Repository: pyviz/pyviz.org
Branch: master
Commit: 927b110bca20
Files: 29
Total size: 110.3 KB
Directory structure:
gitextract_479j_ta9/
├── .github/
│ └── workflows/
│ └── docs.yml
├── .gitignore
├── LICENSE.txt
├── README.md
├── anaconda-project-lock.yml
├── anaconda-project.yml
├── doc/
│ ├── _static/
│ │ └── custom.css
│ ├── conf.py
│ ├── dashboarding/
│ │ ├── index.md
│ │ └── index.rst
│ ├── high-level/
│ │ ├── index.md
│ │ └── index.rst
│ ├── index.md
│ ├── index.rst
│ ├── overviews/
│ │ ├── index.md
│ │ └── index.rst
│ ├── scivis/
│ │ ├── index.md
│ │ └── index.rst
│ ├── tools.md
│ └── tutorials/
│ ├── index.md
│ └── index.rst
└── tools/
├── README.md
├── build.py
├── build_cache.py
├── builtons.yml
├── conda_downloads.py
├── sponsors.yml
├── template.html
└── tools.yml
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/workflows/docs.yml
================================================
name: docs
on:
push:
branches:
- master
pull_request:
branches:
- '*'
schedule:
- cron: "0 8 * 1-12 1" # Every monday at 8 am
workflow_dispatch:
permissions:
# To allow the workflow to push to the origin, when actions/checkout is used.
contents: write
jobs:
pre_ci:
runs-on: 'ubuntu-latest'
steps:
- uses: actions/checkout@v4
with:
# required for PRs
fetch-depth: 2
- name: Get commit message
id: get_commit_message
run: |
if [[ '${{ github.event_name }}' == 'push' ]]; then
echo "commit_message=$(git log --format=%B -n 1 HEAD)" >> $GITHUB_OUTPUT
elif [[ '${{ github.event_name }}' == 'pull_request' ]]; then
echo "commit_message=$(git log --format=%B -n 1 HEAD^2)" >> $GITHUB_OUTPUT
fi
outputs:
commit_message:
echo "${{ steps.get_commit_message.outputs.commit_message }}"
build_docs:
name: Documentation
runs-on: 'ubuntu-latest'
needs: pre_ci
if: "contains(needs.pre_ci.outputs.commit_message, 'website_dev') || github.ref == 'refs/heads/master'"
timeout-minutes: 120
defaults:
run:
shell: bash -l {0}
steps:
- uses: actions/checkout@v3
- uses: conda-incubator/setup-miniconda@v2
with:
auto-activate-base: true
activate-environment: ""
miniconda-version: "latest"
- name: conda setup
run: |
conda install anaconda-project
anaconda-project prepare
- name: Build cache
run: |
anaconda-project run build_cache
git config user.name github-actions
git config user.email github-actions@github.com
mv ./doc/_static/cache ./tmp
git fetch origin cache
git checkout cache
mv ./tmp/* ./doc/_static/cache
git add -f ./doc/_static/cache
ls ./doc/_static/cache
git commit -m "adding cached badges"
git push -f origin HEAD:cache
- uses: actions/checkout@v3
with:
clean: false
- name: Build website
run: |
git checkout -b deploy-tmp
git fetch origin cache # all cached badges are in this branch
git checkout origin/cache -- ./doc/_static/cache
anaconda-project run build_website
- name: git status
run: |
git status
git diff
- name: Deploy main
if: ${{ github.ref == 'refs/heads/master' }}
uses: peaceiris/actions-gh-pages@v3
with:
publish_dir: ./builtdocs
cname: pyviz.org
github_token: ${{ secrets.GITHUB_TOKEN }}
force_orphan: true
================================================
FILE: .gitignore
================================================
# Byte-compiled / DLL / optimized files...
__pycache__/
# OSX
.DS_STORE
# Jupyter notebook
*.ipynb_checkpoints/
# nbsite output
builtdocs/
doc/tools.rst
doc/_static/cache/
# Site building temp output
tools/pypi_invalid_badges.txt
# pytest
.pytest_cache
# anaconda-project
envs
*~
================================================
FILE: LICENSE.txt
================================================
Creative Commons Attribution 4.0 International Public License (CC-BY)
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consequential, punitive, exemplary, or other losses, costs,
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of this Public License.
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Section 8 - Interpretation.
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not be interpreted to, reduce, limit, restrict, or impose
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c. No term or condition of this Public License will be waived and no
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================================================
FILE: README.md
================================================
<img src="doc/_static/logo.png" width=150><br>
# Python tools for data visualization
| | |
| --- | --- |
| Build Status | [](https://github.com/pyviz/pyviz.org/actions) |
| Website | [](https://github.com/pyviz/pyviz.org/tree/gh-pages) [](https://pyviz.org) |
Source material to build [pyviz.org](https://pyviz.org). This site is owned by [NumFocus](https://numfocus.org) and is currently managed by Anaconda, Inc. for the community, but is open to everyone involved in Python data visualization; see [#2](https://github.com/pyviz/website/issues/2).
## Building pyviz.org
Whenever a PR is merged, or a commit is pushed to master, a Github Actions job is triggered that builds pyviz.org.
## Building dev site
To build the [dev site](https://pyviz-dev.github.io/pyviz.org), just push a commit containing the string: `website_dev`. This will start a job on Github Actions that when complete will deploy to the dev site.
**NOTE:** This will work on any branch, so it is recommended that you use it to test builds on PRs, just try not to trample on other people's toes.
## Building website locally
Install anaconda-project:
```bash
conda install anaconda-project
```
Build the cached badges:
```bash
anaconda-project build_cache
```
Build the website:
```bash
anaconda-project build_website
```
View the website locally:
```bash
python -m http.server 8000
```
## Adding a tool to the "All Tools" page
See the [README](tools/README.md) in the tools directory for instructions on adding a tool to the "All Tools" page.
================================================
FILE: anaconda-project-lock.yml
================================================
# This is an Anaconda project lock file.
# The lock file locks down exact versions of all your dependencies.
#
# In most cases, this file is automatically maintained by the `anaconda-project` command or GUI tools.
# It's best to keep this file in revision control (such as git or svn).
# The file is in YAML format, please see http://www.yaml.org/start.html for more.
#
#
# Set to false to ignore locked versions.
#
locking_enabled: true
#
# A key goes in here for each env spec.
#
env_specs:
default:
locked: true
env_spec_hash: 978f894d8eec02b2a98d51e5ac316d783e8c9e0e
platforms:
- linux-64
- osx-64
packages:
unix:
- aiobotocore=1.4.0=pyhd8ed1ab_0
- aioitertools=0.10.0=pyhd8ed1ab_0
- aiosignal=1.2.0=pyhd8ed1ab_0
- alabaster=0.7.12=py_0
- appdirs=1.4.4=pyh9f0ad1d_0
- argon2-cffi=21.3.0=pyhd8ed1ab_0
- async-timeout=4.0.2=pyhd8ed1ab_0
- asynctest=0.13.0=py_0
- attrs=21.4.0=pyhd8ed1ab_0
- babel=2.9.1=pyh44b312d_0
- backcall=0.2.0=pyh9f0ad1d_0
- backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
- backports=1.1=pyhd3eb1b0_0
- beautifulsoup4=4.11.1=pyha770c72_0
- blas=2.114=openblas
- bleach=5.0.0=pyhd8ed1ab_0
- botocore=1.20.106=pyhd8ed1ab_0
- charset-normalizer=2.0.12=pyhd8ed1ab_0
- cloudpickle=2.0.0=pyhd8ed1ab_0
- colorama=0.4.4=pyh9f0ad1d_0
- colorcet=2.0.6=py_0
- dask-core=2021.10.0=pyhd3eb1b0_0
- dask=2021.10.0=pyhd8ed1ab_0
- decorator=5.1.1=pyhd8ed1ab_0
- defusedxml=0.7.1=pyhd8ed1ab_0
- entrypoints=0.4=pyhd8ed1ab_0
- flit-core=3.7.1=pyhd8ed1ab_0
- fsspec=2021.7.0=pyhd8ed1ab_0
- heapdict=1.0.1=py_0
- idna=3.3=pyhd8ed1ab_0
- imagesize=1.3.0=pyhd8ed1ab_0
- importlib_resources=5.7.1=pyhd8ed1ab_0
- intake-parquet=0.2.3=py_0
- intake=0.6.3=pyhd8ed1ab_0
- ipython_genutils=0.2.0=py_1
- jinja2=3.0.1=pyhd8ed1ab_0
- jmespath=0.10.0=pyh9f0ad1d_0
- jsonschema=4.4.0=pyhd8ed1ab_0
- jupyter_client=7.1.2=pyhd8ed1ab_0
- jupyterlab_pygments=0.2.2=pyhd8ed1ab_0
- m2r2=0.3.1=pyhd8ed1ab_1
- markdown=3.3.4=pyhd8ed1ab_0
- matplotlib-inline=0.1.3=pyhd8ed1ab_0
- nbclient=0.6.0=pyhd8ed1ab_0
- nbconvert-core=6.5.0=pyhd8ed1ab_0
- nbconvert-pandoc=6.5.0=pyhd8ed1ab_0
- nbconvert=6.5.0=pyhd8ed1ab_0
- nbformat=5.3.0=pyhd8ed1ab_0
- nbsite=0.6.7=py_0
- nest-asyncio=1.5.5=pyhd8ed1ab_0
- packaging=21.3=pyhd8ed1ab_0
- pandocfilters=1.5.0=pyhd8ed1ab_0
- param=1.12.1=py_0
- parquet-cpp=1.5.1=2
- parso=0.8.3=pyhd8ed1ab_0
- partd=1.2.0=pyhd8ed1ab_0
- pexpect=4.8.0=pyh9f0ad1d_2
- pickleshare=0.7.5=py_1003
- pip=22.0.4=pyhd8ed1ab_0
- prometheus_client=0.14.1=pyhd8ed1ab_0
- prompt-toolkit=3.0.29=pyha770c72_0
- ptyprocess=0.7.0=pyhd3deb0d_0
- pycparser=2.21=pyhd8ed1ab_0
- pyct-core=0.4.8=py_0
- pyct=0.4.8=py_0
- pygments=2.11.2=pyhd8ed1ab_0
- pyopenssl=22.0.0=pyhd8ed1ab_0
- pyparsing=3.0.8=pyhd8ed1ab_0
- python-dateutil=2.8.2=pyhd8ed1ab_0
- python-fastjsonschema=2.15.3=pyhd8ed1ab_0
- pytz=2022.1=pyhd8ed1ab_0
- pyviz_comms=2.2.0=py_0
- requests=2.26.0=pyhd8ed1ab_1
- s3fs=2021.8.0=pyhd8ed1ab_0
- send2trash=1.8.0=pyhd8ed1ab_0
- six=1.16.0=pyh6c4a22f_0
- snowballstemmer=2.2.0=pyhd8ed1ab_0
- sortedcontainers=2.4.0=pyhd8ed1ab_0
- soupsieve=2.3.1=pyhd8ed1ab_0
- sphinx=4.5.0=pyh6c4a22f_0
- sphinxcontrib-applehelp=1.0.2=py_0
- sphinxcontrib-devhelp=1.0.2=py_0
- sphinxcontrib-htmlhelp=2.0.0=pyhd8ed1ab_0
- sphinxcontrib-jsmath=1.0.1=py_0
- sphinxcontrib-qthelp=1.0.3=py_0
- sphinxcontrib-serializinghtml=1.1.5=pyhd8ed1ab_2
- tblib=1.7.0=pyhd8ed1ab_0
- tinycss2=1.1.1=pyhd8ed1ab_0
- toolz=0.11.2=pyhd8ed1ab_0
- traitlets=5.1.1=pyhd8ed1ab_0
- typing-extensions=4.2.0=hd8ed1ab_0
- typing_extensions=4.2.0=pyha770c72_0
- urllib3=1.26.9=pyhd8ed1ab_0
- wcwidth=0.2.5=pyh9f0ad1d_2
- webencodings=0.5.1=py_1
- wheel=0.37.1=pyhd8ed1ab_0
- zict=2.1.0=pyhd8ed1ab_0
- zipp=3.8.0=pyhd8ed1ab_0
linux-64:
- _libgcc_mutex=0.1=conda_forge
- _openmp_mutex=4.5=1_llvm
- abseil-cpp=20210324.2=h9c3ff4c_0
- aiohttp=3.8.1=py37h540881e_1
- argon2-cffi-bindings=21.2.0=py37h540881e_2
- arrow-cpp=6.0.1=py37hbd77c41_5_cpu
- aws-c-auth=0.6.8=hadad3cd_1
- aws-c-cal=0.5.12=h70efedd_7
- aws-c-common=0.6.17=h7f98852_0
- aws-c-compression=0.2.14=h7c7754b_7
- aws-c-event-stream=0.2.7=hd2be095_32
- aws-c-http=0.6.10=h416565a_3
- aws-c-io=0.10.14=he836878_0
- aws-c-mqtt=0.7.10=h885097b_0
- aws-c-s3=0.1.29=h8d70ed6_0
- aws-c-sdkutils=0.1.1=h7c7754b_4
- aws-checksums=0.1.12=h7c7754b_6
- aws-crt-cpp=0.17.10=h6ab17b9_5
- aws-sdk-cpp=1.9.160=h36ff4c5_0
- blas-devel=3.9.0=14_linux64_openblas
- bokeh=2.4.2=py37h89c1867_1
- bottleneck=1.3.4=py37hda87dfa_1
- brotlipy=0.7.0=py37h540881e_1004
- bzip2=1.0.8=h7f98852_4
- c-ares=1.18.1=h7f98852_0
- ca-certificates=2022.3.29=h06a4308_0
- certifi=2021.10.8=py37h89c1867_2
- cffi=1.15.0=py37hd667e15_1
- click=8.1.2=py37h89c1867_0
- cramjam=2.5.0=py37hfd0a3e1_0
- cryptography=36.0.2=py37h38fbfac_1
- cytoolz=0.11.2=py37h540881e_2
- debugpy=1.6.0=py37hd23a5d3_0
- distributed=2021.10.0=py37h06a4308_0
- docutils=0.17.1=py37h89c1867_1
