[ { "path": ".github/workflows/docs.yml", "content": "name: docs\n\non:\n push:\n branches:\n - master\n pull_request:\n branches:\n - '*'\n schedule:\n - cron: \"0 8 * 1-12 1\" # Every monday at 8 am\n workflow_dispatch:\n\npermissions:\n # To allow the workflow to push to the origin, when actions/checkout is used.\n contents: write\n\njobs:\n pre_ci:\n runs-on: 'ubuntu-latest'\n steps:\n - uses: actions/checkout@v4\n with:\n # required for PRs\n fetch-depth: 2\n - name: Get commit message\n id: get_commit_message\n run: |\n if [[ '${{ github.event_name }}' == 'push' ]]; then\n echo \"commit_message=$(git log --format=%B -n 1 HEAD)\" >> $GITHUB_OUTPUT\n elif [[ '${{ github.event_name }}' == 'pull_request' ]]; then\n echo \"commit_message=$(git log --format=%B -n 1 HEAD^2)\" >> $GITHUB_OUTPUT\n fi\n outputs:\n commit_message:\n echo \"${{ steps.get_commit_message.outputs.commit_message }}\"\n build_docs:\n name: Documentation\n runs-on: 'ubuntu-latest'\n needs: pre_ci\n if: \"contains(needs.pre_ci.outputs.commit_message, 'website_dev') || github.ref == 'refs/heads/master'\"\n timeout-minutes: 120\n defaults:\n run:\n shell: bash -l {0}\n steps:\n - uses: actions/checkout@v3\n - uses: conda-incubator/setup-miniconda@v2\n with:\n auto-activate-base: true\n activate-environment: \"\"\n miniconda-version: \"latest\"\n - name: conda setup\n run: |\n conda install anaconda-project\n anaconda-project prepare\n - name: Build cache\n run: |\n anaconda-project run build_cache\n git config user.name github-actions\n git config user.email github-actions@github.com\n mv ./doc/_static/cache ./tmp\n git fetch origin cache\n git checkout cache\n mv ./tmp/* ./doc/_static/cache\n git add -f ./doc/_static/cache\n ls ./doc/_static/cache\n git commit -m \"adding cached badges\"\n git push -f origin HEAD:cache\n - uses: actions/checkout@v3\n with:\n clean: false\n - name: Build website\n run: |\n git checkout -b deploy-tmp\n git fetch origin cache # all cached badges are in this branch\n git checkout origin/cache -- ./doc/_static/cache\n anaconda-project run build_website\n - name: git status\n run: |\n git status\n git diff\n - name: Deploy main\n if: ${{ github.ref == 'refs/heads/master' }}\n uses: peaceiris/actions-gh-pages@v3\n with:\n publish_dir: ./builtdocs\n cname: pyviz.org\n github_token: ${{ secrets.GITHUB_TOKEN }}\n force_orphan: true\n" }, { "path": ".gitignore", "content": "# Byte-compiled / DLL / optimized files...\n__pycache__/\n\n# OSX\n.DS_STORE\n\n# Jupyter notebook\n*.ipynb_checkpoints/\n\n# nbsite output\nbuiltdocs/\ndoc/tools.rst\ndoc/_static/cache/\n\n# Site building temp output\ntools/pypi_invalid_badges.txt\n\n# pytest\n.pytest_cache\n\n# anaconda-project\nenvs\n\n*~\n" }, { "path": "LICENSE.txt", "content": "Creative Commons Attribution 4.0 International Public License (CC-BY)\n\n By exercising the Licensed Rights (defined below), You accept and agree\n to be bound by the terms and conditions of this Creative Commons\n Attribution 4.0 International Public License (\"Public License\"). To the\n extent this Public License may be interpreted as a contract, You are\n granted the Licensed Rights in consideration of Your acceptance of\n these terms and conditions, and the Licensor grants You such rights in\n consideration of benefits the Licensor receives from making the\n Licensed Material available under these terms and conditions.\n\n Section 1 - Definitions.\n a. Adapted Material means material subject to Copyright and Similar\n Rights that is derived from or based upon the Licensed Material and\n in which the Licensed Material is translated, altered, arranged,\n transformed, or otherwise modified in a manner requiring permission\n under the Copyright and Similar Rights held by the Licensor. For\n purposes of this Public License, where the Licensed Material is a\n musical work, performance, or sound recording, Adapted Material is\n always produced where the Licensed Material is synched in timed\n relation with a moving image.\n b. Adapter's License means the license You apply to Your Copyright and\n Similar Rights in Your contributions to Adapted Material in\n accordance with the terms and conditions of this Public License.\n c. Copyright and Similar Rights means copyright and/or similar rights\n closely related to copyright including, without limitation,\n performance, broadcast, sound recording, and Sui Generis Database\n Rights, without regard to how the rights are labeled or\n categorized. For purposes of this Public License, the rights\n specified in Section [5]2(b)(1)-(2) are not Copyright and Similar\n Rights.\n d. Effective Technological Measures means those measures that, in the\n absence of proper authority, may not be circumvented under laws\n fulfilling obligations under Article 11 of the WIPO Copyright\n Treaty adopted on December 20, 1996, and/or similar international\n agreements.\n e. 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Subject to the terms and conditions of this Public License,\n the Licensor hereby grants You a worldwide, royalty-free,\n non-sublicensable, non-exclusive, irrevocable license to\n exercise the Licensed Rights in the Licensed Material to:\n A. reproduce and Share the Licensed Material, in whole or in\n part; and\n B. produce, reproduce, and Share Adapted Material.\n 2. Exceptions and Limitations. For the avoidance of doubt, where\n Exceptions and Limitations apply to Your use, this Public\n License does not apply, and You do not need to comply with its\n terms and conditions.\n 3. Term. The term of this Public License is specified in Section\n [6]6(a).\n 4. Media and formats; technical modifications allowed. The\n Licensor authorizes You to exercise the Licensed Rights in all\n media and formats whether now known or hereafter created, and\n to make technical modifications necessary to do so. The\n Licensor waives and/or agrees not to assert any right or\n authority to forbid You from making technical modifications\n necessary to exercise the Licensed Rights, including technical\n modifications necessary to circumvent Effective Technological\n Measures. For purposes of this Public License, simply making\n modifications authorized by this Section [7]2(a)(4) never\n produces Adapted Material.\n 5. Downstream recipients.\n A. Offer from the Licensor - Licensed Material. Every\n recipient of the Licensed Material automatically receives\n an offer from the Licensor to exercise the Licensed\n Rights under the terms and conditions of this Public\n License.\n B. No downstream restrictions. You may not offer or impose\n any additional or different terms or conditions on, or\n apply any Effective Technological Measures to, the\n Licensed Material if doing so restricts exercise of the\n Licensed Rights by any recipient of the Licensed\n Material.