Lets-Plot

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.

• Hadley Wickham, "ggplot2: Elegant Graphics for Data Analysis"

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 Kermel.

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

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:

• Scientific mode in PyCharm
• Scientific mode in IntelliJ IDEA

What is new in 2.2.0

• Added support for coord_flip().

  See: example notebook.

• Improved plot appearance and better theme support:

  • Bigger fonts across the board;
  • Gridlines;
  • 4 themes from ggplot2 (R) library: theme_grey(), theme_light(), theme_classic(), theme_minimal();
  • Our designer theme: theme_minimal2() (used by default);
  • theme_none() for the case you want to design another theme;
  • A lot more parameters in the theme() function, also helpers: element_line(), element_rect(), element_text().

  See: example notebook.

Note: fonts size, family and face still can not be configured.

• Improved Date-time formatting support:

  • tooltip format() should understand date-time format pattern [#387];
  • scale_x_datetime should apply date-time formatting to the breaks [#392].

  See: example notebook.

• corr_plot() function now also accepts pre-computed correlation coefficients. I.e. the following two expressions are equivalent:

    corr_plot(iris_df).points().labels().build()
    corr_plot(iris_df.corr()).points().labels().build()  # new

Change Log

See CHANGELOG.md for other changes and fixes.

License

Code and documentation released under the MIT license. Copyright © 2019-2021, JetBrains s.r.o.