Scatter plots are one of the most important tools in data visualization. They show the relationship between two variables by representing each observation as a point on the 2D plane.

In this chapter, we’ll cover:

• What scatter plots are and when to use them
• Creating basic scatter plots
• Customizing colors, sizes, and markers
• Adding multiple scatter plots
• Adding transparency and colormaps
• Using scatter plots for data analysis

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1. What is a Scatter Plot?

A scatter plot uses points to represent values of two different variables:

• x-axis → Independent variable
• y-axis → Dependent variable
• Each point → A single observation in the dataset

Scatter plots are useful for:

• Identifying trends, clusters, or outliers
• Showing correlations between variables
• Comparing groups of data

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2. Creating a Basic Scatter Plot

import matplotlib.pyplot as plt

# Data
x = [5, 7, 8, 7, 6, 9, 5, 6, 7, 8]
y = [99, 86, 87, 88, 100, 86, 103, 87, 94, 78]

# Create scatter plot
plt.scatter(x, y)

# Labels and title
plt.xlabel("X values")
plt.ylabel("Y values")
plt.title("Basic Scatter Plot")

plt.show()

✅ Each point represents an (x, y) pair.

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3. Customizing Markers and Colors

You can change the color, shape, and size of points:

plt.scatter(x, y, color='red', marker='o', s=100, edgecolor='black')
plt.title("Customized Scatter Plot")
plt.xlabel("X values")
plt.ylabel("Y values")
plt.show()

• color → fill color of points
• marker → 'o', 's', '^', '*', 'D' etc.
• s → size of points
• edgecolor → outline color

Another Example of IRIS Dataset from Seaborn Library

import seaborn as sns
# iris dataset 

iris = sns.load_dataset("iris") 
plt.scatter(iris.sepal_length, iris.sepal_width, color='orange', marker='*', s=100, edgecolor='black')
plt.title("sepal_length vs sepal_width")
plt.xlabel("sepal_length")
plt.ylabel("sepal_width")
plt.show()

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4. Plotting Multiple Scatter Plots

You can compare different groups by plotting multiple scatter datasets:

x1 = [5, 7, 8, 7, 6]
y1 = [99, 86, 87, 88, 100]

x2 = [6, 9, 5, 6, 7]
y2 = [103, 87, 94, 78, 85]

plt.scatter(x1, y1, color='blue', label="Group 1")
plt.scatter(x2, y2, color='green', label="Group 2")

plt.title("Multiple Scatter Plots")
plt.xlabel("X values")
plt.ylabel("Y values")
plt.legend()
plt.show()

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5. Adding Transparency (alpha)

Transparency helps when points overlap:

plt.scatter(x, y, color='purple', alpha=0.5, s=120)
plt.title("Scatter Plot with Transparency")
plt.show()

• alpha ranges from 0 (fully transparent) to 1 (opaque).

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6. Using Colormap to Show a Third Variable

Scatter plots can represent a third variable using color:

import numpy as np

# Example data
x = np.random.rand(50)
y = np.random.rand(50)
colors = np.random.rand(50)   # third variable

plt.scatter(x, y, c=colors, cmap='viridis', s=100)
plt.colorbar(label="Color scale")
plt.title("Scatter Plot with Colormap")
plt.show()

• c → array of values for color mapping
• cmap → colormap ('viridis', 'plasma', 'cool', etc.)
• plt.colorbar() → adds a color scale legend

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7. Bubble Plot (Using Size as a Variable)

You can use marker size to represent a fourth variable:

sizes = np.random.randint(50, 500, size=50)

plt.scatter(x, y, c=colors, s=sizes, cmap='plasma', alpha=0.6, edgecolor='black')
plt.colorbar(label="Color scale")
plt.title("Bubble Plot")
plt.show()

Here:

• s represents the bubble size
• Larger points mean higher values in the size variable

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8. Adding Annotations

You can highlight specific points:

plt.scatter(x, y, color='orange', s=80)
plt.annotate("Important Point", xy=(x[0], y[0]), xytext=(0.2, 0.9),
             arrowprops=dict(facecolor='black', shrink=0.05))
plt.title("Scatter Plot with Annotation")
plt.show()

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✅ Summary

In this chapter, you learned how to:

• Create basic scatter plots
• Customize color, size, and markers
• Plot multiple groups
• Add transparency and colormaps
• Create bubble plots
• Annotate specific points

Scatter plots are essential for exploring relationships and correlations in data.