\n 6. No endorsement. Nothing in this Public License constitutes or\n may be construed as permission to assert or imply that You\n are, or that Your use of the Licensed Material is, connected\n with, or sponsored, endorsed, or granted official status by,\n the Licensor or others designated to receive attribution as\n provided in Section [8]3(a)(1)(A)(i).\n b. Other rights.\n 1. Moral rights, such as the right of integrity, are not licensed\n under this Public License, nor are publicity, privacy, and/or\n other similar personality rights; however, to the extent\n possible, the Licensor waives and/or agrees not to assert any\n such rights held by the Licensor to the limited extent\n necessary to allow You to exercise the Licensed Rights, but\n not otherwise.\n 2. Patent and trademark rights are not licensed under this Public\n License.\n 3. 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If You Share the Licensed Material (including in modified\n form), You must:\n A. retain the following if it is supplied by the Licensor\n with the Licensed Material:\n i. identification of the creator(s) of the Licensed\n Material and any others designated to receive\n attribution, in any reasonable manner requested by\n the Licensor (including by pseudonym if designated);\n ii. a copyright notice;\n iii. a notice that refers to this Public License;\n iv. a notice that refers to the disclaimer of\n warranties;\n v. a URI or hyperlink to the Licensed Material to the\n extent reasonably practicable;\n B. indicate if You modified the Licensed Material and retain\n an indication of any previous modifications; and\n C. indicate the Licensed Material is licensed under this\n Public License, and include the text of, or the URI or\n hyperlink to, this Public License.\n 2. You may satisfy the conditions in Section [9]3(a)(1) in any\n reasonable manner based on the medium, means, and context in\n which You Share the Licensed Material. For example, it may be\n reasonable to satisfy the conditions by providing a URI or\n hyperlink to a resource that includes the required\n information.\n 3. If requested by the Licensor, You must remove any of the\n information required by Section [10]3(a)(1)(A) to the extent\n reasonably practicable.\n 4. If You Share Adapted Material You produce, the Adapter's\n License You apply must not prevent recipients of the Adapted\n Material from complying with this Public License.\n\n Section 4 - Sui Generis Database Rights.\n\n Where the Licensed Rights include Sui Generis Database Rights that\n apply to Your use of the Licensed Material:\n a. for the avoidance of doubt, Section [11]2(a)(1) grants You the\n right to extract, reuse, reproduce, and Share all or a substantial\n portion of the contents of the database;\n b. if You include all or a substantial portion of the database\n contents in a database in which You have Sui Generis Database\n Rights, then the database in which You have Sui Generis Database\n Rights (but not its individual contents) is Adapted Material; and\n c. You must comply with the conditions in Section [12]3(a) if You\n Share all or a substantial portion of the contents of the database.\n\n For the avoidance of doubt, this Section [13]4 supplements and does not\n replace Your obligations under this Public License where the Licensed\n Rights include other Copyright and Similar Rights.\n\n Section 5 - Disclaimer of Warranties and Limitation of Liability.\n a. Unless otherwise separately undertaken by the Licensor, to the\n extent possible, the Licensor offers the Licensed Material as-is\n and as-available, and makes no representations or warranties of any\n kind concerning the Licensed Material, whether express, implied,\n statutory, or other. This includes, without limitation, warranties\n of title, merchantability, fitness for a particular purpose,\n non-infringement, absence of latent or other defects, accuracy, or\n the presence or absence of errors, whether or not known or\n discoverable. Where disclaimers of warranties are not allowed in\n full or in part, this disclaimer may not apply to You.\n b. To the extent possible, in no event will the Licensor be liable to\n You on any legal theory (including, without limitation, negligence)\n or otherwise for any direct, special, indirect, incidental,\n consequential, punitive, exemplary, or other losses, costs,\n expenses, or damages arising out of this Public License or use of\n the Licensed Material, even if the Licensor has been advised of the\n possibility of such losses, costs, expenses, or damages. Where a\n limitation of liability is not allowed in full or in part, this\n limitation may not apply to You.\n\n c. The disclaimer of warranties and limitation of liability provided\n above shall be interpreted in a manner that, to the extent\n possible, most closely approximates an absolute disclaimer and\n waiver of all liability.\n\n Section 6 - Term and Termination.\n a. 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Sections [16]1, [17]5, [18]6, [19]7, and [20]8 survive termination\n of this Public License.\n\n Section 7 - Other Terms and Conditions.\n a. The Licensor shall not be bound by any additional or different\n terms or conditions communicated by You unless expressly agreed.\n b. Any arrangements, understandings, or agreements regarding the\n Licensed Material not stated herein are separate from and\n independent of the terms and conditions of this Public License.\n\n Section 8 - Interpretation.\n a. For the avoidance of doubt, this Public License does not, and shall\n not be interpreted to, reduce, limit, restrict, or impose\n conditions on any use of the Licensed Material that could lawfully\n be made without permission under this Public License.\n b. To the extent possible, if any provision of this Public License is\n deemed unenforceable, it shall be automatically reformed to the\n minimum extent necessary to make it enforceable. If the provision\n cannot be reformed, it shall be severed from this Public License\n without affecting the enforceability of the remaining terms and\n conditions.\n c. No term or condition of this Public License will be waived and no\n failure to comply consented to unless expressly agreed to by the\n Licensor.\n d. Nothing in this Public License constitutes or may be interpreted as\n a limitation upon, or waiver of, any privileges and immunities that\n apply to the Licensor or You, including from the legal processes of\n any jurisdiction or authority.\n" }, { "path": "README.md", "content": "
