Scatter Plot in Python with Matplotlib – Tutorial and Mini Project

A scatter plot is your quick “does X relate to Y?” detector. Each dot is one data point, plotted by two values (like hours studied vs exam score). If the dots form a pattern (slanting up, clustering, curving), there’s a story worth exploring.

When should you use a scatter plot?

You have two numeric variables and want to check if they move together.
You suspect a trend, cluster, or outliers (the oddballs) in your data.
You want to compare groups by color/shape without making 3 different charts.

Real-Life Examples of Scatter plots

Study habits: Hours studied vs exam score — does more time actually help?
Fitness: Daily steps vs sleep duration — do active days sleep better?
Apps: Session length vs in-app purchases — where’s the sweet spot?
Business: Ad spend vs conversions — are you spending smart or just spending?

Quick Start: Your first scatter plot

Let’s plot hours studied vs scores.

Python


          import matplotlib.pyplot as plt
          import numpy as np
          
          # Sample data
          hours = [1, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8]
          scores = [40, 42, 48, 52, 55, 60, 64, 70, 78, 85, 90]
          
          plt.figure(figsize=(7,4))
          
          # Scatter plot
          plt.scatter(hours, scores, s=75, alpha=0.85)
          
          # Labels & title
          plt.title("Do More Study Hours Lead to Better Scores?")
          plt.xlabel("Hours Studied")
          plt.ylabel("Score (%)")
          plt.grid(True, linestyle="--", alpha=0.35)
          
          plt.tight_layout()
          plt.show()

Explanation

plt.scatter() creates the scatter plot.
hours → x-axis values (study hours).
scores → y-axis values (exam scores).
s=75 sets the size of each point.
alpha=0.85 controls point transparency.
plt.grid() adds dashed grid lines for better readability.

Output of the above code :

Make it pop: easy customizations

Color by category: Use different colors for groups (e.g., class A vs B).
Size encodes a third variable: Pass a list to s=[...] (e.g., study breaks taken).
Transparency: alpha=0.5 helps when many points overlap.
Markers: Try marker='x', '^', or 's' for different vibes.
Annotations: Highlight outliers with plt.annotate().

Mini Project: Weekly Temperature Tracker

In this mini project, we’ll track the daily temperature for a week using a Python list and then plot it visually with Matplotlib. The twist? We’ll automatically find the hottest day of the week and highlight it in a different color on the chart.

This project will help you practice:

Storing and managing data in Python lists
Finding the maximum value and its index
Customizing scatter plots with different colors and sizes
Adding labels, titles, and legends in Matplotlib

Challenge: Try modifying the project so that it highlights both the hottest and coldest days with different colors, and experiment with more than one week of data.

Hint: You’ll need to use max(), min(), list indexing, and multiple plt.scatter() calls.

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