- fastparquet=0.7.1=py37hb1e94ed_0
- freetype=2.11.0=h70c0345_0
- frozenlist=1.3.0=py37h540881e_1
- gflags=2.2.2=he1b5a44_1004
- giflib=5.2.1=h36c2ea0_2
- glog=0.5.0=h48cff8f_0
- grpc-cpp=1.42.0=ha1441d3_1
- importlib-metadata=4.11.3=py37h89c1867_1
- ipykernel=6.11.0=py37h25bab4e_0
- ipython=7.32.0=py37h89c1867_0
- jbig=2.1=h7f98852_2003
- jedi=0.18.1=py37h89c1867_1
- jpeg=9e=h166bdaf_1
- jupyter_core=4.9.2=py37h89c1867_0
- keyutils=1.6.1=h166bdaf_0
- krb5=1.19.3=h3790be6_0
- lcms2=2.12=hddcbb42_0
- ld_impl_linux-64=2.36.1=hea4e1c9_2
- lerc=3.0=h9c3ff4c_0
- libblas=3.9.0=14_linux64_openblas
- libbrotlicommon=1.0.9=h166bdaf_7
- libbrotlidec=1.0.9=h166bdaf_7
- libbrotlienc=1.0.9=h166bdaf_7
- libcblas=3.9.0=14_linux64_openblas
- libcurl=7.82.0=h7bff187_0
- libdeflate=1.10=h7f98852_0
- libedit=3.1.20210910=h7f8727e_0
- libev=4.33=h516909a_1
- libevent=2.1.12=h8f2d780_0
- libffi=3.3=h58526e2_2
- libgcc-ng=11.2.0=h1d223b6_15
- libgfortran-ng=11.2.0=h69a702a_15
- libgfortran5=11.2.0=h5c6108e_15
- liblapack=3.9.0=14_linux64_openblas
- liblapacke=3.9.0=14_linux64_openblas
- libnghttp2=1.47.0=h727a467_0
- libopenblas=0.3.20=pthreads_h78a6416_0
- libpng=1.6.37=h21135ba_2
- libprotobuf=3.19.4=h780b84a_0
- libsodium=1.0.18=h36c2ea0_1
- libssh2=1.10.0=ha56f1ee_2
- libstdcxx-ng=11.2.0=he4da1e4_15
- libthrift=0.15.0=hcc01f38_0
- libtiff=4.3.0=h542a066_3
- libutf8proc=2.7.0=h7f98852_0
- libwebp-base=1.2.2=h7f98852_1
- libwebp=1.2.2=h3452ae3_0
- libxcb=1.13=h7f98852_1004
- libzlib=1.2.11=h166bdaf_1014
- llvm-openmp=13.0.1=he0ac6c6_1
- locket=0.2.1=py37h06a4308_2
- lz4-c=1.9.3=h9c3ff4c_1
- markupsafe=2.1.1=py37h540881e_1
- mistune=0.8.4=py37h5e8e339_1005
- msgpack-python=1.0.3=py37h7cecad7_1
- multidict=6.0.2=py37h540881e_1
- ncurses=6.3=h27087fc_1
- notebook=6.4.0=py37h06a4308_0
- numexpr=2.8.1=py37hecfb737_0
- numpy=1.21.6=py37h976b520_0
- openblas=0.3.20=pthreads_h320a7e8_0
- openjpeg=2.4.0=hb52868f_1
- openssl=1.1.1n=h166bdaf_0
- orc=1.7.1=h1be678f_1
- pandas=1.3.5=py37h8c16a72_0
- pandoc=2.18=ha770c72_0
- pillow=9.1.0=py37h44f0d7a_2
- psutil=5.9.0=py37h540881e_1
- pthread-stubs=0.4=h36c2ea0_1001
- pyarrow=6.0.1=py37h20dbb2a_5_cpu
- pyrsistent=0.18.1=py37h540881e_1
- pysocks=1.7.1=py37h89c1867_5
- python-snappy=0.6.0=py37h614b16a_2
- python=3.7.11=h12debd9_0
- python_abi=3.7=1_cp37m
- pyyaml=5.4.1=py37h5e8e339_1
- pyzmq=22.3.0=py37h0c0c2a8_2
- re2=2021.11.01=h9c3ff4c_0
- readline=8.1.2=h7f8727e_1
- s2n=1.3.0=h9b69904_0
- setuptools=62.1.0=py37h89c1867_0
- snappy=1.1.9=h295c915_0
- sqlite=3.38.2=h4ff8645_0
- terminado=0.13.3=py37h89c1867_1
- thrift=0.16.0=py37hd23a5d3_1
- tk=8.6.12=h27826a3_0
- tornado=5.1.1=py37h14c3975_1000
- wrapt=1.14.0=py37h540881e_1
- xorg-libxau=1.0.9=h7f98852_0
- xorg-libxdmcp=1.1.3=h7f98852_0
- xz=5.2.5=h516909a_1
- yaml=0.2.5=h7f98852_2
- yarl=1.7.2=py37h540881e_2
- zeromq=4.3.4=h9c3ff4c_1
- zlib=1.2.11=h166bdaf_1014
- zstd=1.5.2=ha95c52a_0
osx-64:
- abseil-cpp=20210324.2=he49afe7_0
- aiohttp=3.8.1=py37h69ee0a8_1
- appnope=0.1.3=pyhd8ed1ab_0
- argon2-cffi-bindings=21.2.0=py37h69ee0a8_2
- arrow-cpp=6.0.1=py37hc0a5d74_5_cpu
- aws-c-auth=0.6.8=h8f5e388_1
- aws-c-cal=0.5.12=hda7428a_7
- aws-c-common=0.6.17=h0d85af4_0
- aws-c-compression=0.2.14=h8451fdb_7
- aws-c-event-stream=0.2.7=ha663dc4_32
- aws-c-http=0.6.10=heb655c9_3
- aws-c-io=0.10.14=h3cf48f6_1
- aws-c-mqtt=0.7.10=h6d234a2_0
- aws-c-s3=0.1.29=h73af6b9_0
- aws-c-sdkutils=0.1.1=h8451fdb_4
- aws-checksums=0.1.12=h8451fdb_6
- aws-crt-cpp=0.17.10=haa61d5f_5
- aws-sdk-cpp=1.9.160=h075ee0a_0
- blas-devel=3.9.0=14_osx64_openblas
- bokeh=2.4.2=py37hf985489_1
- bottleneck=1.3.4=py37h49e79e5_1
- brotlipy=0.7.0=py37h69ee0a8_1004
- bzip2=1.0.8=h0d85af4_4
- c-ares=1.18.1=h0d85af4_0
- ca-certificates=2022.3.29=hecd8cb5_0
- certifi=2021.10.8=py37hf985489_2
- cffi=1.15.0=py37hc55c11b_1
- click=8.1.2=py37hf985489_0
- cramjam=2.5.0=py37h5210ebb_0
- cryptography=36.0.2=py37h20b3391_1
- cytoolz=0.11.2=py37h69ee0a8_2
- debugpy=1.6.0=py37h0582d14_0
- distributed=2021.10.0=py37hecd8cb5_0
- docutils=0.17.1=py37hf985489_1
- fastparquet=0.7.1=py37h032687b_0
- freetype=2.11.0=hd8bbffd_0
- frozenlist=1.3.0=py37h69ee0a8_1
- gflags=2.2.2=hb1e8313_1004
- giflib=5.2.1=hbcb3906_2
- glog=0.5.0=h25b26a9_0
- grpc-cpp=1.42.0=h6da9ac5_1
- importlib-metadata=4.11.3=py37hf985489_1
- ipykernel=6.11.0=py37h0a7177a_0
- ipython=7.32.0=py37hf985489_0
- jbig=2.1=h0d85af4_2003
- jedi=0.18.1=py37hf985489_1
- jpeg=9e=h5eb16cf_1
- jupyter_core=4.9.2=py37hf985489_0
- krb5=1.19.3=hb49756b_0
- lcms2=2.12=h577c468_0
- lerc=3.0=he49afe7_0
- libblas=3.9.0=14_osx64_openblas
- libbrotlicommon=1.0.9=h5eb16cf_7
- libbrotlidec=1.0.9=h5eb16cf_7
- libbrotlienc=1.0.9=h5eb16cf_7
- libcblas=3.9.0=14_osx64_openblas
- libcurl=7.82.0=h9f20792_0
- libcxx=13.0.1=hc203e6f_0
- libdeflate=1.10=h0d85af4_0
- libedit=3.1.20210910=hca72f7f_0
- libev=4.33=haf1e3a3_1
- libevent=2.1.12=h0a4fc7d_0
- libffi=3.3=h046ec9c_2
- libgfortran5=9.3.0=h6c81a4c_23
- libgfortran=5.0.0=9_3_0_h6c81a4c_23
- liblapack=3.9.0=14_osx64_openblas
- liblapacke=3.9.0=14_osx64_openblas
- libnghttp2=1.47.0=h942079c_0
- libopenblas=0.3.20=openmp_hb3cd9ec_0
- libpng=1.6.37=h7cec526_2
- libprotobuf=3.19.4=hcf210ce_0
- libsodium=1.0.18=hbcb3906_1
- libssh2=1.10.0=h52ee1ee_2
- libthrift=0.15.0=h054ceb0_0
- libtiff=4.3.0=h17f2ce3_3
- libutf8proc=2.7.0=h0d85af4_0
- libwebp-base=1.2.2=h0d85af4_1
- libwebp=1.2.2=h28dabe5_0
- libxcb=1.13=h0d85af4_1004
- libzlib=1.2.11=h6c3fc93_1014
- llvm-openmp=13.0.1=hcb1a161_1
- locket=0.2.1=py37hecd8cb5_2
- lz4-c=1.9.3=he49afe7_1
- markupsafe=2.1.1=py37h69ee0a8_1
- mistune=0.8.4=py37h271585c_1005
- msgpack-python=1.0.3=py37h18621fa_1
- multidict=6.0.2=py37h69ee0a8_1
- ncurses=6.3=h96cf925_1
- notebook=6.4.0=py37hecd8cb5_0
- numexpr=2.8.1=py37h9c3cb84_0
- numpy=1.21.6=py37h345d48f_0
- openblas=0.3.20=openmp_h5ad848b_0
- openjpeg=2.4.0=h6e7aa92_1
- openssl=1.1.1n=h6c3fc93_0
- orc=1.7.1=h84518c8_1
- pandas=1.3.5=py37h743cdd8_0
- pandoc=2.18=h694c41f_0
- pillow=9.1.0=py37h2540ef4_2
- psutil=5.9.0=py37h69ee0a8_1
- pthread-stubs=0.4=hc929b4f_1001
- pyarrow=6.0.1=py37hd1ae41a_5_cpu
- pyrsistent=0.18.1=py37h69ee0a8_1
- pysocks=1.7.1=py37hf985489_5
- python-snappy=0.6.0=py37h1f5a272_2
- python=3.7.11=h88f2d9e_0
- python_abi=3.7=2_cp37m
- pyyaml=5.4.1=py37h271585c_1
- pyzmq=22.3.0=py37h8f778e5_1
- re2=2021.11.01=he49afe7_0
- readline=8.1.2=hca72f7f_1
- setuptools=62.1.0=py37hf985489_0
- snappy=1.1.9=he9d5cce_0
- sqlite=3.38.2=hb516253_0
- terminado=0.13.3=py37hf985489_1
- thrift=0.16.0=py37h0582d14_1
- tk=8.6.12=h5dbffcc_0
- tornado=5.1.1=py37h1de35cc_1000
- wrapt=1.14.0=py37h69ee0a8_1
- xorg-libxau=1.0.9=h35c211d_0
- xorg-libxdmcp=1.1.3=h35c211d_0
- xz=5.2.5=haf1e3a3_1
- yaml=0.2.5=h0d85af4_2
- yarl=1.7.2=py37h69ee0a8_2
- zeromq=4.3.4=he49afe7_1
- zlib=1.2.11=h6c3fc93_1014
- zstd=1.5.2=h582d3a0_0
================================================
FILE: anaconda-project.yml
================================================
name: pyviz.org
description: pyviz.org
commands:
build_cache:
unix: |
python tools/conda_downloads.py
BADGE=stars python tools/build_cache.py
BADGE=contributors python tools/build_cache.py
BADGE=license python tools/build_cache.py
BADGE=pypi_downloads python tools/build_cache.py
build_website:
unix: |
python tools/build.py
mv tools/index.rst doc/tools.rst
nbsite generate-rst --org pyviz --project-name pyviz
nbsite build --what=html --output=builtdocs
channels:
- defaults
- pyviz
- conda-forge
packages:
- python==3.7.11
- jinja2==3.0.1
- markdown==3.3.4
- nbsite==0.6.7
- pyyaml==5.4.1
- requests==2.26.0
- tornado==5.1.1
- m2r2==0.3.1
- colorcet==2.0.6
- fastparquet==0.7.1
- intake==0.6.3
- intake-parquet==0.2.3
- s3fs==2021.8.0
- python-snappy==0.6.0
platforms:
- linux-64
- osx-64
================================================
FILE: doc/_static/custom.css
================================================
div.body {
max-width: 2000px;
}
iframe {
-moz-transform: scale(0.25, 0.25);
-webkit-transform: scale(0.25, 0.25);
-o-transform: scale(0.25, 0.25);
-ms-transform: scale(0.25, 0.25);
transform: scale(0.25, 0.25);
-moz-transform-origin: top left;
-webkit-transform-origin: top left;
-o-transform-origin: top left;
-ms-transform-origin: top left;
transform-origin: top left;
width: 3100px;
margin-right: -2500px;
height: 1600px;
margin-bottom: -1200px;
}
#tools-wrapper a {
text-decoration:none;
}
#tools-wrapper .sponsor-logo, #tools-wrapper .builton-logo {
max-height: 20px;
}
#tools-wrapper .empty-cell {
text-align: center;
}
#tools-wrapper table img {
max-width: fit-content;
}
================================================
FILE: doc/conf.py
================================================
# noqa
from nbsite.shared_conf import *
project = u'PyViz'
authors = u'PyViz authors'
copyright = u' 2019, ' + authors
description = 'How to solve visualization problems with Python tools.'
version = release = '0.0.1'
extensions.extend(['m2r2'])
html_static_path += ['_static']
html_favicon = '_static/favicon.ico'
html_theme_options = {
'logo': 'logo.png',
'logo_name': False,
'page_width': '90%',
'font_family': "Ubuntu, sans-serif",
'font_size': '0.9em',
'link': '#347ab4',
'link_hover': '#1c4669',
'extra_nav_links': {
'Github': 'https://github.com/pyviz/website',
},
'show_powered_by': False,
}
html_context.update({
'PROJECT': project,
'DESCRIPTION': description,
'AUTHOR': authors,
# WEBSITE_SERVER is optional for tests and local builds, but allows defining a canonical URL for search engines
'WEBSITE_SERVER': 'https://pyviz.org',
})
================================================
FILE: doc/dashboarding/index.md
================================================
# Dashboarding tools
Just about any Python library can be used to create a "static" PNG, SVG, HTML, or other output that can be pasted into a presentation, sent in an email, published as a figure in a paper, and so on. Many people also want or need to create "live" Python-backed applications or dashboards that a user can interact with to explore or analyze some data. Python offers several libraries for this purpose. When PyViz.org was created, the four main tools designed specifically for web-based dashboarding in Python were:
- [Dash](https://plot.ly/products/dash) (from [Plotly](https://plot.ly)); see the [blog post](https://medium.com/@plotlygraphs/introducing-dash-5ecf7191b503)
- [Panel](https://panel.pyviz.org) (from [Anaconda](http://anaconda.com)); see the [blog post](https://medium.com/@philipp.jfr/panel-announcement-2107c2b15f52)
- [Voila](https://github.com/QuantStack/voila) (from [QuantStack](http://quantstack.net)); see the [blog post](https://blog.jupyter.org/and-voil%C3%A0-f6a2c08a4a93); used with separate layout tools like
[jupyter-flex](https://github.com/danielfrg/jupyter-flex) or templates like [voila-vuetify](https://github.com/voila-dashboards/voila-vuetify).
