Charts

Data

Every layer may have some data associated with it. The “data” refers to a table of data where each row contains an observation and each column represents a variable that describes some property of each observation.

Data in this format is sometimes referred to as tidy data, flat data, primary data, atomic data, and unit record data.

You can pass tidy data to Lets-Plot in form of a Pandas Dataframe, a Polars Dataframe or just a dictionary: example notebook.

Basic Building Blocks

Points: points, jittered points

Lines: line, path, diagonal line, horizontal line, vertical line, segment, curve, spoke, step-function

Areas: area, ribbon

Polygons: polygon, map

Tiles: tiles, rectangles, raster plot

Text: text, label

Examples:

• Population mobility and COVID-19
• Bayesian inference
• Line vs. path
• Inset map of Kotlin island
• Drawing graph edges
• Formatting labels on plots
• Text geoms
• Image on map, the 'use_crs' parameter
• Variadic lines in geom_path() and geom_line()
• Spoke geometry
• Curve geometry

Discrete

bar, pie, lollipop, boxplot, count/sum

Examples:

• Bar geometry
• Annotations for barchart
• Pie
• Pie chart on map
• Annotations for pie
• Parameter size_unit in geom_pie()
• Pie chart stroke and spacers
• 'boxplot_outlier' statistics
• Lollipop plot
• Count stat
• Handling an overplotting on a scatter plot: geom_count()/stat_sum()
• Using scales
• Viridis colors

Ordering Categories, as_discrete()

as_discrete()

Learn more: Function as_discrete().

Examples:

• Guide to ordering
• Factor levels
• Discover trends with smoothing

Contours

contours, filled contours

Examples:

• Contours
• How to draw curve fast

Visualization of Distribution

histogram, density, dotplot, ydotplot, violin, ridgeline, frequency polygon

Examples:

• Histogram
• Distributions
• Dot plots
• Violin plot
• Ridgeline plot
• Netflix movies
• The orientation parameter
• Using scales

Stats

stat_ecdf(), stat_summary(), stat_summary_bin()

Examples:

• ECDF stat
• Summary stat
• Binned summary stat

Function

function

Examples:

• Function geometry

Marginal Plots

ggmarginal

See also: Joint Plot, Residual Plot.

Examples:

• Marginal plots

Visualization of Errors

crossbar, errorbar, linerange, pointrange

Examples:

• Error bars
• Horizontal geoms
• Horizontal error bars and vertical 'dodge'

Smoothing

smoothing line

Examples:

• Draw a scatter plot
• Discover trends with smoothing

Bivariate Distribution

2d bins, 2d density, filled 2d density

Examples:

• 2d density

Time Series

scale_x_datetime(), scale_y_datetime(), scale_x_time(), scale_y_time()

Examples:

• Scale time
• Time series visualizations

Images

geom_imshow(), matrix of images

Examples:

• geom_imshow()
• The 'extent' parameter
• Parameters 'norm', 'vmain', 'vmax' and 'cmap'
• The alpha parameter in geom_imshow()
• NaN values in geom_imshow()
• "Fisher boat": 'geom_imshow()' and 'geom_raster()'
• DEM image on map
• Image on map, the 'use_crs' parameter
• Image matrix

Facets

facet_grid(), facet_wrap()

Examples:

• Facets
• Free scales on a faceted plot
• Population mobility and COVID-19

Coordinate Systems

coord_cartesian(), coord_fixed(), coord_polar(), coord_flip(), coord_map()

Examples:

• Coordinate systems
• Flipping coordinates
• Polar coordinate system

‘bistro’ Plots

Exploratory Data Analysis (EDA) is an open-ended, highly interactive, iterative process, whose actual steps are segments of a stubbily branching, tree-like pattern of possible actions.

Learn more about instruments for EDA in Lets-Plot: ‘bistro’ Plots.

GeoPandas Shapes

GeoPandas GeoDataFrame is supported by the following geometry layers: polygon, map, point, pie, text, path, rect.

Learn more: GeoPandas Support.

Examples:

• Inset map of Kotlin island
• Using Lets-Plot with GeoPandas to Create Maps
• Plotting Airbnb prices Boston
• Spatial prediction of soil pollutants with multi-output Gaussian processes
• DEM image on map
• Using exotic map projections

Grouping Plots

GGBunch and gggrid shows a collection of plots on one figure.

Examples:

• The GGBunch class
• The gggrid() function
• Applying common theme to a plot group
• Sharing X,Y-axis scale limits

Presentation Options

theme(), ggtitle(), ggsize(), xlab(), ylab(), labs(), guide_legend(), guide_colorbar(), guides()

Predefined themes:

minimal2, bw, grey, classic, light, minimal, void, none

Color schemes (flavors):

darcula, solarized light, solarized dark, high contrast light, high contrast dark

Examples:

• Default theme
• Themes overview
• Theme colors for geometries
• Theme flavors
• Applying common theme to a plot group
• Legend and axis
• Plot margins
• Multiple lines for legend text
• Tooltip customization
• Title, subtitle, caption
• Tooltips theme
• Set font faces
• Panel borders
• Axis position
• Rotation of axis labels
• Option to show/hide plot messages
• Exponent format in Lets-Plot
• Text setting for annotations

Cookbooks

• Lets-Plot API overview
• Multiple color scales
• Showing quantiles on density plots
• Scale functions with parameter aesthetic
• Stackable position adjustments

Resources

• EDA Examples

• Picking the Perfect Data Visualization: Line Plots

• Picking the Perfect Data Visualization: Barplots

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 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.

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 for any geometry layer in your plot. Learn more.

Formatting

Lets-Plot supports formatting of numeric and date-time values in tooltips, 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.