Get Started

Python versions: 3.8-3.13

OS: Linux, macOS, Windows

Installation

pip install lets-plot

Quick Start

import numpy as np
from lets_plot import *
LetsPlot.setup_html()

np.random.seed(12)
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(700, 300) + \
    geom_density(color='dark_green', alpha=.7) + scale_fill_brewer(type='seq') + \
    theme(panel_grid_major_x='blank')

import numpy as np
from lets_plot import *
LetsPlot.setup_html()

np.random.seed(12)
data = dict(
    cond=np.repeat(['A', 'B'], 200),
    rating=np.concatenate((np.random.normal(0, 1, 200), np.random.normal(1, 1.5, 200)))
)

background = element_rect(fill='#14181e')
ggplot(data, aes(x='rating', fill='cond')) + ggsize(700, 300) + \
    geom_density(color='dark_green', alpha=.7) + scale_fill_brewer(type='seq') + \
    flavor_high_contrast_dark() + \
    theme(panel_grid_major_x='blank', plot_background=background, legend_background=background)

User Guide

Coding for Economists by Arthur Turrell

• Easy Data Visualisation for Tidy Data with Lets-Plot - how to make plots quickly using the declarative plotting.

Python4DS by Arthur Turrell

• Data Visualisation - will teach you how to visualise your data using Lets-Plot.

• Layers - a deeper dive into aesthetic mappings, geometric objects, and facets.

• Exploratory Data Analysis - search for answers by visualising, transforming, and modelling your data.

Data Science with Sanjaya by Sanjaya Subedi

• Create Beautiful Plots with Python Let’s Plot Library - Using the Lets-Plot library, the author shows you how to create 10 different but very common types of plots that you’ll see and create often.

Explore Your Data with Lets-Plot

Visit Charts

Visit Maps

Visit Geocoding

Key Features

Inspired by ggplot2

A faithful port of R’s ggplot2 to Python.

You can learn R’s ggplot2 and the grammar of graphics in the “ggplot2: Elegant Graphics for Data Analysis” book by Hadley Wickham.

Multiplatform

A Grammar of Graphics for Python - works in Python notebooks (Jupyter, Datalore, Kaggle, Colab, Deepnote, Nextjournal) as well as in PyCharm and Intellij IDEA IDEs.

A Grammar of Graphics for Kotlin - a Kotlin multiplatform visualization library which fulfills your needs in the Kotlin ecosystem: from Kotlin notebooks to Compose-Multiplatform apps.

Geospatial Visualization

Find spatial objects with the help of our powerful and easy to use Geocoding module. In case you already have GeoDataFrame on hand - plot it straight away.

Interactive Maps

Interactive maps allow zooming and panning around your geospatial data with customizable vector or raster basemaps as a backdrop. Learn more.

Customizable Tooltips and Annotations

You can customize the content, values formatting and appearance of tooltip and annotation for layers of your plot.

Formatting

Lets-Plot supports formatting of numeric and date-time values in tooltips, annotations, legends, on the axes and text geometry layer. Learn more.

Export to SVG, HTML, PNG and PDF

Use the ggsave() function to save your plot to a file. Alternatively, leverage the to_svg(), to_html(), to_png(), or to_pdf() methods of the plot object to save it to a file or an in-memory file-like object. Learn more.

Option to Display Plots in External Browser

With the “show externally” mode enabled, you can easily display a plot in an external browser by invoking its show() method. Learn more.

Sampling

Sampling is a special technique of data transformation, which helps to deal with large datasets and overplotting. Learn more.

‘No Javascript’ and Offline Mode

In the ‘no javascript’ mode Lets-Plot generates plots as bare-bones SVG images. Plots in the notebook with option offline=True will be working without an Internet connection. Learn more.