- [Streamlit](https://www.streamlit.io); see the [blog post](https://towardsdatascience.com/coding-ml-tools-like-you-code-ml-models-ddba3357eace)
Since then, dozens of other libraries have been created, all of which are listed at [pyviz.org/tools#dashboarding](https://pyviz.org/tools#dashboarding). Some of them are compared in these overview articles:
- [A Survey of Python Frameworks](https://ploomber.io/blog/survey-python-frameworks/), 25 Sep 2024: Ellie Ko. Comparing Streamlit, Shiny for Python, Panel, Flask, Chainlit, Dash, Voila, and Gradio.
- [Streamlit vs Dash vs Voilà vs Panel — Battle of The Python Dashboarding Giants](https://medium.datadriveninvestor.com/streamlit-vs-dash-vs-voil%C3%A0-vs-panel-battle-of-the-python-dashboarding-giants-177c40b9ea57)
30 Mar 2021: Stephen Kilcommins. Comparing Streamlit, Dash, Voilà, and Panel for dashboarding. Links to more detailed explorations for each library individually.
- [Are Dashboards for Me?](https://towardsdatascience.com/are-dashboards-for-me-7f66502986b1)
7 Jul 2020: Dan Lester. Overview of Python and R dashboard tools, including Voila, ipywidgets, binder, Shiny, Dash, Streamlit, Bokeh, and Panel.
There are also other tools that can be used for some aspects of dashboarding as well as many other tasks:
- [Bokeh](http://bokeh.org) is a plotting library, a widget and app library, and a server for both plots and dashboards. [Panel](https://panel.pyviz.org) is built on Bokeh, providing a higher-level toolkit specifically focused on app and dashboard creation and supporting multiple plotting libraries (not just Bokeh).
- [ipywidgets](https://ipywidgets.readthedocs.io) provides a wide array of Jupyter-compatible widgets and an interface supported by many Python libraries, but sharing as a dashboard requires a separate deployable server like [Voila](https://github.com/QuantStack/voila).
- [matplotlib](http://matplotlib.org) supports many different backends, including several native GUI toolkit interfaces such as Qt that can be used for building arbitrarily complex native applications that can be used instead of a web-based dashboard like those above.
- [Bowtie](https://github.com/jwkvam/bowtie) (from Jacques Kvam) allows users to build dashboards in pure Python.
- [flask](http://flask.pocoo.org/) is a Python-backed web server that can be used to build arbitrary web sites, including those with Python plots that then function as [flask dashboards](https://pusher.com/tutorials/live-dashboard-python), but is not specifically set up to make dashboarding easier.
================================================
FILE: doc/dashboarding/index.rst
================================================
.. mdinclude:: index.md
.. toctree::
:titlesonly:
:hidden:
:maxdepth: 2
Dash <https://plot.ly/products/dash>
Panel <https://panel.pyviz.org>
Voila <https://github.com/QuantStack/voila>
================================================
FILE: doc/high-level/index.md
================================================
# High-level tools
The full list of [Python viz tools](../tools.html) is very long and covers a wide range of functionality. Many users share similar needs, and can get very far using a high-level tool that covers the most common tasks succinctly and conveniently, typically by providing a simpler API on top of an existing plotting tool.
## Pandas .plot() API
The longest-established of these tools is the [Pandas .plot() API](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html). This basic plotting interface uses [Matplotlib](http://matplotlib.org) to render static PNGs in a Jupyter notebook or for exporting from Python, with a command that can be as simple as `df.plot()` for a DataFrame with two columns.
The Pandas .plot() API has emerged as a de-facto standard for high-level plotting APIs in Python, and is now supported by many different libraries that use other underlying plotting engines to provide additional power and flexibility. Thus learning this API allows you to access capabilities provided by a wide variety of underlying tools, with relatively little additional effort. The libraries currently supporting this API include:
- [Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html) -- Matplotlib-based API included with Pandas. Static PNG output in Jupyter notebooks.
- [xarray](https://xarray.pydata.org/en/stable/plotting.html) -- Matplotlib-based API included with xarray, based on pandas .plot API. Static PNG output in Jupyter notebooks.
- [hvPlot](https://hvplot.pyviz.org) -- HoloViews and Bokeh-based interactive plots for Pandas, GeoPandas, xarray, Dask, Intake, and Streamz data.
- [Pandas Bokeh](https://github.com/PatrikHlobil/Pandas-Bokeh) -- Bokeh-based interactive plots, for Pandas, GeoPandas, and PySpark data.
- [Cufflinks](https://github.com/santosjorge/cufflinks) -- Plotly-based interactive plots for Pandas data.
- [PdVega](https://altair-viz.github.io/pdvega) -- Vega-lite-based, JSON-encoded interactive plots for Pandas data.
## Other high-level APIs
- [Seaborn](https://seaborn.pydata.org) -- Matplotlib-based high-level interface for drawing statistical graphics.
- [Altair](https://altair-viz.github.io/) -- Declarative Vega-lite-based interactive plots.
- [HoloViews](https://holoviews.org) -- Declarative Bokeh, Matplotlib, or Plotly-based interactive plots for tidy data.
- [Chartify](https://github.com/spotify/chartify) -- Bokeh-based interactive plots for tidy data.
- [Plotly Express](https://www.plotly.express/) -- Plotly-based interactive plots.
================================================
FILE: doc/high-level/index.rst
================================================
.. mdinclude:: index.md
.. toctree::
:titlesonly:
:hidden:
:maxdepth: 2
Pandas .plot <https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html>
xarray .plot <http://xarray.pydata.org/en/stable/plotting.html>
hvPlot <https://hvplot.pyviz.org>
Pandas Bokeh <https://github.com/PatrikHlobil/Pandas-Bokeh>
Cufflinks <https://github.com/santosjorge/cufflinks>
PdVega <https://altair-viz.github.io/pdvega>
Seaborn <https://seaborn.pydata.org>
Altair <https://altair-viz.github.io>
HoloViews <https://holoviews.org>
Chartify <https://github.com/spotify/chartify>
Plotly Express <https://www.plotly.express>
================================================
FILE: doc/index.md
================================================
# Python tools for data visualization
Welcome to PyViz! The PyViz.org website is an open platform for helping users decide on the best open-source (OSS) Python data visualization tools for their purposes, with links, overviews, comparisons, and examples. Contents:
- [Overviews](overviews/index.html) of the OSS visualization packages available in Python, how they relate to each other, and the core concepts that underlie them.
- [High-level tools](high-level/index.html) for getting started with Python viz, creating powerful plots in just a few lines of code.
- [All tools](tools.html) available for doing viz in Python OSS, as a live table for comparing maturity, popularity, and support.
- [Dashboarding](dashboarding/index.html) tools for sharing live Python-backed visualizations.
- [SciVis](scivis/index.html) tools for rendering data embedded in three-dimensional space.
- [Tutorials](tutorials/index.html) showing how to use the available tools to accomplish various categories of tasks.
- [Topic examples](https://examples.pyviz.org) of using Python viz tools to analyze or describe specific datasets in a particular domain or field of research.
## This site
If you are a part of the Python data visualization landscape, then this is _your_ site! All content has been contributed by individual library authors and users, and you could be next! Please open an issue or PR on [this GitHub repo](https://github.com/pyviz/website) to start a conversation. The goal is to make everyone doing viz in Python more productive, have more power, and make a greater impact from their work.
**NOTE:** The contents of PyViz.org from June 2019 or earlier, focusing on Datashader, HoloViews, GeoViews, Panel, Param, and hvPlot, are now at [HoloViz.org](https://holoviz.org). PyViz.org is now a fully open guide to all Python visualization tools. If you are looking for Brian Thomas's
PyViz smart-home visualization tool, check out his `paper <http://ieeexplore.ieee.org/document/5766889/>`_.
================================================
FILE: doc/index.rst
================================================
.. mdinclude:: index.md
.. toctree::
:titlesonly:
:hidden:
:maxdepth: 2
Home <self>
Overviews <overviews/index>
High-level tools <high-level/index>
All tools <tools>
Dashboarding <dashboarding/index>
SciVis <scivis/index>
Tutorials <tutorials/index>
================================================
FILE: doc/overviews/index.md
================================================
# Overviews
The Python visualization landscape can seem daunting at first. These overviews attempt to shine light on common patterns and use cases, comparing or discussing multiple plotting libraries. Note that some of the projects discussed in the overviews are no longer maintained, so be sure to check the list of [dormant projects](../tools.html#dormant-projects) before choosing that library.
<iframe src="https://rougier.github.io/python-visualization-landscape/landscape-colors.html" frameborder="0" allowfullscreen></iframe>
<em>Adaptation of <a href="https://www.youtube.com/watch?v=FytuB8nFHPQ">Jake VanderPlas' graphic</a> about the Python visualization landscape, by Nicolas P. Rougier</em>
- [PyViz: Data Visualization in Python](https://docs.google.com/presentation/d/19PWM-9oKkcKlVHycVg2kBHK84UEA55Df2yi5lFQ_7PQ/), 18 Apr 2026: James A. Bednar. Brief survey of PyViz.org as slides, with examples of each category.
- [7 Python Libraries That Make Visualization Beautiful](https://medium.com/@abdur.rahman12/7-python-libraries-that-make-visualization-beautiful-3d2ffb308611),
22 Sep 2025: Abdur Rahman. Brief overview of PyWaffle, Plotnine, Datashader, JoyPy, Sankeyview, PyCirclize, and Weave.
- [10 Python Libraries That Build Dashboards in Minutes](https://medium.com/@abdur.rahman12/10-python-libraries-that-build-dashboards-in-minutes-f1b6724946fa), 25 Dec 2025: Abdur Rahman. Brief overview of Streamlit, Dash, Panel, Plotly, Bokeh, Voilà, Gradio, Altair, NiceGUI, and Flask + HTMX.
- [I built the same dashboard 8 times | Which Python framework was best?](https://www.youtube.com/watch?v=k_fWYqCBUCE), 24 Aug 2025: Fanilo Andrianasolo. 1-hour video comparing Streamlit, Gradio, Panel, Dash, Shiny, Solara, NiceGUI and Reflex for building web applications in Python.
- [Practical Python Dashboards: The Best 5 Frameworks For Interactive Maps](https://medium.com/data-science-collective/practical-python-dashboards-the-best-5-frameworks-for-interactive-maps-0834ca7f0637), 12 Jun 2025: John Loewen. Comparing Streamlit, Dash, Shiny, Voila, and Panel for plotting choropleth maps.
- [The Best Python Dashboard Tools: Comparative Analysis With Practical Examples](https://medium.com/data-storytelling-corner/the-best-python-dashboard-tools-comparative-analysis-with-practical-examples-759636cc48ef), 11 Jun 2025: John Loewen. Comparing Streamlit, Dash, Shiny, Voila, and Panel.
- [Python Packages for Data Visualization in 2025](https://python.plainenglish.io/python-packages-for-data-visualization-in-2025-9cb2132c9a7e), 27 January 2025: Zlatan B. Comparing Matplotlib, Datashader, Seaborn, Plotnine, Altair, hvPlot, HoloViews, Bokeh, Plotly, and PyVista.
- [Matplotlib Alternatives That Actually Save You Time](https://nathanrosidi.medium.com/matplotlib-alternatives-that-actually-save-you-time-75631616cc4e), 13 May 2025: Nathan Rosidi. Comparing Plotly, Seaborn, Bokeh, Altair, and Plotnine as Matplotlib alternatives.
- [Which Python Dashboard Is Better? Dash, Panel And Streamlit Showdown](https://pub.towardsai.net/which-python-dashboard-is-better-dash-panel-and-streamlit-showdown-8d4f8bf744f9), 5 Feb 2025: John Loewen. Comparing Plotly, Dash, and Streamlit generation from LLMs.
- [The Essential Guide to R and Python Libraries for Data Visualization](https://towardsdatascience.com/the-essential-guide-to-r-and-python-libraries-for-data-visualization-33be8511c976), 16 Dec 2024: Sarah Lea. Comparing Matplotlib, Seaborn, Plotly, Pandas .plot(), Bokeh, Altair, HoloViews, and Folium.
- [A Survey of Python Frameworks](https://ploomber.io/blog/survey-python-frameworks/), 25 Sep 2024: Ellie Ko. Comparing Streamlit, Shiny for Python, Panel, Flask, Chainlit, Dash, Voila, and Gradio.