\n\n# Python tools for data visualization\n\n| | |\n| --- | --- |\n| Build Status | [![Build Status](https://github.com/pyviz/pyviz.org/actions/workflows/docs.yml/badge.svg)](https://github.com/pyviz/pyviz.org/actions) |\n| 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) |\n\nSource 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).\n\n## Building pyviz.org\n\nWhenever a PR is merged, or a commit is pushed to master, a Github Actions job is triggered that builds pyviz.org.\n\n## Building dev site\n\nTo 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.\n\n**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.\n\n## Building website locally\n\nInstall anaconda-project:\n\n```bash\nconda install anaconda-project\n```\n\nBuild the cached badges:\n\n```bash\nanaconda-project build_cache\n```\n\nBuild the website:\n\n```bash\nanaconda-project build_website\n```\n\nView the website locally:\n\n```bash\npython -m http.server 8000\n```\n\n## Adding a tool to the \"All Tools\" page\n\nSee the [README](tools/README.md) in the tools directory for instructions on adding a tool to the \"All Tools\" page.\n" }, { "path": "anaconda-project-lock.yml", "content": "# This is an Anaconda project lock file.\n# The lock file locks down exact versions of all your dependencies.\n#\n# In most cases, this file is automatically maintained by the `anaconda-project` command or GUI tools.\n# It's best to keep this file in revision control (such as git or svn).\n# The file is in YAML format, please see http://www.yaml.org/start.html for more.\n#\n\n#\n# Set to false to ignore locked versions.\n#\nlocking_enabled: true\n\n#\n# A key goes in here for each env spec.\n#\nenv_specs:\n default:\n locked: true\n env_spec_hash: 978f894d8eec02b2a98d51e5ac316d783e8c9e0e\n platforms:\n - linux-64\n - osx-64\n packages:\n unix:\n - aiobotocore=1.4.0=pyhd8ed1ab_0\n - aioitertools=0.10.0=pyhd8ed1ab_0\n - aiosignal=1.2.0=pyhd8ed1ab_0\n - alabaster=0.7.12=py_0\n - appdirs=1.4.4=pyh9f0ad1d_0\n - argon2-cffi=21.3.0=pyhd8ed1ab_0\n - async-timeout=4.0.2=pyhd8ed1ab_0\n - asynctest=0.13.0=py_0\n - attrs=21.4.0=pyhd8ed1ab_0\n - babel=2.9.1=pyh44b312d_0\n - backcall=0.2.0=pyh9f0ad1d_0\n - backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0\n - backports=1.1=pyhd3eb1b0_0\n - beautifulsoup4=4.11.1=pyha770c72_0\n - blas=2.114=openblas\n - bleach=5.0.0=pyhd8ed1ab_0\n - botocore=1.20.106=pyhd8ed1ab_0\n - charset-normalizer=2.0.12=pyhd8ed1ab_0\n - cloudpickle=2.0.0=pyhd8ed1ab_0\n - colorama=0.4.4=pyh9f0ad1d_0\n - colorcet=2.0.6=py_0\n - dask-core=2021.10.0=pyhd3eb1b0_0\n - dask=2021.10.0=pyhd8ed1ab_0\n - decorator=5.1.1=pyhd8ed1ab_0\n - defusedxml=0.7.1=pyhd8ed1ab_0\n - entrypoints=0.4=pyhd8ed1ab_0\n - flit-core=3.7.1=pyhd8ed1ab_0\n - fsspec=2021.7.0=pyhd8ed1ab_0\n - heapdict=1.0.1=py_0\n - idna=3.3=pyhd8ed1ab_0\n - imagesize=1.3.0=pyhd8ed1ab_0\n - importlib_resources=5.7.1=pyhd8ed1ab_0\n - intake-parquet=0.2.3=py_0\n - intake=0.6.3=pyhd8ed1ab_0\n - ipython_genutils=0.2.0=py_1\n - jinja2=3.0.1=pyhd8ed1ab_0\n - jmespath=0.10.0=pyh9f0ad1d_0\n - jsonschema=4.4.0=pyhd8ed1ab_0\n - jupyter_client=7.1.2=pyhd8ed1ab_0\n - jupyterlab_pygments=0.2.2=pyhd8ed1ab_0\n - m2r2=0.3.1=pyhd8ed1ab_1\n - markdown=3.3.4=pyhd8ed1ab_0\n - matplotlib-inline=0.1.3=pyhd8ed1ab_0\n - nbclient=0.6.0=pyhd8ed1ab_0\n - nbconvert-core=6.5.0=pyhd8ed1ab_0\n - nbconvert-pandoc=6.5.0=pyhd8ed1ab_0\n - nbconvert=6.5.0=pyhd8ed1ab_0\n - nbformat=5.3.0=pyhd8ed1ab_0\n - nbsite=0.6.7=py_0\n - nest-asyncio=1.5.5=pyhd8ed1ab_0\n - packaging=21.3=pyhd8ed1ab_0\n - pandocfilters=1.5.0=pyhd8ed1ab_0\n - param=1.12.1=py_0\n - parquet-cpp=1.5.1=2\n - parso=0.8.3=pyhd8ed1ab_0\n - partd=1.2.0=pyhd8ed1ab_0\n - pexpect=4.8.0=pyh9f0ad1d_2\n - pickleshare=0.7.5=py_1003\n - pip=22.0.4=pyhd8ed1ab_0\n - prometheus_client=0.14.1=pyhd8ed1ab_0\n - prompt-toolkit=3.0.29=pyha770c72_0\n - ptyprocess=0.7.0=pyhd3deb0d_0\n - pycparser=2.21=pyhd8ed1ab_0\n - pyct-core=0.4.8=py_0\n - pyct=0.4.8=py_0\n - pygments=2.11.2=pyhd8ed1ab_0\n - pyopenssl=22.0.0=pyhd8ed1ab_0\n - pyparsing=3.0.8=pyhd8ed1ab_0\n - python-dateutil=2.8.2=pyhd8ed1ab_0\n - python-fastjsonschema=2.15.3=pyhd8ed1ab_0\n - pytz=2022.1=pyhd8ed1ab_0\n - pyviz_comms=2.2.0=py_0\n - requests=2.26.0=pyhd8ed1ab_1\n - s3fs=2021.8.0=pyhd8ed1ab_0\n - send2trash=1.8.0=pyhd8ed1ab_0\n - six=1.16.0=pyh6c4a22f_0\n - snowballstemmer=2.2.0=pyhd8ed1ab_0\n - sortedcontainers=2.4.0=pyhd8ed1ab_0\n - soupsieve=2.3.1=pyhd8ed1ab_0\n - sphinx=4.5.0=pyh6c4a22f_0\n - sphinxcontrib-applehelp=1.0.2=py_0\n - sphinxcontrib-devhelp=1.0.2=py_0\n - sphinxcontrib-htmlhelp=2.0.0=pyhd8ed1ab_0\n - sphinxcontrib-jsmath=1.0.1=py_0\n - sphinxcontrib-qthelp=1.0.3=py_0\n - sphinxcontrib-serializinghtml=1.1.5=pyhd8ed1ab_2\n - tblib=1.7.0=pyhd8ed1ab_0\n - tinycss2=1.1.1=pyhd8ed1ab_0\n - toolz=0.11.2=pyhd8ed1ab_0\n - traitlets=5.1.1=pyhd8ed1ab_0\n - typing-extensions=4.2.0=hd8ed1ab_0\n - typing_extensions=4.2.0=pyha770c72_0\n - urllib3=1.26.9=pyhd8ed1ab_0\n - wcwidth=0.2.5=pyh9f0ad1d_2\n - webencodings=0.5.1=py_1\n - wheel=0.37.1=pyhd8ed1ab_0\n - zict=2.1.0=pyhd8ed1ab_0\n - zipp=3.8.0=pyhd8ed1ab_0\n linux-64:\n - _libgcc_mutex=0.1=conda_forge\n - _openmp_mutex=4.5=1_llvm\n - abseil-cpp=20210324.2=h9c3ff4c_0\n - aiohttp=3.8.1=py37h540881e_1\n - argon2-cffi-bindings=21.2.0=py37h540881e_2\n - arrow-cpp=6.0.1=py37hbd77c41_5_cpu\n - aws-c-auth=0.6.8=hadad3cd_1\n - aws-c-cal=0.5.12=h70efedd_7\n - aws-c-common=0.6.17=h7f98852_0\n - aws-c-compression=0.2.14=h7c7754b_7\n - aws-c-event-stream=0.2.7=hd2be095_32\n - aws-c-http=0.6.10=h416565a_3\n - aws-c-io=0.10.14=he836878_0\n - aws-c-mqtt=0.7.10=h885097b_0\n - aws-c-s3=0.1.29=h8d70ed6_0\n - aws-c-sdkutils=0.1.1=h7c7754b_4\n - aws-checksums=0.1.12=h7c7754b_6\n - aws-crt-cpp=0.17.10=h6ab17b9_5\n - aws-sdk-cpp=1.9.160=h36ff4c5_0\n - blas-devel=3.9.0=14_linux64_openblas\n - bokeh=2.4.2=py37h89c1867_1\n - bottleneck=1.3.4=py37hda87dfa_1\n - brotlipy=0.7.0=py37h540881e_1004\n - bzip2=1.0.8=h7f98852_4\n - c-ares=1.18.1=h7f98852_0\n - ca-certificates=2022.3.29=h06a4308_0\n - certifi=2021.10.8=py37h89c1867_2\n - cffi=1.15.0=py37hd667e15_1\n - click=8.1.2=py37h89c1867_0\n - cramjam=2.5.0=py37hfd0a3e1_0\n - cryptography=36.0.2=py37h38fbfac_1\n - cytoolz=0.11.2=py37h540881e_2\n - debugpy=1.6.0=py37hd23a5d3_0\n - distributed=2021.10.0=py37h06a4308_0\n - docutils=0.17.1=py37h89c1867_1\n - fastparquet=0.7.1=py37hb1e94ed_0\n - freetype=2.11.0=h70c0345_0\n - frozenlist=1.3.0=py37h540881e_1\n - gflags=2.2.2=he1b5a44_1004\n - giflib=5.2.1=h36c2ea0_2\n - glog=0.5.0=h48cff8f_0\n - grpc-cpp=1.42.0=ha1441d3_1\n - importlib-metadata=4.11.3=py37h89c1867_1\n - ipykernel=6.11.0=py37h25bab4e_0\n - ipython=7.32.0=py37h89c1867_0\n - jbig=2.1=h7f98852_2003\n - jedi=0.18.1=py37h89c1867_1\n - jpeg=9e=h166bdaf_1\n - jupyter_core=4.9.2=py37h89c1867_0\n - keyutils=1.6.1=h166bdaf_0\n - krb5=1.19.3=h3790be6_0\n - lcms2=2.12=hddcbb42_0\n - ld_impl_linux-64=2.36.1=hea4e1c9_2\n - lerc=3.0=h9c3ff4c_0\n - libblas=3.9.0=14_linux64_openblas\n - libbrotlicommon=1.0.9=h166bdaf_7\n - libbrotlidec=1.0.9=h166bdaf_7\n - libbrotlienc=1.0.9=h166bdaf_7\n - libcblas=3.9.0=14_linux64_openblas\n - libcurl=7.82.0=h7bff187_0\n - libdeflate=1.10=h7f98852_0\n - libedit=3.1.20210910=h7f8727e_0\n - libev=4.33=h516909a_1\n - libevent=2.1.12=h8f2d780_0\n - libffi=3.3=h58526e2_2\n - libgcc-ng=11.2.0=h1d223b6_15\n - libgfortran-ng=11.2.0=h69a702a_15\n - libgfortran5=11.2.0=h5c6108e_15\n - liblapack=3.9.0=14_linux64_openblas\n - liblapacke=3.9.0=14_linux64_openblas\n - libnghttp2=1.47.0=h727a467_0\n - libopenblas=0.3.20=pthreads_h78a6416_0\n - libpng=1.6.37=h21135ba_2\n - libprotobuf=3.19.4=h780b84a_0\n - libsodium=1.0.18=h36c2ea0_1\n - libssh2=1.10.0=ha56f1ee_2\n - libstdcxx-ng=11.2.0=he4da1e4_15\n - libthrift=0.15.0=hcc01f38_0\n - libtiff=4.3.0=h542a066_3\n - libutf8proc=2.7.0=h7f98852_0\n - libwebp-base=1.2.2=h7f98852_1\n - libwebp=1.2.2=h3452ae3_0\n - libxcb=1.13=h7f98852_1004\n - libzlib=1.2.11=h166bdaf_1014\n - llvm-openmp=13.0.1=he0ac6c6_1\n - locket=0.2.1=py37h06a4308_2\n - lz4-c=1.9.3=h9c3ff4c_1\n - markupsafe=2.1.1=py37h540881e_1\n - mistune=0.8.4=py37h5e8e339_1005\n - msgpack-python=1.0.3=py37h7cecad7_1\n - multidict=6.0.2=py37h540881e_1\n - ncurses=6.3=h27087fc_1\n - notebook=6.4.0=py37h06a4308_0\n - numexpr=2.8.1=py37hecfb737_0\n - numpy=1.21.6=py37h976b520_0\n - openblas=0.3.20=pthreads_h320a7e8_0\n - openjpeg=2.4.0=hb52868f_1\n - openssl=1.1.1n=h166bdaf_0\n - orc=1.7.1=h1be678f_1\n - pandas=1.3.5=py37h8c16a72_0\n - pandoc=2.18=ha770c72_0\n - pillow=9.1.0=py37h44f0d7a_2\n - psutil=5.9.0=py37h540881e_1\n - pthread-stubs=0.4=h36c2ea0_1001\n - pyarrow=6.0.1=py37h20dbb2a_5_cpu\n - pyrsistent=0.18.1=py37h540881e_1\n - pysocks=1.7.1=py37h89c1867_5\n - python-snappy=0.6.0=py37h614b16a_2\n - python=3.7.11=h12debd9_0\n - python_abi=3.7=1_cp37m\n - pyyaml=5.4.1=py37h5e8e339_1\n - pyzmq=22.3.0=py37h0c0c2a8_2\n - re2=2021.11.01=h9c3ff4c_0\n - readline=8.1.2=h7f8727e_1\n - s2n=1.3.0=h9b69904_0\n - setuptools=62.1.0=py37h89c1867_0\n - snappy=1.1.9=h295c915_0\n - sqlite=3.38.2=h4ff8645_0\n - terminado=0.13.3=py37h89c1867_1\n - thrift=0.16.0=py37hd23a5d3_1\n - tk=8.6.12=h27826a3_0\n - tornado=5.1.1=py37h14c3975_1000\n - wrapt=1.14.0=py37h540881e_1\n - xorg-libxau=1.0.9=h7f98852_0\n - xorg-libxdmcp=1.1.3=h7f98852_0\n - xz=5.2.5=h516909a_1\n - yaml=0.2.5=h7f98852_2\n - yarl=1.7.2=py37h540881e_2\n - zeromq=4.3.4=h9c3ff4c_1\n - zlib=1.2.11=h166bdaf_1014\n - zstd=1.5.2=ha95c52a_0\n osx-64:\n - abseil-cpp=20210324.2=he49afe7_0\n - aiohttp=3.8.1=py37h69ee0a8_1\n - appnope=0.1.3=pyhd8ed1ab_0\n - argon2-cffi-bindings=21.2.0=py37h69ee0a8_2\n - arrow-cpp=6.0.1=py37hc0a5d74_5_cpu\n - aws-c-auth=0.6.8=h8f5e388_1\n - aws-c-cal=0.5.12=hda7428a_7\n - aws-c-common=0.6.17=h0d85af4_0\n - aws-c-compression=0.2.14=h8451fdb_7\n - aws-c-event-stream=0.2.7=ha663dc4_32\n - aws-c-http=0.6.10=heb655c9_3\n - aws-c-io=0.10.14=h3cf48f6_1\n - aws-c-mqtt=0.7.10=h6d234a2_0\n - aws-c-s3=0.1.29=h73af6b9_0\n - aws-c-sdkutils=0.1.1=h8451fdb_4\n - aws-checksums=0.1.12=h8451fdb_6\n - aws-crt-cpp=0.17.10=haa61d5f_5\n - aws-sdk-cpp=1.9.160=h075ee0a_0\n - blas-devel=3.9.0=14_osx64_openblas\n - bokeh=2.4.2=py37hf985489_1\n - bottleneck=1.3.4=py37h49e79e5_1\n - brotlipy=0.7.0=py37h69ee0a8_1004\n - bzip2=1.0.8=h0d85af4_4\n - c-ares=1.18.1=h0d85af4_0\n - ca-certificates=2022.3.29=hecd8cb5_0\n - certifi=2021.10.8=py37hf985489_2\n - cffi=1.15.0=py37hc55c11b_1\n - click=8.1.2=py37hf985489_0\n - cramjam=2.5.0=py37h5210ebb_0\n - cryptography=36.0.2=py37h20b3391_1\n - cytoolz=0.11.2=py37h69ee0a8_2\n - debugpy=1.6.0=py37h0582d14_0\n - distributed=2021.10.0=py37hecd8cb5_0\n - docutils=0.17.1=py37hf985489_1\n - fastparquet=0.7.1=py37h032687b_0\n - freetype=2.11.0=hd8bbffd_0\n - frozenlist=1.3.0=py37h69ee0a8_1\n - gflags=2.2.2=hb1e8313_1004\n - giflib=5.2.1=hbcb3906_2\n - glog=0.5.0=h25b26a9_0\n - grpc-cpp=1.42.0=h6da9ac5_1\n - importlib-metadata=4.11.3=py37hf985489_1\n - ipykernel=6.11.0=py37h0a7177a_0\n - ipython=7.32.0=py37hf985489_0\n - jbig=2.1=h0d85af4_2003\n - jedi=0.18.1=py37hf985489_1\n - jpeg=9e=h5eb16cf_1\n - jupyter_core=4.9.2=py37hf985489_0\n - krb5=1.19.3=hb49756b_0\n - lcms2=2.12=h577c468_0\n - lerc=3.0=he49afe7_0\n - libblas=3.9.0=14_osx64_openblas\n - libbrotlicommon=1.0.9=h5eb16cf_7\n - libbrotlidec=1.0.9=h5eb16cf_7\n - libbrotlienc=1.0.9=h5eb16cf_7\n - libcblas=3.9.0=14_osx64_openblas\n - libcurl=7.82.0=h9f20792_0\n - libcxx=13.0.1=hc203e6f_0\n - libdeflate=1.10=h0d85af4_0\n - libedit=3.1.20210910=hca72f7f_0\n - libev=4.33=haf1e3a3_1\n - libevent=2.1.12=h0a4fc7d_0\n - libffi=3.3=h046ec9c_2\n - libgfortran5=9.3.0=h6c81a4c_23\n - libgfortran=5.0.0=9_3_0_h6c81a4c_23\n - liblapack=3.9.0=14_osx64_openblas\n - liblapacke=3.9.0=14_osx64_openblas\n - libnghttp2=1.47.0=h942079c_0\n - libopenblas=0.3.20=openmp_hb3cd9ec_0\n - libpng=1.6.37=h7cec526_2\n - libprotobuf=3.19.4=hcf210ce_0\n - libsodium=1.0.18=hbcb3906_1\n - libssh2=1.10.0=h52ee1ee_2\n - libthrift=0.15.0=h054ceb0_0\n - libtiff=4.3.0=h17f2ce3_3\n - libutf8proc=2.7.0=h0d85af4_0\n - libwebp-base=1.2.2=h0d85af4_1\n - libwebp=1.2.2=h28dabe5_0\n - libxcb=1.13=h0d85af4_1004\n - libzlib=1.2.11=h6c3fc93_1014\n - llvm-openmp=13.0.1=hcb1a161_1\n - locket=0.2.1=py37hecd8cb5_2\n - lz4-c=1.9.3=he49afe7_1\n - markupsafe=2.1.1=py37h69ee0a8_1\n - mistune=0.8.4=py37h271585c_1005\n - msgpack-python=1.0.3=py37h18621fa_1\n - multidict=6.0.2=py37h69ee0a8_1\n - ncurses=6.3=h96cf925_1\n - notebook=6.4.0=py37hecd8cb5_0\n - numexpr=2.8.1=py37h9c3cb84_0\n - numpy=1.21.6=py37h345d48f_0\n - openblas=0.3.20=openmp_h5ad848b_0\n - openjpeg=2.4.0=h6e7aa92_1\n - openssl=1.1.1n=h6c3fc93_0\n - orc=1.7.1=h84518c8_1\n - pandas=1.3.5=py37h743cdd8_0\n - pandoc=2.18=h694c41f_0\n - pillow=9.1.0=py37h2540ef4_2\n - psutil=5.9.0=py37h69ee0a8_1\n - pthread-stubs=0.4=hc929b4f_1001\n - pyarrow=6.0.1=py37hd1ae41a_5_cpu\n - pyrsistent=0.18.1=py37h69ee0a8_1\n - pysocks=1.7.1=py37hf985489_5\n - python-snappy=0.6.0=py37h1f5a272_2\n - python=3.7.11=h88f2d9e_0\n - python_abi=3.7=2_cp37m\n - pyyaml=5.4.1=py37h271585c_1\n - pyzmq=22.3.0=py37h8f778e5_1\n - re2=2021.11.01=he49afe7_0\n - readline=8.1.2=hca72f7f_1\n - setuptools=62.1.0=py37hf985489_0\n - snappy=1.1.9=he9d5cce_0\n - sqlite=3.38.2=hb516253_0\n - terminado=0.13.3=py37hf985489_1\n - thrift=0.16.0=py37h0582d14_1\n - tk=8.6.12=h5dbffcc_0\n - tornado=5.1.1=py37h1de35cc_1000\n - wrapt=1.14.0=py37h69ee0a8_1\n - xorg-libxau=1.0.9=h35c211d_0\n - xorg-libxdmcp=1.1.3=h35c211d_0\n - xz=5.2.5=haf1e3a3_1\n - yaml=0.2.5=h0d85af4_2\n - yarl=1.7.2=py37h69ee0a8_2\n - zeromq=4.3.4=he49afe7_1\n - zlib=1.2.11=h6c3fc93_1014\n - zstd=1.5.2=h582d3a0_0\n" }, { "path": "anaconda-project.yml", "content": "name: pyviz.org\n\ndescription: pyviz.org\n\ncommands:\n build_cache:\n unix: |\n python tools/conda_downloads.py\n BADGE=stars python tools/build_cache.py\n BADGE=contributors python tools/build_cache.py\n BADGE=license python tools/build_cache.py\n BADGE=pypi_downloads