Skip to main content

An open source library for statistical plotting

Project description

Lets-Plot official JetBrains project

Couldn't load MIT license svg

Lets-Plot is an open-source plotting library for statistical data.

The design of Lets-Plot library is heavily influenced by Leland Wilkinson work The Grammar of Graphics describing the deep features that underlie all statistical graphics.

This grammar [...] is made up of a set of independent components that can be composed in many different ways. This makes [it] very powerful because you are not limited to a set of pre-specified graphics, but you can create new graphics that are precisely tailored for your problem.

We provide ggplot2-like plotting API for Python and Kotlin users.

Lets-Plot for Python

A bridge between R (ggplot2) and Python data visualization.

Learn more about Lets-Plot for Python installation and usage at the documentation website: https://lets-plot.org.

Lets-Plot for Kotlin

Lets-Plot for Kotlin adds plotting capabilities to scientific notebooks built on the Jupyter Kotlin Kernel.

You can use this API to embed charts into Kotlin/JVM and Kotlin/JS applications as well.

Lets-Plot for Kotlin at GitHub: https://github.com/JetBrains/lets-plot-kotlin.

"Lets-Plot in SciView" plugin

JetBrains Plugins JetBrains plugins

Scientific mode in PyCharm and in IntelliJ IDEA provides support for interactive scientific computing and data visualization.

Lets-Plot in SciView plugin adds support for interactive plotting to IntelliJ-based IDEs with the Scientific mode enabled.

Note: The Scientific mode is NOT available in communinty editions of JetBrains IDEs.

Also read:

What is new in 3.1.0

Change Log

See CHANGELOG.md for other changes and fixes.

License

Code and documentation released under the MIT license. Copyright © 2019-2023, JetBrains s.r.o.

Project details


Release history Release notifications | RSS feed

This version

3.1.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

lets_plot-3.1.0-cp311-cp311-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.11Windows x86-64

lets_plot-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

lets_plot-3.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.6 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ ARM64

lets_plot-3.1.0-cp311-cp311-macosx_11_0_arm64.whl (3.6 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

lets_plot-3.1.0-cp311-cp311-macosx_10_9_x86_64.whl (3.7 MB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

lets_plot-3.1.0-cp310-cp310-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.10Windows x86-64

lets_plot-3.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

lets_plot-3.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.6 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ ARM64

lets_plot-3.1.0-cp310-cp310-macosx_11_0_arm64.whl (3.6 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

lets_plot-3.1.0-cp310-cp310-macosx_10_9_x86_64.whl (3.7 MB view details)

Uploaded CPython 3.10macOS 10.9+ x86-64

lets_plot-3.1.0-cp39-cp39-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.9Windows x86-64

lets_plot-3.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

lets_plot-3.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.6 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

lets_plot-3.1.0-cp39-cp39-macosx_11_0_arm64.whl (3.6 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

lets_plot-3.1.0-cp39-cp39-macosx_10_9_x86_64.whl (3.7 MB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

lets_plot-3.1.0-cp38-cp38-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.8Windows x86-64

lets_plot-3.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

lets_plot-3.1.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.6 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ ARM64

lets_plot-3.1.0-cp38-cp38-macosx_11_0_arm64.whl (3.6 MB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

lets_plot-3.1.0-cp38-cp38-macosx_10_9_x86_64.whl (3.7 MB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

lets_plot-3.1.0-cp37-cp37m-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.7mWindows x86-64

lets_plot-3.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.17+ x86-64

lets_plot-3.1.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.6 MB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.17+ ARM64

lets_plot-3.1.0-cp37-cp37m-macosx_10_9_x86_64.whl (3.7 MB view details)

Uploaded CPython 3.7mmacOS 10.9+ x86-64

File details

Details for the file lets_plot-3.1.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: lets_plot-3.1.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 3.5 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.7.16

