Lecture Notes on Matplotlib in Python

Overview

• Continuing with Matplotlib basics.
• Recap of last session: Hierarchical data structures in Matplotlib.
  • Figure: The top-level container (blank canvas).
  • Axes: Multiple plots within a figure.
  • X-axis and Y-axis: For labeling and customization.

---

Customizing Plots

• Customization Features:
  • Assign colors (e.g., 'r' for red, 'b' for blue, etc.).
  • Different line styles (solid, dashed, dotted).
• Examples:
  • Red solid line: pl.plot(x, y, 'r-')
  • Green dashed line: pl.plot(x, y, 'g--')
  • Blue dotted line: pl.plot(x, y, 'b:')
  • Marker customizations (e.g., circles, stars, triangles).

---

Multiple Plots in a Single Figure

• Challenges with Shared Y-axis: Different scales can hide data.
• Creating Multiple Axes:
  • Use plt.subplot() to create a 1x2 grid for two plots.
  • Example: plt.subplot(1, 2, 1) for the first plot, plt.subplot(1, 2, 2) for the second.
• Independent Axes: Each subplot can have its own labels and legends.

---

Advanced Charting Techniques

• Creating Larger Figures: Use the figsize parameter to adjust dimensions.
• Spanning Axes: To create a plot spanning multiple axes, specify the axis indices.
• Common Types of Plots:
  • Line Charts: For time series data.
  • Bar Charts: For categorical comparisons.
  • Box Plots: For visualizing distributions and outliers.
  • Histograms: For visualizing frequency distributions.
  • Scatter Plots: For observing relationships between two numerical columns.

---

Using Pandas with Matplotlib

• Pandas Integration: Simplifies plotting with DataFrames.
  • Example: dataframe.plot(x='date', y='count') for a time series.
• Creating Bar Charts: Use grouped data for categorical vs numerical analysis.
  • Example: Aggregating average premiums by age.

---

Summary of Plot Types

• Line Charts: Good for trends over time.
• Bar Charts: Useful for category comparisons.
• Box Plots: Identify outliers and distribution.
• Histograms: Visualize frequency distributions.
• Scatter Plots: Analyze relationships between two variables.

---

Next Session Preview

• Introduction to Seaborn: A more visually appealing library for data visualization.
• Comparison with Matplotlib regarding ease of use and aesthetics.