Plotly and Dash¶
Plotly¶
Plotly Open Source Graphing Libraries make interactive, publication-quality graphs. Line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, bubble charts, and maps.
Details
The supported programming languages / libraries / frameworks are Python, R, Julia, JavaScript, ggplot2, F#, MATLAB®, and Dash.
Based on Plotly, Dash is a low-code framework for rapidly building data apps in Python.
Dash¶
Dash is a low-code framework for rapidly building data apps in Python, based on Plotly. Built on top of Plotly.js, React and Flask, Dash ties modern UI elements like dropdowns, sliders, and graphs, directly to your analytical Python code.
Details
Dash is a trusted Python framework for building ML & data science web apps. Many specialized open-source Dash libraries exist that are tailored for building domain-specific Dash components and applications.
Dash Enterprise
Dash Enterprise is Plotly’s paid product for building, testing, deploying, managing, and scaling Dash applications organization-wide, advertised as the Premier Data App Platform for Python.
When building Dash apps in a business setting, Dash Enterprise supports you to deploy and scale them, plus integrate them with IT infrastructure such as authentication and VPC services, in order to deliver faster and more impactful business outcomes on AI and data science initiatives.
Dash Enterprise enables the rapid development of production-grade data apps within your business. Python has taken over the world, and traditional BI dashboards no longer cut it in today’s AI and ML driven world. Production-grade, low-code Python data apps are needed to visualize the sophisticated data analytics and data pipelines that run modern businesses.
Learn¶
CrateDB for time series modeling, exploration, and visualization
Access time series data from CrateDB via SQL, load it into pandas DataFrames, and visualize it using Plotly.
About advanced time series operations in SQL, like aggregations, window functions, interpolation of missing data, common table expressions, moving averages, relational JOINs, and the handling of JSON data.
Time series visualization
Python pandas Plotly Dash
Plotly Dash Course - Session 1
This is the first session of the course on “Building Data Apps in Python and Plotly Dash”.
Alternatively, you are welcome to explore the canonical Dash Examples.
Plotly Dash Course - Session 1.
Fundamentals
Plotly