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 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.
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 Skia 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.
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:
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- Support for Python
timeanddateobjects. - Support for timezone-aware
datetimeobjects and Pandas/PolarsSeries.
See example notebook.
- Support for Python
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Exporting to PNG and PDF formats now uses the
ImageMagicklibrary bundled with Lets-Plot Python wheels and available out-of-the-box.
This replaces the previous dependency on theCairoSVGlibrary and comes with improved support for LaTeX labels rasterization. -
See example notebook.
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See example notebook.
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Annotations support via
relative_labelsandabsolute_labelsparameters.
See example notebook.
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Support for combining waterfall bars with other geometry layers.
See example notebook.
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Continuous data when used with discrete positional scales is no longer transformed to discrete data.
Instead, it remains continuous, allowing for precise positioning of continuous elements relative to discrete ones.
See: example notebook.
Tip
New way of handling continuous data on discrete scales could potentially break existing plots.
If you want to restore a broken plot to its original form, you can use the as_discrete() function to annotate continuous data as discrete.
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The default plot layout has been improved to better accommodate axis labels and titles.
Also, newtheme()optionsaxis_text_spacing,axis_text_spacing_x, andaxis_text_spacing_ycontrol spacing between axis ticks and labels.
See the plot layout diagram showing various layout options and their effects on plot appearance.
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See CHANGELOG.md for a full list of changes.
Recent Updates in the Gallery
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Code and documentation released under the MIT license. Copyright © 2019-2025, JetBrains s.r.o.