📘 Overview

• What is it?: Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It is the core graphics engine behind many other plotting libraries (such as Seaborn and Pandas plotting).
• Key Features:
  • Highly Customizable: Total control over fonts, axes, line styles, colors, and layout configurations.
  • Two Interfaces: State-based interface (via pyplot - good for quick plots) and Object-Oriented interface (recommended for clean, robust layouts).
  • Export Options: Export figures to multiple high-quality formats (PNG, PDF, SVG, etc.).
• Installation:

  pip install matplotlib

🧾 Core Concepts

• Figure: The overall window or page that contains all plot elements (axes, title, legend, etc.). Think of it as a canvas.
• Axes: A coordinate area within a Figure where data is plotted. A single Figure can contain multiple Axes (subplots).
• Axis: The actual helper lines and ticks marking scale values (e.g. X-axis, Y-axis).

💻 Common Code Patterns & Cheat Sheet

• Basic Line Plot (Object-Oriented Style):

  import matplotlib.pyplot as plt
  import numpy as np
   
  # Generate data
  x = np.linspace(0, 10, 100)
  y = np.sin(x)
   
  # Create Figure and Axes
  fig, ax = plt.subplots(figsize=(8, 4))
   
  # Plot data
  ax.plot(x, y, label="Sine Wave", color="purple", linewidth=2, linestyle="-")
   
  # Customize labels & grid
  ax.set_title("Simple Sine Plot", fontsize=14)
  ax.set_xlabel("X-Axis")
  ax.set_ylabel("Y-Axis")
  ax.legend(loc="upper right")
  ax.grid(True, linestyle="--", alpha=0.6)
   
  # Render / Show
  plt.tight_layout()
  plt.show()

• Creating Subplots (Multi-plot Layouts):

  # Create 1 row with 2 columns of plots
  fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
   
  # Left plot: Scatter
  ax1.scatter(np.random.rand(50), np.random.rand(50), color="blue", alpha=0.5)
  ax1.set_title("Scatter Plot")
   
  # Right plot: Histogram
  ax2.hist(np.random.randn(1000), bins=30, color="orange", edgecolor="black")
  ax2.set_title("Histogram")
   
  plt.tight_layout()
  plt.show()

• Saving Plots:

  fig, ax = plt.subplots()
  ax.plot([1, 2, 3], [4, 5, 6])
   
  # Save as PNG with transparent background and high resolution
  fig.savefig("my_plot.png", dpi=300, bbox_inches="tight", transparent=True)

💡 Best Practices & Tips

• Object-Oriented API: Always prefer using fig, ax = plt.subplots() rather than calling plt.plot() directly. The OO API keeps your code cleaner when working with subplots or complex layouts.
• Tight Layout: Always call plt.tight_layout() before saving or showing a plot to prevent axes labels and titles from overlapping.
• Closing Figures: If you are generating many plots in a loop (e.g., in a background job or web server), always call plt.close(fig) or plt.close('all') to prevent memory leaks.

🔗 Navigation & Internal Links

• Parent: Python
• Related Notes: Data Science | Machine Learning | NumPy | Pandas | Seaborn | Plotly