frontend-libraries-for-data-science-91261a65e366), 24 Dec 2023: Artem Shelamanov. Comparing Streamlit, Solara, Trame, ReactPy, and PyQt. - [Declarative vs. Imperative Plotting: An overview for Python beginners](https://towardsdatascience.com/declarative-vs-imperative-plotting-3ee9952d6bf3), 9 January 2024: Lee Vaughan. Comparing Matplotlib, Seaborn, Plotly Express, and hvPlot/HoloViews. - [Is Matplotlib Still the Best Python Library for Static Plots?](https://towardsdatascience.com/is-matplotlib-still-the-best-python-library-for-static-plots-a933c092cd49), 19 January 2024: Mike Clayton. Comparing Matplotlib, Seaborn, plotnine, Altair, and Plotly. - [Top-5 Python Frontend Libraries for Data Science](https://python.plainenglish.io/top-5-python-frontend-libraries-for-data-science-91261a65e366), 24 December 2023: Artem Shelamanov. Comparing Streamlit, Solara, Trame, ReactPy, and PyQt. - [Python on the Web](https://towardsdatascience.com/python-on-the-web-b819a6a55ec7), 11 October 2023: Pier Paolo Ippolito. Comparing Panel, Shiny for Python, and PyScript. - [Data Visualization with Streamlit, Dash, and Panel. Part 1](https://sunscrapers.com/blog/data-viz-streamlit-dash-panel-part-1) and [Part 2](https://sunscrapers.com/blog/streamlit-dash-panel-features-part-2), 20 September 2023: Patryk Młynarek. Comparing Panel, Dash, and Streamlit. - [Low Code With Dash, Streamlit, and Panel](https://betterprogramming.pub/technical-encounter-low-code-with-dash-streamlit-and-panel-part-1-364cf67f8b71), 9 July 2023: Petrica Leuca. Comparing Dash, Streamlit, and Panel. Separate followups focus individually on [Dash](https://medium.com/better-programming/technical-encounter-low-code-with-dash-43c6a4f2da5c), [Streamlit](https://medium.com/better-programming/technical-encounter-low-code-with-streamlit-9e3f730c0cd), and [Panel](https://medium.com/@petrica.leuca/technical-encounter-low-code-with-panel-7757d6a00876). - [Interactive Dashboards in Python 2023](https://medium.com/@marktopacio/interactive-dashboards-in-python-2023-7d6cd4bda40c), 8 July 2023: Mark Topacio. Comparing Streamlit, Solara, Dash, Datasette, and Shiny for Python. - [One library to rule them all? Geospatial visualisation tools in Python](https://gregorhd.github.io/geospatial-visualisation-in-python/), November 2022: Gregor Herda. Comparing Altair, Bokeh, Cartopy, Datashader, GeoPandas, Geoplot, GeoViews, hvPlot, and Plotly. - [What Are the Best Python Plotting Libraries?](https://towardsdatascience.com/what-are-the-best-python-plotting-libraries-df234a356aec), May 2022: Will Norris. Comparing Matplotlib, Seaborn, Plotly, and Folium. - [Python Dashboarding Shootout and Showdown | PyData Global 2021](https://www.youtube.com/watch?v=4a-Db1zhTEw) October 2021: James Bednar, Nicolas Kruchten, Marc Skov Madsen, Sylvain Corlay and Adrien Treuille - [Why *Interactive* Data Visualization Matters for Data Science in Python | PyData Global 2021](https://www.youtube.com/watch?v=tlcMlOVbEpw) October 2021: Nicolas Kruchten - [Beyond Matplotlib and Seaborn: Python Data Visualization Tools That Work](https://medium.com/codex/beyond-matplotlib-and-seaborn-python-data-visualization-tools-that-work-3ef7f8d1500e) 1 Feb 2021 Stephanie Kirmer. Comparing Matplotlib, Seaborn, Bokeh, Altair, Plotnine, and Plotly, with example github repo for code. - [Plotly vs. Bokeh: Interactive Python Visualisation Pros and Cons](https://pauliacomi.com/2020/06/07/plotly-v-bokeh.html) 7 June 2020 Paul Iacomi. In-depth comparison of Bokeh and Plotly+Dash for dashboarding. - [Complete Guide to Data Visualization with Python](https://towardsdatascience.com/complete-guide-to-data-visualization-with-python-2dd74df12b5e) 29 Feb 2020 Albert Sanchez Lafuente. Example code for Pandas tables, Matplotlib, Seaborn, Bokeh, Altair, and Folium. - [Python Visualization Landscape](https://medium.com/@lulunana/python-visualization-landscape-3b95ede3d030) 24 Oct 2019 Sophia Yang. High-level overview of various categories of Python viz libraries, without example code. - [Python Grids: Data Visualization](http://www.pythongrids.org/grids/g/data-visualization) 19 Sep 2019 Jared Chung. Table comparing stats on 14 Python plotting libraries. - [Python Data Visualization 2018](https://www.anaconda.com/python-data-visualization-2018-why-so-many-libraries) 15 Nov 2018 - 14 Dec 2018 James A. Bednar, Anaconda, Inc. Three blog posts surveying the history and breadth of several dozen Python viz libraries, without example code. [Updated in 2019 as an eBook](https://know.anaconda.com/eBook-PyVizeBookLP_ReportRegistration.html?utm_source=pyviz.org&utm_campaign=pyviz&utm_content=ebook). - [pythonplot.com](http://pythonplot.com) 23 Jun 2017 - 12 Jun 2019 Timothy Hopper. Website with examples of plots made with Pandas+Matplotlib, Seaborn, plotnine, plotly, and R ggplot2, with output and Python code. - [Plotting business locations on maps using multiple Plotting libraries in Python](https://towardsdatascience.com/plotting-business-locations-on-maps-using-multiple-plotting-libraries-in-python-45a00ea770af) 30 Apr 2018 Karan Bhanot. Blog post comparing plotting business locations using gmplot, geopandas, plotly, and bokeh. - [Python Data Visualization — Comparing 5 Tools](https://codeburst.io/overview-of-python-data-visualization-tools-e32e1f716d10) 6 Dec 2017 Elena Kirzhner, Codeburst. Blog post with simple comparisons of Pandas, Seaborn, Bokeh, Pygal, and Plotly code and output. - [10 Heatmaps 10 Libraries](https://blog.algorexhealth.com/2017/09/10-heatmaps-10-python-libraries/) 10 Sep 2017 Luke Shulman. Comparing heatmap code across 10 different viz libraries. - [The Python Visualization Landscape](https://www.youtube.com/watch?v=FytuB8nFHPQ) 20 May 2017 Jake VanderPlas, U. Washington. 