Choose a useful visualization
Match charts to comparisons, distributions, and relationships.
- Choose a chart type that makes the intended comparison readable.
Charts make patterns easier to see when their visual form matches the question. Use a histogram to inspect the distribution of one numeric variable, a bar chart to compare categories, and a scatter plot to examine the relationship between two numeric variables. Label axes with units, show meaningful scales, and avoid design choices that exaggerate differences.
temperatures = [18, 19, 19, 21, 24, 24, 25]
counts = {value: temperatures.count(value) for value in sorted(set(temperatures))}
print(counts){18: 1, 19: 2, 21: 1, 24: 2, 25: 1}A chart should make the question and evidence legible. Include a clear title, axis labels, units, and relevant context. A visualization can reveal an association, but it does not by itself identify a cause.
Key takeaways
Choose a chart type that makes the intended comparison readable.
Check how the data was collected before drawing conclusions.
Explain important assumptions and limitations.
Lesson quiz
3 questions · pass with 3 correct · up to 50 XP
Passing this quiz completes the lesson and keeps your streak going. Questions you miss come back in review sessions later.
Practice: write Python
Write Python in the editor and run it against sample inputs. Python runs locally in your browser using a WebAssembly runtime.
Build category counts
Read a JSON array of category strings. Print each distinct category and its count in alphabetical order, one per line as category count.
- Repeated labels
- One category
Python runs in a sandboxed browser worker with a 60 second time limit. Its runtime loads from the Pyodide CDN; your code stays in this browser.
Questions about this lesson
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