- I bet you didn’t use these Python visualization libraries!, 10-14 Sep 2024: Abhinaba Banerjee. Comparing [Altair and Plotnine (part 1)](https://python.plainenglish.io/i-bet-you-didnt-use-these-python-visualization-libraries-9da9531a1855), [Datashader, Pygal, and Geoplot (part 2)](https://python.plainenglish.io/i-bet-you-didnt-use-these-python-visualization-libraries-part-2-1f201a0a0547), VisPy, and typlot (part 3).
- [The Power of Pandas Plots: Backends](https://towardsdatascience.com/the-power-of-pandas-plots-backends-6a08d52071d2), 29 Aug 2024: Pierre-Etienne Toulemonde. Comparing matplotlib, plotly, and hvPlot for plotting with Pandas.
- [7 Best Python Libraries For Data Visualisation](https://medium.com/@inverita/7-best-python-libraries-for-data-visualisation-517020f725a4), 25 Jan 2024: inVerita. Comparing Matplotlib, Seaborn, Plotly, Bokeh, Altair, and HoloViews.
- [Top-5 Python Frontend Libraries for Data Science, part 2](https://python.plainenglish.io/top-5-python-frontend-libraries-for-data-science-part-2-4d07a48d2fde), 31 Mar 2024: Artem Shelamanov. Comparing Voila, PyWebIO, Gradio, Panel, and Dash.
- [Top-5 Python Frontend Libraries for Data Science, part 1](https://python.plainenglish.io/top-5-python-frontend-libraries-for-data-science-91261a65e366), 24 Dec 2023: Artem Shelamanov. Comparing Streamlit, Solara, Trame, ReactPy, and PyQt.
- [Declarative vs. Imperative Plotting: An overview for Python beginners](https://towardsdatascience.com/declarative-vs-imperative-plotting-3ee9952d6bf3), 9 January 2024: Lee Vaughan. Comparing Matplotlib, Seaborn, Plotly Express, and hvPlot/HoloViews.
- [Is Matplotlib Still the Best Python Library for Static Plots?](https://towardsdatascience.com/is-matplotlib-still-the-best-python-library-for-static-plots-a933c092cd49), 19 January 2024: Mike Clayton. Comparing Matplotlib, Seaborn, plotnine, Altair, and Plotly.
- [Top-5 Python Frontend Libraries for Data Science](https://python.plainenglish.io/top-5-python-frontend-libraries-for-data-science-91261a65e366), 24 December 2023: Artem Shelamanov. Comparing Streamlit, Solara, Trame, ReactPy, and PyQt.
- [Python on the Web](https://towardsdatascience.com/python-on-the-web-b819a6a55ec7), 11 October 2023: Pier Paolo Ippolito. Comparing Panel, Shiny for Python, and PyScript.
- [Data Visualization with Streamlit, Dash, and Panel. Part 1](https://sunscrapers.com/blog/data-viz-streamlit-dash-panel-part-1) and [Part 2](https://sunscrapers.com/blog/streamlit-dash-panel-features-part-2), 20 September 2023: Patryk Młynarek. Comparing Panel, Dash, and Streamlit.
- [Low Code With Dash, Streamlit, and Panel](https://betterprogramming.pub/technical-encounter-low-code-with-dash-streamlit-and-panel-part-1-364cf67f8b71), 9 July 2023: Petrica Leuca. Comparing Dash, Streamlit, and Panel. Separate followups focus individually on [Dash](https://medium.com/better-programming/technical-encounter-low-code-with-dash-43c6a4f2da5c), [Streamlit](https://medium.com/better-programming/technical-encounter-low-code-with-streamlit-9e3f730c0cd), and [Panel](https://medium.com/@petrica.leuca/technical-encounter-low-code-with-panel-7757d6a00876).
- [Interactive Dashboards in Python 2023](https://medium.com/@marktopacio/interactive-dashboards-in-python-2023-7d6cd4bda40c), 8 July 2023: Mark Topacio. Comparing Streamlit, Solara, Dash, Datasette, and Shiny for Python.
- [One library to rule them all? Geospatial visualisation tools in Python](https://gregorhd.github.io/geospatial-visualisation-in-python/), November 2022: Gregor Herda. Comparing Altair, Bokeh, Cartopy, Datashader, GeoPandas, Geoplot, GeoViews, hvPlot, and Plotly.
- [What Are the Best Python Plotting Libraries?](https://towardsdatascience.com/what-are-the-best-python-plotting-libraries-df234a356aec), May 2022: Will Norris. Comparing Matplotlib, Seaborn, Plotly, and Folium.
- [Python Dashboarding Shootout and Showdown | PyData Global 2021](https://www.youtube.com/watch?v=4a-Db1zhTEw)
October 2021: James Bednar, Nicolas Kruchten, Marc Skov Madsen, Sylvain Corlay and Adrien Treuille
- [Why *Interactive* Data Visualization Matters for Data Science in Python | PyData Global 2021](https://www.youtube.com/watch?v=tlcMlOVbEpw)
October 2021: Nicolas Kruchten
- [Beyond Matplotlib and Seaborn: Python Data Visualization Tools That Work](https://medium.com/codex/beyond-matplotlib-and-seaborn-python-data-visualization-tools-that-work-3ef7f8d1500e)
1 Feb 2021 Stephanie Kirmer. Comparing Matplotlib, Seaborn, Bokeh, Altair, Plotnine, and Plotly, with example github repo for code.
- [Plotly vs. Bokeh: Interactive Python Visualisation Pros and Cons](https://pauliacomi.com/2020/06/07/plotly-v-bokeh.html)
7 June 2020 Paul Iacomi. In-depth comparison of Bokeh and Plotly+Dash for dashboarding.
- [Complete Guide to Data Visualization with Python](https://towardsdatascience.com/complete-guide-to-data-visualization-with-python-2dd74df12b5e)
29 Feb 2020 Albert Sanchez Lafuente. Example code for Pandas tables, Matplotlib, Seaborn, Bokeh, Altair, and Folium.
- [Python Visualization Landscape](https://medium.com/@lulunana/python-visualization-landscape-3b95ede3d030)
24 Oct 2019 Sophia Yang. High-level overview of various categories of Python viz libraries, without example code.
- [Python Grids: Data Visualization](http://www.pythongrids.org/grids/g/data-visualization) 19 Sep 2019 Jared Chung. Table comparing stats on 14 Python plotting libraries.
- [Python Data Visualization 2018](https://www.anaconda.com/python-data-visualization-2018-why-so-many-libraries)
15 Nov 2018 - 14 Dec 2018 James A. Bednar, Anaconda, Inc. Three blog posts surveying the history and breadth of several dozen Python viz libraries, without example code.
[Updated in 2019 as an eBook](https://know.anaconda.com/eBook-PyVizeBookLP_ReportRegistration.html?utm_source=pyviz.org&utm_campaign=pyviz&utm_content=ebook).
- [pythonplot.com](http://pythonplot.com)
23 Jun 2017 - 12 Jun 2019 Timothy Hopper. Website with examples of plots made with Pandas+Matplotlib, Seaborn, plotnine, plotly, and R ggplot2, with output and Python code.
- [Plotting business locations on maps using multiple Plotting libraries in Python](https://towardsdatascience.com/plotting-business-locations-on-maps-using-multiple-plotting-libraries-in-python-45a00ea770af)
30 Apr 2018 Karan Bhanot. Blog post comparing plotting business locations using gmplot, geopandas, plotly, and bokeh.
- [Python Data Visualization — Comparing 5 Tools](https://codeburst.io/overview-of-python-data-visualization-tools-e32e1f716d10)
6 Dec 2017 Elena Kirzhner, Codeburst. Blog post with simple comparisons of Pandas, Seaborn, Bokeh, Pygal, and Plotly code and output.
- [10 Heatmaps 10 Libraries](https://blog.algorexhealth.com/2017/09/10-heatmaps-10-python-libraries/)
10 Sep 2017 Luke Shulman. Comparing heatmap code across 10 different viz libraries.
- [The Python Visualization Landscape](https://www.youtube.com/watch?v=FytuB8nFHPQ)
20 May 2017 Jake VanderPlas, U. Washington. 30-minute talk surveying the history and breadth of Python viz libraries. [[slides]](https://speakerdeck.com/jakevdp/pythons-visualization-landscape-pycon-2017).
- [Python Graph Gallery](https://python-graph-gallery.com)
30 Apr 2017 - 7 Jan 2018 Yan Holtz. Website with examples of plots made with Seaborn, Matplotlib, Pandas, with output and Python code, used in [data-to-viz.com](https://www.data-to-viz.com).
- [Overview of Python Visualization Tools](https://pbpython.com/visualization-tools-1.html)
20 Jan 2015 - 25 Apr 2017 Chris Moffitt, Practical Business Python. Three blog posts with examples of using pandas, seaborn, ggplot, bokeh, pygal, plotly, altair, matplotlib.
- [A Dramatic Tour through Python’s Data Visualization Landscape (including ggplot and Altair)](https://dsaber.com/2016/10/02/a-dramatic-tour-through-pythons-data-visualization-landscape-including-ggplot-and-altair) 02 Oct 2016 Dan Saber. Comparison of Matplotlib, Pandas .plot(), Seaborn, ggplot/ggpy (now superseded by plotnine), and Altair, with example code.
- [10 Useful Python Data Visualization Libraries for Any Discipline](https://mode.com/blog/python-data-visualization-libraries)
8 Jun 2016 Melissa Bierly, Mode.com. Blog post briefly describing matplotlib, seaborn, ggplot, bokeh, pygal, plotly, geoplotlib, gleam, missingno, and leather (now retired), with examples running on the Mode server.
- [Comparing 7 Tools For Data Visualization in Python](https://www.dataquest.io/blog/python-data-visualization-libraries)
12 Nov 2015 Vik Paruchuri, Dataquest. Blog post illustrating usage of matplotlib, vispy, bokeh, seaborn, pygal, folium, and networkx, with code, for an airport/flight dataset.
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# SciVis Libraries
Most of the libraries listed at PyViz.org fall into the [InfoVis](http://ieeevis.org/year/2019/info/call-participation/infovis-paper-types) (Information Visualization) category of tools, visualizing arbitrary and potentially abstract types of information, typically in 2D or 2D+time plots with axes and numerical scales. Tools in the separate [SciVis](http://ieeevis.org/year/2019/info/call-participation/scivis-paper-types) (Scientific Visualization) category focus on visualizing physically situated gridded data in 3D and 3D+time, often without spatial axes and instead providing an immersive visual experience of real-world physical datasets (see [Weiskopf et al](https://pdfs.semanticscholar.org/86aa/dffeae1912a404ee66223774d6a45eefb438.pdf) for a comparison). Desktop-GUI targeted SciVis tools build on the OpenGL graphics standard, while browser-based web applications usually leverage the related WebGL graphics standard.
SciVis libraries supporting Python:
- The Visualization Toolkit - [VTK](https://vtk.org) (from [Kitware](https://www.kitware.com/)) supports manipulating and displaying scientific data by enabling 3D rendering, widgets for 3D interaction, and 2D plotting capability.
- [VisPy](http://vispy.org) is a high-performance interactive 2D/3D data visualization library leveraging the computational power of modern Graphics Processing Units (GPUs) through the OpenGL library to display very large datasets.
- [Glumpy](https://glumpy.github.io) is an OpenGL-based interactive visualization library in Python. Its goal is to make it easy to create fast, scalable, beautiful, interactive and dynamic visualizations.
- [GR](https://gr-framework.org) is a universal framework for cross-platform visualization applications. It offers developers a compact, portable and consistent graphics library for their programs.
- [Mayavi](https://docs.enthought.com/mayavi/mayavi) (from [Enthought](https://www.enthought.com/)) is a general purpose, cross-platform tool for 3-D scientific data visualization.
- [ParaView](https://www.paraview.org) (from [Kitware](https://www.kitware.com/)) is an application built on the Visualization Toolkit (VTK) with extensions for distributed computing. ParaView allows users to quickly build visualizations to analyze their data using qualitative and quantitative techniques. The data exploration can be done interactively in 3D or programmatically using batch processing capabilities.
- [yt](https://yt-project.org) is a package for analyzing and visualizing volumetric data. yt supports structured, variable-resolution meshes, unstructured meshes, and discrete or sampled data such as particles.
- [PyVista](http://www.pyvista.org) is a streamlined interface for the Visualization Toolkit (VTK) providing 3D plotting and mesh analysis with NumPy support being at its core. PyVista supports point clouds, structured/unstructured meshes, and volumetric datasets.
- [vedo](https://vedo.embl.es) is a lightweight module for scientific analysis and visualization of polygonal meshes, point clouds and volumetric data. It offers an intuitive API which can be combined with VTK seamlessly in a program, whilst mantaining access to the full range of VTK native classes.
- [itk-jupyter-widgets](https://github.com/InsightSoftwareConsortium/itk-jupyter-widgets), based on the Visualization Toolkit for JavaScript [vtk.js](https://kitware.github.io/vtk-js/index.html) and the [Insight Toolkit (ITK)](https://www.itk.org/), provides interactive 3D widgets for Jupyter to visualize and analyze images, point sets, and meshes.
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FILE: doc/scivis/index.rst
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.. toctree::
:titlesonly:
:hidden:
:maxdepth: 2
VTK <https://vtk.org>
VisPy <http://vispy.org>
Glumpy <https://glumpy.github.io>
GR <https://gr-framework.org>
Mayavi <https://docs.enthought.com/mayavi/mayavi>
ParaView <https://www.paraview.org>
yt <https://yt-project.org>
PyVista <http://www.pyvista.org>
vedo <http://vedo.embl.es>
================================================
FILE: doc/tools.md
================================================
This page lists OSS libraries for visualizing data in Python. If you see any missing Python tools, please open a [PR](https://help.github.com/en/articles/about-pull-requests) for [tools.yml](https://github.com/pyviz/pyviz.org/blob/master/tools/tools.yml). Tools are sorted in each category according to their total downloads (pypi + conda) per month when added to the list. Note that conda downloads are computed by summing total downloads across the defaults channel, conda-forge, and bioconda; data for other channels is not currently included. Also note that the stars, contributors, license and PyPi downloads badges are cached to prevent users hitting the badges rate limits. Caching fails occasionally for some PyPi downloads badges, in which case their live counterpart is instead displayed (identified by a grey background).
