Interactive Dashboards with Python: Plotly/Dash Masterclass

Learn dash layout basics using HTML divs and core components, build a layout with a heading, drop down, and bar chart, and implement a callback to update the output container.

Add interactivity to Dash applications by using callbacks to update outputs from input changes, and display interactive graphs that respond to dropdown selections.

Explore styling dashboards in Dash by applying inline and external CSS, linking style.css, and using class names to craft interactive, server-driven visuals with callbacks.

Deploy dashboards locally by setting up the Python environment, installing Dash, and running app.py. Switch the port from 8050 to 8051 or 8052 as needed.

Explore advanced chart types in Plotly/Dash, including box plots with the five-number summary and outliers, heatmaps for pattern detection, and waterfall charts showing cumulative effects.

Explore visualizations with hover text on iris scatter plots in plotly and dash, revealing sepal and petal measurements by species, and use legends plus zoom and pan for exploration.

Explore 3D scatter plots, geo scatter plots, and choropleth maps using Plotly and Dash to visualize multi-dimensional and geographic data, with interactive features like hover, zoom, and rotate.

Customize the layout of your plots in Plotly and Dash by adjusting axis labels, fonts, gridlines, and colors by species, then add annotations to highlight key data points.

Explore the core components of Dash—sliders, dropdowns, and input fields—and learn how they enable interactive and dynamic dashboards built with Plotly Dash.

Explore the Dash graph and data table components to create line charts, bar charts, scatter plots, and tables that visualize and organize large datasets for interactive dashboards.

Master interactive dashboards with Plotly Dash by building tabs and multi-page apps, using the DCC tabs component, callbacks, and URL-based navigation to organize content.

Learn to implement file upload and download in Plotly Dash, including drag-and-drop, CSS styling, decoding uploaded content, and generating CSV downloads.

Learn to read data from csv, excel, and json files in a dash app using pandas, covering headers, sheets, and json structures to enable data integration for dashboards.

Master pandas data processing by filtering with boolean indexing, grouping by columns, and aggregating with sum and mean to build interactive dashboards with Dash and Plotly.

Connect to an SQLite database in Python, create an engine and table, insert and read data, then integrate with a Dash app for visualization.

Learn to fetch data from a rest api using the requests library, parse json to a data frame, and create a Dash visualization from api endpoints.

Learn form handling in Dash and Plotly dashboards, validating inputs on client and server to ensure security, while building a simple name and age form with a callback.

Explore how to implement notifications and alerts in Dash, including error, success, and informational messages, with callbacks and styling to deliver timely, non intrusive updates in dashboards.

Explore interactive data filtering with Dash and Plotly to dynamically update a bar chart using dropdowns, ranges, and checkboxes, with real-time feedback and flexible views.

Learn basic authentication in Dash apps with the Dash auth package for local sharing, including password-based, multi-factor, and third-party options, plus setup steps on a local server.

Explore dynamic layouts in Dash to build flexible, responsive dashboards that update in real time with conditional callbacks and rendering; swap layouts and charts using checkboxes, dropdowns, and sliders.

Optimize performance in dash and plotly dashboards by minimizing callbacks and using asynchronous processing with threading. Caching results and showing loading indicators reduce latency and keep the user interface responsive.

Master error handling in Dash apps by validating input, catching exceptions, and displaying precise, actionable error messages to prevent crashes and guide users.

Build responsive Dash apps with Dash bootstrap components (DBC), using container, row, and column layouts, plus styled buttons and cards via class names.

Learn to tailor Dash apps with custom themes from Dash Bootstrap Components, applying predefined styles like cyborg, darkly, and flatly via external stylesheets and CSS tweaks.

Master accessibility for Dash and Plotly dashboards by applying ARIA attributes, semantic HTML, and color contrast. Ensure keyboard navigation and ARIA roles to help assistive technologies interpret headings and regions.

Build interactive financial dashboards with Dash and Plotly by visualizing stock price trends, volume analysis, and comparative performance across Apple, Google, and more using Yahoo Finance data.

Build an interactive covid-19 dashboard using Plotly Dash, transforming Kaggle data, with line charts, country comparisons through dropdowns, and global vs. country views of confirmed cases and deaths.

Explore interactive sales dashboards with Dash and Plotly, using raw sales data from Cargill to visualize trends, distribution, and product performance across countries on localhost.

Explore operational dashboards with Plotly and Dash, using the sample superstore dataset to visualize sales trends, distributions, and regional performance by category.

Create an interactive Dash dashboard using the Titanic dataset with multi-page navigation (home, data overview, visualizations, settings) and Plotly-driven charts on survival by class, gender, age, and time series.