We will be working on a variety of datasets in this book, and we will analyze their data. We will make many charts along the way. Here is how we will go about it:
- Visualizing data distributions:
- Headlines
- Distributions
- Comparisons
- Finding trends in time series or multi-feature datasets:
- Joint distributions with time series data
- Joint distributions with a size feature
- Joint distributions
- Discovering hierarchical and graphical relationships between features:
- Hierarchical maps
- Path maps
- Plotting features with location information on maps:
- Heatmaps using Mapbox
- 2D maps using Mapbox
- 3D maps using MapGL
- World map
Superset plugs into any SQL database that has a Python SQLAlchemy connector, such as PostgreSQL, MySQL, SQLite, MongoDB, and Snowflake. The data stored in any of the databases is fetched for making charts. Most database documents have a requirement for the Python SQLAlchemy connector.
In this book, we will use Google BigQuery and PostgreSQL as our database. Our datasets will be public tables from Google BigQuery and .csv files from a variety of web resources, which we will upload to PostgreSQL. The datasets cover topics such as Ethereum, globally traded commodities, airports, flight routes, and a reading list of books, because the generating process for each of these datasets is different. It will be interesting to visualize and analyze the datasets.
Hopefully, the experience that we will gain over the course of this book will help us in becoming effective at using Superset for data visualization and dashboarding.
