Machine Learning

fig, ax = plt.subplots() # Single subplot
fig, axes = plt.subplots(2, 2) # 2x2 grid of subplots

• Plot on a specific subplot (Axes object).

axes[0, 0].plot(x, np.sin(x))

• Set the title for a specific subplot.

axes[0, 0].set_title('Subplot 1')

• Set labels for a specific subplot.

axes[0, 0].set_xlabel('X-axis')
axes[0, 0].set_ylabel('Y-axis')

• Add a legend to a specific subplot.

axes[0, 0].legend(['Sine'])

• Add a main title for the entire figure.

fig.suptitle('Main Figure Title')

• Automatically adjust subplot parameters for a tight layout.

plt.tight_layout()

• Share x or y axes between subplots.

fig, axes = plt.subplots(2, 1, sharex=True)

• Get the current Axes instance.

ax = plt.gca()

• Create a second y-axis that shares the x-axis.

ax2 = ax.twinx()

VI. Specialized Plots

• Create a contour plot.

X, Y = np.meshgrid(x, x)
Z = np.sin(X) * np.cos(Y)
plt.contour(X, Y, Z, levels=10)

• Create a filled contour plot.

plt.contourf(X, Y, Z)

• Create a stream plot for vector fields.

U, V = np.cos(X), np.sin(Y)
plt.streamplot(X, Y, U, V)

• Create a 3D surface plot.

from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z)

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