Basic Building Blocks¶
Learn more: Function as_discrete().
Visualization of Distribution¶
Visualization of Errors¶
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.
Learn more: GeoPandas Support.
Color schemes (flavors):
There are a few notebooks that contain overviews of many features at once. Check them out:
GGBunch shows a collection of plots on one figure. Each plot in the collection can have an arbitrary location and size.
You can customize the content, values formatting and appearance of tooltip for any geometry layer in your plot. Learn more.
R, Python, what’s next? Right. Lets-Plot Kotlin API enables data visualization in JVM and Kotlin/JS applications as well as in scientific notebooks like Jupyter and Datalore.
Lets-Plot supports formatting of numeric and date-time values in tooltips, legends, on the axes and text geometry layer. Learn more.
Sampling is a special technique of data transformation, which helps to deal with large datasets and overplotting. Learn more.
Interactive maps allow zooming and panning around your geospatial data with customizable vector or raster basemaps as a backdrop. Learn more.
Export to SVG and HTML
ggsave() function is an easy way to export plot to a file in SVG or HTML formats.
offline=True will be working without an Internet connection. Learn more.