File hashes

Hashes for lets_plot-3.1.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 8cdc100598f2ac5774a0d1099baa2c19157aa3e7dd7b1e696c024cfdcd0d39ac
MD5 862cce9e144aafdd27dd94cd42d274bf
BLAKE2b-256 b131e73c6bcf18b4d1fa909d8372d0220964bdd4d6003dd308e96f6941912377

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b1c771c3fd8486ed8124693f8cb0c7812a72bdd20d165b42b7b1d2c0a1e22a5f
MD5 b63a68561cbf8e0da42cbdda3db3d3d2
BLAKE2b-256 501c1ce144066f646c9399e5798b40e4d35a5f9d9bf2a163a5454aaac26839a7

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 0774f5088cc0c72d8ce242030ce12b46037ef6f42fed5e15c98822ba70340f1d
MD5 0994f5fca99dcfc2c6c91ffff4e88dc3
BLAKE2b-256 8cbe78cd8ad599a99775440f5ad803f0f992c638fc5898556e4bd8f599a712bd

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 25b3900e47b8838dcc0b98aafb48dfb87d748a7d9454bd670960b5415767a185
MD5 3ec2ef5933b46e9cade3fd1b78718fdd
BLAKE2b-256 bb9c0691ad418b6e9b438b6d261215e440ab8b7cfb962ed6118765e0166db8ae

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 dc7488591df695768bc39d6e844570770380e9b5c8011b7348dbca98bc322551
MD5 789795ddff0369e19e9e5ef8fbfb6c2c
BLAKE2b-256 313e4b6e5a8e131ff7b0aa625da0c0a3be4230a407d4497f6114a8b6ebc67f63

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: lets_plot-3.1.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 3.5 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.7.16

File hashes

Hashes for lets_plot-3.1.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 830429ba3f6d7cf83179a6e27ec7ae7b6bc66e9db895598532ae0cf6aa98388c
MD5 9a3c4954e98017bbc1ed3dc0c3e39bc0
BLAKE2b-256 b9ce80a076b5856d939c28fe4ff68f4118371080dd22fc16eca13a8c640cf8c0

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 adecb6caa5a3b7a36b291c04d5814f709b75b4124094e3dbed894420d7e8d915
MD5 b39ab058bc9d70613f60a2649c5890a4
BLAKE2b-256 5fc0b1f8fe02a2238593cd46c9c2fb11cdc2af779c244d7433c107fe496c653c

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 a475f4aea70fdb8a055688395375c96c090739039dedea54ae02371212dcfc50
MD5 278e0b101d7b2a5c0ba577cf8de5743b
BLAKE2b-256 47d736c2458cb69344353c6f06d355e08e675cd1c4511e66dac0663108b91276

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 79a6cc67ecbab0f452628a83ac768579f4bd298d55a24e8331de25a20abec974
MD5 5e4b460f327195658bbb621d3951462f
BLAKE2b-256 da43f4a01e8fd3d56f9c95528efdc1dcb7db31a87555aec9ad7807e9106b608f

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 dfdaf46a44c6c369732d1eb69eec30702ed4d5596dcb3b5a4ed74f28dde8491b
MD5 57299fa21066274d611f5a4734b9881c
BLAKE2b-256 887b8558e51ec6201427b36fb9080354ce2c4585dc437e6509dd1df12243ac94

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: lets_plot-3.1.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 3.5 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.7.16

File hashes

Hashes for lets_plot-3.1.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 ee62e783cbb49a47febc3f0025865e05d287952f35bb09f884c4bfa6ea531c28
MD5 da0c92b58a100b5849ce6c69bc81559d
BLAKE2b-256 fbfaa0d65881b8b80a99bae0e80f328c10ffdc6498e5a3f2b1ed751c5aaf2e9a

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 97b9c038e1db3cdfb31c41f9e1ca23cf79226939d51a5e463ea50c12a27612fa
MD5 bed3c55f3e0033347baf70b3dcd9a0c2
BLAKE2b-256 32978d196bc33d0302c4aead3465d68597f444b5d8a51cf193cc126f1b35fd17

