The document provides an introduction to creating subplots in Matplotlib, explaining how to organize multiple axes within a figure for comparative data visualization. It covers basic subplot creation, the use of grids for layout, and advanced options with gridspec for flexible arrangements. Example code snippets illustrate how to implement these concepts for different subplot configurations.

1. Introduction to Subplots in Matplotlib
• Definition: Subplots are smaller axes grouped
within a single figure, allowing multiple views
of data side-by-side.
• Uses: Helpful for comparing different datasets
or visualizations together.
• Example Code:
• import matplotlib.pyplot as plt
• import numpy as np

2. Creating Basic Subplots with Axes
• Standard Axes:
• ax1 = plt.axes() # Creates main axes
• ax2 = plt.axes([0.65, 0.65, 0.2, 0.2]) # Inset subplot
• Figure with Custom Axes:
• fig = plt.figure()
• ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4], ylim=(-1.2, 1.2))
• ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.4], ylim=(-1.2, 1.2))
• x = np.linspace(0, 10)
• ax1.plot(np.sin(x))
• ax2.plot(np.cos(x))

3. Output:

4. Using plt.subplot for Simple Grids
• Definition: plt.subplot(rows, cols, index) creates
subplots in a grid layout.
• Example Code:
• for i in range(1, 7):
• plt.subplot(2, 3, i)
• plt.text(0.5, 0.5, str((2, 3, i)), fontsize=18,
ha='center')
• Explanation: The grid layout makes it easy to organize
multiple subplots in a matrix format.

5. Output:

6. Flexible Grids with GridSpec
• Definition: GridSpec allows subplots to span multiple
rows/columns.
• Example Code:
• grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3)
• plt.subplot(grid[0, 0]) # First row, first column
• plt.subplot(grid[0, 1:]) # First row, spans columns 2 and 3
• plt.subplot(grid[1, 2]) # Second row, third column
• Explanation: More control over layout with Python slicing.

7. Output: