8.9.3: Hands-On Lab Exercise

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Page ID: 58325

Felix Amoruwa
De Anza College

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Hands-On Lab Exercise

Lab: Tool Comparison Through Implementation

Scenario:

You have a dataset of 500 movies: title, genre, budget, revenue, rating, release_year, runtime.

Instructions:

Matplotlib (20 min): Create a bar chart of average revenue by genre and a scatter plot of budget vs. revenue. Customize: add titles, axis labels, color one genre differently, add a trend line to the scatter plot.
Seaborn (15 min): Create a box plot of ratings by genre and a heatmap of the correlation matrix for numeric variables. Compare the code length and visual quality to Matplotlib.
Plotly (15 min): Create an interactive scatter plot of budget vs. revenue, colored by genre, with hover tooltips showing movie title. Export as HTML.
Tool Comparison Matrix (20 min): Create a table comparing Matplotlib, Seaborn, Plotly, and one commercial tool across: learning curve, interactivity, customization, code required, best use case, output format.
Recommendation Report (20 min): A marketing team at a movie studio needs weekly genre performance dashboards plus ad-hoc deep-dive analysis. Recommend which tool(s) to use for each need and justify.

Deliverables:

Matplotlib code + outputs, Seaborn code + outputs, Plotly interactive HTML, comparison matrix, recommendation report.

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