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 a75f547593fe180bb95ee24f55394b95f15363db412f262b22b68b6352e93ea7
MD5 ee255e17896aafa5abf40f197d2fa5cc
BLAKE2b-256 19c85a5a1e59b07aad1f764f1c9c28f250e4867de6b50dcc66f7a3bc5ad97900

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 e2c7aa742d7381e980d0a8d331963f1ba5ad2c51b1711b6e7abb8c026191e41c
MD5 ad777233e8d62d759051a39f1d6c2383
BLAKE2b-256 72fa746b6c71945788d32f37a80f2eca8a3140e6d80d798566ae2696ec714495

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 b5e5b198ae74b0202376c5f375567f5b458ec2b41cb9e759187021f7e17f4f85
MD5 b1765a4f44c56a349f445ba304524e23
BLAKE2b-256 58773ccb93709e52b5545a8fd6c796918df358b307d64bdc46d551b4efd636f1

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: lets_plot-3.1.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 3.5 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.7.16

File hashes

Hashes for lets_plot-3.1.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 407b9a48dce2aa1682b64d5aed478885a74d0c50ac36639a50de79985a2d7442
MD5 4c612e7e862da7b692dff01f69f3f0f2
BLAKE2b-256 f5bc13d0fbfe70837f7cacc8eb43ad18f3753f94f87e24552d10fedc56b22b34

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3d7c143a26f14867d79726c991d9e5a71162ed6d3a2416023f99da8e2a4b39e8
MD5 0d4a1a9c02cbf6a03c4c2ad000b78ee3
BLAKE2b-256 3a871049cd805132ed4c38de6357b850c8d5bacca021fe8c84f19983f869875c

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 74f957311845e99c462c6ec818eb61afc46481f45957304218d32635e9e4359e
MD5 4e6ce0e2d7aa3f0ab946fc35c3b2b01e
BLAKE2b-256 32552adcb7ea654ade2c6141bd6cbf28cbcda2d4d5795f1c5597bb3fab2e1b1e

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 bb9206ace1533ebf1f08404e58f8126b130859b0d6bfaebe0fe56037d299866b
MD5 e60fccdb140f0ab6110f489c0c81b97e
BLAKE2b-256 26866914119d2ebec8e0e7badb4427229c53ab9c668295a730e9b90b8fe94c6d

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 d532e5aa23c9bc0a782c1fa451e092553366a46b28f5e2e523b128248d4c5516
MD5 87d890e6899aca9e11d01ae09a381e1a
BLAKE2b-256 1ff53b6c3dd941f72e118f8cd7893c835088189c3c916cba15238501af1e73b8

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: lets_plot-3.1.0-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 3.5 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.7.16

File hashes

Hashes for lets_plot-3.1.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 a1ede54f0478e00da07c1851bbf6d38579d1bbcd6449b95bf1b12ebd58d88665
MD5 4a01127bb8bff1baedda1484586dd4c5
BLAKE2b-256 aaa28ca143da15872295ce415a4b384d87db1bab9ac0068a82cb9f31e882300c

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 7dea9f0da7cb1101f8aa80e76d99e632c1bb4083566d0f0c618fddc7f7e4ea22
MD5 ee4f966ab66e2d9b2c63d7d6490f45e9
BLAKE2b-256 d251b6c549e1562d065c43c5d451393943099c3a01db3e23092210f68006ec5f

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f2e3e95946a4d61af13550248ecf996e6f9c1c9156961743e8019d53f682d289
MD5 5b9d50c6c613142db295119fc20cd833
BLAKE2b-256 97fa4d3aa674721f2b362f6023a0892965a7f3d39e7bb121101ca57601682586

See more details on using hashes here.

File details

Details for the file lets_plot-3.1.0-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for lets_plot-3.1.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 e02fceed747a1bd08628cf338bef002c26a79fac19dc524150b101b95e2861f8
MD5 f9c35b1187b43ecc9b7bbb1df7307d1e
BLAKE2b-256 57939a82b59a9c57f65d72a1fa5f8f9844da32137986d738f8b1645006946ede

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page