Deephaven Plotly Express

Deephaven Plotly Express is a powerful plotting library built on top of Plotly Express that enhances its capabilities by adding support for real-time Deephaven tables, automatic downsampling, and server-side data grouping and aggregation using the Deephaven query engine. This library seamlessly integrates real-time data from Deephaven with the interactive and expressive visualizations of Plotly Express, allowing you to easily plot or aggregate millions of data points.

Key Features

  • Live Dataframe Support: Direct integration with real-time Deephaven tables, allowing you to visualize and analyze data as it updates in real time, in addition to Pandas DataFrames.
  • Automatic Downsampling: Pixel accurate automatic downsampling that reduces the number of data points displayed, ensuring smooth and responsive visualizations even with large datasets.
  • Server-Side Data Grouping and Aggregation: Uses server-side processing capabilities to perform data grouping and aggregation directly within Deephaven query engine, enabling efficient analysis of huge datasets without requiring data transfer.
  • Plotly Express Compatibility: Built on top of Plotly Express, the library inherits its comprehensive set of features, enabling you to create stunning and interactive visualizations effortlessly. In most cases you can directly swap px for dx for instant compatibility.
  • Interactive Visualizations: Supports interactive features such as zooming, panning, and hovering, allowing you to explore and interact with your data conveniently.
  • Easy-to-Use API: Simple and intuitive API, making it easy for users to generate a wide range of visualizations and customize them according to their specific needs.
  • Single entry point: from deephaven.plot import express as dx and get easy access to all plotting functions and built-in demo datasets.

Plot Types

This page contains a collection of links to examples demonstrating different plot types and usage scenarios. You can explore these examples to gain a better understanding of how to leverage the library in your projects.

Basic Plots

Scatter plot - Dots show relationships between two numerical measures

Dots show relationships between two numerical measures

Line plot- Line connects points, showing trends over time or sequence

Line connects points, showing trends over time or sequence

Bar plot - Rectangles depict comparisons between values

Rectangles depict comparisons between values

Area plot - Colored areas highlight trends, emphasizing change over time

Colored areas highlight trends, emphasizing change over time

Pie plot - Slices represent proportion of parts to a whole

Slices represent proportion of parts to a whole

Indicator - Displays a single value with an optional gauge

Displays a single value with an optional gauge

1D Distribution Plots

Histogram - Displays the distribution of a continuous variable using bars

Displays the distribution of a continuous variable using bars

Box plot - Shows the distribution of a continuous variable across different categories

Shows the distribution of a continuous variable across different categories

Violin plot - Shows the distribution of numeric data for one or more groups using density curves

Shows the distribution of numeric data for one or more groups using density curves

Strip plot - Displays the distribution of a continuous variable as individual data points

Displays the distribution of a continuous variable as individual data points

2D Distribution Plots

Density Heatmap - Displays the distribution of continuous variables using a grid

Displays the distribution of continuous variables using a grid

Financial Plots

Candlestick plot - Uses candles to display open, high, low, and close prices of a financial instrument

Uses candles to display open, high, low, and close prices of a financial instrument

OHLC plot - Uses vertical lines to display the open, high, low, and close prices of a financial instrument

Uses vertical lines to display the open, high, low, and close prices of a financial instrument

Hierarchical Plots

Treemap plot - Represents a hierarchy using nested rectangles

Represents a hierarchy using nested rectangles

Icicle plot - Similar to treemap, uses rectangles to represent hierarchical data

Similar to treemap, uses rectangles to represent hierarchical data

Sunburst plot - Represents hierarchical data using concentric circles

Represents hierarchical data using concentric circles

Funnel plot - Visualizes stages in a process with decreasing areas

Visualizes stages in a process with decreasing areas

Funnel area plot - Similar to funnel plot, but uses filled area to emphasize the magnitude of changes

Similar to funnel plot, but uses filled area to emphasize the magnitude of changes