30-minute talk surveying the history and breadth of Python viz libraries. [[slides]](https://speakerdeck.com/jakevdp/pythons-visualization-landscape-pycon-2017). - [Python Graph Gallery](https://python-graph-gallery.com) 30 Apr 2017 - 7 Jan 2018 Yan Holtz. Website with examples of plots made with Seaborn, Matplotlib, Pandas, with output and Python code, used in [data-to-viz.com](https://www.data-to-viz.com). - [Overview of Python Visualization Tools](https://pbpython.com/visualization-tools-1.html) 20 Jan 2015 - 25 Apr 2017 Chris Moffitt, Practical Business Python. Three blog posts with examples of using pandas, seaborn, ggplot, bokeh, pygal, plotly, altair, matplotlib. - [A Dramatic Tour through Python’s Data Visualization Landscape (including ggplot and Altair)](https://dsaber.com/2016/10/02/a-dramatic-tour-through-pythons-data-visualization-landscape-including-ggplot-and-altair) 02 Oct 2016 Dan Saber. Comparison of Matplotlib, Pandas .plot(), Seaborn, ggplot/ggpy (now superseded by plotnine), and Altair, with example code. - [10 Useful Python Data Visualization Libraries for Any Discipline](https://mode.com/blog/python-data-visualization-libraries) 8 Jun 2016 Melissa Bierly, Mode.com. Blog post briefly describing matplotlib, seaborn, ggplot, bokeh, pygal, plotly, geoplotlib, gleam, missingno, and leather (now retired), with examples running on the Mode server. - [Comparing 7 Tools For Data Visualization in Python](https://www.dataquest.io/blog/python-data-visualization-libraries) 12 Nov 2015 Vik Paruchuri, Dataquest. Blog post illustrating usage of matplotlib, vispy, bokeh, seaborn, pygal, folium, and networkx, with code, for an airport/flight dataset. ================================================ FILE: doc/overviews/index.rst ================================================ .. mdinclude:: index.md .. toctree:: :titlesonly: :hidden: :maxdepth: 2 ================================================ FILE: doc/scivis/index.md ================================================ # SciVis Libraries Most of the libraries listed at PyViz.org fall into the [InfoVis](http://ieeevis.org/year/2019/info/call-participation/infovis-paper-types) (Information Visualization) category of tools, visualizing arbitrary and potentially abstract types of information, typically in 2D or 2D+time plots with axes and numerical scales. Tools in the separate [SciVis](http://ieeevis.org/year/2019/info/call-participation/scivis-paper-types) (Scientific Visualization) category focus on visualizing physically situated gridded data in 3D and 3D+time, often without spatial axes and instead providing an immersive visual experience of real-world physical datasets (see [Weiskopf et al](https://pdfs.semanticscholar.org/86aa/dffeae1912a404ee66223774d6a45eefb438.pdf) for a comparison). Desktop-GUI targeted SciVis tools build on the OpenGL graphics standard, while browser-based web applications usually leverage the related WebGL graphics standard. SciVis libraries supporting Python: - The Visualization Toolkit - [VTK](https://vtk.org) (from [Kitware](https://www.kitware.com/)) supports manipulating and displaying scientific data by enabling 3D rendering, widgets for 3D interaction, and 2D plotting capability. - [VisPy](http://vispy.org) is a high-performance interactive 2D/3D data visualization library leveraging the computational power of modern Graphics Processing Units (GPUs) through the OpenGL library to display very large datasets. - [Glumpy](https://glumpy.github.io) is an OpenGL-based interactive visualization library in Python. Its goal is to make it easy to create fast, scalable, beautiful, interactive and dynamic visualizations. - [GR](https://gr-framework.org) is a universal framework for cross-platform visualization applications. It offers developers a compact, portable and consistent graphics library for their programs. - [Mayavi](https://docs.enthought.com/mayavi/mayavi) (from [Enthought](https://www.enthought.com/)) is a general purpose, cross-platform tool for 3-D scientific data visualization. - [ParaView](https://www.paraview.org) (from [Kitware](https://www.kitware.com/)) is an application built on the Visualization Toolkit (VTK) with extensions for distributed computing. ParaView allows users to quickly build visualizations to analyze their data using qualitative and quantitative techniques. The data exploration can be done interactively in 3D or programmatically using batch processing capabilities. - [yt](https://yt-project.org) is a package for analyzing and visualizing volumetric data. yt supports structured, variable-resolution meshes, unstructured meshes, and discrete or sampled data such as particles. - [PyVista](http://www.pyvista.org) is a streamlined interface for the Visualization Toolkit (VTK) providing 3D plotting and mesh analysis with NumPy support being at its core. PyVista supports point clouds, structured/unstructured meshes, and volumetric datasets. - [vedo](https://vedo.embl.es) is a lightweight module for scientific analysis and visualization of polygonal meshes, point clouds and volumetric data. It offers an intuitive API which can be combined with VTK seamlessly in a program, whilst mantaining access to the full range of VTK native classes. - [itk-jupyter-widgets](https://github.com/InsightSoftwareConsortium/itk-jupyter-widgets), based on the Visualization Toolkit for JavaScript [vtk.js](https://kitware.github.io/vtk-js/index.html) and the [Insight Toolkit (ITK)](https://www.itk.org/), provides interactive 3D widgets for Jupyter to visualize and analyze images, point sets, and meshes. ================================================ FILE: doc/scivis/index.rst ================================================ .. mdinclude:: index.md .. toctree:: :titlesonly: :hidden: :maxdepth: 2 VTK VisPy Glumpy GR Mayavi ParaView yt PyVista vedo ================================================ FILE: doc/tools.md ================================================ This page lists OSS libraries for visualizing data in Python. If you see any missing Python tools, please open a [PR](https://help.github.com/en/articles/about-pull-requests) for [tools.yml](https://github.com/pyviz/pyviz.org/blob/master/tools/tools.yml). Tools are sorted in each category according to their total downloads (pypi + conda) per month when added to the list. Note that conda downloads are computed by summing total downloads across the defaults channel, conda-forge, and bioconda; data for other channels is not currently included. Also note that the stars, contributors, license and PyPi downloads badges are cached to prevent users hitting the badges rate limits. Caching fails occasionally for some PyPi downloads badges, in which case their live counterpart is instead displayed (identified by a grey background). ================================================ FILE: doc/tutorials/index.md ================================================ # Tutorials Most of the projects listed at PyViz.org contain examples explaining how to solve various problems using that specific tool. This section lists additional in-depth, comprehensive tutorials designed for users new to Python viz, helping them to get started with a variety of different types of plot and situations. Each tutorial should include at least an hour's worth of work, with links to a repository of runnable materials along with text to describe what to do. - [Bokeh tutorial](https://nbviewer.jupyter.org/github/bokeh/bokeh-notebooks/blob/master/tutorial/00%20-%20Introduction%20and%20Setup.ipynb): How to use the