Python Data Visualization: Dashboards with Plotly & Dash

Learn why interactive visuals matter with Plotly Dash, build a first interactive dashboard, and master callbacks, layouts, and embedding plots to explore data with zoom and tooltips.

Install plotly and dash for Python via conda or pip, verify in notebooks, and explore building interactive visualizations and dashboards.

Explore the anatomy of a Dash application, connecting a front end and back end via a callback that updates a graph from user inputs.

Learn how to build the world's simplest Dash app by importing Dash and HTML, creating an app, providing a layout, and running a local server for quick development.

Explore dash components in depth, including dash, html, dcc, and callback inputs and outputs, to build interactive dashboards with Plotly Express and pandas.

Discover how dash core components enable interactive elements like dropdowns and sliders, and learn how callbacks connect front-end inputs to the back-end for dynamic dashboards.

Explore how callback functions connect a color input dropdown to a color output div in a dash app, updating text dynamically via app.callback with inputs and outputs.

Explore building a Dash app with a country dropdown and a callback that updates a dynamic output div, using Dash dependencies input and output, and defining a country picker function.

Apply prevent updates to avoid runtime errors when no value is selected, and configure run options like debug, host, port, mode inline, and sizing.

Build a Dash app with a state dropdown (California, Oregon, Washington) and a callback outputting the selected state, starting in Jupyter Dash and later switching to Dash deployment for interactivity.

Create a simple dash application with Jupyter Dash, a div and dropdown, plus a callback to display the selected state, noting multiple valid approaches and optional prevent update.

Build interactive Dash apps by embedding Plotly visuals and linking callbacks. Prototype scatter plots with pandas and Plotly Express, then filter Anscombe's quartet via a dropdown and update the graph.

Connect interactive controls to visuals by filtering a data frame with a dropdown of unique values, updating a scatterplot via a Dash callback and graph component.

Create an interactive Dash app that embeds a Plotly Express line chart of total expenditure by year, filtered by a three-state dropdown (California, Oregon, Washington) with dynamic titles.

The lecture demonstrates building a Dash app by rendering a Plotly graph, filtering a dataframe by state with all-caps data, and updating the callback to display year and total expenditure.

Explore the fundamental components of a dash application—the front end and back end—connected by callback functions that respond to user inputs, using pandas to build interactive charts with Plotly.

Explore how Plotly enables interactive dashboard visualizations, covering line, bar, pie, scatter, histogram, and maps, and compare graph objects with Plotly Express for building and customizing charts.

Explore creating interactive visuals with Plotly graph objects and Plotly Express, building a two-series scatter plot from education data with expenditure per student and above/below average math scores.

Explore creating scatterplots with Plotly Express and graph objects using data frames, color by category, and customize axes, labels, and legends for interactive dashboards and Dash apps.

Learn to create basic charts with plotly express by passing a data frame and selecting x and y columns for line, scatter, bar, horizontal bar, histogram, and pie charts.

Demonstrate scatterplots and line charts with Plotly Express, color by state, add data labels and OLS trend lines, and explore animation and time-based data.

Complete assignment one by building a line chart of percent skiers by year from the Spanish skiers file using Plotly Express, with a chart title and optional y-axis styling.

Explore building and styling line charts with Plotly Express using a sample data frame, including renaming columns, customizing axis labels, and applying color palettes.

Plot multiple series by using the color argument in line and bar charts, enabling stacked or grouped bars with bar mode, and customize colors with color discrete map or palettes.

Build and customize bar charts from a data frame by mapping x and y columns, grouping by state to display average expenditure per student, with sorting, labeling, and color scales.

Create bar charts of total ski lifts by country and a stacked chart breaking lifts down by surface lifts, chairlifts, and gondola lifts using the European ski resorts data.

Create bar charts in Plotly Dash from ski resorts data, aggregate lifts by country, render lifts, and show a stacked chart by lift type with a legend titled lift type.

Explore bubble charts, a variation of scatterplots that size dots by a numeric variable such as expenditure per student, and add a regression line to reveal trends.

Convert pie charts to donut charts by setting hole and category orders, define discrete color sequences in Plotly, and explore sorting, hover data, and readability for dashboards.

Plot a bubble chart of resorts comparing total lifts (x) and lift capacity (y), with marker size by gondola lifts, and a donut chart for highest-capacity resort identified by argmax.

Plot bubble charts with Plotly by mapping x to total lifts, y to total capacity, and size to gondola lifts; use argmax to create the donut chart and a histogram.

Learn to create histograms in Plotly Dash by adjusting bins, using percent-based relative frequencies, adding labels, color for multiple series, stacked or overlayed views, and marginal distributions.

Explore Plotly Dash update methods to customize charts by updating layout, traces, and x and y axes, altering title, legends, fonts, figure size, colors, opacity, markers, and axis formatting.

Explore updating layout and traces in Plotly dashboards, center titles, adjust color scales and color bars, tweak axes, and apply defaults for consistent Python data visualization.

Explore how to update x and y axes in Plotly Dash dashboards, adjusting range, grid, titles, and ticks, and use category type to avoid gaps.