================================================
FILE: doc/tutorials/index.md
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# Tutorials
Most of the projects listed at PyViz.org contain examples explaining how to solve various problems using that specific tool. This section lists additional in-depth, comprehensive tutorials designed for users new to Python viz, helping them to get started with a variety of different types of plot and situations. Each tutorial should include at least an hour's worth of work, with links to a repository of runnable materials along with text to describe what to do.
- [Bokeh tutorial](https://nbviewer.jupyter.org/github/bokeh/bokeh-notebooks/blob/master/tutorial/00%20-%20Introduction%20and%20Setup.ipynb): How to use the native [Bokeh](https://bokeh.org) API directly to create interactive plots, apps, and dashboards.
- [HoloViz tutorial](https://holoviz.org/tutorial): How to use the high-level [HoloViz](http://holoviz.org) tools from Anaconda to plot gridded, tabular, streaming, large, and graph/network data, focusing on [Panel](https://panel.pyviz.org), [Datashader](http://datashader.org), [HoloViews](https://holoviews.org), [GeoViews](http://geoviews.org), and [hvPlot](https://hvplot.pyviz.org), which build on [Bokeh](http://bokeh.org) and [Matplotlib](http://matplotlib.org). [[2018 SciPy recording](https://www.youtube.com/watch?v=aZ1G_Q7ovmc)]
- [Jupyter widgets tutorial](https://github.com/jupyter-widgets/tutorial): How to make interactive plots, apps, and dashboards using
ipywidgets, bqplot, vaex, ipympl, vue, ipysheet, ipyvolume, ipyleaflet, pythreejs, voila, and other ipywidgets-compatible libraries. [[2018 SciPy recording](https://www.youtube.com/watch?v=NBZBjEjN-rU)]
- [Matplotlib tutorial](https://github.com/matplotlib/AnatomyOfMatplotlib): Guide to the building blocks of Matplotlib and how to use them to create many different types of plots. [[SciPy 2018 recording](https://www.youtube.com/watch?v=6gdNUDs6QPc)]
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.. want to include these in the toctree
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Bokeh tutorial <https://nbviewer.jupyter.org/github/bokeh/bokeh-notebooks/blob/master/tutorial/00%20-%20Introduction%20and%20Setup.ipynb>
HoloViz <https://holoviz.org/tutorial>
Jupyter widgets tutorial <https://github.com/jupyter-widgets/tutorial>
Matplotlib tutorial <https://github.com/matplotlib/AnatomyOfMatplotlib>
================================================
FILE: tools/README.md
================================================
## PyViz Tools
This directory is used to generate a tools dashboard for comparing various Python visualization packages.
The main configuration of the dashboard is done via the ``tools.yml`` file, which can contain several sections with a list of packages, and a list of services for each section.
The dashboard is created during the website build process on Github Actions and can be seen at [pyviz.org/tools.html](http://pyviz.org/tools.html).
### Introductory text
The intro text is located in `doc/tools.md`, whose contents will be included immediately after the title on the page.
Every section can also have an `intro` in `tools.yml`. This text should also be written as markdown.
### Adding a tool
To add a tool, just create a new entry under the desired section in ``tools.yml``. At a minimum, include the GitHub org/repo for the project's source code. This will result in a project with just the badges that come from github and pypi.
** Minimal entry **
```yaml
- repo: SciTools/cartopy
```
To include more badges, add a list of sponsors, the site that the documentation can be found at. Also feel free to add the CI information, although this information isn't currently displayed, it could easily be added later.
** More complete entry **
```yaml
- repo: SciTools/cartopy
sponsors: [metoffice]
site: scitools.org.uk/cartopy
conda_channel: conda-forge
badges: travis, coveralls, pypi, conda
```
### Adding a sponsor
If you add a new tool that has a sponsor that is not yet found on the page, the name will not be linked and there won't be a logo. To get those assets, add an entry to ``sponsors.yml``. Use the same key as in ``tools.yml`` and include a `label` and optionally `url` and/or `logo`:
```yaml
numfocus:
label: NumFocus
url: https://numfocus.org
logo: _static/badges/numfocus.png
```
If using a logo, don't forget to include a small version of the logo at `doc/_static/badges/`.
================================================
FILE: tools/build.py
================================================
#!/usr/bin/env python
import datetime
import os
from jinja2 import Template
from yaml import safe_load
from markdown import markdown
here = os.path.abspath(os.path.dirname(__file__))
today = datetime.date.today().strftime("%B %-d, %Y")
print("Opening config file")
with open(os.path.join(here, 'tools.yml')) as f:
config = safe_load(f)
try:
with open(os.path.join(here, 'pypi_invalid_badges.txt')) as f:
pypi_invalid_badges = f.read().splitlines()
except FileNotFoundError:
pypi_invalid_badges = []
for section in config:
print(f"Building {section.get('name', '')}")
if section.get('intro'):
section['intro'] = markdown(section['intro'])
for package in section['packages']:
try:
package['user'], package['name'] = package['repo'].split('/')
except:
raise Warning('Package.repo is not in correct format', package)
package['conda_package'] = package.get('conda_package', package['name'])
package['pypi_name'] = package.get('pypi_name', package['name'])
if package['pypi_name'] in pypi_invalid_badges:
package['pypi_invalid'] = True
if package.get('badges'):
package['badges'] = [x.strip() for x in package['badges'].split(',')]
else:
package['badges'] = ['pypi', 'conda']
if package.get('conda_channel') and 'conda' not in package['badges']:
package['badges'].append('conda')
if package.get('sponsors') and 'sponsor' not in package['badges']:
package['badges'].append('sponsor')
if package.get('builtons') and 'builton' not in package['badges']:
package['badges'].append('builton')
if package.get('site') and 'site' not in package['badges']:
package['badges'].append('site')
if package.get('dormant') and 'dormant' not in package['badges']:
package['badges'].append('dormant')
if 'rtd' in package['badges'] and 'rtd_name' not in package:
package['rtd_name'] = package['name']
if 'conda' in package['badges'] and 'conda_channel' not in package:
package['conda_channel'] = 'anaconda'
if 'site' in package['badges']:
if 'site' not in package:
package['site'] = '{}.org'.format(package['name'])
package['site_protocol'] = 'https'
else:
package['site_protocol'], package['site'] = package['site'].rstrip('/').split('://')
with open(os.path.join(here, 'sponsors.yml')) as f:
sponsors = safe_load(f)
with open(os.path.join(here, 'builtons.yml')) as f:
builtons = safe_load(f)
template = Template(open(os.path.join(here, 'template.html'), 'r').read())
with open(os.path.join(here, 'index.rst'), 'w') as f:
f.write("All Tools\n")
f.write("=========\n\n")
f.write(".. mdinclude:: tools.md\n\n")
f.write(".. raw:: html\n\n")
f.write(template.render(config=config, sponsors=sponsors, builtons=builtons, date=today))
================================================
FILE: tools/build_cache.py
================================================
#!/usr/bin/env python
import os
import time
from yaml import safe_load
import requests
here = os.path.abspath(os.path.dirname(__file__))
cache_path = os.path.join(here, '..', 'doc', '_static', 'cache')
badge = os.getenv('BADGE')
cache = {
# Override the label with a space to disable it and reduce the badge size
"stars": "https://img.shields.io/github/stars/{repo}.svg?style=flat&logo=github&color=blue&label=%20",
"contributors": "https://img.shields.io/github/contributors/{repo}.svg?style=flat&logo=github&color=blue&label=%20",
"pypi_downloads": "https://img.shields.io/pypi/dm/{pypi_name}.svg?label=pypi",
"license": "https://img.shields.io/pypi/l/{pypi_name}.svg?label",
}
url = cache.get(badge)
if url is None:
raise ValueError((f'{badge} not in {", ".join(cache.keys())}, use env '
'var BADGE to set.'))
# The pypi download badge cannot occasionally be properly fetched
# by shields and cached here. We list those that failed, so that
# in the template we can put the actual badge link rather than
# the cached one.
pypi_invalid_file = os.path.join(here, "pypi_invalid_badges.txt")
if os.path.exists(pypi_invalid_file):
os.remove(pypi_invalid_file)
print(f"\nBuilding a cache of {badge} badges.\n")
if not os.path.exists(cache_path):
os.mkdir(cache_path)
with open(os.path.join(here, 'tools.yml')) as f:
config = safe_load(f)
for section in config:
print(f"Building cache for {section.get('name', '')}")
for package in section['packages']:
try:
package['user'], package['name'] = package['repo'].split('/')
except:
raise Warning('Package.repo is not in correct format', package)
package['pypi_name'] = package.get('pypi_name', package['name'])
print(f" * package: {package.get('pypi_name', '')}")
rendered_url = url.format(repo=package['repo'], pypi_name=package['pypi_name'])
r = requests.get(rendered_url)
content = r.content
# Pypistats implements IP rate limiting, so let's slow things
# down and retry a few times when failing.
if badge == 'pypi_downloads':
time.sleep(2.5)
nb_retries = 4
retry_duration = 5 # In seconds, multiplied by two after each retry.
retry_count = 1
while 'pypi: invalid' in r.text and retry_count <= nb_retries:
print(f"PyPI badge returned as 'invalid'. Retrying after {retry_duration} seconds.")
time.sleep(retry_duration)
r = requests.get(rendered_url)
content = r.content
if retry_count == nb_retries:
print(f"Failed a getting a valid Pypi Downloads badge for {package['pypi_name']}.")
break
retry_count += 1
retry_duration *= 2
if 'pypi: invalid' in r.text:
with open(pypi_invalid_file, 'a') as f:
f.write(package['pypi_name'] + '\n')
with open(os.path.join(cache_path, f"{package['name']}_{badge}_badge.svg"), 'wb') as f:
f.write(content)
================================================
FILE: tools/builtons.yml
================================================
bokeh:
label: Bokeh
url: https://docs.bokeh.org/en/latest/
logo: _static/badges/builtons/bokeh.png
plotly:
label: Plotly
url: https://plotly.com/
logo: _static/badges/builtons/plotly.png
matplotlib:
label: Matplotlib
url: https://matplotlib.org/
logo: _static/badges/builtons/matplotlib.png
vega:
label: Vega
url: https://vega.github.io/vega/
logo: _static/badges/builtons/vega.png
vtk:
label: VTK
url: https://vtk.org/
logo: _static/badges/builtons/vtk.png
opengl:
label: OpenGL
url: https://www.opengl.org/
logo: _static/badges/builtons/opengl.png
webgl:
label: WebGL
url: https://www.khronos.org/webgl/
logo: _static/badges/builtons/webgl.png
vispy:
label: VisPy
url: https://vispy.org/
logo: _static/badges/builtons/vispy.png
leaflet:
label: Leaflet
url: https://leafletjs.com/
logo: _static/badges/builtons/leaflet.png
graphviz:
label: Graphviz
url: https://graphviz.org/
logo: _static/badges/builtons/graphviz.png
d3:
label: D3
url: https://d3js.org/
logo: _static/badges/builtons/d3.png
qt:
label: qt
url: https://qt.io/
logo: _static/badges/builtons/qt.png
gmt:
label: gmt
url: https://www.generic-mapping-tools.org/
logo: _static/badges/builtons/gmt.png
vulkan:
label: vulkan
url: https://www.vulkan.org/
logo: _static/badges/builtons/vulkan.png
panel:
label: Panel
url: https://panel.holoviz.org
logo: _static/badges/builtons/panel.png
pyvista:
label: PyVista
url: https://docs.pyvista.org/version/stable/
logo: _static/badges/builtons/pyvista.png
================================================
FILE: tools/conda_downloads.py
================================================
#!/usr/bin/env python
"""
Run this script at the beginning of each month to build new conda downloads badges
from the previous month.