native [Bokeh](https://bokeh.org) API directly to create interactive plots, apps, and dashboards. - [HoloViz tutorial](https://holoviz.org/tutorial): How to use the high-level [HoloViz](http://holoviz.org) tools from Anaconda to plot gridded, tabular, streaming, large, and graph/network data, focusing on [Panel](https://panel.pyviz.org), [Datashader](http://datashader.org), [HoloViews](https://holoviews.org), [GeoViews](http://geoviews.org), and [hvPlot](https://hvplot.pyviz.org), which build on [Bokeh](http://bokeh.org) and [Matplotlib](http://matplotlib.org). [[2018 SciPy recording](https://www.youtube.com/watch?v=aZ1G_Q7ovmc)] - [Jupyter widgets tutorial](https://github.com/jupyter-widgets/tutorial): How to make interactive plots, apps, and dashboards using ipywidgets, bqplot, vaex, ipympl, vue, ipysheet, ipyvolume, ipyleaflet, pythreejs, voila, and other ipywidgets-compatible libraries. [[2018 SciPy recording](https://www.youtube.com/watch?v=NBZBjEjN-rU)] - [Matplotlib tutorial](https://github.com/matplotlib/AnatomyOfMatplotlib): Guide to the building blocks of Matplotlib and how to use them to create many different types of plots. [[SciPy 2018 recording](https://www.youtube.com/watch?v=6gdNUDs6QPc)] ================================================ FILE: doc/tutorials/index.rst ================================================ .. want to include these in the toctree .. mdinclude:: index.md .. toctree:: :titlesonly: :hidden: :maxdepth: 2 Bokeh tutorial HoloViz Jupyter widgets tutorial Matplotlib tutorial ================================================ FILE: tools/README.md ================================================ ## PyViz Tools This directory is used to generate a tools dashboard for comparing various Python visualization packages. The main configuration of the dashboard is done via the ``tools.yml`` file, which can contain several sections with a list of packages, and a list of services for each section. The dashboard is created during the website build process on Github Actions and can be seen at [pyviz.org/tools.html](http://pyviz.org/tools.html). ### Introductory text The intro text is located in `doc/tools.md`, whose contents will be included immediately after the title on the page. Every section can also have an `intro` in `tools.yml`. This text should also be written as markdown. ### Adding a tool To add a tool, just create a new entry under the desired section in ``tools.yml``. At a minimum, include the GitHub org/repo for the project's source code. This will result in a project with just the badges that come from github and pypi. ** Minimal entry ** ```yaml - repo: SciTools/cartopy ``` To include more badges, add a list of sponsors, the site that the documentation can be found at. Also feel free to add the CI information, although this information isn't currently displayed, it could easily be added later. ** More complete entry ** ```yaml - repo: SciTools/cartopy sponsors: [metoffice] site: scitools.org.uk/cartopy conda_channel: conda-forge badges: travis, coveralls, pypi, conda ``` ### Adding a sponsor If you add a new tool that has a sponsor that is not yet found on the page, the name will not be linked and there won't be a logo. To get those assets, add an entry to ``sponsors.yml``. Use the same key as in ``tools.yml`` and include a `label` and optionally `url` and/or `logo`: ```yaml numfocus: label: NumFocus url: https://numfocus.org logo: _static/badges/numfocus.png ``` If using a logo, don't forget to include a small version of the logo at `doc/_static/badges/`. ================================================ FILE: tools/build.py ================================================ #!/usr/bin/env python import datetime import os from jinja2 import Template from yaml import safe_load from markdown import markdown here = os.path.abspath(os.path.dirname(__file__)) today = datetime.date.today().strftime("%B %-d, %Y") print("Opening config file") with open(os.path.join(here, 'tools.yml')) as f: config = safe_load(f) try: with open(os.path.join(here, 'pypi_invalid_badges.txt')) as f: pypi_invalid_badges = f.read().splitlines() except FileNotFoundError: pypi_invalid_badges = [] for section in config: print(f"Building {section.get('name', '')}") if section.get('intro'): section['intro'] = markdown(section['intro']) for package in section['packages']: try: package['user'], package['name'] = package['repo'].split('/') except: raise Warning('Package.repo is not in correct format', package) package['conda_package'] = package.get('conda_package', package['name']) package['pypi_name'] = package.get('pypi_name', package['name']) if package['pypi_name'] in pypi_invalid_badges: package['pypi_invalid'] = True if package.get('badges'): package['badges'] = [x.strip() for x in package['badges'].split(',')] else: package['badges'] = ['pypi', 'conda'] if package.get('conda_channel') and 'conda' not in package['badges']: package['badges'].append('conda') if package.get('sponsors') and 'sponsor' not in package['badges']: package['badges'].append('sponsor') if package.get('builtons') and 'builton' not in package['badges']: package['badges'].append('builton') if package.get('site') and 'site' not in package['badges']: package['badges'].append('site') if package.get('dormant') and 'dormant' not in package['badges']: package['badges'].append('dormant') if 'rtd' in package['badges'] and 'rtd_name' not in package: package['rtd_name'] = package['name'] if 'conda' in package['badges'] and 'conda_channel' not in package: package['conda_channel'] = 'anaconda' if 'site' in package['badges']: if 'site' not in package: package['site'] = '{}.org'.format(package['name']) package['site_protocol'] = 'https' else: package['site_protocol'], package['site'] = package['site'].rstrip('/').split('://') with open(os.path.join(here, 'sponsors.yml')) as f: sponsors = safe_load(f) with open(os.path.join(here, 'builtons.yml')) as f: builtons = safe_load(f) template = Template(open(os.path.join(here, 'template.html'), 'r').read()) with open(os.path.join(here, 'index.rst'), 'w') as f: f.write("All Tools\n") f.write("=========\n\n") f.write(".. mdinclude:: tools.md\n\n") f.write(".. raw:: html\n\n") f.write(template.render(config=config, sponsors=sponsors, builtons=builtons, date=today)) ================================================ FILE: tools/build_cache.py ================================================ #!