Advanced Matplotlib: Styling, Subplots & Layouts

1. Subplots: One Figure, Multiple Graphs

Often you want to compare different views side-by-side. We use plt.subplots().

Code Example:

# Create a 2x1 grid (2 rows, 1 column)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 10))

# Top Plot (Axes 1)
ax1.plot(x, y, color='blue')
ax1.set_title("Linear Trend")

# Bottom Plot (Axes 2)
ax2.bar(x, y, color='orange')
ax2.set_title("Bar View")

# Adjust spacing so titles don't overlap
plt.tight_layout()
plt.show()

2. Deep Styling Reference

Matplotlib allows granular control over aesthetics.

3. Additional Plot Types

4. Annotations & Text

Annotating specific data points makes charts more informative:

plt.annotate('Peak Value', xy=(x_peak, y_peak),
             xytext=(x_peak+1, y_peak+10),
             arrowprops=dict(arrowstyle='->', color='red'),
             fontsize=12, color='red')

5. Legends

A legend maps colors/lines to meanings.

1. Label: Add label="My Label" inside the plot function.
2. Display: Call plt.legend() at the end.

• Positioning: plt.legend(loc='upper right') or loc='best'.

6. Saving Figures

plt.savefig('my_chart.png', dpi=300, bbox_inches='tight')
# dpi=300: High resolution for publications
# bbox_inches='tight': Removes extra whitespace