"""
import os
from yaml import safe_load
import requests
import datetime
import intake
import colorcet as cc
import numpy as np
here = os.path.abspath(os.path.dirname(__file__))
cache_path = os.path.join(here, '..', 'doc', '_static', 'cache')
cat = intake.open_catalog('https://raw.githubusercontent.com/ContinuumIO/anaconda-package-data/master/catalog/anaconda_package_data.yaml')
colors = cc.palette_n.rainbow[-20:80:-1]
top_of_colormap = 1e6
step = len(colors) /np.log10(top_of_colormap)
today = datetime.date.today()
first = today.replace(day=1)
last_month = first - datetime.timedelta(days=1)
try:
monthly = cat.anaconda_package_data_by_month(year=last_month.year, month=last_month.month,
columns=['pkg_name', 'counts']).to_dask()
except:
# if the last month isn't available, get the month before
month_before = last_month.replace(day=1) - datetime.timedelta(days=1)
monthly = cat.anaconda_package_data_by_month(year=month_before.year, month=month_before.month,
columns=['pkg_name', 'counts']).to_dask()
per_package_downloads = monthly.groupby('pkg_name').sum().compute()
if not os.path.exists(cache_path):
os.mkdir(cache_path)
def get_conda_badge(conda_package):
conda_package = conda_package.lower()
if conda_package in per_package_downloads.index:
downloads = per_package_downloads.counts.loc[conda_package]
else:
downloads = 0
if downloads == 0:
color_index = 0
elif downloads > top_of_colormap:
color_index = -1
else:
color_index = int(np.log10(downloads) * step)
color = colors[color_index][1:]
if downloads > 1e6:
downloads = '{}M'.format(int(downloads/1e6))
elif downloads > 1e3:
downloads = '{}k'.format(int(downloads/1e3))
else:
downloads = int(downloads)
return f"https://img.shields.io/badge/conda-{downloads}/month-{color}.svg"
with open(os.path.join(here, 'tools.yml')) as f:
config = safe_load(f)
for section in config:
print(f"Building conda downloads badge for: {section['name']}")
for package in section['packages']:
try:
package['user'], package['name'] = package['repo'].split('/')
except:
raise Warning('Package.repo is not in correct format', package)
continue
url = get_conda_badge(package.get('conda_package', package['name']))
rendered_url = url
r = requests.get(rendered_url)
with open(os.path.join(cache_path, f"{package['name']}_conda_downloads_badge.svg"), 'wb') as f:
f.write(r.content)
================================================
FILE: tools/sponsors.yml
================================================
numfocus:
label: NumFocus
url: https://numfocus.org
logo: _static/badges/numfocus.png
anaconda:
label: Anaconda
url: https://www.anaconda.com
logo: _static/badges/anaconda.png
metoffice:
label: Met Office
url: https://www.metoffice.gov.uk
logo: _static/badges/metoffice.png
spotify:
label: Spotify
url: https://www.spotify.com
logo: _static/badges/spotify.png
plotly:
label: Plot.ly
url: https://plot.ly
logo: _static/badges/plotly.png
kitware:
label: Kitware
url: https://www.kitware.com
logo: _static/badges/kitware.svg
EMBL:
label: EMBL
url: https://www.embl.es
logo: _static/badges/embl.png
enthought:
label: Enthought
url: https://www.enthought.com
logo: _static/badges/enthought.svg
nvidia:
label: NVIDIA
url: https://www.nvidia.com
logo: _static/badges/nvidia.jpg
posit:
label: Posit, PBC
URL: https://posit.co/
logo: _static/badges/positpbc.png
sandia:
label: Sandia
url: https://www.sandia.gov
logo: _static/badges/sandia.png
quantstack:
label: QuantStack
url: http://quantstack.net
logo: _static/badges/quantstack.svg
vaexio:
label: vaex.io
url: https://vaex.io
logo: _static/badges/vaexio.png
unidata:
label: Unidata
url: https://www.unidata.ucar.edu
logo: _static/badges/unidata.png
cgs:
label: cgs
url: https://spatial.ucr.edu
logo: _static/badges/cgs.svg
streamlit:
label: streamlit
url: https://streamlit.io
logo: _static/badges/streamlit.png
jetbrains:
label: jetbrains
url: https://www.jetbrains.com
logo: _static/badges/jetbrains.png
h2o:
label: h2o.ai
url: https://h2o.ai
logo: _static/badges/h2o.png
huggingface:
label: huggingface
url: https://huggingface.co
logo: _static/badges/huggingface.png
mljar:
label: mljar
url: https://mljar.com
logo: _static/badges/mljar.png
wandb:
label: Weights and Biases
url: https://wandb.ai
logo: _static/badgets/weights_and_biases.svg
mckinsey:
label: McKinsey
url: https://mckinsey.com
reflex:
label: Reflex
url: https://reflex.dev
logo: _static/badges/reflex.svg
zauberzeug:
label: Zauberzeug
url: https://zauberzeug.com
logo: _static/badges/zauberzeug.webp
widgetti:
label: Widgetti
url: https://widgetti.io/
logo: _static/badges/widgetti.png
marimo:
label: marimo
url: https://marimo.io/
logo: _static/badges/marimo.png
taipy:
label: taipy
url: https://www.taipy.io/
logo: _static/badges/taipy.png
================================================
FILE: tools/template.html
================================================
<div idid="date">
<i>Last updated: {{ date }}</i>
</div>
<div id="tools-wrapper">
{% for section in config %}
<h3 id="{{ section.name.lower().replace(' ', '-') }}">{{ section.name }}<a class="headerlink" href="#{{ section.name.lower().replace(' ', '-') }}" title="Permalink to {{ section.name }}">¶</a></h3>
{{ section.get('intro', '') }}
<table>
<tr>
<th>Name</th>
<th></th>
<th>Stars</th>
<th>Contributors</th>
<th>Downloads</th>
<th></th>
<th>License</th>
<th>Docs</th>
<th>PyPI</th>
<th>Conda</th>
<th>Sponsors</th>
<th>Built on</th>
</tr>
{% for package in section.packages %}
<tr>
<td align='left'>
<a href="http://github.com/{{ package.repo }}">{{ package.name }}</a>
</td>
<td align='left'>
{% if 'dormant' in package.badges %}
<a href="{{ package.dormant }}">
<img src="_static/dormant.svg">
</a>
{% endif %}
</td>
<td align='left'>
<a href="https://github.com/{{ package.repo }}/stargazers">
<img src="_static/cache/{{ package.name }}_stars_badge.svg">
</a>
</td>
<td align='left'>
<a href="https://github.com/{{ package.repo }}/graphs/contributors">
<img src="_static/cache/{{ package.name }}_contributors_badge.svg">
</a>
</td>
{% if 'pypi_invalid' in package %}
<td align='left'>
<img src="https://img.shields.io/pypi/dm/{{ package.name }}?color=%20%23868686&label=pypi">
</td>
{% elif 'pypi' in package.badges %}
<td align='left'>
<img src="_static/cache/{{ package.name }}_pypi_downloads_badge.svg">
</td>
{% else %}
<td align='center' class='empty-cell'>-</td>
{% endif %}
{% if 'conda' in package.badges %}
<td align='left'>
<img src="_static/cache/{{ package.name }}_conda_downloads_badge.svg">
</td>
{% else %}
<td align='center' class='empty-cell'>-</td>
{% endif %}
{% if 'pypi' in package.badges %}
<td align='left'>
<img src="_static/cache/{{ package.name }}_license_badge.svg">
</td>
{% else %}
<td align='center' class='empty-cell'>-</td>
{% endif %}
{% if 'site' in package.badges %}
<td align='left'>
<a href="{{ package.site_protocol }}://{{ package.site }}">
<img src="https://img.shields.io/website-up-down-green-red/{{ package.site_protocol }}/{{ package.site }}.svg?label=%20">
</a>
</td>
{% elif 'rtd' in package.badges %}
<td align='left'>
<a href="https://{{ package.rtd_name }}.readthedocs.io">
<img src="https://readthedocs.org/projects/{{ package.rtd_name }}/badge/?version=latest">
</a>
</td>
{% else %}
<td align='center' class='empty-cell'>-</td>
{% endif %}
{% if 'pypi' in package.badges %}
<td align='left'>
<a href="https://pypi.python.org/pypi/{{ package.pypi_name }}">
<img src="https://img.shields.io/pypi/v/{{ package.pypi_name }}.svg?label">
</a>
</td>
{% else %}
<td align='center' class='empty-cell'>-</td>
{% endif %}
{% if 'conda' in package.badges %}
<td align='left'>
<a href="https://anaconda.org/{{ package.conda_channel }}/{{ package.conda_package }}">
<img src="https://img.shields.io/conda/vn/{{ package.conda_channel }}/{{ package.conda_package }}.svg?style=flat">
</a>
</td>
{% else %}
<td align='center' class='empty-cell'>-</td>
{% endif %}
{% if 'sponsor' in package.badges %}
<td align='left'>
{% for sponsor in package.sponsors %}
{% if sponsors.get(sponsor) %}
<a href="{{ sponsors[sponsor].get('url') }}">
{% if sponsors[sponsor].get('logo') %}
<img class='sponsor-logo' src="{{ sponsors[sponsor]['logo'] }}" title="{{ sponsors[sponsor].get('label', sponsor) }}">
{% else %}
{{ sponsors[sponsor].get('label', sponsor) }}
{% endif %}
</a>
{% else %}
{{ sponsor }}
{% endif %}
{% endfor %}
</td>
{% else %}
<td align='center' class='empty-cell'>-</td>
{% endif %}
{% if 'builton' in package.badges %}
<td align='center'>
{% for builton in package.builtons %}
{% if builtons.get(builton) %}
<a href="{{ builtons[builton].get('url') }}">
{% if builtons[builton].get('logo') %}
<img class='builton-logo' src="{{ builtons[builton]['logo'] }}" title="{{ builtons[builton].get('label', builton) }}">
{% else %}
{{ builtons[builton].get('label', builton) }}
{% endif %}
</a>
{% else %}
{{ builton }}
{% endif %}
{% endfor %}
</td>
{% else %}
<td align='center' class='empty-cell'>-</td>
{% endif %}
</tr>
{% endfor %}
</table>
{% endfor %}
</div>
================================================
FILE: tools/tools.yml
================================================
- name: Core
intro: Python libraries on which multiple higher-level libraries are built.
packages:
- repo: matplotlib/matplotlib
sponsors: [numfocus]
badges: travis, appveyor, codecov, pypi, conda, site
builtons: [matplotlib]
- repo: plotly/plotly.py
sponsors: [plotly]
pypi_name: plotly
conda_package: plotly
site: https://plot.ly
badges: circleci, pypi, conda, site
builtons: [plotly]
- repo: bokeh/bokeh
sponsors: [numfocus, anaconda]
badges: travis, pypi, conda, site
builtons: [bokeh]
- name: High-Level Shared API
intro: Libraries sharing the Pandas .plot() API, built upon the core Python or JS libraries.
packages:
- repo: pandas-dev/pandas
sponsors: [numfocus]
site: https://pandas.pydata.org
badges: travis, appveyor, codecov, rtd, pypi, conda, site
builtons: [matplotlib]
- repo: pydata/xarray
sponsors: [numfocus]
appveyor_project: shoyer/xray
site: https://xarray.dev/
conda_channel: conda-forge
badges: travis, appveyor, coveralls, rtd, pypi, conda, site
builtons: [matplotlib]
- repo: holoviz/hvplot
sponsors: [anaconda,numfocus]
site: https://hvplot.holoviz.org
badges: pypi, conda, site
builtons: [bokeh]
- repo: santosjorge/cufflinks
site: https://github.com/santosjorge/cufflinks/blob/master/README.md
conda_channel: conda-forge
badges: circleci, pypi, conda, site
builtons: [plotly]
- repo: PatrikHlobil/Pandas-Bokeh
site: https://github.com/PatrikHlobil/Pandas-Bokeh/blob/master/README.md
conda_channel: PatrikHlobil
badges: pypi, conda, site
builtons: [bokeh]
- name: High-Level
intro: InfoVis Libraries focusing on high-level operations for working with data visually, built upon the core Python or JS libraries.
packages:
- repo: altair-viz/altair
site: https://altair-viz.github.io
conda_channel: conda-forge
badges: travis, pypi, conda, site
builtons: [vega]
- repo: mwaskom/seaborn
site: https://seaborn.pydata.org
badges: travis, codecov, pypi, conda, site
builtons: [matplotlib]
- repo: plotly/plotly_express
sponsors: [plotly]
site: https://plotly.express
conda_channel: conda-forge
badges: pypi, conda, site
builtons: [plotly]
- repo: holoviz/holoviews
sponsors: [anaconda,numfocus]
badges: coveralls, pypi, conda, site
builtons: [bokeh, matplotlib, plotly]
- repo: pyecharts/pyecharts
site: https://pyecharts.org/#/en-us/
badges: pypi
builtons: [echarts]
- repo: JetBrains/lets-plot
sponsors: [jetbrains]
site: https://lets-plot.org
badges: pypi, site
- repo: Marsilea-viz/marsilea
pypi_name: marsilea
site: https://marsilea.rtfd.io/
conda_channel: conda-forge
badges: pypi, conda, site, rtd
builtons: [matplotlib]
- repo: finos/perspective
site: https://perspective.finos.org
conda_channel: conda-forge
badges: pypi, conda, site
- repo: AutoViML/AutoViz
site: https://github.com/AutoViML/AutoViz
badges: travis, pypi, site
builtons: [matplotlib]
- repo: spotify/chartify
sponsors: [spotify]
conda_channel: conda-forge
site: https://github.com/spotify/chartify/blob/master/README.rst
badges: travis, pypi, conda, site
builtons: [bokeh]
- repo: lukelbd/proplot
site: https://proplot.readthedocs.io
conda_channel: conda-forge
badges: travis, pypi, conda, site
builtons: [matplotlib]
- repo: vizzuhq/ipyvizzu
site: https://ipyvizzu.vizzuhq.com
conda_channel: conda-forge
badges: travis, pypi, conda, site
- repo: vizzuhq/ipyvizzu-story
site: https://vizzuhq.github.io/ipyvizzu-story/
conda_channel: conda-forge
badges: travis, pypi, conda, site
- repo: Technion-Kishony-lab/quibbler
pypi_name: pyquibbler
site: https://github.com/Technion-Kishony-lab/quibbler
badges: pypi, site
builtons: [matplotlib]
- repo: koonimaru/omniplot
- name: Native-GUI
intro: InfoVis Libraries targetting native-desktop GUI interfaces for interactive plots.
packages:
- repo: matplotlib/matplotlib
sponsors: [numfocus]
badges: travis, appveyor, codecov, pypi, conda, site
builtons: [matplotlib]
- repo: pyqtgraph/pyqtgraph
site: http://www.pyqtgraph.org
builtons: [qt, opengl]
- repo: newville/wxmplot
site: https://newville.github.io/wxmplot/
conda_channel: conda-forge
conda_package: wxmplot
badges: pypi, conda, site
builtons: [matplotlib]
- repo: glue-viz/glue
site: http://docs.glueviz.org
conda_channel: conda-forge
conda_package: glueviz
badges: pypi, conda, site
builtons: [matplotlib]
- repo: enthought/chaco
sponsors: [enthought]
site: https://docs.enthought.com/chaco
badges: pypi
builtons: [kiva]
- repo: sciapp/gr
site: https://gr-framework.org/python.html
badges: pypi, site
- repo: veusz/veusz
conda_channel: conda-forge
site: https://veusz.github.io
builtons: [qt]
- name: Other InfoVis
intro: InfoVis plotting libraries not fitting into other categories above.
packages:
- repo: has2k1/plotnine
conda_channel: conda-forge
badges: pypi, conda, rtd
builtons: [matplotlib]
- repo: Kozea/pygal
site: http://pygal.org
conda_channel: conda-forge
- repo: bloomberg/bqplot
conda_channel: conda-forge
badges: pypi, conda, rtd
- repo: sandialabs/toyplot
sponsors: [sandia]
conda_channel: conda-forge
badges: pypi, conda, rtd
- repo: flekschas/jupyter-scatter
badges: pypi, site
builtons: [webgl]
pypi_name: jupyter-scatter
site: https://jupyter-scatter.dev/
- repo: maxhumber/chart
badges: pypi
- name: SciVis
intro: Libraries for visualizing scientific data situated in real-world coordinates, typically using OpenGL, WebGL, or Vulkan.