python tools/build_cache.py\n build_website:\n unix: |\n python tools/build.py\n mv tools/index.rst doc/tools.rst\n nbsite generate-rst --org pyviz --project-name pyviz\n nbsite build --what=html --output=builtdocs\n\nchannels:\n- defaults\n- pyviz\n- conda-forge\n\npackages:\n- python==3.7.11\n- jinja2==3.0.1\n- markdown==3.3.4\n- nbsite==0.6.7\n- pyyaml==5.4.1\n- requests==2.26.0\n- tornado==5.1.1\n- m2r2==0.3.1\n- colorcet==2.0.6\n- fastparquet==0.7.1\n- intake==0.6.3\n- intake-parquet==0.2.3\n- s3fs==2021.8.0\n- python-snappy==0.6.0\n\nplatforms:\n- linux-64\n- osx-64\n" }, { "path": "doc/_static/custom.css", "content": "div.body {\n max-width: 2000px;\n}\n\niframe {\n -moz-transform: scale(0.25, 0.25);\n -webkit-transform: scale(0.25, 0.25);\n -o-transform: scale(0.25, 0.25);\n -ms-transform: scale(0.25, 0.25);\n transform: scale(0.25, 0.25);\n -moz-transform-origin: top left;\n -webkit-transform-origin: top left;\n -o-transform-origin: top left;\n -ms-transform-origin: top left;\n transform-origin: top left;\n\n width: 3100px;\n margin-right: -2500px;\n height: 1600px;\n margin-bottom: -1200px;\n}\n\n#tools-wrapper a {\n\ttext-decoration:none;\n}\n\n#tools-wrapper .sponsor-logo, #tools-wrapper .builton-logo {\n max-height: 20px;\n}\n\n#tools-wrapper .empty-cell {\n text-align: center;\n}\n\n#tools-wrapper table img {\n max-width: fit-content;\n }\n" }, { "path": "doc/conf.py", "content": "# noqa\nfrom nbsite.shared_conf import *\n\nproject = u'PyViz'\nauthors = u'PyViz authors'\ncopyright = u' 2019, ' + authors\ndescription = 'How to solve visualization problems with Python tools.'\n\nversion = release = '0.0.1'\nextensions.extend(['m2r2'])\n\nhtml_static_path += ['_static']\nhtml_favicon = '_static/favicon.ico'\nhtml_theme_options = {\n 'logo': 'logo.png',\n 'logo_name': False,\n 'page_width': '90%',\n 'font_family': \"Ubuntu, sans-serif\",\n 'font_size': '0.9em',\n 'link': '#347ab4',\n 'link_hover': '#1c4669',\n 'extra_nav_links': {\n 'Github': 'https://github.com/pyviz/website',\n },\n 'show_powered_by': False,\n}\n\nhtml_context.update({\n 'PROJECT': project,\n 'DESCRIPTION': description,\n 'AUTHOR': authors,\n # WEBSITE_SERVER is optional for tests and local builds, but allows defining a canonical URL for search engines\n 'WEBSITE_SERVER': 'https://pyviz.org',\n})\n" }, { "path": "doc/dashboarding/index.md", "content": "# Dashboarding tools\n\nJust 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:\n\n- [Dash](https://plot.ly/products/dash) (from [Plotly](https://plot.ly)); see the [blog post](https://medium.com/@plotlygraphs/introducing-dash-5ecf7191b503)\n- [Panel](https://panel.pyviz.org) (from [Anaconda](http://anaconda.com)); see the [blog post](https://medium.com/@philipp.jfr/panel-announcement-2107c2b15f52)\n- [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 \n[jupyter-flex](https://github.com/danielfrg/jupyter-flex) or templates like [voila-vuetify](https://github.com/voila-dashboards/voila-vuetify).\n- [Streamlit](https://www.streamlit.io); see the [blog post](https://towardsdatascience.com/coding-ml-tools-like-you-code-ml-models-ddba3357eace)\n\nSince 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:\n\n- [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.\n\n- [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)\n 30 Mar 2021: Stephen Kilcommins. Comparing Streamlit, Dash, Voilà, and Panel for dashboarding. Links to more detailed explorations for each library individually.\n\n- [Are Dashboards for Me?](https://towardsdatascience.com/are-dashboards-for-me-7f66502986b1)\n 7 Jul 2020: Dan Lester. Overview of Python and R dashboard tools, including Voila, ipywidgets, binder, Shiny, Dash, Streamlit, Bokeh, and Panel.\n\nThere are also other tools that can be used for some aspects of dashboarding as well as many other tasks:\n\n- [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).\n\n- [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).\n\n- [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.\n\n- [Bowtie](https://github.com/jwkvam/bowtie) (from Jacques Kvam) allows users to build dashboards in pure Python.\n\n- [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.\n" }, { "path": "doc/dashboarding/index.rst", "content": ".. mdinclude:: index.md\n\n.. toctree::\n :titlesonly:\n :hidden:\n :maxdepth: 2\n\n Dash \n Panel \n Voila \n" }, { "path": "doc/high-level/index.md", "content": "# High-level tools\n\nThe 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.\n\n\n## Pandas .plot() API\n\nThe 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.\n\nThe 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:\n\n- [Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html) -- Matplotlib-based API included with Pandas. Static PNG output in Jupyter notebooks.\n- [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.\n- [hvPlot](https://hvplot.pyviz.org) -- HoloViews and Bokeh-based interactive plots for Pandas, GeoPandas, xarray, Dask, Intake, and Streamz data.\n- [Pandas Bokeh](https://github.com/PatrikHlobil/Pandas-Bokeh) -- Bokeh-based interactive plots, for Pandas, GeoPandas, and PySpark data.\n- [Cufflinks](https://github.com/santosjorge/cufflinks) -- Plotly-based interactive plots for Pandas data.\n- [PdVega](https://altair-viz.github.io/pdvega) -- Vega-lite-based, JSON-encoded interactive plots for Pandas data.\n\n\n## Other high-level APIs\n\n- [Seaborn](https://seaborn.pydata.org) -- Matplotlib-based high-level interface for drawing statistical graphics.\n- [Altair](https://altair-viz.github.io/) -- Declarative Vega-lite-based interactive plots.\n- [HoloViews](https://holoviews.org) -- Declarative Bokeh, Matplotlib, or Plotly-based interactive plots for tidy data.\n- [Chartify](https://github.com/spotify/chartify) -- Bokeh-based interactive plots for tidy data.\n- [Plotly Express](https://www.plotly.express/) -- Plotly-based interactive plots.\n" }, { "path": "doc/high-level/index.rst", "content": ".. mdinclude:: index.md\n\n.. toctree::\n :titlesonly:\n :hidden:\n :maxdepth: 2\n\n Pandas .plot \n xarray .plot \n hvPlot \n Pandas Bokeh \n Cufflinks \n PdVega \n Seaborn \n Altair \n HoloViews \n Chartify \n Plotly Express \n" }, { "path": "doc/index.md", "content": "# Python tools for data visualization\n\nWelcome 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:\n\n - [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.\n - [High-level tools](high-level/index.html) for getting started with Python viz, creating powerful plots in just a few lines of code.\n - [All tools](tools.html) available for doing viz in Python OSS, as a live table for comparing maturity, popularity, and support.\n - [Dashboarding](dashboarding/index.html) tools for sharing live Python-backed visualizations.