Recall & Review
beginner
What is the main difference between the Axes interface and the pyplot interface in matplotlib?
The Axes interface works with explicit axes objects, giving more control and flexibility. The pyplot interface is state-based and simpler, managing figures and axes behind the scenes.
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beginner
How do you create a plot using the Axes interface?
You first create a Figure and Axes object using plt.subplots(), then call plotting methods on the Axes object, e.g., ax.plot(x, y).
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intermediate
What is a benefit of using the Axes interface over pyplot?
It allows precise control over multiple plots in the same figure and avoids confusion from pyplot's global state.
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beginner
Show a simple example of plotting a line using the pyplot interface.
import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5, 6])
plt.show()Click to reveal answer
beginner
Why might pyplot be easier for quick plots?
Because it manages figure and axes creation automatically, so you can plot with fewer lines of code.
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Which interface requires you to explicitly create axes objects before plotting?
✗ Incorrect
The Axes interface requires explicit creation of axes objects, while pyplot manages them automatically.
What does pyplot interface use to manage figures and axes?
✗ Incorrect
Pyplot uses a global state to keep track of the current figure and axes.
Which interface is better for creating multiple subplots in one figure?
✗ Incorrect
The Axes interface provides explicit control over each subplot, making it better for multiple subplots.
Which code snippet uses the pyplot interface?
✗ Incorrect
plt.plot() and plt.show() are pyplot interface functions.
What is a disadvantage of pyplot interface?
✗ Incorrect
Pyplot is less flexible for complex plots because it relies on global state and implicit axes.
Explain the difference between the Axes interface and the pyplot interface in matplotlib.
Think about how each interface manages figures and axes.
You got /4 concepts.
Describe a situation where you would prefer to use the Axes interface over pyplot.
Consider when you need more control over your plots.
You got /4 concepts.