Mastering Data Visualization with Matplotlib in Python

Chapter 3: Advanced Plotting Techniques in Matplotlib

🔹 1. Introduction

Now that we have covered the basics of Matplotlib, it’s time to dive deeper into more advanced plotting techniques. This chapter will cover more sophisticated plots and customizations that will help you present data in a more insightful and interactive manner. We will explore:

• Multiple plot types in a single figure (including scatter plots, bar charts, and histograms)
• Customizing axes for better data presentation
• Adding annotations, text, and shapes to plots
• Using legends and color maps for improved clarity
• Creating animated plots for dynamic data visualization

By the end of this chapter, you’ll be able to handle more complex visualizations, which can help you reveal patterns and insights more effectively.

🔹 2. Creating Multiple Plots in One Figure

Sometimes, it’s helpful to display multiple plots in the same figure to compare different datasets. Matplotlib makes this easy with subplots.

✅ Creating Subplots

A subplot allows you to create multiple plots in a single figure and organize them into a grid. You can customize the number of rows and columns to arrange the plots accordingly.

import matplotlib.pyplot as plt

# Data for plotting

x = [1, 2, 3, 4, 5]

y = [1, 4, 9, 16, 25]

z = [25, 20, 15, 10, 5]

# Create a 1x2 grid of subplots (1 row, 2 columns)

fig, ax = plt.subplots(1, 2, figsize=(10, 5))

# First subplot: Line plot

ax[0].plot(x, y, color='blue', label='Line plot')

ax[0].set_title('Line Plot')

# Second subplot: Scatter plot

ax[1].scatter(x, z, color='red', label='Scatter plot')

ax[1].set_title('Scatter Plot')

# Display the plot

plt.tight_layout()  # Adjust spacing

plt.show()

This code will create a figure with two plots:

1. A line plot on the left
2. A scatter plot on the right

The plt.subplots() function allows you to define the layout of the figure, where figsize=(10, 5) sets the size of the overall figure.

🔹 3. Customizing Axes

Customizing the axes can improve the readability and presentation of your plot. You can adjust axis limits, set custom tick marks, and even control the aspect ratio.

✅ Customizing Axis Limits

To change the limits of the axes, use the plt.xlim() and plt.ylim() functions:

plt.plot(x, y)

plt.xlim(0, 6)  # Set the x-axis limit

plt.ylim(0, 30)  # Set the y-axis limit

plt.show()

✅ Setting Custom Ticks

To set custom ticks on the axes, you can use plt.xticks() and plt.yticks():

plt.plot(x, y)

plt.xticks([1, 2, 3, 4, 5])  # Custom x-axis ticks

plt.yticks([1, 10, 20, 30])  # Custom y-axis ticks

plt.show()

🔹 4. Adding Annotations, Text, and Shapes

Matplotlib allows you to add annotations, text, and shapes to highlight key points on your plot.

✅ Adding Text Annotations

You can use plt.text() to add text at specific coordinates:

plt.plot(x, y)

plt.text(2, 10, 'Point (2,10)', fontsize=12, color='red')  # Add text annotation

plt.show()

✅ Adding Arrows for Annotations

You can add arrows using plt.annotate() to point to specific data points:

plt.plot(x, y)

plt.annotate('Max Point', xy=(5, 25), xytext=(3, 20),

             arrowprops=dict(facecolor='blue', shrink=0.05))  # Add arrow annotation

plt.show()

This adds an arrow pointing to the point (5, 25) with a label "Max Point."

✅ Adding Shapes

You can draw shapes like circles, rectangles, or lines on your plot:

plt.plot(x, y)

plt.axhline(y=15, color='green', linestyle='--', label='Horizontal line')  # Horizontal line

plt.axvline(x=3, color='purple', linestyle=':', label='Vertical line')  # Vertical line

plt.show()

🔹 5. Using Legends and Color Maps

Legends and color maps help make your plots more informative by explaining different plot elements.

✅ Adding a Legend

You can use plt.legend() to add a legend that explains the different lines, bars, or points in your plot:

plt.plot(x, y, label='y = x^2')

plt.plot(x, z, label='z = 30 - x')

plt.legend(loc='best')  # Automatically place the legend

plt.show()

✅ Using Color Maps for Heatmaps

Color maps are used to represent data values in a visually appealing way, especially in heatmaps.

import numpy as np

# Create a 2D array for heatmap

data = np.random.rand(10, 10)

plt.imshow(data, cmap='viridis', interpolation='nearest')

plt.colorbar()  # Show color bar

plt.title('Heatmap Example')

plt.show()

This creates a heatmap using plt.imshow(), with viridis as the color map.

🔹 6. Creating Animated Plots

Matplotlib also supports animations which are useful for visualizing changes over time.

✅ Creating Simple Animations

Here’s an example of creating an animated sine wave:

import matplotlib.pyplot as plt

import numpy as np

from matplotlib.animation import FuncAnimation

# Create a figure

fig, ax = plt.subplots()

x = np.linspace(0, 2*np.pi, 100)

line, = ax.plot(x, np.sin(x))

# Update function for animation

def update(frame):

    line.set_ydata(np.sin(x + frame / 10))  # Update the data

    return line,

# Create an animation

ani = FuncAnimation(fig, update, frames=100, interval=100)

# Display the animation

plt.show()

This example uses FuncAnimation to create a sine wave animation, updating the plot every 100 milliseconds.

🔹 7. Summary Table