This week we are looking at the top grossing movies of all time. Using Hal9, you won’t even have to worry about grabbing a snack to perform visualizations similar to this. We can walk you through a handful of short steps that create the visualization in minutes. You can read this post and modify the pipeline directly in Hal9 by opening here. Popcorn not required.
Top Grossing Movies of All Time
This post makes use of data imports, transformations and visualizations to compare various movies’ box office sales throughout the years.
Analyzing Movies with Hal9
To analyze movies we first scrape data from wikipedia’s highest-grossing films page using Hal9’s “Web Table” block. All the block needs is the Wikipedia’s page’s URL and “Year” as the “Table Text” to let Hal9 know that the table we are interested in contains a column named “Year”. Once data is scraped by Hal9, it will look as follows:
The Pipeline for Analyzing Movies
Once the “Web Table” block has imported our movies data, we use the “Select” block to select the most important columns (film, gross income and year). We then use a “Map” block to clean the dollar signs from this dataset and convert them to actual numbers we can plot. We complete this analysis by using a “Scatter” plot to visualize all the movies, we set the Y Axis to display the gross income of each movie, and the X axis to display the year in which each movie was published.
Hal9’s Interface provides you with various different types of charts, transformations and ready-to-use AI models to analyze data with ease.
If you are interested in using AI models in your data analysis, please give hal9.ai a try and let us know what you think. If you’re ready for a bigger challenge, you can create entirely new data sources, transformations, visualizations or predictive models, and contribute them to our open source GitHub repository.
We also have a Twitter Hal9 account, worth following to learn more about Artificial Intelligence, visualizations, and data analysis.