/usr/bin/env python import os import time from yaml import safe_load import requests here = os.path.abspath(os.path.dirname(__file__)) cache_path = os.path.join(here, '..', 'doc', '_static', 'cache') badge = os.getenv('BADGE') cache = { # Override the label with a space to disable it and reduce the badge size "stars": "https://img.shields.io/github/stars/{repo}.svg?style=flat&logo=github&color=blue&label=%20", "contributors": "https://img.shields.io/github/contributors/{repo}.svg?style=flat&logo=github&color=blue&label=%20", "pypi_downloads": "https://img.shields.io/pypi/dm/{pypi_name}.svg?label=pypi", "license": "https://img.shields.io/pypi/l/{pypi_name}.svg?label", } url = cache.get(badge) if url is None: raise ValueError((f'{badge} not in {", ".join(cache.keys())}, use env ' 'var BADGE to set.')) # The pypi download badge cannot occasionally be properly fetched # by shields and cached here. We list those that failed, so that # in the template we can put the actual badge link rather than # the cached one. pypi_invalid_file = os.path.join(here, "pypi_invalid_badges.txt") if os.path.exists(pypi_invalid_file): os.remove(pypi_invalid_file) print(f"\nBuilding a cache of {badge} badges.\n") if not os.path.exists(cache_path): os.mkdir(cache_path) with open(os.path.join(here, 'tools.yml')) as f: config = safe_load(f) for section in config: print(f"Building cache for {section.get('name', '')}") for package in section['packages']: try: package['user'], package['name'] = package['repo'].split('/') except: raise Warning('Package.repo is not in correct format', package) package['pypi_name'] = package.get('pypi_name', package['name']) print(f" * package: {package.get('pypi_name', '')}") rendered_url = url.format(repo=package['repo'], pypi_name=package['pypi_name']) r = requests.get(rendered_url) content = r.content # Pypistats implements IP rate limiting, so let's slow things # down and retry a few times when failing. if badge == 'pypi_downloads': time.sleep(2.5) nb_retries = 4 retry_duration = 5 # In seconds, multiplied by two after each retry. retry_count = 1 while 'pypi: invalid' in r.text and retry_count <= nb_retries: print(f"PyPI badge returned as 'invalid'. Retrying after {retry_duration} seconds.") time.sleep(retry_duration) r = requests.get(rendered_url) content = r.content if retry_count == nb_retries: print(f"Failed a getting a valid Pypi Downloads badge for {package['pypi_name']}.") break retry_count += 1 retry_duration *= 2 if 'pypi: invalid' in r.text: with open(pypi_invalid_file, 'a') as f: f.write(package['pypi_name'] + '\n') with open(os.path.join(cache_path, f"{package['name']}_{badge}_badge.svg"), 'wb') as f: f.write(content) ================================================ FILE: tools/builtons.yml ================================================ bokeh: label: Bokeh url: https://docs.bokeh.org/en/latest/ logo: _static/badges/builtons/bokeh.png plotly: label: Plotly url: https://plotly.com/ logo: _static/badges/builtons/plotly.png matplotlib: label: Matplotlib url: https://matplotlib.org/ logo: _static/badges/builtons/matplotlib.png vega: label: Vega url: https://vega.github.io/vega/ logo: _static/badges/builtons/vega.png vtk: label: VTK url: https://vtk.org/ logo: _static/badges/builtons/vtk.png opengl: label: OpenGL url: https://www.opengl.org/ logo: _static/badges/builtons/opengl.png webgl: label: WebGL url: https://www.khronos.org/webgl/ logo: _static/badges/builtons/webgl.png vispy: label: VisPy url: https://vispy.org/ logo: _static/badges/builtons/vispy.png leaflet: label: Leaflet url: https://leafletjs.com/ logo: _static/badges/builtons/leaflet.png graphviz: label: Graphviz url: https://graphviz.org/ logo: _static/badges/builtons/graphviz.png d3: label: D3 url: https://d3js.org/ logo: _static/badges/builtons/d3.png qt: label: qt url: https://qt.io/ logo: _static/badges/builtons/qt.png gmt: label: gmt url: https://www.generic-mapping-tools.org/ logo: _static/badges/builtons/gmt.png vulkan: label: vulkan url: https://www.vulkan.org/ logo: _static/badges/builtons/vulkan.png panel: label: Panel url: https://panel.holoviz.org logo: _static/badges/builtons/panel.png pyvista: label: PyVista url: https://docs.pyvista.org/version/stable/ logo: _static/badges/builtons/pyvista.png ================================================ FILE: tools/conda_downloads.py ================================================ #!/usr/bin/env python """ Run this script at the beginning of each month to build new conda downloads badges from the previous month. """ import os from yaml import safe_load import requests import datetime import intake import colorcet as cc import numpy as np here = os.path.abspath(os.path.dirname(__file__)) cache_path = os.path.join(here, '..', 'doc', '_static', 'cache') cat = intake.open_catalog('https://raw.githubusercontent.com/ContinuumIO/anaconda-package-data/master/catalog/anaconda_package_data.yaml') colors = cc.palette_n.rainbow[-20:80:-1] top_of_colormap = 1e6 step = len(colors) /np.log10(top_of_colormap) today = datetime.date.today() first = today.replace(day=1) last_month = first - datetime.timedelta(days=1) try: monthly = cat.anaconda_package_data_by_month(year=last_month.year, month=last_month.month, columns=['pkg_name', 'counts']).to_dask() except: # if the last month isn't available, get the month before month_before = last_month.replace(day=1) - datetime.timedelta(days=1) monthly = cat.anaconda_package_data_by_month(year=month_before.year, month=month_before.month, columns=['pkg_name', 'counts']).to_dask() per_package_downloads = monthly.groupby('pkg_name').sum().compute() if not os.path.exists(cache_path): os.mkdir(cache_path) def get_conda_badge(conda_package): conda_package = conda_package.lower() if conda_package in per_package_downloads.index: downloads = per_package_downloads.counts.loc[conda_package] else: downloads = 0 if downloads == 0: color_index = 0 elif downloads > top_of_colormap: color_index = -1 else: color_index = int(np.log10(downloads) * step) color = colors[color_index][1:] if downloads > 1e6: downloads = '{}M'.format(int(downloads/1e6)) elif downloads > 1e3: downloads = '{}k'.format(int(downloads/1e3)) else: downloads = int(downloads) return f"https://img.shields.io/badge/conda-{downloads}/month-{color}.svg" with open(os.path.join(here, 'tools.yml')) as f: config = safe_load(f) for section in config: print(f"Building conda downloads badge for: {section['name']}") for package in section['packages']: try: package['user'], package['name'] = package['repo'].split('/') except: raise Warning('Package.repo is not in correct format', package) continue url = get_conda_badge(package.get('conda_package', package['name'])) rendered_url = url r = requests.get(rendered_url) with open(os.path.join(cache_path, f"{package['name']}_conda_downloads_badge.svg"), 'wb') as f: f.write(r.content) ================================================ FILE: tools/sponsors.yml ================================================ numfocus: label: NumFocus url: https://numfocus.org logo: _static/badges/numfocus.png anaconda: label: Anaconda url: https://www.anaconda.com logo: _static/badges/anaconda.png metoffice: label: Met Office url: https://www.metoffice.gov.uk logo: _static/badges/metoffice.png spotify: label: Spotify url: https://www.spotify.com logo: _static/badges/spotify.png plotly: label: Plot.ly url: https://plot.ly logo: _static/badges/plotly.png kitware: label: Kitware url: https://www.kitware.com logo: _static/badges/kitware.svg EMBL: label: EMBL url: https://www.embl.es logo: _static/badges/embl.png enthought: label: Enthought url: https://www.enthought.com logo: _static/badges/enthought.svg nvidia: label: NVIDIA url: https://www.nvidia.com logo: _static/badges/nvidia.jpg posit: label: Posit, PBC URL: https://posit.co/ logo: _static/badges/positpbc.png sandia: label: Sandia url: https://www.sandia.gov logo: _static/badges/sandia.png quantstack: label: QuantStack url: http://quantstack.net logo: _static/badges/quantstack.svg vaexio: label: vaex.io url: https://vaex.io logo: _static/badges/vaexio.png unidata: label: Unidata url: https://www.unidata.ucar.edu logo: _static/badges/unidata.png cgs: label: cgs url: https://spatial.ucr.edu logo: _static/badges/cgs.svg streamlit: label: streamlit url: https://streamlit.io logo: _static/badges/streamlit.png jetbrains: label: jetbrains url: https://www.jetbrains.com logo: _static/badges/jetbrains.png h2o: label: h2o.ai url: https://h2o.ai logo: _static/badges/h2o.png huggingface: label: huggingface url: https://huggingface.co logo: _static/badges/huggingface.png mljar: label: mljar url: https://mljar.com logo: _static/badges/mljar.png wandb: label: Weights and Biases url: https://wandb.ai logo: _static/badgets/weights_and_biases.svg mckinsey: label: McKinsey url: https://mckinsey.com reflex: label: Reflex url: https://reflex.dev logo: _static/badges/reflex.svg zauberzeug: label: Zauberzeug url: https://zauberzeug.com logo: _static/badges/zauberzeug.webp widgetti: label: Widgetti url: https://widgetti.io/ logo: _static/badges/widgetti.png marimo: label: marimo url: https://marimo.io/ logo: _static/badges/marimo.png taipy: label: taipy url: https://www.taipy.io/ logo: _static/badges/taipy.png ================================================ FILE: tools/template.html ================================================
Last updated: {{ date }}
{% for section in config %}

{{ section.name }}

{{ section.get('intro', '') }} {% for package in section.packages %} {% if 'pypi_invalid' in package %} {% elif 'pypi' in package.badges %} {% else %} {% endif %} {% if 'conda' in package.badges %} {% else %} {% endif %} {% if 'pypi' in package.badges %} {% else %} {% endif %} {% if 'site' in package.badges %} {% elif 'rtd' in package.badges %} {% else %} {% endif %} {% if 'pypi' in package.badges %} {% else %} {% endif %} {% if 'conda' in package.badges %} {% else %} {% endif %} {% if 'sponsor' in package.badges %} {% else %} {% endif %} {% if 'builton' in package.badges %} {% else %} {% endif %} {% endfor %}
Name Stars Contributors Downloads License Docs PyPI Conda Sponsors Built on
{{ package.name }} {% if 'dormant' in package.badges %} {% endif %} - - - - - - {% for sponsor in package.sponsors %} {% if sponsors.get(sponsor) %} {% if sponsors[sponsor].get('logo') %} {% else %} {{ sponsors[sponsor].get('label', sponsor) }} {% endif %} {% else %} {{ sponsor }} {% endif %} {% endfor %} - {% for builton in package.builtons %} {% if builtons.get(builton) %} {% if builtons[builton].get('logo') %} {% else %} {{ builtons[builton].get('label', builton) }} {% endif %} {% else %} {{ builton }} {% endif %} {% endfor %} -
{% endfor %}
================================================ FILE: tools/tools.yml ================================================ - name: Core intro: Python libraries on which multiple higher-level libraries are built. packages: - repo: matplotlib/matplotlib sponsors: [numfocus] badges: travis, appveyor, codecov, pypi, conda, site builtons: [matplotlib] - repo: plotly/plotly.py sponsors: [plotly] pypi_name: plotly conda_package: plotly site: https://plot.ly badges: circleci, pypi, conda, site builtons: [plotly] - repo: bokeh/bokeh sponsors: [numfocus, anaconda] badges: travis, pypi, conda, site builtons: [bokeh] - name: High-Level Shared API intro: Libraries sharing the Pandas .plot() API, built upon the core Python or JS libraries. packages: - repo: pandas-dev/pandas sponsors: [numfocus] site: https://pandas.pydata.org badges: travis, appveyor, codecov, rtd, pypi, conda, site builtons: [matplotlib] - repo: pydata/xarray sponsors: [numfocus] appveyor_project: shoyer/xray site: https://xarray.dev/ conda_channel: conda-forge badges: travis, appveyor, coveralls, rtd, pypi, conda, site builtons: [matplotlib] - repo: holoviz/hvplot sponsors: [anaconda,numfocus] site: https://hvplot.holoviz.org badges: pypi, conda, site builtons: [bokeh] - repo: santosjorge/cufflinks site: https://github.com/santosjorge/cufflinks/blob/master/README.md conda_channel: conda-forge badges: circleci, pypi, conda, site builtons: [plotly] - repo: PatrikHlobil/Pandas-Bokeh site: https://github.com/PatrikHlobil/Pandas-Bokeh/blob/master/README.md conda_channel: PatrikHlobil badges: pypi, conda, site builtons: [bokeh] - name: High-Level intro: InfoVis Libraries focusing on high-level operations for working with data visually, built upon the core Python or JS libraries. packages: - repo: altair-viz/altair site: https://altair-viz.github.io conda_channel: conda-forge badges: travis, pypi, conda, site builtons: [vega] - repo: mwaskom/seaborn site: https://seaborn.pydata.org badges: travis, codecov, pypi, conda, site builtons: [matplotlib] - repo: plotly/plotly_express sponsors: [plotly] site: https://plotly.express conda_channel: conda-forge badges: pypi, conda, site builtons: [plotly] - repo: holoviz/holoviews sponsors: [anaconda,numfocus] badges: coveralls, pypi, conda, site builtons: [bokeh, matplotlib, plotly] - repo: pyecharts/pyecharts site: https://pyecharts.org/#/en-us/ badges: pypi builtons: [echarts] - repo: JetBrains/lets-plot sponsors: [jetbrains] site: https://lets-plot.org badges: pypi, site - repo: Marsilea-viz/marsilea pypi_name: marsilea site: https://marsilea.rtfd.io/ conda_channel: conda-forge badges: pypi, conda, site, rtd builtons: [matplotlib] - repo: finos/perspective site: https://perspective.finos.org conda_channel: conda-forge badges: pypi, conda, site - repo: AutoViML/AutoViz site: https://github.com/AutoViML/AutoViz badges: travis, pypi, site builtons: [matplotlib] - repo: