Introduction to Matplotlib

What is Matplotlib?

• Matplotlib is a Python library used for creating static, animated, and interactive visualizations.
• It simplifies common tasks and enables complex customizations.
• It produces publication-quality plots.
• Interactive figures allow zooming, panning, and updating.
• You can customize visual styles and layouts.
• Provides wide support for file formats.
• Can be embedded in JupyterLab and graphical user interfaces.
• Matplotlib supports third-party packages.

Installation

• To install Matplotlib, use pip: pip install matplotlib.
• Alternatively, if using conda, use: conda install matplotlib.
• Import the pyplot module to use Matplotlib in Python: import matplotlib.pyplot as plt.

Basic Plotting

• Line plots, scatter plots, bar charts, and histograms are among the basic plot types.
• Use plt.plot(x, y) to create a line plot with specified x and y values.
• Use plt.scatter(x, y) to create a scatter plot with specified x and y values.
• Use plt.bar(categories, values) to create a bar chart with specified categories and values.
• Use plt.hist(data, bins=5) to create a histogram with specified data and bin count.
• Labels and titles can be added to plots using plt.xlabel, plt.ylabel, and plt.title.
• Use plt.show() to display the plots.

Customization

• Plots can be customized with different colors, markers, and line styles.
• Customize the line plot with plt.plot(x, y, color='red', linestyle='--', marker='o', linewidth=2, markersize=8).
• Add a legend to differentiate the plots using plt.legend().
• Implement grid lines using plt.grid(True).

Subplots

• Multiple plots can be displayed within the same figure using subplots.
• Use fig, axs = plt.subplots(2, 1) to create a figure and a set of subplots with 2 rows and 1 column.
• Plot data on specific subplots with axs.plot(x, y1), and set titles with axs.set_title('Subplot 1').
• Add a title to the entire figure with fig.suptitle('Subplots Example').
• Use plt.tight_layout() to prevent overlapping.

3D Plotting

• Matplotlib supports 3D plotting using the mpl_toolkits.mplot3d toolkit.
• Use fig = plt.figure() and ax = fig.add_subplot(111, projection='3d') to create a 3D subplot.
• 3D line plots can be created using ax.plot(x, y, z, label='parametric curve').
• 3D scatter plots can be created using ax.scatter(x, y, z, c='r', marker='o').
• Set axis labels using ax.set_xlabel, ax.set_ylabel, and ax.set_zlabel.
• Set the title with ax.set_title('3D Line Plot').

Saving Plots

• Plots can be saved to a file using plt.savefig('filename.png or other extension').