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, band, 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
• Customizing line type
• Expanding plot limits with expand_limits()

Discrete

bar, pie, lollipop, boxplot, count/sum

Learn more: Working with Categorical Variables and the as_discrete() Function.

Examples:

• Bar geometry
• Pie chart
• Annotation labels
• 'boxplot_outlier' statistics
• Lollipop plot
• Count stat
• Handling an overplotting on a scatter plot: geom_count()/stat_sum()
• Using scales
• Viridis colors
• Guide to ordering

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
• Parameter threshold in geom_histogram()

Stats

stat_ecdf(), stat_summary(), stat_summary_bin()

Examples:

• Explicit use of statistics
• ECDF stat
• Summary stat
• Binned summary stat

Function

function

Examples:

• Function geometry

Visualization of Errors

crossbar, errorbar, linerange, pointrange

Examples:

• Plotting means and error ranges

Smoothing

smoothing line

Examples:

• Draw a scatter plot
• Discover trends with smoothing

Bivariate Distribution

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

Examples:

• 2d density

Marginal Plots

ggmarginal

See also: Joint Plot, Residual Plot.

Examples:

• Marginal plots

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:

• Multipanel plots with facets
• 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

Legends and Guides

guide_legend(), guide_colorbar(), guides(), layer_key()

Examples:

• Legend customization
• Manual legend
• Customize legend appearance

‘bistro’ Plots

corr_plot(), qq_plot(), joint_plot(), residual_plot(), waterfall_plot()

“Bistro” plots is a collection of “compound plots” allowing users to generate intricate charts without the need for extensive coding.

With these high-level functions you can create visualizations like correlation matrices, quantile-quantile plots, and joint distribution plots using single function calls.

Learn more: ‘bistro’ Plots.

GeoPandas Shapes

GeoPandas GeoDataFrame is supported by the following geometry layers: polygon, map, point, pie, text, label, 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
• Sharing X,Y-axis scale limits
• Custom theme

Presentation Options

theme(), ggtitle(), ggsize(), xlab(), ylab(), labs()

Examples:

• Custom theme
• Default theme
• Theme colors for geometries
• Tooltip customization
• Set font faces
• Axis position
• Rotation of axis labels
• Exponent format in Lets-Plot
• Annotation labels

Predefined themes

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

Examples:

• Default theme
• Theme flavors
• Annotation labels

Color schemes (flavors)

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

Examples:

• Theme colors for geometries
• Theme flavors

Miscellaneous

Panning and Zooming

Use the ggtb() function to enable Pan and Zoom interactivity on a chart.

This function adds a toolbar containing three tool-buttons: pan, rubber-band zoom, and center-point zoom.

Examples:

• Zoom and Pan interactivity

Extended Text Markup

In tooltips/labels/texts and wherever else there is text, you can use:

• Interactive links, e.g. <a href="https://github.com">GitHub</a>.

• LaTeX formulas with

  • superscript, e.g. \( a^b \);

  • subscript, e.g. \( x_i \);

  • Greek letters, e.g. \( \Omega \), and

  • some special symbols, e.g. \( a \cdot b \neq c \).

Examples:

• The observable LP-verse
• LaTeX support

Manual Legend

In Lets-Plot, as in ggplot2, legends are automatically generated based on the aesthetic mappings in the plot. Sometimes, however, this automatic generation doesn’t provide the precise control needed for complex visualizations. Options manual_key and override_aes addresses this limitation.

Examples:

• Manual legend

Multiple Color Scales

Use color_by/fill_by parameters and paint_a/paint_b/paint_c aesthetics if you need to display two different layers with the same color aesthetic but different color scales.

Examples:

• Multiple color scales
• MPG correlogram
• Montenegrin independence referendum

Scale Functions

To specify a scale for any group of aesthetics, use the special scale functions: scale_manual(), scale_continuous(), scale_gradient(), etc.

Examples:

• Scale functions with parameter aesthetic
• Palmer penguins
• Montenegrin independence referendum
• Google play store EDA

Quantiles

Density-like plots let you show the quantiles by mapping them to a particular colour palette.

Examples:

• Showing quantiles on density plots
• Ridgeline plot

Stackable Position Adjustments

To configure positioning where groups are stacked on top of each other, use the position_stack() and position_fill() functions.

Examples:

• Stackable position adjustments
• Nobel prize EDA

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.

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.

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.