spotify/chartify sponsors: [spotify] conda_channel: conda-forge site: https://github.com/spotify/chartify/blob/master/README.rst badges: travis, pypi, conda, site builtons: [bokeh] - repo: lukelbd/proplot site: https://proplot.readthedocs.io conda_channel: conda-forge badges: travis, pypi, conda, site builtons: [matplotlib] - repo: vizzuhq/ipyvizzu site: https://ipyvizzu.vizzuhq.com conda_channel: conda-forge badges: travis, pypi, conda, site - repo: vizzuhq/ipyvizzu-story site: https://vizzuhq.github.io/ipyvizzu-story/ conda_channel: conda-forge badges: travis, pypi, conda, site - repo: Technion-Kishony-lab/quibbler pypi_name: pyquibbler site: https://github.com/Technion-Kishony-lab/quibbler badges: pypi, site builtons: [matplotlib] - repo: koonimaru/omniplot - name: Native-GUI intro: InfoVis Libraries targetting native-desktop GUI interfaces for interactive plots. packages: - repo: matplotlib/matplotlib sponsors: [numfocus] badges: travis, appveyor, codecov, pypi, conda, site builtons: [matplotlib] - repo: pyqtgraph/pyqtgraph site: http://www.pyqtgraph.org builtons: [qt, opengl] - repo: newville/wxmplot site: https://newville.github.io/wxmplot/ conda_channel: conda-forge conda_package: wxmplot badges: pypi, conda, site builtons: [matplotlib] - repo: glue-viz/glue site: http://docs.glueviz.org conda_channel: conda-forge conda_package: glueviz badges: pypi, conda, site builtons: [matplotlib] - repo: enthought/chaco sponsors: [enthought] site: https://docs.enthought.com/chaco badges: pypi builtons: [kiva] - repo: sciapp/gr site: https://gr-framework.org/python.html badges: pypi, site - repo: veusz/veusz conda_channel: conda-forge site: https://veusz.github.io builtons: [qt] - name: Other InfoVis intro: InfoVis plotting libraries not fitting into other categories above. packages: - repo: has2k1/plotnine conda_channel: conda-forge badges: pypi, conda, rtd builtons: [matplotlib] - repo: Kozea/pygal site: http://pygal.org conda_channel: conda-forge - repo: bloomberg/bqplot conda_channel: conda-forge badges: pypi, conda, rtd - repo: sandialabs/toyplot sponsors: [sandia] conda_channel: conda-forge badges: pypi, conda, rtd - repo: flekschas/jupyter-scatter badges: pypi, site builtons: [webgl] pypi_name: jupyter-scatter site: https://jupyter-scatter.dev/ - repo: maxhumber/chart badges: pypi - name: SciVis intro: Libraries for visualizing scientific data situated in real-world coordinates, typically using OpenGL, WebGL, or Vulkan. packages: - repo: Kitware/VTK sponsors: [kitware] site: https://vtk.org/ conda_channel: conda-forge badges: travis, pypi, conda, site builtons: [vtk] - repo: pyvista/pyvista site: https://docs.pyvista.org conda_channel: conda-forge builtons: [vtk] - repo: vispy/vispy conda_channel: conda-forge site: http://vispy.org badges: travis, appveyor, coveralls, pypi, conda, site builtons: [opengl] - repo: nmwsharp/polyscope site: http://polyscope.run badges: pypi builtons: [opengl] - repo: marcomusy/vedo sponsors: [EMBL] site: https://vedo.embl.es conda_channel: conda-forge badges: pypi, conda, circleci builtons: [vtk] - repo: maartenbreddels/ipyvolume conda_channel: conda-forge builtons: [opengl, webgl] - repo: InsightSoftwareConsortium/itkwidgets pypi_name: itkwidgets conda_package: itkwidgets conda_channel: conda-forge badges: pypi, conda, circleci builtons: [webgl] - repo: enthought/mayavi sponsors: [enthought] site: https://docs.enthought.com/mayavi/mayavi appveyor_project: EnthoughtOSS/mayavi badges: travis, appveyor, codecov, pypi, conda, site builtons: [vtk, opengl] - repo: glumpy/glumpy badges: pypi, rtd builtons: [opengl] - repo: datoviz/datoviz site: https://datoviz.org conda_channel: mark.harfouche badges: conda builtons: [vulkan] - name: Geospatial intro: Tools for working with data in geographic coordinates. packages: - repo: pydata/xarray sponsors: [numfocus] appveyor_project: shoyer/xray site: https://xarray.dev/ conda_channel: conda-forge badges: travis, appveyor, coveralls, rtd, pypi, conda, site builtons: [matplotlib] - repo: geopandas/geopandas site: http://geopandas.org conda_channel: conda-forge builtons: [matplotlib] - repo: python-visualization/folium site: https://python-visualization.github.io/folium conda_channel: conda-forge builtons: [leaflet] - repo: SciTools/cartopy sponsors: [metoffice] site: https://scitools.org.uk/cartopy badges: travis, appveyor, coveralls, pypi, conda, site builtons: [matplotlib] - repo: holoviz/hvplot sponsors: [anaconda,numfocus] site: https://hvplot.holoviz.org badges: pypi, conda, site builtons: [bokeh] - repo: gboeing/osmnx site: https://osmnx.readthedocs.io badges: pypi - repo: keplergl/kepler.gl site: https://docs.kepler.gl/docs/keplergl-jupyter badges: pypi pypi_name: keplergl - repo: jupyter-widgets/ipyleaflet conda_channel: conda-forge badges: pypi, conda, rtd builtons: [leaflet] - repo: vgm64/gmplot badges: pypi - repo: JetBrains/lets-plot sponsors: [jetbrains] site: https://lets-plot.org badges: pypi, site - repo: giswqs/leafmap site: https://leafmap.org conda_channel: conda-forge badges: pypi, conda builtons: [leaflet, plotly] - repo: gee-community/geemap site: https://geemap.org badges: pypi - repo: holoviz/geoviews sponsors: [anaconda,numfocus] site: http://geoviews.org badges: pypi, conda, site builtons: [bokeh, matplotlib, plotly] - repo: pysal/splot sponsors: [cgs] site: https://splot.readthedocs.io conda_channel: conda-forge badges: coveralls, pypi, conda, site builtons: [matplotlib] - repo: HTenkanen/pyrosm site: https://pyrosm.readthedocs.io badges: pypi - repo: GenericMappingTools/pygmt site: https://www.pygmt.org conda_channel: conda-forge badges: pypi, conda, codecov builtons: [gmt] - repo: ResidentMario/geoplot site: https://residentmario.github.io/geoplot conda_channel: conda-forge badges: pypi, conda, site builtons: [matplotlib] - repo: raphaelquast/eomaps site: https://eomaps.readthedocs.io conda_channel: conda-forge badges: pypi, conda, codecov, site builtons: [matplotlib, cartopy] - repo: opengeos/mapwidget site: https://mapwidget.gishub.org/ badges: pypi - repo: bjlittle/geovista site: https://geovista.readthedocs.io conda_channel: conda-forge badges: pypi, conda, codecov, site builtons: [pyvista] - repo: andrea-cuttone/geoplotlib site: https://github.com/andrea-cuttone/geoplotlib/wiki/User-Guide badges: pypi builtons: [opengl] - repo: ambeelabs/gspatial_plot site: https://gspatial-plot.readthedocs.io badges: pypi builtons: [matplotlib] - name: Graphs and networks intro: Tools specifically focused on visualizing graphs (networks). Several of the other plotting libraries listed in other sections can also plot network graphs, including