Lets-Plot is a multiplatform plotting library built on the principles of the Grammar of Graphics.

The library design is heavily influenced by Leland Wilkinson's 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"

A bridge between R (ggplot2) and Python data visualization.
To learn more, see the documentation site at lets-plot.org/python.

Create plots in Kotlin Notebook, Datalore, Jupyter with Kotlin Kernel
or any other notebook that supports Kotlin Kernel.
To learn more, see the Lets-Plot Kotlin API project at GitHub.

Embed Lets-Plot charts in Compose Multiplatform applications.
To learn more, see the Lets-Plot Compose Frontend project at GitHub.

Embed Lets-Plot charts in JVM (Swing, JavaFX) and Kotlin/JS applications.
To learn more, see the Lets-Plot Kotlin API project at GitHub.

Kotlin API documentation site: lets-plot.org/kotlin.

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

• Statistical Summaries Directly on geom_smooth() Plot Layer

  The geom_smooth() layer now includes a labels parameter designed to display statistical summaries of the fitted model directly on the plot.
  This parameter accepts a smooth_labels() object, which provides access to model-specific variables like $R^2$ and the regression equation.

  

  See example notebook.

• Plot Tags

  Plot tags are short labels attached to a plot.

  

  See example notebook.

• New geom_bracket() and geom_bracket_dodge() Geometries

  New geometries designed primarily for significance bars (p-values) annotations in categorical plots.

  

  See: example notebook.

• Custom Color Palettes in geom_imshow()

  The cmap parameter now allows you to specify a list of hex color codes for visualizing grayscale images.
  Also, the new cguide parameter lets you customize the colorbar for grayscale images.

  

  See example notebook.

• New palette() Method in Color Scales

  Generates a list of hex color codes that can be used with scale_color_manual() to maintain consistent colors across multiple plots.

  See: example notebook.

• New overflow parameter in scale_color_brewer(), scale_fill_brewer()

  Controls how colors are generated when more colors are needed than the palette provides.
  Options: 'interpolate' ('i'), 'cycle' ('c'), 'generate' ('g').

  See: example notebook.

• New break_width Parameter in Positional Scales

  Specifies a fixed distance between axis breaks.

  See examples:

  • datetime scale
  • time (duration) scale
  • log10 scale

• Axis Minor Ticks Customization

  The axis_minor_ticks and axis_minor_ticks_length parameters in theme().

  See: example notebook.

• Pan/Zoom in gggrid() with Shared Axes

  Pan/Zoom now propagates across subplots with shared axes (sharex/sharey).

  See: example notebook.

• And More

  See CHANGELOG.md for a full list of changes.

CHANGELOG.md

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Code and documentation released under the MIT license. Copyright © 2019-2025, JetBrains s.r.o.