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An open source library for statistical plotting

Project description

Lets-Plot for Python

Latest Release
OS Linux, MacOS
Python versions 3.7, 3.8

Implementation Overview

The Lets-Plot python extension includes native backend and a Python API, which was mostly based on the ggplot2 package well-known to data scientists who use R.

R ggplot2 has extensive documentation and a multitude of examples and therefore is an excellent resource for those who want to learn the grammar of graphics.

Note that the Python API being very similar yet is different in detail from R. Although we have not implemented the entire ggplot2 API in our Python package, we have added a few new functions and built-in sampling to our Python API.

You can try the Lets-Plot library in Datalore. Lets-Plot is available in Datalore out-of-the-box and is almost identical to the one we ship as PyPI package. This is because Lets-Plot is an offshoot of the Datalore project from which it was extracted to a separate plotting library.

One important difference is that the python package in Datalore is named datalore.plot and the package you install from PyPI has name lets_plot.

The advantage of Datalore as a learning tool in comparison to Jupyter is that it is equipped with very friendly Python editor which comes with auto-completion, intentions, and other useful coding assistance features.


To install the Lets-Plot library, run the following command:

pip install lets-plot

Quickstart in Jupyter

To evaluate the plotting capabilities of Lets-Plot, add the following code to a Jupyter notebook:

import numpy as np
from lets_plot import *

data = dict(
    cond=np.repeat(['A','B'], 200),
    rating=np.concatenate((np.random.normal(0, 1, 200), np.random.normal(1, 1.5, 200)))

ggplot(data, aes(x='rating', fill='cond')) + ggsize(500, 250) \
+ geom_density(color='dark_green', alpha=.7) + scale_fill_brewer(type='seq') \
+ theme(axis_line_y='blank')
Couldn't load quickstart.png


Try the following examples to study more features of the Lets-Plot library.

Quickstart in Jupyter: quickstart.ipynb

Histogram, density plot, box plot and facets: distributions.ipynb

Error-bars, points, lines, bars, dodge position: error_bars.ipynb

Points, point shapes, linear regression, jitter position: scatter_plot.ipynb

Points, density2d, polygons, density2df: density_2d.ipynb

Tiles, contours, polygons, contourf: contours.ipynb

Various presentation options: legend_and_axis.ipynb

Unfamiliar functions used in the examples

  • ggsize() - sets size of the plot. Used in many examples starting from quickstart

  • geom_density2df() - fills space between equal density lines on 2D density plot. Similar to geom_density2d but supports fill aesthetic.

    Example: density_2d.ipynb

  • geom_contourf() - fills space between lines of equal level of bivariate function. Similar to geom_contour but supports fill aesthetic.

    Example: contours.ipynb


Sampling is a special technique of data transformation, which helps dealing with large datasets and overplotting.

Learn more about sampling in Lets-Plot.


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

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