Libraries like Pandas

Data Visualization with Matplotlib

This note covers the basics of data visualization using the Matplotlib library in Python.

Key aspects to cover:

• Basic Plotting:

  • matplotlib.pyplot.plot() function: Creating line plots. Experiment with different line styles, markers, and colors.

  import matplotlib.pyplot as plt
  plt.plot(1, 2, 3, 4, 5, 6, 7, 8)
  plt.xlabel("X-axis")
  plt.ylabel("Y-axis")
  plt.title("Simple Line Plot")
  plt.show()
  

  • Scatter plots using matplotlib.pyplot.scatter(): Show relationships between two variables. Example with color-coding.

  plt.scatter(1, 2, 3, 4, 5, 6, 7, 8, c=['red', 'green', 'blue', 'yellow']])
  plt.show()
  

  • Bar charts and Histograms using matplotlib.pyplot.bar() and matplotlib.pyplot.hist(): Visualizing categorical and numerical data distributions.

• Customizing Plots:

  • Adding titles, labels, legends, and annotations.
  • Adjusting plot size, aspect ratio, and fonts.
  • Working with different colormaps.
  • Using plt.subplots() for creating multiple subplots within a single figure.

• More Advanced Plotting:

  • Matplotlib Subplots - Detailed explanation of creating and customizing subplots.
  • Matplotlib Styling - Advanced customization options, including stylesheets and rcParams.
  • 3D plotting with mpl_toolkits.mplot3d: Creating 3D visualizations (requires extra import).
  • Working with Images in Matplotlib - Importing and displaying images. Examples using imread and imshow.

• Saving Plots:

  • Saving plots in various formats (e.g., PNG, JPG, PDF) using plt.savefig().

• Useful Resources:

  • Matplotlib official documentation: https://matplotlib.org/
  • Matplotlib tutorials and examples: [Numerous online tutorials available](Search online for “Matplotlib tutorials”)

• Related Notes: Data Wrangling with Pandas (for preparing data for visualization)