Bokeh, HoloViews, hvPlot, Matplotlib, and Plotly. packages: - repo: networkx/networkx site: https://networkx.github.io appveyor_project: dschult/networkx-pqott badges: travis, appveyor, codecov, pypi, conda, site builtons: [matplotlib, graphviz] - repo: xflr6/graphviz site: https://graphviz.readthedocs.io conda_package: python-graphviz conda_channel: conda-forge badges: travis, codecov, pypi, conda, rtd, site builtons: [graphviz] - repo: pydot/pydot badges: pypi, conda conda_channel: conda-forge builtons: [graphviz] - repo: WestHealth/pyvis site: https://pyvis.readthedocs.io conda_channel: conda-forge badges: pypi, conda, site - repo: pygraphviz/pygraphviz site: https://pygraphviz.github.io conda_channel: conda-forge badges: travis, pypi, conda, site builtons: [graphviz] - repo: timkpaine/ipydagred3 conda_channel: conda-forge badges: azure, pypi, conda builtons: [d3] - repo: igraph/python-igraph site: https://igraph.org/python conda_channel: conda-forge appveyor_project: ntamas/python-igraph badges: travis, appveyor, pypi, conda, site - repo: QuantStack/ipycytoscape conda_channel: conda-forge badges: pypi, conda - repo: Yomguithereal/ipysigma badges: pypi - repo: epfl-lts2/pygsp site: https://pygsp.readthedocs.io conda_channel: conda-forge badges: travis, coveralls, pypi, conda, rtd, site - repo: ericmjl/nxviz site: https://nxviz.readthedocs.io conda_channel: conda-forge badges: pypi, conda, site - repo: benmaier/netwulf site: https://netwulf.readthedocs.io badges: travis, pypi, rtd, site - repo: cytoscape/py2cytoscape site: https://py2cytoscape.readthedocs.io conda_channel: conda-forge badges: travis, pypi, conda, rtd, site - repo: SkBlaz/Py3Plex site: https://py3plex.readthedocs.io badges: pypi, site - repo: dblarremore/webweb site: https://webwebpage.github.io badges: pypi, conda, site - repo: skewed/graph-tool # repo: https://git.skewed.de/count0/graph-tool site: http://graph-tool.skewed.de conda_channel: conda-forge badges: pypi, conda, site - name: Table display intro: Tools for displaying and potentially interacting with data displayed as tables. Data libraries like Pandas and Polars also display tables, as do most dashboarding tools (e.g. Panel includes the Tabulator JS library for interactive tables). packages: - repo: mwouts/itables pypi_name: itables site: https://mwouts.github.io/itables badges: codecov, pypi, site, conda conda_package: itables conda_channel: conda-forge - repo: posit-dev/great-tables pypi_name: great-tables site: https://posit-dev.github.io/great-tables badges: codecov, pypi, site, conda conda_package: great_tables conda_channel: conda-forge sponsors: [posit] - repo: Kanaries/pygwalker pypi_name: pygwalker site: https://kanaries.net/pygwalker badges: pypi, conda conda_package: pygwalker conda_channel: conda-forge - repo: jupyter-widgets/ipydatagrid pypi_name: ipydatagrid badges: pypi, conda conda_package: ipydatagrid conda_channel: conda-forge - repo: finos/perspective pypi_name: perspective-python site: https://perspective.finos.org badges: pypi, conda conda_package: perspective # newer of two conda packages (the other is `perspective-python`) conda_channel: conda-forge - repo: paddymul/buckaroo pypi_name: buckaroo badges: pypi site: https://paddymul.github.io/buckaroo - repo: manzt/quak pypi_name: quak badges: pypi site: https://manzt.github.io/quak - repo: machow/reactable-py pypi_name: reactable badges: pypi site: https://machow.github.io/reactable-py - name: Other domain-specific intro: Tools focused on specific plot types, research areas, or application types other than those above. packages: - repo: scikit-image/scikit-image site: https://scikit-image.org badges: travis, appveyor, codecov, pypi, conda, site builtons: [matplotlib] - repo: arviz-devs/arviz sponsors: [numfocus] site: https://arviz-devs.github.io/arviz conda_channel: conda-forge badges: pypi, travis, azure, coveralls, conda, site builtons: [matplotlib] - repo: DistrictDataLabs/yellowbrick site: https://www.scikit-yb.org sponsors: [numfocus] conda_channel: DistrictDataLabs badges: travis, appveyor, pypi, conda, site builtons: [matplotlib] - repo: Unidata/MetPy sponsors: [unidata] site: https://unidata.github.io/MetPy conda_channel: conda-forge badges: travis, appveyor, codecov, pypi, conda, site builtons: [matplotlib] - repo: reiinakano/scikit-plot site: https://github.com/reiinakano/scikit-plot conda_channel: conda-forge badges: pypi, conda, site builtons: [matplotlib] - repo: ResidentMario/missingno conda_channel: conda-forge builtons: [matplotlib] - repo: napari/napari site: https://napari.org/ conda_channel: conda-forge badges: codecov, pypi, conda builtons: [vispy] - repo: gyli/PyWaffle badges: pypi, site builtons: [matplotlib] pypi_name: pywaffle site: https://pywaffle.readthedocs.io/ - repo: yt-project/yt sponsors: [numfocus] site: https://yt-project.org badges: travis, codecov, pypi, conda, site builtons: [matplotlib] - repo: saulpw/visidata site: https://visidata.org/ badges: pypi - repo: leotac/joypy badges: pypi, site builtons: [matplotlib] - repo: moshi4/pyCirclize badges: pypi - repo: ismms-himc/clustergrammer2 site: https://clustergrammer.readthedocs.io badges: pypi, site builtons: [webgl] - repo: ricklupton/floweaver badges: pypi builtons: [d3] - repo: ContextLab/hypertools badges: pypi, rtd builtons: [matplotlib] - repo: PAIR-code/facets site: https://pair-code.github.io/facets/ badges: pypi - name: Large-data rendering intro: Tools for visualizing especially large datasets, e.g. by automatic subsampling, dynamic aggregation, server-side rasterization, or dynamic colormapping packages: - repo: holoviz/datashader sponsors: [anaconda,numfocus] site: https://datashader.org badges: pypi, conda, site - repo: vaexio/vaex sponsors: [vaexio] site: https://vaex.io conda_channel: conda-forge badges: travis, appveyor, pypi, conda, site - repo: astrofrog/mpl-scatter-density site: https://github.com/astrofrog/mpl-scatter-density badges: travis, appveyor, pypi, conda, site builtons: [matplotlib] - repo: flekschas/jupyter-scatter badges: pypi, site builtons: [webgl] pypi_name: jupyter-scatter site: https://jupyter-scatter.dev/ - name: Dashboarding intro: Libraries for creating live Python-backed web applications or dashboards that a user can interact with to explore or analyze data. packages: - repo: streamlit/streamlit sponsors: [snowflake] site: https://streamlit.io conda_channel: conda-forge badges: pypi, conda, site - repo: gradio-app/gradio sponsors: [huggingface] site: https://gradio.app conda_channel: conda-forge badges: circleci, codecov, pypi, conda, site - repo: plotly/dash sponsors: [plotly] conda_channel: conda-forge