3D, Polar, Ternary and Other Plots

3D scatter plot - Shows data points in three dimensions

Shows data points in three dimensions

3D line plot - Connects data points in three dimensions to show trends

Connects data points in three dimensions to show trends

Polar scatter plot - Represents data points on a circular coordinate system

Represents data points on a circular coordinate system

Polar line plot - Connects data points on a circular coordinate system to show trends

Connects data points on a circular coordinate system to show trends

Ternary scatter plot - Represents data points in a triangular coordinate system

Represents data points in a triangular coordinate system

Ternary line plot - Connects data points in a triangular coordinate system to show trends

Connects data points in a triangular coordinate system to show trends

Timeline plot - Visualizes events over time on a horizontal axis also known as a Gantt

Visualizes events over time on a horizontal axis also known as a Gantt

Concepts

Plot by group - Creates multiple series to compare data across different groups

Creates multiple series to compare data across different groups

Subplot - Combines multiple plots into a single layout

Combines multiple plots into a single layout

Layer plot - Overlays multiple plots on top of each other

Overlays multiple plots on top of each other

Multiple axes plot - Uses multiple axes to represent different data dimensions

Uses multiple axes to represent different data dimensions

Quickstart

  1. Install with Docker, use a Docker image with it already installed, or pip install with:
pip install deephaven-plugin-plotly-express
  1. To create a real-time plot using Deephaven Plotly Express, run the following example within Deephaven:
import deephaven.plot.express as dx

# Deephaven plotly express includes a number of generated data sets for examples
my_table = dx.data.stocks()

# Create a line plot, and assign colors by distinct values in the `sym` column
my_plot = dx.line(table=my_table, x="Timestamp", y="Price", color="Sym")

In this example, we create a Deephaven table and create a line plot of Timestamp against Price with automatic downsampling. A trace is created for each value in the Sym column, each of which has a unique color.

Documentation Terminology

The documentation for Deephaven Express routinely uses some common terms to help clarify how plots are intended to be used:

  • Variable: Variables, usually represented as columns in a Deephaven table, are a series of data points or observations of a particular characteristic in the data set. Examples include age, GDP, stock price, wind direction, sex, zip code, shoe size, square footage, and height.

The following terms define different types of variables. Variable types are important because any given plot is usually only intended to be used with specific variable types:

  • Categorical variable: This is a variable with a countable (often small) number of possible measurements for which an average cannot be computed. Examples include sex, country, flower species, stock symbol, and last name. Zip code is also a categorical variable, because while it is made of numbers and can technically be averaged, the “average zip code” is not a sensible concept.
  • Discrete numeric variable (often abbreviated to discrete variable): This is a variable with a countable number of possible measurements for which an average can be computed. These are typically represented with whole numbers. Examples include the number of wins in a season, number of bedrooms in a house, the size of one’s immediate family, and the number of letters in a word.
  • Continuous numberic variable (often abbreviated to continuous variable): This is a variable with a spectrum of possible measurements for which an average can be computed. These are typically represented with decimal or fractional numbers. Examples include height, square footage of a home, length of a flower petal, price of a stock, and the distance between two stars.

The following terms define relationships between variables. They do not describe attributes of a variable, but describe how variables relate to one another:

  • Explanatory variable: A variable that other variables depend on in some important way. A common example is time. If explanatory variables are displayed in a plot, they are presented on the x-axis by convention.
  • Response variable: A variable that depends directly on another variable (the explanatory variable) in some important way. A rule of thumb is that explanatory variables are used to make predictions about response variables, but not conversely. If response variables are displayed in a plot, they are presented on the y-axis by convention.

Contributing

We welcome contributions to Deephaven Plotly Express! If you encounter any issues, have ideas for improvements, or would like to add new features, please open an issue or submit a pull request on the GitHub repository.

License

Deephaven’s Plotly Express plugin is licensed under the Apache License 2.0. You are free to use, modify, and distribute this library in compliance with the terms of the license.

Acknowledgments

We would like to express our gratitude to the Plotly and the Plotly Express team for creating a remarkable plotting library and making it open-source. Their work forms the foundation of the Deephaven Plotly Express plugin.