Learn to annotate charts with text and arrows, add traces like reference lines, and use vertical or horizontal lines to highlight key metrics in Plotly dashboards.

Explore bar chart formatting by selecting the top ten countries, centering the title, and adjusting font color, white background, and black grid lines, while updating the y axis title.

Modify chart formatting in a Plotly dashboard by using iloc indexing for data, adjusting title position and font, updating the y axis, and setting plot and paper backgrounds.

Explore choropleth maps with Plotly Express to visualize geographic data using latitude and longitude coordinates, with built-in country and state options and GeoJSON for subregions.

Build a Plotly Express choropleth map of US states by converting state names to two-letter codes, then color by average math scores and scope the visualization to the USA.

Discover Mapbox maps in Python with Plotly, using latitude and longitude in scatter_geo and density_mapbox, OpenStreetMap styles, and center-zoom controls to visualize regional fast food data.

Explore density maps in Plotly dashboards by mapping revenue to dot size and color, using density map box to visualize intensity as you zoom, with hover tips showing revenue.

Create a choropleth map of lift capacity by country, using country as the locations and a Europe scope, after aggregating data to avoid incorrect plots.

Group the ski resorts data by country, sum lift capacity, and map by country names to create an interactive map with hover details.

Compare plot graph objects for deep customization with Plot Express for quick charting, and learn to build dashboards by creating line, bar, pie, scatter, and choropleth maps in Dash.

Identify Dash core components to add interactivity to Plotly visuals using dropdowns, filters, and multiple input/output callbacks. Explore tooltips, zoom, pan, and legend-based filtering to enhance dashboards.

Explore Dash core components for interactive dashboards, including dropdowns (single and multi-select), checklists, sliders, range sliders, and date pickers, to filter data frames and toggle visuals.

Explore how to implement Dash dropdowns (dcc) with single and multi-select options, value vs label, and dynamic updates to scatter plots of numeric columns and regression lines.

Build a Dash app with a dropdown to filter education data by state, then plot year vs total expenditure and explore multi-select, labels, and filtering with the query method.

Explore checklists in Dash to multi-select options, set defaults, and filter data for histograms; compare score distributions and learn when to use dropdowns for many options.

Learn to build a dash app: a choropleth map of total lifts by country with a checkbox-driven filter for snow parks and night ski features.

Explore building interactive dashboards with a checklist-driven callback in Dash, filtering data by has snowpark and has night ski to generate a choropleth map of country lifts in Europe.

Use radio buttons for a single-select ux, wired with DCC radio items and callbacks to toggle a trend line and choose aggregations like mean, sum, or median.

Master interactive dashboards using sliders to filter expenditure per student and update scatter plots with dynamic titles, thresholds, and range sliders for k-means clustering insights.

Explore using a dash DCC range slider with two handles to select a spending range, using a value list to filter expenditure per student between the first and second values.

Build a Dash app that visualizes the number of ski resorts by country, filtered by an elevation slider using count aggregation.

Create an interactive dash app by turning notebook cells into standalone scripts, use an elevation slider to filter data, and format marks with f-strings and dictionary comprehension.

Learn to implement date pickers, single and range, via a calendar dropdown to filter New York City collisions dataset by date and borough, and visualize with bar and line charts.

Explore how to implement and customize date pickers in a Dash app, including single and range date inputs, min/max ranges, date formats, and building dynamic filters with callbacks.

Explore how multiple input callbacks empower dashboards with Plotly Dash, using date range pickers and dropdowns to select the X and Y columns, metrics, and colors for a dynamic scatterplot.

Discover how to implement multiple output callbacks to drive header text and multiple charts from shared inputs, including dynamic titles and coordinated line and bar charts.

Explore building an interactive dashboard that uses a slider and checkboxes to filter a map of ski resort counts by country, based on elevation, night skiing, and snow parks.

Build a Plotly dashboard by wiring an elevation slider and features checklist to filter ski resorts, update a dynamic title, and render the map via callbacks.

Learn to select dash interactive elements by data type and filtering needs, implement multi-input, multi-output callbacks, and use dropdowns, checklists, radio buttons, sliders, and date pickers for Plotly Dash dashboards.

Introduce dashboards for US and Canada ski resorts using Plotly Dash, featuring density Mapbox, price slider, night skiing toggle, top-resorts bar chart with a metric dropdown and country filter.

Explore building interactive dashboards with dash and plotly through a midcourse project solution, including a price slider, night skiing toggle, data filtering, and maps and bar charts.

Learn to design coherent Dash dashboards using HTML layouts, Dash Bootstrap components, and styling, and explore the basics, types, and principles that support exploratory data insights.

Identify metrics and visuals for dashboards and apply filters and interactivity with Plotly and Dash. Sketch an inverse pyramid layout and drill down from national to state views.

Explore how Dash uses HTML layouts to build the front end with the HTML module and div containers, linking a dropdown to a line chart and inspecting the generated HTML.

Explore the basics of HTML components, including divs, headers, paragraphs, and spans, and compare markdown as a simpler option for composing rich text in dashboards with Plotly and Dash.

Practice basics of HTML and Markdown by building a simple web page layout using headers, paragraphs, spans, and Markdown blocks; modify style arguments and run the server to preview results.

