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¶
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):
This book will walk you through the main tools and technics of data science in Python: importing, cleaning, transforming, and visualising data.
In the visualization chapter you will find easy to read and comprehencive guides to data visualization using the Lets-Plot.
You can customize the content, values formatting and appearance of tooltip for any geometry layer in your plot. Learn more.
Lets-Plot supports formatting of numeric and date-time values in tooltips, legends, on the axes and text geometry layer. 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.
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.
offline=True will be working without an Internet connection. Learn more.