packages:
- repo: Kitware/VTK
sponsors: [kitware]
site: https://vtk.org/
conda_channel: conda-forge
badges: travis, pypi, conda, site
builtons: [vtk]
- repo: pyvista/pyvista
site: https://docs.pyvista.org
conda_channel: conda-forge
builtons: [vtk]
- repo: vispy/vispy
conda_channel: conda-forge
site: http://vispy.org
badges: travis, appveyor, coveralls, pypi, conda, site
builtons: [opengl]
- repo: nmwsharp/polyscope
site: http://polyscope.run
badges: pypi
builtons: [opengl]
- repo: marcomusy/vedo
sponsors: [EMBL]
site: https://vedo.embl.es
conda_channel: conda-forge
badges: pypi, conda, circleci
builtons: [vtk]
- repo: maartenbreddels/ipyvolume
conda_channel: conda-forge
builtons: [opengl, webgl]
- repo: InsightSoftwareConsortium/itkwidgets
pypi_name: itkwidgets
conda_package: itkwidgets
conda_channel: conda-forge
badges: pypi, conda, circleci
builtons: [webgl]
- repo: enthought/mayavi
sponsors: [enthought]
site: https://docs.enthought.com/mayavi/mayavi
appveyor_project: EnthoughtOSS/mayavi
badges: travis, appveyor, codecov, pypi, conda, site
builtons: [vtk, opengl]
- repo: glumpy/glumpy
badges: pypi, rtd
builtons: [opengl]
- repo: datoviz/datoviz
site: https://datoviz.org
conda_channel: mark.harfouche
badges: conda
builtons: [vulkan]
- name: Geospatial
intro: Tools for working with data in geographic coordinates.
packages:
- repo: pydata/xarray
sponsors: [numfocus]
appveyor_project: shoyer/xray
site: https://xarray.dev/
conda_channel: conda-forge
badges: travis, appveyor, coveralls, rtd, pypi, conda, site
builtons: [matplotlib]
- repo: geopandas/geopandas
site: http://geopandas.org
conda_channel: conda-forge
builtons: [matplotlib]
- repo: python-visualization/folium
site: https://python-visualization.github.io/folium
conda_channel: conda-forge
builtons: [leaflet]
- repo: SciTools/cartopy
sponsors: [metoffice]
site: https://scitools.org.uk/cartopy
badges: travis, appveyor, coveralls, pypi, conda, site
builtons: [matplotlib]
- repo: holoviz/hvplot
sponsors: [anaconda,numfocus]
site: https://hvplot.holoviz.org
badges: pypi, conda, site
builtons: [bokeh]
- repo: gboeing/osmnx
site: https://osmnx.readthedocs.io
badges: pypi
- repo: keplergl/kepler.gl
site: https://docs.kepler.gl/docs/keplergl-jupyter
badges: pypi
pypi_name: keplergl
- repo: jupyter-widgets/ipyleaflet
conda_channel: conda-forge
badges: pypi, conda, rtd
builtons: [leaflet]
- repo: vgm64/gmplot
badges: pypi
- repo: JetBrains/lets-plot
sponsors: [jetbrains]
site: https://lets-plot.org
badges: pypi, site
- repo: giswqs/leafmap
site: https://leafmap.org
conda_channel: conda-forge
badges: pypi, conda
builtons: [leaflet, plotly]
- repo: gee-community/geemap
site: https://geemap.org
badges: pypi
- repo: holoviz/geoviews
sponsors: [anaconda,numfocus]
site: http://geoviews.org
badges: pypi, conda, site
builtons: [bokeh, matplotlib, plotly]
- repo: pysal/splot
sponsors: [cgs]
site: https://splot.readthedocs.io
conda_channel: conda-forge
badges: coveralls, pypi, conda, site
builtons: [matplotlib]
- repo: HTenkanen/pyrosm
site: https://pyrosm.readthedocs.io
badges: pypi
- repo: GenericMappingTools/pygmt
site: https://www.pygmt.org
conda_channel: conda-forge
badges: pypi, conda, codecov
builtons: [gmt]
- repo: ResidentMario/geoplot
site: https://residentmario.github.io/geoplot
conda_channel: conda-forge
badges: pypi, conda, site
builtons: [matplotlib]
- repo: raphaelquast/eomaps
site: https://eomaps.readthedocs.io
conda_channel: conda-forge
badges: pypi, conda, codecov, site
builtons: [matplotlib, cartopy]
- repo: opengeos/mapwidget
site: https://mapwidget.gishub.org/
badges: pypi
- repo: bjlittle/geovista
site: https://geovista.readthedocs.io
conda_channel: conda-forge
badges: pypi, conda, codecov, site
builtons: [pyvista]
- repo: andrea-cuttone/geoplotlib
site: https://github.com/andrea-cuttone/geoplotlib/wiki/User-Guide
badges: pypi
builtons: [opengl]
- repo: ambeelabs/gspatial_plot
site: https://gspatial-plot.readthedocs.io
badges: pypi
builtons: [matplotlib]
- name: Graphs and networks
intro: Tools specifically focused on visualizing graphs (networks). Several of the other plotting libraries listed in other sections can also plot network graphs, including Bokeh, HoloViews, hvPlot, Matplotlib, and Plotly.
packages:
- repo: networkx/networkx
site: https://networkx.github.io
appveyor_project: dschult/networkx-pqott
badges: travis, appveyor, codecov, pypi, conda, site
builtons: [matplotlib, graphviz]
- repo: xflr6/graphviz
site: https://graphviz.readthedocs.io
conda_package: python-graphviz
conda_channel: conda-forge
badges: travis, codecov, pypi, conda, rtd, site
builtons: [graphviz]
- repo: pydot/pydot
badges: pypi, conda
conda_channel: conda-forge
builtons: [graphviz]
- repo: WestHealth/pyvis
site: https://pyvis.readthedocs.io
conda_channel: conda-forge
badges: pypi, conda, site
- repo: pygraphviz/pygraphviz
site: https://pygraphviz.github.io
conda_channel: conda-forge
badges: travis, pypi, conda, site
builtons: [graphviz]
- repo: timkpaine/ipydagred3
conda_channel: conda-forge
badges: azure, pypi, conda
builtons: [d3]
- repo: igraph/python-igraph
site: https://igraph.org/python
conda_channel: conda-forge
appveyor_project: ntamas/python-igraph
badges: travis, appveyor, pypi, conda, site
- repo: QuantStack/ipycytoscape
conda_channel: conda-forge
badges: pypi, conda
- repo: Yomguithereal/ipysigma
badges: pypi
- repo: epfl-lts2/pygsp
site: https://pygsp.readthedocs.io
conda_channel: conda-forge
badges: travis, coveralls, pypi, conda, rtd, site
- repo: ericmjl/nxviz
site: https://nxviz.readthedocs.io
conda_channel: conda-forge
badges: pypi, conda, site
- repo: benmaier/netwulf
site: https://netwulf.readthedocs.io
badges: travis, pypi, rtd, site
- repo: cytoscape/py2cytoscape
site: https://py2cytoscape.readthedocs.io
conda_channel: conda-forge
badges: travis, pypi, conda, rtd, site
- repo: SkBlaz/Py3Plex
site: https://py3plex.readthedocs.io
badges: pypi, site
- repo: dblarremore/webweb
site: https://webwebpage.github.io
badges: pypi, conda, site
- repo: skewed/graph-tool
# repo: https://git.skewed.de/count0/graph-tool
site: http://graph-tool.skewed.de
conda_channel: conda-forge
badges: pypi, conda, site
- name: Table display
intro: Tools for displaying and potentially interacting with data displayed as tables. Data libraries like Pandas and Polars also display tables, as do most dashboarding tools (e.g. Panel includes the Tabulator JS library for interactive tables).
packages:
- repo: mwouts/itables
pypi_name: itables
site: https://mwouts.github.io/itables
badges: codecov, pypi, site, conda
conda_package: itables
conda_channel: conda-forge
- repo: posit-dev/great-tables
pypi_name: great-tables
site: https://posit-dev.github.io/great-tables
badges: codecov, pypi, site, conda
conda_package: great_tables
conda_channel: conda-forge
sponsors: [posit]
- repo: Kanaries/pygwalker
pypi_name: pygwalker
site: https://kanaries.net/pygwalker
badges: pypi, conda
conda_package: pygwalker
conda_channel: conda-forge
- repo: jupyter-widgets/ipydatagrid
pypi_name: ipydatagrid
badges: pypi, conda
conda_package: ipydatagrid
conda_channel: conda-forge
- repo: finos/perspective
pypi_name: perspective-python
site: https://perspective.finos.org
badges: pypi, conda
conda_package: perspective # newer of two conda packages (the other is `perspective-python`)
conda_channel: conda-forge
- repo: paddymul/buckaroo
pypi_name: buckaroo
badges: pypi
site: https://paddymul.github.io/buckaroo
- repo: manzt/quak
pypi_name: quak
badges: pypi
site: https://manzt.github.io/quak
- repo: machow/reactable-py
pypi_name: reactable
badges: pypi
site: https://machow.github.io/reactable-py
- name: Other domain-specific
intro: Tools focused on specific plot types, research areas, or application types other than those above.
packages:
- repo: scikit-image/scikit-image
site: https://scikit-image.org
badges: travis, appveyor, codecov, pypi, conda, site
builtons: [matplotlib]
- repo: arviz-devs/arviz
sponsors: [numfocus]
site: https://arviz-devs.github.io/arviz
conda_channel: conda-forge
badges: pypi, travis, azure, coveralls, conda, site
builtons: [matplotlib]
- repo: DistrictDataLabs/yellowbrick
site: https://www.scikit-yb.org
sponsors: [numfocus]
conda_channel: DistrictDataLabs
badges: travis, appveyor, pypi, conda, site
builtons: [matplotlib]
- repo: Unidata/MetPy
sponsors: [unidata]
site: https://unidata.github.io/MetPy
conda_channel: conda-forge
badges: travis, appveyor, codecov, pypi, conda, site
builtons: [matplotlib]
- repo: reiinakano/scikit-plot
site: https://github.com/reiinakano/scikit-plot
conda_channel: conda-forge
badges: pypi, conda, site
builtons: [matplotlib]
- repo: ResidentMario/missingno
conda_channel: conda-forge
builtons: [matplotlib]
- repo: napari/napari
site: https://napari.org/
conda_channel: conda-forge
badges: codecov, pypi, conda
builtons: [vispy]
- repo: gyli/PyWaffle
badges: pypi, site
builtons: [matplotlib]
pypi_name: pywaffle
site: https://pywaffle.readthedocs.io/
- repo: yt-project/yt
sponsors: [numfocus]
site: https://yt-project.org
badges: travis, codecov, pypi, conda, site
builtons: [matplotlib]
- repo: saulpw/visidata
site: https://visidata.org/
badges: pypi
- repo: leotac/joypy
badges: pypi, site
builtons: [matplotlib]
- repo: moshi4/pyCirclize
badges: pypi
- repo: ismms-himc/clustergrammer2
site: https://clustergrammer.readthedocs.io
badges: pypi, site
builtons: [webgl]
- repo: ricklupton/floweaver
badges: pypi
builtons: [d3]
- repo: ContextLab/hypertools
badges: pypi, rtd
builtons: [matplotlib]
- repo: PAIR-code/facets
site: https://pair-code.github.io/facets/
badges: pypi
- name: Large-data rendering
intro: Tools for visualizing especially large datasets, e.g. by automatic subsampling, dynamic aggregation, server-side rasterization, or dynamic colormapping
packages:
- repo: holoviz/datashader
sponsors: [anaconda,numfocus]
site: https://datashader.org
badges: pypi, conda, site
- repo: vaexio/vaex
sponsors: [vaexio]
site: https://vaex.io
conda_channel: conda-forge
badges: travis, appveyor, pypi, conda, site
- repo: astrofrog/mpl-scatter-density
site: https://github.com/astrofrog/mpl-scatter-density
badges: travis, appveyor, pypi, conda, site
builtons: [matplotlib]
- repo: flekschas/jupyter-scatter
badges: pypi, site
builtons: [webgl]
pypi_name: jupyter-scatter
site: https://jupyter-scatter.dev/
- name: Dashboarding
intro: Libraries for creating live Python-backed web applications or dashboards that a user can interact with to explore or analyze data.