\n - [SciVis](scivis/index.html) tools for rendering data embedded in three-dimensional space.\n - [Tutorials](tutorials/index.html) showing how to use the available tools to accomplish various categories of tasks.\n - [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.\n\n## This site\n\nIf 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.\n\n**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 \nPyViz smart-home visualization tool, check out his `paper `_.\n" }, { "path": "doc/index.rst", "content": ".. mdinclude:: index.md\n\n.. toctree::\n :titlesonly:\n :hidden:\n :maxdepth: 2\n\n Home \n Overviews \n High-level tools \n All tools \n Dashboarding \n SciVis \n Tutorials \n" }, { "path": "doc/overviews/index.md", "content": "# Overviews\n\nThe 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.\n\n\n\nAdaptation of Jake VanderPlas' graphic about the Python visualization landscape, by Nicolas P. Rougier\n\n- [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.\n\n- [7 Python Libraries That Make Visualization Beautiful](https://medium.com/@abdur.rahman12/7-python-libraries-that-make-visualization-beautiful-3d2ffb308611), \n22 Sep 2025: Abdur Rahman. Brief overview of PyWaffle, Plotnine, Datashader, JoyPy, Sankeyview, PyCirclize, and Weave.\n\n- [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. \n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- 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).\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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).\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [Python Dashboarding Shootout and Showdown | PyData Global 2021](https://www.youtube.com/watch?v=4a-Db1zhTEw)\n October 2021: James Bednar, Nicolas Kruchten, Marc Skov Madsen, Sylvain Corlay and Adrien Treuille\n\n- [Why *Interactive* Data Visualization Matters for Data Science in Python | PyData Global 2021](https://www.youtube.com/watch?v=tlcMlOVbEpw)\n October 2021: Nicolas Kruchten\n\n- [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)\n 1 Feb 2021 Stephanie Kirmer. Comparing Matplotlib, Seaborn, Bokeh, Altair, Plotnine, and Plotly, with example github repo for code.\n\n- [Plotly vs. Bokeh: Interactive Python Visualisation Pros and Cons](https://pauliacomi.com/2020/06/07/plotly-v-bokeh.html) \n 7 June 2020 Paul Iacomi. In-depth comparison of Bokeh and Plotly+Dash for dashboarding.\n\n- [Complete Guide to Data Visualization with Python](https://towardsdatascience.com/complete-guide-to-data-visualization-with-python-2dd74df12b5e)\n 29 Feb 2020 Albert Sanchez Lafuente. Example code for Pandas tables, Matplotlib, Seaborn, Bokeh, Altair, and Folium.\n\n- [Python Visualization Landscape](https://medium.com/@lulunana/python-visualization-landscape-3b95ede3d030)\n 24 Oct 2019 Sophia Yang. High-level overview of various categories of Python viz libraries, without example code.\n\n- [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.\n\n- [Python Data Visualization 2018](https://www.anaconda.com/python-data-visualization-2018-why-so-many-libraries)\n 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.\n [Updated in 2019 as an eBook](https://know.anaconda.com/eBook-PyVizeBookLP_ReportRegistration.html?utm_source=pyviz.org&utm_campaign=pyviz&utm_content=ebook).\n\n- [pythonplot.com](http://pythonplot.com)\n 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.\n\n- [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)\n 30 Apr 2018 Karan Bhanot. Blog post comparing plotting business locations using gmplot, geopandas, plotly, and bokeh.\n\n- [Python Data Visualization — Comparing 5 Tools](https://codeburst.io/overview-of-python-data-visualization-tools-e32e1f716d10)\n 6 Dec 2017 Elena Kirzhner, Codeburst. Blog post with simple comparisons of Pandas, Seaborn, Bokeh, Pygal, and Plotly code and output.\n\n- [10 Heatmaps 10 Libraries](https://blog.algorexhealth.com/2017/09/10-heatmaps-10-python-libraries/)\n 10 Sep 2017 Luke Shulman. Comparing heatmap code across 10 different viz libraries.\n\n- [The Python Visualization Landscape](https://www.youtube.com/watch?v=FytuB8nFHPQ)\n 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).\n\n- [Python Graph Gallery](https://python-graph-gallery.com)\n 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).\n\n- [Overview of Python Visualization Tools](https://pbpython.com/visualization-tools-1.html)\n 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.\n\n- [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.\n\n- [10 Useful Python Data Visualization Libraries for Any Discipline](https://mode.com/blog/python-data-visualization-libraries)\n 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.\n\n- [Comparing 7 Tools For Data Visualization in Python](https://www.dataquest.io/blog/python-data-visualization-libraries)\n 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.\n\n\n" }, { "path": "doc/overviews/index.rst", "content": ".. mdinclude:: index.md\n\n.. toctree::\n :titlesonly:\n :hidden:\n :maxdepth: 2\n" }, { "path": "doc/scivis/index.md", "content": "# SciVis Libraries\n\nMost 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.\n\nSciVis libraries supporting Python:\n\n- 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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n\n- [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.\n" }, { "path": "doc/scivis/index.rst", "content": ".. mdinclude:: index.md\n\n.. toctree::\n :titlesonly:\n :hidden:\n :maxdepth: 2\n\n VTK \n VisPy \n Glumpy \n GR \n Mayavi \n ParaView \n yt \n PyVista \n vedo \n" }, { "path": "doc/tools.md", "content": "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).\n" }, { "path": "doc/tutorials/index.md", "content": "# Tutorials\n\nMost 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.\n\n- [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.\n\n- [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)]\n\n- [Jupyter widgets tutorial](https://github.com/jupyter-widgets/tutorial): How to make interactive plots, apps, and dashboards using \nipywidgets, 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)]\n\n- [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)]\n\n\n" }, { "path": "doc/tutorials/index.rst", "content": ".. want to include these in the toctree\n\n.. mdinclude:: index.md\n\n.. toctree::\n :titlesonly:\n :hidden:\n :maxdepth: 2\n\n Bokeh tutorial \n HoloViz \n Jupyter widgets tutorial \n Matplotlib tutorial \n" }, { "path": "tools/README.md", "content": "## PyViz Tools\n\nThis directory is used to generate a tools dashboard for comparing various Python visualization packages.\n\nThe 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.\n\nThe 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).