Difference between plt.plot() and ax.plot()
Description:
This is a fundamental distinction in Matplotlib. As a beginner, you’ll often see plt.plot() used, and that’s okay for quick plots. But as you grow and your projects scale, you’ll need to understand ax.plot() — which is part of the object-oriented (OO) approach — to build more structured, reusable, and professional-quality visualizations.
Code Explanation ():
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plt.plot()is part of Matplotlib's state-based (pyplot) interface. It's designed for simplicity and mimics MATLAB-style coding. It works by implicitly tracking the current figure and axes, which is why you don’t need to explicitly create them. -
ax.plot()belongs to the object-oriented interface. You explicitly create the figure and Axes objects usingfig, ax = plt.subplots(), and then you call methods on these objects. -
Why the difference matters:
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plt.plot()is convenient but less flexible when you need multiple subplots or want to customize individual axes. -
ax.plot()gives you precise control over each subplot, legend, title, and styling — it’s the professional way of doing things.
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Program:
import matplotlib.pyplot as plt
# Using state-based interface
plt.plot([1, 2, 3], [4, 5, 6])
plt.title("Using plt.plot()")
plt.show()
# Using object-oriented interface
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [4, 5, 6])
ax.set_title("Using ax.plot()")
plt.show()
Output:
