Scatter plots are a fundamental tool in data visualization, providing a clear way to display the relationship between two variables. Enhancing these plots with lines, such as trend lines or lines of best fit, can offer additional insights. This article will guide you through the process of drawing a line inside a scatter plot, using Python’s popular data visualization libraries: Matplotlib and Seaborn.

Introduction

Scatter plots are used to observe and show relationships between two numeric variables. By plotting data points on two axes, we can quickly identify patterns, correlations, or anomalies. Adding lines to scatter plots can help us further understand these patterns, whether we are looking to show trends, fit models, or mark thresholds.

Prerequisites

To follow this guide, you should have a basic understanding of Python programming and have Python installed on your system. Additionally, you’ll need to install the following libraries if you haven’t already:

• Matplotlib
• Seaborn
• NumPy (optional, for generating data)

You can install these libraries using pip:

pip install matplotlib seaborn numpy

3. Creating a Scatter Plot

Let’s start by creating a simple scatter plot using Matplotlib and Seaborn.

3.1. Generating Sample Data

We’ll generate some sample data using NumPy:

import numpy as np

# Generate random data
np.random.seed(0)
x = np.random.rand(50)
y = 2 * x + np.random.normal(0, 0.1, 50)

3.2. Plotting with Matplotlib

Now, let’s create a scatter plot with Matplotlib:

import matplotlib.pyplot as plt

# Create a scatter plot
plt.scatter(x, y, color='blue')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Scatter Plot with Matplotlib')
plt.show()

3.3. Plotting with Seaborn

Similarly, we can create a scatter plot with Seaborn:

import seaborn as sns

# Create a scatter plot
sns.scatterplot(x=x, y=y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Scatter Plot with Seaborn')
plt.show()

4. Adding a Line to the Scatter Plot

Adding a line inside a scatter plot can be done for various purposes, such as fitting a regression line, adding a reference line, or indicating thresholds.

4.1. Adding a Line of Best Fit

A common use case is to add a line of best fit, which helps visualize the relationship between the variables.

1. With Matplotlib

To add a line of best fit using Matplotlib, we can use NumPy to calculate the line:

# Calculate the line of best fit
slope, intercept = np.polyfit(x, y, 1)
line = slope * x + intercept

# Plot the scatter plot and line of best fit
plt.scatter(x, y, color='blue')
plt.plot(x, line, color='red', label='Line of Best Fit')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Scatter Plot with Line of Best Fit')
plt.legend()
plt.show()

2. With Seaborn

Seaborn simplifies this process with its regplot function:

# Plot the scatter plot and line of best fit
sns.regplot(x=x, y=y, ci=None, line_kws={"color": "red"})
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Scatter Plot with Line of Best Fit')
plt.show()

4.2. Adding Custom Lines

You might also want to add custom lines, such as horizontal or vertical lines, or lines with specific slopes and intercepts.

1. Horizontal and Vertical Lines

Adding horizontal and vertical lines with Matplotlib:

# Plot the scatter plot
plt.scatter(x, y, color='blue')

# Add a horizontal line at y = 1
plt.axhline(y=1, color='green', linestyle='--', label='y=1')

# Add a vertical line at x = 0.5
plt.axvline(x=0.5, color='purple', linestyle='--', label='x=0.5')

plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Scatter Plot with Custom Lines')
plt.legend()
plt.show()

2. Custom Slope and Intercept

Adding a custom line with a specified slope and intercept:

# Define the slope and intercept
slope = 1.5
intercept = -0.2
line = slope * x + intercept

# Plot the scatter plot and custom line
plt.scatter(x, y, color='blue')
plt.plot(x, line, color='orange', linestyle='-', label='Custom Line')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Scatter Plot with Custom Line')
plt.legend()
plt.show()

Conclusion

Drawing lines inside scatter plots enhances their utility by highlighting trends, making comparisons, and adding context. Whether you’re using Matplotlib or Seaborn, adding lines is a straightforward process that can significantly improve the clarity and interpretability of your data visualizations.

Colab Link: https://colab.research.google.com/drive/1wkBaenqBMWK4F8gkxA3mL3EilGEOErQp#scrollTo=oKSyYUFAytLj