Matplotlib is Python's foundational 2D and 3D data visualization powerhouse. Created by John D. Hunter in 2003, it allows engineers, researchers, and data scientists to render publication-quality figures with complete control over every pixel, axis, and tick.
Think of Matplotlib as an artist's studio: the `Figure` is the wooden easel canvas, the `Axes` is the sheet of paper taped onto the easel, and pyplot functions are paintbrushes drawing curves and data points.
By global Python convention, the pyplot module is always imported under the short alias 'plt'.
import matplotlib.pyplot as plt
import numpy as npPyplot tracks the 'current' active figure automatically (like MATLAB). The OO interface creates explicit Figure and Axes objects (`fig, ax = plt.subplots()`), offering ultimate control.
fig, ax = plt.subplots()
ax.plot(x, y)Avoid the top errors and bad plotting practices that trip up new Python developers
Switch from abstract numbers to relatable Cricket, Cinema & Business charts
Write Matplotlib code to solve the objective and see your live plot render
Plot `x = [1, 2, 3, 4, 5]` against `y = [1, 4, 9, 16, 25]` using `plt.plot()` with `color='#00D9C0'` and add a title 'Quadratic Curve'.
✓ plt.plot✓ plt.titleTest your understanding to unlock the lesson completion badge