An open source library for statistical plotting
Project description
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, DataSpell 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 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.
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