Explore building HTML and markdown components in a notebook, applying Arial sans serif fonts, black background, and light gray text to organize headers, paragraphs, and lists.

Master HTML styling in dash by creating style dictionaries that adjust color, background, font family, and font size, with outer div styles cascading to children.

Style DCC components with style dictionaries and prebuilt css, tweak dropdown colors and labels with spans, and explore practical tricks for cohesive app aesthetics.

Learn to style Plotly figures independently from HTML components using update layout to adjust paper and plot backgrounds, fonts, and axis fonts for polished dashboards.

Style the ski resorts map app by adjusting background and font colors, font family, and resizing the slider and checklist components.

Apply style elements to Dash components, center titles, set fonts and colors, adjust map background and color bar, and explore a transition to the Bootstrap Components library for better styling.

Master Dash Bootstrap Components to build polished dashboards with fewer lines of code, using pre-built themes, containers, cards, and a grid layout for cohesive visuals.

Apply bootstrap themes to Dash dashboards using Dash Bootstrap Components, choosing from 26 themes, and apply themes to Plotly figures with Dash Bootstrap Templates for a cohesive, responsive UI.

Apply and compare bootstrap themes in a Dash app by loading figure templates and using Dash bootstrap templates to achieve cohesive styling across charts, dropdowns, and layouts.

Learn to build grid-based dashboards in Dash using DBC rows and columns, with cards for KPIs and charts, enabling interactive layouts.

Explore grid-based layouts for dashboards by creating a traditional sidebar interactivity pane with a dropdown and two charts, arranged in responsive columns and rows.

Organize dashboards with multiple tabs to separate sections from national KPIs to state insights. Build tabs using Dash Core Components (and Dash Bootstrap Components) to host charts and interactive elements.

Learn to build a multi-tab dashboard by creating tabs, labeling each tab, applying a class name for styling, and embedding layouts and content within each tab.

Modify the map application layout with a left sidebar for interactive elements and a right-side map, and apply a DVC theme using a grid-based layout.

Explore building a grid-based dashboard layout in Dash, using Dash Bootstrap Components, external CSS and themes, with a left interactive panel (slider and checklist) and a right graph.

Dashboards group visuals to help users understand data at a glance, with filters, interactivity, top KPI cards, and layouts built with HTML, Markdown, and Dash Bootstrap Components.

Explore advanced topics in dash apps, including chained and conditional callbacks, cross filtering, debug mode, data tables with sorting, filtering, and exporting, and deploying apps to online servers.

Discover how to add interactive data tables to Dash dashboards with the dash_table module, defining columns and data, enabling sort, filter, and CSV export.

Discover how to render interactive data tables in dash: read a data frame, use dash table, enable sorting, filtering, and csv export, and apply header and data styling.

Build a Dash app that uses a country dropdown and an elevation slider to display a sortable, filterable, exportable table of resorts with the highest point above the chosen elevation.

Build a data table with the data table function, filter by elevation and country, convert the frame to a dictionary, and compare native and DVC/DBC sorting and export options.

Explore conditional, chain, cross-filtering, manual, and periodic callbacks in Dash to dynamically render graphs or tables based on user input.

Master chained callbacks in dash: link one callback's output to another to create dependent dropdowns and filter bar charts using state, city, and subject or grade options.

Enable debug mode to diagnose callback structure, monitor performance, and catch real-time errors in a Dash app, then inspect the debug diagram and fix issues before deployment.

Explore interactive cross-filtering where chart selections or hover feed callbacks to filter other charts in real time, using a scatter plot of states and a dynamically updating line chart.

Enable user-triggered, manual callbacks by adding a submit button to delay updates until users finalize their inputs. Use the NCLEX variable to prevent updates and drive a scatter plot.

Use periodic callbacks with the DCC interval to refresh dashboards every two seconds, generating new data from a random normal generator and updating titles and visuals in real time.

Explore building real-time dashboards in dash using an interval-driven callback. Update a histogram of random normal draws and a stock line chart from the Fin Hub API, with timestamped titles.

Explore advanced callbacks in Dash to return a bar chart or a table based on user selection, with a button to trigger output and data filtered by country and price.

Explore advanced callbacks in a Dash dashboard, using a radial toggle to switch between chart and table outputs, with a run button triggering updates and easy free web deployment.

Discover how to deploy your dashboard from a local setup to a server, exploring cloud options like AWS, Azure, Dash Enterprise, Python anywhere, and Heroku.

Deploy dash apps on Python Anywhere by uploading your project, creating a web app, configuring a virtual environment, and updating the wsgi file to run your dash project.

Embed data tables in dash apps to let users sort, filter, and export raw data for analysis with advanced, manual, and periodic callbacks. Plan deployment from local to cloud.

the final project guides you to build a multi-tab, grid-based dashboard with interactive filters and cross-filtering to explore worldwide ski resorts, including a map, country reports, and top-ten bar charts.

Explore a final project solution for a Python Dash dashboard with Plotly, featuring a resort map and country profiler, using interactive sliders, checkboxes, dropdowns, and chained callbacks.