\n\n\n### Introductory text\n\nThe intro text is located in `doc/tools.md`, whose contents will be included immediately after the title on the page.\n\nEvery section can also have an `intro` in `tools.yml`. This text should also be written as markdown.\n\n### Adding a tool\n\nTo 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.\n\n** Minimal entry **\n\n```yaml\n - repo: SciTools/cartopy\n```\n\nTo 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.\n\n** More complete entry **\n\n```yaml\n - repo: SciTools/cartopy\n sponsors: [metoffice]\n site: scitools.org.uk/cartopy\n conda_channel: conda-forge\n badges: travis, coveralls, pypi, conda\n```\n\n### Adding a sponsor\n\nIf 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`:\n\n```yaml\nnumfocus:\n label: NumFocus\n url: https://numfocus.org\n logo: _static/badges/numfocus.png\n```\n\nIf using a logo, don't forget to include a small version of the logo at `doc/_static/badges/`.\n" }, { "path": "tools/build.py", "content": "#!/usr/bin/env python\nimport datetime\nimport os\nfrom jinja2 import Template\nfrom yaml import safe_load\nfrom markdown import markdown\n\n\nhere = os.path.abspath(os.path.dirname(__file__))\ntoday = datetime.date.today().strftime(\"%B %-d, %Y\")\n\nprint(\"Opening config file\")\nwith open(os.path.join(here, 'tools.yml')) as f:\n config = safe_load(f)\n\ntry:\n with open(os.path.join(here, 'pypi_invalid_badges.txt')) as f:\n pypi_invalid_badges = f.read().splitlines()\nexcept FileNotFoundError:\n pypi_invalid_badges = []\n\nfor section in config:\n print(f\"Building {section.get('name', '')}\")\n if section.get('intro'):\n section['intro'] = markdown(section['intro'])\n for package in section['packages']:\n try:\n package['user'], package['name'] = package['repo'].split('/')\n except:\n raise Warning('Package.repo is not in correct format', package)\n package['conda_package'] = package.get('conda_package', package['name'])\n package['pypi_name'] = package.get('pypi_name', package['name'])\n\n if package['pypi_name'] in pypi_invalid_badges:\n package['pypi_invalid'] = True\n\n if package.get('badges'):\n package['badges'] = [x.strip() for x in package['badges'].split(',')]\n else:\n package['badges'] = ['pypi', 'conda']\n if package.get('conda_channel') and 'conda' not in package['badges']:\n package['badges'].append('conda')\n if package.get('sponsors') and 'sponsor' not in package['badges']:\n package['badges'].append('sponsor')\n if package.get('builtons') and 'builton' not in package['badges']:\n package['badges'].append('builton')\n if package.get('site') and 'site' not in package['badges']:\n package['badges'].append('site')\n if package.get('dormant') and 'dormant' not in package['badges']:\n package['badges'].append('dormant')\n\n if 'rtd' in package['badges'] and 'rtd_name' not in package:\n package['rtd_name'] = package['name']\n if 'conda' in package['badges'] and 'conda_channel' not in package:\n package['conda_channel'] = 'anaconda'\n if 'site' in package['badges']:\n if 'site' not in package:\n package['site'] = '{}.org'.format(package['name'])\n package['site_protocol'] = 'https'\n else:\n package['site_protocol'], package['site'] = package['site'].rstrip('/').split('://')\n\nwith open(os.path.join(here, 'sponsors.yml')) as f:\n sponsors = safe_load(f)\n\nwith open(os.path.join(here, 'builtons.yml')) as f:\n builtons = safe_load(f)\n\ntemplate = Template(open(os.path.join(here, 'template.html'), 'r').read())\n\nwith open(os.path.join(here, 'index.rst'), 'w') as f:\n f.write(\"All Tools\\n\")\n f.write(\"=========\\n\\n\")\n f.write(\".. mdinclude:: tools.md\\n\\n\")\n f.write(\".. raw:: html\\n\\n\")\n f.write(template.render(config=config, sponsors=sponsors, builtons=builtons, date=today))\n" }, { "path": "tools/build_cache.py", "content": "#!/usr/bin/env python\n\nimport os\nimport time\nfrom yaml import safe_load\nimport requests\n\nhere = os.path.abspath(os.path.dirname(__file__))\ncache_path = os.path.join(here, '..', 'doc', '_static', 'cache')\nbadge = os.getenv('BADGE')\n\ncache = {\n # Override the label with a space to disable it and reduce the badge size\n \"stars\": \"https://img.shields.io/github/stars/{repo}.svg?style=flat&logo=github&color=blue&label=%20\",\n \"contributors\": \"https://img.shields.io/github/contributors/{repo}.svg?style=flat&logo=github&color=blue&label=%20\",\n \"pypi_downloads\": \"https://img.shields.io/pypi/dm/{pypi_name}.svg?label=pypi\",\n \"license\": \"https://img.shields.io/pypi/l/{pypi_name}.svg?label\",\n}\nurl = cache.get(badge)\nif url is None:\n raise ValueError((f'{badge} not in {\", \".join(cache.keys())}, use env '\n 'var BADGE to set.'))\n\n# The pypi download badge cannot occasionally be properly fetched\n# by shields and cached here. We list those that failed, so that\n# in the template we can put the actual badge link rather than\n# the cached one.\npypi_invalid_file = os.path.join(here, \"pypi_invalid_badges.txt\")\nif os.path.exists(pypi_invalid_file):\n os.remove(pypi_invalid_file)\n\nprint(f\"\\nBuilding a cache of {badge} badges.\\n\")\n\nif not os.path.exists(cache_path):\n os.mkdir(cache_path)\n\nwith open(os.path.join(here, 'tools.yml')) as f:\n config = safe_load(f)\n\nfor section in config:\n print(f\"Building cache for {section.get('name', '')}\")\n for package in section['packages']:\n try:\n package['user'], package['name'] = package['repo'].split('/')\n except:\n raise Warning('Package.repo is not in correct format', package)\n package['pypi_name'] = package.get('pypi_name', package['name'])\n\n print(f\" * package: {package.get('pypi_name', '')}\")\n rendered_url = url.format(repo=package['repo'], pypi_name=package['pypi_name'])\n r = requests.get(rendered_url)\n content = r.content\n # Pypistats implements IP rate limiting, so let's slow things\n # down and retry a few times when failing.\n if badge == 'pypi_downloads':\n\n time.sleep(2.5)\n \n nb_retries = 4\n retry_duration = 5 # In seconds, multiplied by two after each retry.\n retry_count = 1\n while 'pypi: invalid' in r.text and retry_count <= nb_retries:\n print(f\"PyPI badge returned as 'invalid'. Retrying after {retry_duration} seconds.\")\n time.sleep(retry_duration)\n r = requests.get(rendered_url)\n content = r.content\n if retry_count == nb_retries:\n print(f\"Failed a getting a valid Pypi Downloads badge for {package['pypi_name']}.