Back To Course Home

Interactive Dashboards and Data Apps with Plotly and Dash

0% completed

Plotly's Dash Framework

Introduction to the Course

Overview of the Dash Ecosystem

Introduction: The Dash Ecosystem Exploring Dash and Other Supporting Packages Creating and Running a Simple App Structuring the Layout and Managing Themes Summary: The Dash Ecosystem Quiz: The Dash Ecosystem

Exploring the Structure of a Dash App

Introduction: Structure of the Dash App Using Jupyter Notebooks to Run Dash Apps Understanding the ID Parameter of Dash Components Incorporating the Function into the App Properties of Dash's Callback Functions Summary: Structure of the Dash App Quiz: Structure of the Dash App

Working with Plotly's Figure Objects

Introduction: Plotly's Figure Objects Figure Object Data and Layout Attributes Exploring the Different Ways of Converting Figures Adding the New Functionality to Our App Summary: Plotly's Figure Objects Quiz: Plotly's Figure Objects

Data Manipulation and Preparation using Plotly Express

Introduction: Data Manipulation and Preparation

Understanding Long Format (Tidy) Data

Understanding the Role of Data Manipulation Skills

Reshaping DataFrames

Learning Plotly Express

Creating a Plotly Express Chart Using the Dataset

Summary: Data Manipulation and Preparation

Quiz: Data Manipulation and Preparation

Exercise: Merging Datasets

Solution: Merging Datasets

Interactively Comparing Values with Bar Charts and Drop-Down Menus

Introduction: Bar Charts and Drop-Down Menus Plotting Bar Charts Linking Bar Charts and Drop-Downs Exploring Different Ways of Displaying Multiple Bar Charts Incorporating the Functionality into the App Exploring Additional Features of Drop-Downs Summary: Bar Charts and Drop-Down Menus Quiz: Bar Charts and Drop-Down Menus

Exploring Variables and Filtering Subsets

Introduction: Variables and Filtering Subsets Learning about the Different Ways of Using Scatterplots Creating Multiple Scatter Traces in a Single Plot Mapping and Setting Colors with Scatterplots Handling Overplotting and Outlier Values Introducing Sliders and Range Sliders Customizing the Marks and Values of Sliders Summary: Variables and Filtering Subsets Quiz: Variables and Filtering Subsets

Exploring Map Plots and Enriching Dashboards with Markdown

Introduction: Map Plots and Enriching Dashboards with Markdown Exploring Choropleth Maps Creating a Markdown Component Understanding Map Projections Incorporating an Interactive Map into the App Summary: Map Plots and Enriching Dashboards with Markdown Quiz: Map Plots and Enriching Dashboards with Markdown

Calculating the Frequency of Data with Histograms and Tables

Introduction: Histograms and Tables Creating a Histogram Adding Interactivity to Histograms Creating a DataTable Adding Histograms and Tables to the App Summary: Histograms and Tables Quiz: Histograms and Tables Exercise: Visualizing Datasets Solution: Visualizing Datasets

Letting the Data Speak for Itself with Machine Learning

Introduction: Letting Data Speak with Machine Learning Understanding Clustering Finding the Optimal Number of Clusters Clustering Countries by Population Creating an Interactive KMeans Clustering App Preparing Data with Scikit-learn Summary: Letting the Data Speak for Itself with Machine Learning Quiz: Letting Data Speak with Machine Learning Exercise: Creating and Analyzing a KMeans Model Solution: Creating and Analyzing a Kmeans Model

Turbocharge Apps with Advanced Callbacks

Introduction: Advanced Callbacks Understanding State Creating Components That Control Other Components Allowing Users to Add Dynamic Components to the App Introducing Pattern-Matching Callbacks Summary: Advanced Callbacks Quiz: Advanced Callbacks

URLs and Multipage Apps

Introduction: URLs and Multipage Apps Location and Link Components Modify the App by Parsing URLs and Using Their Components Adding Dynamically Generated URLs to the App Incorporating the New URL Interactivity into the App Summary: URLs and Multipage Apps Quiz: URLs and Multipage Apps

Deploying the App

Introduction: Deploying Dash Apps to the Internet Establishing the Deployment Workflow Creating a Hosting Account and Virtual Server Running the App on the Server Setting Up and Running the App with a WSGI Managing Maintenance and Updates Deploying and Scaling Dash Apps with Dash Enterprise Summary: Deploying Dash Apps to the Internet

Next Steps

Introduction: Additional Aspects of Development Expanding Data Manipulation and Preparation Skills Operationalizing and Visualizing Machine Learning Models Going Large Scale with Dash Enterprise Summary: Additional Aspects of Development

Appendix

Setting up the environment

Quiz: Data Manipulation and Preparation

Test your knowledge of data formatting and creating visualizations using Plotly Express.

1

Which of the following operations creates a boolean column is_present that identifies missing values in the dataset (df)and sets the value to false for that entry?

A)

df['is_present'] = df['target_column'].notna()

B)

df['is_present'] = df['target_column'].na()

C)

df['is_present'] = df['target_column'].isna()

D)

df['is_present'] = df['target_column'].value_counts

Question 1 of 40 attempted

Get hands-on with 1400+ tech skills courses.