packages:
- repo: streamlit/streamlit
sponsors: [snowflake]
site: https://streamlit.io
conda_channel: conda-forge
badges: pypi, conda, site
- repo: gradio-app/gradio
sponsors: [huggingface]
site: https://gradio.app
conda_channel: conda-forge
badges: circleci, codecov, pypi, conda, site
- repo: plotly/dash
sponsors: [plotly]
conda_channel: conda-forge
site: https://dash.plot.ly
badges: circleci, pypi, conda, site
builtons: [plotly]
- repo: bokeh/bokeh
site: http://bokeh.org/
sponsors: [numfocus, anaconda]
badges: travis, pypi, conda, site
builtons: [bokeh]
- repo: holoviz/panel
sponsors: [anaconda,numfocus]
site: https://panel.holoviz.org
badges: codecov, pypi, conda, site
builtons: [bokeh]
- repo: marimo-team/marimo
badges: pypi, conda
conda_channel: conda-forge
site: https://marimo.io
sponsors: [marimo]
- repo: AnswerDotAI/fasthtml
pypi_name: python-fasthtml
site: https://fastht.ml/
badges: pypi
- repo: zauberzeug/nicegui
site: https://nicegui.io/
badges: pypi, conda
conda_channel: conda-forge
sponsors: [zauberzeug]
- repo: kitware/trame
conda_channel: conda-forge
badges: pypi, conda, site
site: https://kitware.github.io/trame
sponsors: [Kitware]
- repo: QuantStack/voila
site: https://voila.readthedocs.io
sponsors: [quantstack]
conda_channel: conda-forge
badges: travis, pypi, conda, rtd
- repo: rstudio/py-shiny
site: https://shiny.posit.co/py/
pypi_name: shiny
conda_package: shiny
conda_channel: conda-forge
badges: pypi, conda
sponsors: [posit]
- repo: wandb/weave
site: https://wandb.ai/site/weave
badges: pypi
sponsors: [wandb]
- repo: widgetti/reacton
site: https://reacton.solara.dev
badges: pypi, conda
conda_channel: conda-forge
sponsors: [widgetti]
- repo: widgetti/solara
site: https://solara.dev
badges: pypi, conda
conda_channel: conda-forge
sponsors: [widgetti]
- repo: reflex-dev/reflex
site: https://reflex.dev/
badges: pypi
sponsors: [reflex]
- repo: mckinsey/vizro
site: https://vizro.readthedocs.io
badges: pypi, conda
conda_channel: conda-forge
sponsors: [mckinsey]
- repo: fossasia/visdom
site: https://github.com/fossasia/visdom/blob/master/README.md#visdom
badges: pypi, conda
conda_channel: conda-forge
builtons: [plotly]
- repo: h2oai/wave
site: https://wave.h2o.ai
badges: pypi
sponsors: [h2o]
- repo: google/mesop
sponsors: [Google]
badges: pypi
- repo: datapane/datapane
site: https://docs.datapane.com
conda_channel: conda-forge
badges: pypi, conda, site
sponsors: [datapane]
- repo: Avaiga/taipy
site: https://www.taipy.io
badges: pypi
sponsors: [taipy]
- repo: pywebio/PyWebIO
site: https://www.pyweb.io
badges: pypi, site
- repo: mljar/mercury
site: https://mljar.com/mercury
conda_channel: conda-forge
badges: pypi, conda, site
sponsors: [mljar]
- repo: danielfrg/jupyter-flex
site: https://jupyter-flex.extrapolations.dev
badges: pypi
- repo: pycob/pyvibe
badges: pypi
- repo: causalens/dara
badges: pypi
site: https://dara.causalens.com
pypi_name: create-dara-app
- repo: trungleduc/ipyflex
site: https://ipyflex.readthedocs.io
conda_channel: conda-forge
badges: pypi, conda, rtd
- repo: Zen-Reportz/zen_dash
badges: pypi
- repo: streamsync-cloud/streamsync
badges: pypi
site: https://www.streamsync.cloud/
- repo: h2oai/nitro
badges: pypi
sponsors: [h2o]
pypi_name: h2o-nitro
- repo: mljar/bloxs
badges: pypi
sponsors: [mljar]
- repo: hyperdiv/hyperdiv
badges: pypi
site: https://hyperdiv.io
- repo: sansyrox/starfyre
badges: pypi
- repo: jrc-bdap/vois
# repo: https://code.europa.eu/jrc-bdap/vois
badges: pypi
site: https://code.europa.eu/jrc-bdap/vois
- repo: ifpen/chalk-it
badges: pypi
site: https://ifpen.github.io/chalk-it
pypi_name: py-chalk-it
- repo: briefercloud/briefer
badges: pypi
- repo: rio-labs/rio
badges: pypi
site: https://rio.dev
- repo: davialabs/davia
badges: pypi
site: https://davia.ai
- repo: data-stack-hub/DataStack
badges: pypi
- repo: LCL-CAVE/manganite
badges: pypi
builtons: [panel]
- repo: dropseed/plain
badges: pypi
- name: Colormapping
intro: Collections of colormaps and tools for generating new colormaps.
packages:
- repo: holoviz/colorcet
sponsors: [anaconda,numfocus]
site: https://colorcet.holoviz.org
badges: pypi, conda, site
- repo: jiffyclub/palettable
site: https://jiffyclub.github.io/palettable
- repo: matplotlib/cmocean
site: https://matplotlib.org/cmocean
conda_channel: conda-forge
badges: travis, codecov, pypi, conda
- repo: 1313e/CMasher
site: https://cmasher.readthedocs.io
conda_channel: conda-forge
badges: travis, appveyor, pypi, conda, site
- repo: callumrollo/cmcrameri
conda_channel: conda-forge
badges: pypi, conda
- repo: y-sunflower/pypalettes
site: https://python-graph-gallery.com/color-palette-finder/
conda_channel: conda-forge
badges: pypi, conda, site
builtons: [matplotlib]
- repo: matplotlib/viscm
conda_channel: conda-forge
badges: travis, codecov, pypi, conda
- name: Dormant projects
intro: Tools no longer developed or endorsed by the authors.
packages:
- repo: biggles-plot/biggles
site: https://biggles-plot.github.io
badges: pypi
dormant: https://github.com/biggles-plot/biggles/graphs/contributors
- repo: matplotlib/basemap
site: https://matplotlib.org/basemap
dormant: https://matplotlib.org/basemap/users/intro.html#cartopy-new-management-and-eol-announcement
builtons: [matplotlib]
- repo: adrn/d3po
site: https://d3po.org
dormant: https://github.com/adrn/d3po/graphs/contributors
badges: site, dormant
- repo: rossant/galry
dormant: https://github.com/rossant/galry/blob/master/README.md
badges: pypi, dormant
builtons: [opengl]
- repo: yhat/ggpy
site: http://ggplot.yhathq.com
badges: pypi, site
dormant: https://github.com/yhat/ggpy/graphs/contributors
builtons: [matplotlib]
- repo: dgrtwo/gleam
dormant: https://github.com/dgrtwo/gleam/graphs/contributors
badges: pypi, dormant
- repo: wireservice/leather
dormant: https://github.com/wireservice/leather/graphs/contributors
badges: pypi, rtd
- repo: lightning-viz/lightning
site: http://lightning-viz.org
dormant: https://gitter.im/lightning-viz/lightning
badges: pypi, dormant
builtons: [d3, leaflet]
- repo: mpld3/mpld3
site: https://mpld3.github.io
dormant: http://www.xavierdupre.fr/app/pymyinstall/helpsphinx/blog/2017/2017-09-02_mpld3.html
builtons: [matplotlib, d3]
- repo: altair-viz/pdvega
site: https://altair-viz.github.io/pdvega
conda_channel: conda-forge
badges: travis, pypi, conda, site
builtons: [d3, vega]
dormant: https://github.com/altair-viz/pdvega
- repo: olgabot/prettyplotlib
dormant: https://github.com/olgabot/prettyplotlib/commit/089263c8574b03126a638c8c00bf7880695bc93c
- repo: PyQwt/PyQwt
site: http://www.pyqtgraph.org
dormant: https://github.com/PyQwt
badges: site
builtons: [qt]
- repo: PierreRaybaut/guiqwt
site: https://pythonhosted.org/guiqwt/
dormant: https://github.com/PierreRaybaut/guiqwt/graphs/contributors
badges: site, pypi
builtons: [qt]
- repo: wrobstory/vincent
dormant: https://github.com/wrobstory/vincent/graphs/contributors
badges: pypi, rtd
builtons: [d3, vega]
- repo: almarklein/visvis
dormant: https://github.com/almarklein/visvis#status
builtons: [opengl]
gitextract_479j_ta9/
├── .github/
│ └── workflows/
│ └── docs.yml
├── .gitignore
├── LICENSE.txt
├── README.md
├── anaconda-project-lock.yml
├── anaconda-project.yml
├── doc/
│ ├── _static/
│ │ └── custom.css
│ ├── conf.py
│ ├── dashboarding/
│ │ ├── index.md
│ │ └── index.rst
│ ├── high-level/
│ │ ├── index.md
│ │ └── index.rst
│ ├── index.md
│ ├── index.rst
│ ├── overviews/
│ │ ├── index.md
│ │ └── index.rst
│ ├── scivis/
│ │ ├── index.md
│ │ └── index.rst
│ ├── tools.md
│ └── tutorials/
│ ├── index.md
│ └── index.rst
└── tools/
├── README.md
├── build.py
├── build_cache.py
├── builtons.yml
├── conda_downloads.py
├── sponsors.yml
├── template.html
└── tools.yml
SYMBOL INDEX (1 symbols across 1 files) FILE: tools/conda_downloads.py function get_conda_badge (line 40) | def get_conda_badge(conda_package):
Condensed preview — 29 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (117K chars).
[
{
"path": ".github/workflows/docs.yml",
"chars": 2737,
"preview": "name: docs\n\non:\n push:\n branches:\n - master\n pull_request:\n branches:\n - '*'\n schedule:\n - cron: \""
},
{
"path": ".gitignore",
"chars": 287,
"preview": "# Byte-compiled / DLL / optimized files...\n__pycache__/\n\n# OSX\n.DS_STORE\n\n# Jupyter notebook\n*.ipynb_checkpoints/\n\n# nbs"
},
{
"path": "LICENSE.txt",
"chars": 14990,
"preview": "Creative Commons Attribution 4.0 International Public License (CC-BY)\n\n By exercising the Licensed Rights (defined bel"
},
{
"path": "README.md",
"chars": 1786,
"preview": "<img src=\"doc/_static/logo.png\" width=150><br>\n\n# Python tools for data visualization\n\n| | |\n| --- | --- |\n| Build"
},
{
"path": "anaconda-project-lock.yml",
"chars": 13821,
"preview": "# This is an Anaconda project lock file.\n# The lock file locks down exact versions of all your dependencies.\n#\n# In most"
},
{
"path": "anaconda-project.yml",
"chars": 862,
"preview": "name: pyviz.org\n\ndescription: pyviz.org\n\ncommands:\n build_cache:\n unix: |\n python tools/conda_downloads.py\n "
},
{
"path": "doc/_static/custom.css",
"chars": 729,
"preview": "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"
},
{
"path": "doc/conf.py",
"chars": 915,
"preview": "# noqa\nfrom nbsite.shared_conf import *\n\nproject = u'PyViz'\nauthors = u'PyViz authors'\ncopyright = u' 2019, ' + authors\n"
},
{
"path": "doc/dashboarding/index.md",
"chars": 3764,
"preview": "# Dashboarding tools\n\nJust about any Python library can be used to create a \"static\" PNG, SVG, HTML, or other output tha"
},
{
"path": "doc/dashboarding/index.rst",
"chars": 205,
"preview": ".. mdinclude:: index.md\n\n.. toctree::\n :titlesonly:\n :hidden:\n :maxdepth: 2\n\n Dash <https://plot.ly/products/das"
},
{
"path": "doc/high-level/index.md",
"chars": 2569,
"preview": "# High-level tools\n\nThe full list of [Python viz tools](../tools.html) is very long and covers a wide range of functiona"
},
{
"path": "doc/high-level/index.rst",
"chars": 662,
"preview": ".. mdinclude:: index.md\n\n.. toctree::\n :titlesonly:\n :hidden:\n :maxdepth: 2\n\n Pandas .plot <https://pandas.pydat"
},
{
"path": "doc/index.md",
"chars": 2002,
"preview": "# Python tools for data visualization\n\nWelcome to PyViz! The PyViz.org website is an open platform for helping users de"
},
{
"path": "doc/index.rst",
"chars": 282,
"preview": ".. mdinclude:: index.md\n\n.. toctree::\n :titlesonly:\n :hidden:\n :maxdepth: 2\n\n Home <self>\n Overviews <overview"
},
{
"path": "doc/overviews/index.md",
"chars": 12477,
"preview": "# Overviews\n\nThe Python visualization landscape can seem daunting at first. These overviews attempt to shine light on co"
},
{
"path": "doc/overviews/index.rst",
"chars": 82,
"preview": ".. mdinclude:: index.md\n\n.. toctree::\n :titlesonly:\n :hidden:\n :maxdepth: 2\n"
},
{
"path": "doc/scivis/index.md",
"chars": 3592,
"preview": "# SciVis Libraries\n\nMost of the libraries listed at PyViz.org fall into the [InfoVis](http://ieeevis.org/year/2019/info/"
},
{
"path": "doc/scivis/index.rst",
"chars": 395,
"preview": ".. mdinclude:: index.md\n\n.. toctree::\n :titlesonly:\n :hidden:\n :maxdepth: 2\n\n VTK <https://vtk.org>\n VisPy <ht"
},
{
"path": "doc/tools.md",
"chars": 834,
"preview": "This page lists OSS libraries for visualizing data in Python. If you see any missing Python tools, please open a [PR](h"
},
{
"path": "doc/tutorials/index.md",
"chars": 1866,
"preview": "# Tutorials\n\nMost of the projects listed at PyViz.org contain examples explaining how to solve various problems using th"
},
{
"path": "doc/tutorials/index.rst",
"chars": 456,
"preview": ".. want to include these in the toctree\n\n.. mdinclude:: index.md\n\n.. toctree::\n :titlesonly:\n :hidden:\n :maxdepth:"
},
{
"path": "tools/README.md",
"chars": 1957,
"preview": "## PyViz Tools\n\nThis directory is used to generate a tools dashboard for comparing various Python visualization packages"
},
{
"path": "tools/build.py",
"chars": 3018,
"preview": "#!/usr/bin/env python\nimport datetime\nimport os\nfrom jinja2 import Template\nfrom yaml import safe_load\nfrom markdown imp"
},
{
"path": "tools/build_cache.py",
"chars": 3154,
"preview": "#!/usr/bin/env python\n\nimport os\nimport time\nfrom yaml import safe_load\nimport requests\n\nhere = os.path.abspath(os.path."
},
{
"path": "tools/builtons.yml",
"chars": 1569,
"preview": "bokeh:\n label: Bokeh\n url: https://docs.bokeh.org/en/latest/\n logo: _static/badges/builtons/bokeh.png\n\nplotly:\n labe"
},
{
"path": "tools/conda_downloads.py",
"chars": 2802,
"preview": "#!/usr/bin/env python\n\"\"\"\nRun this script at the beginning of each month to build new conda downloads badges\nfrom the pr"
},
{
"path": "tools/sponsors.yml",
"chars": 2444,
"preview": "numfocus:\n label: NumFocus\n url: https://numfocus.org\n logo: _static/badges/numfocus.png\n\nanaconda:\n label: Anaconda"
},
{
"path": "tools/template.html",
"chars": 5632,
"preview": " <div idid=\"date\">\n <i>Last updated: {{ date }}</i>\n </div>\n <div id=\"tools-wrapper\">\n {% for section i"
},
{
"path": "tools/tools.yml",
"chars": 27091,
"preview": "- name: Core\n intro: Python libraries on which multiple higher-level libraries are built.\n packages:\n\n - repo: matp"
}
]
About this extraction
This page contains the full source code of the pyviz/pyviz.org GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 29 files (110.3 KB), approximately 34.7k tokens, and a symbol index with 1 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.