7 The CEO strikes back: Supercharging our dashboard

 

This chapter covers

  • Critically evaluating an app and resolving user feedback
  • Adding flexibility to Streamlit visualizations
  • Improving usability by making commonly used features easy to access
  • Creating modal dialogs in Streamlit
  • Using query parameters to enable deeplinks in a Streamlit app

No software is perfect at launch. Instead, it is refined over time, with bug fixes here and new features added there. The projects we build in this book are no exception.

In the previous chapter, we created a metrics dashboard for a company called Note n' Nib. In this one, we'll skip forward and see how users responded. We'll use their opinions and comments to critically review our app and make improvements. As we go through this process, we'll learn more about Streamlit visualizations, introduce modal dialogs and query parameters, and understand how to program an advanced, flexible dashboard.

If chapter 6 was about launching a dashboard based on user requirements, chapter 7 is about landing it, addressing our users' concerns, and iterating on the app to ensure quality and satisfaction.

Note

The GitHub repo for this book is https://github.com/aneevdavis/streamlit-in-action. The chapter_07 folder has this chapter's code and a requirements.txt file with exact versions of all the required Python libraries.

7.1 Feedback on the dashboard

7.2 Granularity in the time series chart

7.2.1 Enabling different time grains

7.2.2 Creating a time grain selector

7.3 Interdependent filters

7.4 Date range comparisons

7.4.1 Adding another date range selector

7.4.2 Showing the comparison in the metric bar

7.5 A drilldown view

7.5.1 Inserting a modal dialog

7.5.2 Designing the content of the drilldown

7.5.3 Implementing the drilldown view

7.6 Enabling deeplinks

7.6.1 Using st.query_params

7.6.2 Setting widget defaults through st.session_state

7.6.3 Implementing deeplinks

7.7 Sourcing data from a data warehouse

7.7.1 Getting our data into BigQuery

7.7.2 Setting up the Python-BigQuery connection

7.7.3 Updating the dashboard to load data from BigQuery

7.7.4 Notes on deployment to Streamlit Community Cloud

7.8 Summary