\")\n break\n retry_count += 1\n retry_duration *= 2\n \n if 'pypi: invalid' in r.text:\n with open(pypi_invalid_file, 'a') as f:\n f.write(package['pypi_name'] + '\\n')\n\n with open(os.path.join(cache_path, f\"{package['name']}_{badge}_badge.svg\"), 'wb') as f:\n f.write(content)\n" }, { "path": "tools/builtons.yml", "content": "bokeh:\n label: Bokeh\n url: https://docs.bokeh.org/en/latest/\n logo: _static/badges/builtons/bokeh.png\n\nplotly:\n label: Plotly\n url: https://plotly.com/\n logo: _static/badges/builtons/plotly.png\n\nmatplotlib:\n label: Matplotlib\n url: https://matplotlib.org/\n logo: _static/badges/builtons/matplotlib.png\n\nvega:\n label: Vega\n url: https://vega.github.io/vega/\n logo: _static/badges/builtons/vega.png\n\nvtk:\n label: VTK\n url: https://vtk.org/\n logo: _static/badges/builtons/vtk.png\n\nopengl:\n label: OpenGL\n url: https://www.opengl.org/\n logo: _static/badges/builtons/opengl.png\n\nwebgl:\n label: WebGL\n url: https://www.khronos.org/webgl/\n logo: _static/badges/builtons/webgl.png\n\nvispy:\n label: VisPy\n url: https://vispy.org/\n logo: _static/badges/builtons/vispy.png\n\nleaflet:\n label: Leaflet\n url: https://leafletjs.com/\n logo: _static/badges/builtons/leaflet.png\n\ngraphviz:\n label: Graphviz\n url: https://graphviz.org/\n logo: _static/badges/builtons/graphviz.png\n\nd3:\n label: D3\n url: https://d3js.org/\n logo: _static/badges/builtons/d3.png\n\nqt:\n label: qt\n url: https://qt.io/\n logo: _static/badges/builtons/qt.png\n\ngmt:\n label: gmt\n url: https://www.generic-mapping-tools.org/\n logo: _static/badges/builtons/gmt.png\n\nvulkan:\n label: vulkan\n url: https://www.vulkan.org/\n logo: _static/badges/builtons/vulkan.png\n\npanel:\n label: Panel\n url: https://panel.holoviz.org\n logo: _static/badges/builtons/panel.png\n\npyvista:\n label: PyVista\n url: https://docs.pyvista.org/version/stable/\n logo: _static/badges/builtons/pyvista.png\n" }, { "path": "tools/conda_downloads.py", "content": "#!/usr/bin/env python\n\"\"\"\nRun this script at the beginning of each month to build new conda downloads badges\nfrom the previous month.\n\"\"\"\n\nimport os\nfrom yaml import safe_load\nimport requests\nimport datetime\nimport intake\nimport colorcet as cc\nimport numpy as np\n\n\nhere = os.path.abspath(os.path.dirname(__file__))\ncache_path = os.path.join(here, '..', 'doc', '_static', 'cache')\ncat = intake.open_catalog('https://raw.githubusercontent.com/ContinuumIO/anaconda-package-data/master/catalog/anaconda_package_data.yaml')\n\ncolors = cc.palette_n.rainbow[-20:80:-1]\ntop_of_colormap = 1e6\nstep = len(colors) /np.log10(top_of_colormap)\n\ntoday = datetime.date.today()\nfirst = today.replace(day=1)\nlast_month = first - datetime.timedelta(days=1)\ntry:\n monthly = cat.anaconda_package_data_by_month(year=last_month.year, month=last_month.month,\n columns=['pkg_name', 'counts']).to_dask()\nexcept:\n # if the last month isn't available, get the month before\n month_before = last_month.replace(day=1) - datetime.timedelta(days=1)\n monthly = cat.anaconda_package_data_by_month(year=month_before.year, month=month_before.month,\n columns=['pkg_name', 'counts']).to_dask()\nper_package_downloads = monthly.groupby('pkg_name').sum().compute()\n\nif not os.path.exists(cache_path):\n os.mkdir(cache_path)\n\ndef get_conda_badge(conda_package):\n conda_package = conda_package.lower()\n if conda_package in per_package_downloads.index:\n downloads = per_package_downloads.counts.loc[conda_package]\n else:\n downloads = 0\n\n if downloads == 0:\n color_index = 0\n elif downloads > top_of_colormap:\n color_index = -1\n else:\n color_index = int(np.log10(downloads) * step)\n color = colors[color_index][1:]\n\n if downloads > 1e6:\n downloads = '{}M'.format(int(downloads/1e6))\n elif downloads > 1e3:\n downloads = '{}k'.format(int(downloads/1e3))\n else:\n downloads = int(downloads)\n\n return f\"https://img.shields.io/badge/conda-{downloads}/month-{color}.svg\"\n\nwith open(os.path.join(here, 'tools.yml')) as f:\n config = safe_load(f)\n\nfor section in config:\n print(f\"Building conda downloads badge for: {section['name']}\")\n for package in section['packages']:\n try:\n package['user'], package['name'] = package['repo'].split('/')\n except:\n raise Warning('Package.repo is not in correct format', package)\n continue\n url = get_conda_badge(package.get('conda_package', package['name']))\n rendered_url = url\n r = requests.get(rendered_url)\n with open(os.path.join(cache_path, f\"{package['name']}_conda_downloads_badge.svg\"), 'wb') as f:\n f.write(r.content)\n" }, { "path": "tools/sponsors.yml", "content": "numfocus:\n label: NumFocus\n url: https://numfocus.org\n logo: _static/badges/numfocus.png\n\nanaconda:\n label: Anaconda\n url: https://www.anaconda.com\n logo: _static/badges/anaconda.png\n\nmetoffice:\n label: Met Office\n url: https://www.metoffice.gov.uk\n logo: _static/badges/metoffice.png\n\nspotify:\n label: Spotify\n url: https://www.spotify.com\n logo: _static/badges/spotify.png\n\nplotly:\n label: Plot.ly\n url: https://plot.ly\n logo: _static/badges/plotly.png\n\nkitware:\n label: Kitware\n url: https://www.kitware.com\n logo: _static/badges/kitware.svg\n\nEMBL:\n label: EMBL\n url: https://www.embl.es\n logo: _static/badges/embl.png\n\nenthought:\n label: Enthought\n url: https://www.enthought.com\n logo: _static/badges/enthought.svg\n\nnvidia:\n label: NVIDIA\n url: https://www.nvidia.com\n logo: _static/badges/nvidia.jpg\n\nposit:\n label: Posit, PBC\n URL: https://posit.co/\n logo: _static/badges/positpbc.png\n\nsandia:\n label: Sandia\n url: https://www.sandia.gov\n logo: _static/badges/sandia.png\n\nquantstack:\n label: QuantStack\n url: http://quantstack.net\n logo: _static/badges/quantstack.svg\n\nvaexio:\n label: vaex.io\n url: https://vaex.io\n logo: _static/badges/vaexio.png\n\nunidata:\n label: Unidata\n url: https://www.unidata.ucar.edu\n logo: _static/badges/unidata.png\n\ncgs:\n label: cgs\n url: https://spatial.ucr.edu\n logo: _static/badges/cgs.svg\n\nstreamlit:\n label: streamlit\n url: https://streamlit.io\n logo: _static/badges/streamlit.png\n\njetbrains:\n label: jetbrains\n url: https://www.jetbrains.com\n logo: _static/badges/jetbrains.png\n\nh2o:\n label: h2o.ai\n url: https://h2o.ai\n logo: _static/badges/h2o.png\n\nhuggingface:\n label: huggingface\n url: https://huggingface.co\n logo: _static/badges/huggingface.png\n\nmljar:\n label: mljar\n url: https://mljar.com\n logo: _static/badges/mljar.png\n\nwandb:\n label: Weights and Biases\n url: https://wandb.ai\n logo: _static/badgets/weights_and_biases.svg\n\nmckinsey:\n label: McKinsey\n url: https://mckinsey.com\n\nreflex:\n label: Reflex\n url: https://reflex.dev\n logo: _static/badges/reflex.svg\n\nzauberzeug:\n label: Zauberzeug\n url: https://zauberzeug.com\n logo: _static/badges/zauberzeug.webp\n\nwidgetti:\n label: Widgetti\n url: https://widgetti.io/\n logo: _static/badges/widgetti.png\n\nmarimo:\n label: marimo\n url: https://marimo.io/\n logo: _static/badges/marimo.png\n\ntaipy:\n label: taipy\n url: https://www.taipy.io/\n logo: _static/badges/taipy.png\n" }, { "path": "tools/template.html", "content": "
\n Last updated: {{ date }}\n
\n
\n {% for section in config %}\n

{{ section.name }}

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