site: https://dash.plot.ly badges: circleci, pypi, conda, site builtons: [plotly] - repo: bokeh/bokeh site: http://bokeh.org/ sponsors: [numfocus, anaconda] badges: travis, pypi, conda, site builtons: [bokeh] - repo: holoviz/panel sponsors: [anaconda,numfocus] site: https://panel.holoviz.org badges: codecov, pypi, conda, site builtons: [bokeh] - repo: marimo-team/marimo badges: pypi, conda conda_channel: conda-forge site: https://marimo.io sponsors: [marimo] - repo: AnswerDotAI/fasthtml pypi_name: python-fasthtml site: https://fastht.ml/ badges: pypi - repo: zauberzeug/nicegui site: https://nicegui.io/ badges: pypi, conda conda_channel: conda-forge sponsors: [zauberzeug] - repo: kitware/trame conda_channel: conda-forge badges: pypi, conda, site site: https://kitware.github.io/trame sponsors: [Kitware] - repo: QuantStack/voila site: https://voila.readthedocs.io sponsors: [quantstack] conda_channel: conda-forge badges: travis, pypi, conda, rtd - repo: rstudio/py-shiny site: https://shiny.posit.co/py/ pypi_name: shiny conda_package: shiny conda_channel: conda-forge badges: pypi, conda sponsors: [posit] - repo: wandb/weave site: https://wandb.ai/site/weave badges: pypi sponsors: [wandb] - repo: widgetti/reacton site: https://reacton.solara.dev badges: pypi, conda conda_channel: conda-forge sponsors: [widgetti] - repo: widgetti/solara site: https://solara.dev badges: pypi, conda conda_channel: conda-forge sponsors: [widgetti] - repo: reflex-dev/reflex site: https://reflex.dev/ badges: pypi sponsors: [reflex] - repo: mckinsey/vizro site: https://vizro.readthedocs.io badges: pypi, conda conda_channel: conda-forge sponsors: [mckinsey] - repo: fossasia/visdom site: https://github.com/fossasia/visdom/blob/master/README.md#visdom badges: pypi, conda conda_channel: conda-forge builtons: [plotly] - repo: h2oai/wave site: https://wave.h2o.ai badges: pypi sponsors: [h2o] - repo: google/mesop sponsors: [Google] badges: pypi - repo: datapane/datapane site: https://docs.datapane.com conda_channel: conda-forge badges: pypi, conda, site sponsors: [datapane] - repo: Avaiga/taipy site: https://www.taipy.io badges: pypi sponsors: [taipy] - repo: pywebio/PyWebIO site: https://www.pyweb.io badges: pypi, site - repo: mljar/mercury site: https://mljar.com/mercury conda_channel: conda-forge badges: pypi, conda, site sponsors: [mljar] - repo: danielfrg/jupyter-flex site: https://jupyter-flex.extrapolations.dev badges: pypi - repo: pycob/pyvibe badges: pypi - repo: causalens/dara badges: pypi site: https://dara.causalens.com pypi_name: create-dara-app - repo: trungleduc/ipyflex site: https://ipyflex.readthedocs.io conda_channel: conda-forge badges: pypi, conda, rtd - repo: Zen-Reportz/zen_dash badges: pypi - repo: streamsync-cloud/streamsync badges: pypi site: https://www.streamsync.cloud/ - repo: h2oai/nitro badges: pypi sponsors: [h2o] pypi_name: h2o-nitro - repo: mljar/bloxs badges: pypi sponsors: [mljar] - repo: hyperdiv/hyperdiv badges: pypi site: https://hyperdiv.io - repo: sansyrox/starfyre badges: pypi - repo: jrc-bdap/vois # repo: https://code.europa.eu/jrc-bdap/vois badges: pypi site: https://code.europa.eu/jrc-bdap/vois - repo: ifpen/chalk-it badges: pypi site: https://ifpen.github.io/chalk-it pypi_name: py-chalk-it - repo: briefercloud/briefer badges: pypi - repo: rio-labs/rio badges: pypi site: https://rio.dev - repo: davialabs/davia badges: pypi site: https://davia.ai - repo: data-stack-hub/DataStack badges: pypi - repo: LCL-CAVE/manganite badges: pypi builtons: [panel] - repo: dropseed/plain badges: pypi - name: Colormapping intro: Collections of colormaps and tools for generating new colormaps. packages: - repo: holoviz/colorcet sponsors: [anaconda,numfocus] site: https://colorcet.holoviz.org badges: pypi, conda, site - repo: jiffyclub/palettable site: https://jiffyclub.github.io/palettable - repo: matplotlib/cmocean site: https://matplotlib.org/cmocean conda_channel: conda-forge badges: travis, codecov, pypi, conda - repo: 1313e/CMasher site: https://cmasher.readthedocs.io conda_channel: conda-forge badges: travis, appveyor, pypi, conda, site - repo: callumrollo/cmcrameri conda_channel: conda-forge badges: pypi, conda - repo: y-sunflower/pypalettes site: https://python-graph-gallery.com/color-palette-finder/ conda_channel: conda-forge badges: pypi, conda, site builtons: [matplotlib] - repo: matplotlib/viscm conda_channel: conda-forge badges: travis, codecov, pypi, conda - name: Dormant projects intro: Tools no longer developed or endorsed by the authors. packages: - repo: biggles-plot/biggles site: https://biggles-plot.github.io badges: pypi dormant: https://github.com/biggles-plot/biggles/graphs/contributors - repo: matplotlib/basemap site: https://matplotlib.org/basemap dormant: https://matplotlib.org/basemap/users/intro.html#cartopy-new-management-and-eol-announcement builtons: [matplotlib] - repo: adrn/d3po site: https://d3po.org dormant: https://github.com/adrn/d3po/graphs/contributors badges: site, dormant - repo: rossant/galry dormant: https://github.com/rossant/galry/blob/master/README.md badges: pypi, dormant builtons: [opengl] - repo: yhat/ggpy site: http://ggplot.yhathq.com badges: pypi, site dormant: https://github.com/yhat/ggpy/graphs/contributors builtons: [matplotlib] - repo: dgrtwo/gleam dormant: https://github.com/dgrtwo/gleam/graphs/contributors badges: pypi, dormant - repo: wireservice/leather dormant: https://github.com/wireservice/leather/graphs/contributors badges: pypi, rtd - repo: lightning-viz/lightning site: http://lightning-viz.org dormant: https://gitter.im/lightning-viz/lightning badges: pypi, dormant builtons: [d3, leaflet] - repo: mpld3/mpld3 site: https://mpld3.github.io dormant: http://www.xavierdupre.fr/app/pymyinstall/helpsphinx/blog/2017/2017-09-02_mpld3.html builtons: [matplotlib, d3] - repo: altair-viz/pdvega site: https://altair-viz.github.io/pdvega conda_channel: conda-forge badges: travis, pypi, conda, site builtons: [d3, vega] dormant: https://github.com/altair-viz/pdvega - repo: olgabot/prettyplotlib dormant: https://github.com/olgabot/prettyplotlib/commit/089263c8574b03126a638c8c00bf7880695bc93c - repo: PyQwt/PyQwt site: http://www.pyqtgraph.org dormant: https://github.com/PyQwt badges: site builtons: [qt] - repo: PierreRaybaut/guiqwt site: https://pythonhosted.org/guiqwt/ dormant: https://github.com/PierreRaybaut/guiqwt/graphs/contributors badges: site, pypi builtons: [qt] - repo: wrobstory/vincent dormant: https://github.com/wrobstory/vincent/graphs/contributors badges: pypi, rtd builtons: [d3, vega] - repo: almarklein/visvis dormant: https://github.com/almarklein/visvis#status builtons: [opengl]