OOP Introduction, Capstone Project & What's Next

7.1 Object-Oriented Programming - A Gentle Introduction

You do not need to master Object-Oriented Programming (OOP) before your course - but you need to understand it well enough to read library documentation and use classes confidently. Every pandas DataFrame, every scikit-learn model and every Airflow DAG is a class instance.

What is a class?

A class is a blueprint for creating objects. An object (or instance) is a concrete thing built from that blueprint. Think of a class as a form template and an object as a filled-in form.

class Student:
    
    def __init__(self, name, age, programme):
        """Initialise a new Student object."""
        self.name = name
        self.age = age
        self.programme = programme
        self.scores = []
    
    def add_score(self, score):
        """Add a score to the student's record."""
        self.scores.append(score)
    
    def average_score(self):
        """Return the average score, or None if no scores recorded."""
        if not self.scores:
            return None
        return round(sum(self.scores) / len(self.scores), 2)
    
    def __str__(self):
        """Return a human-readable string representation."""
        avg = self.average_score()
        avg_str = str(avg) if avg is not None else "No scores yet"
        return f"{self.name} ({self.programme}) | Avg: {avg_str}"

Creating and using objects

alice = Student("Alice", 28, "Data Analytics")
bob = Student("Bob", 34, "Data Engineering")

alice.add_score(85)
alice.add_score(92)
alice.add_score(78)

bob.add_score(91)

print(alice)   # Alice (Data Analytics) | Avg: 85.0
print(bob)     # Bob (Data Engineering) | Avg: 91.0

print(alice.name)          # Alice - accessing an attribute
print(alice.average_score())   # 85.0 - calling a method

Key terms

Why this matters for data work

When you write df = pd.read_csv("data.csv"), you are creating an instance of the DataFrame class. When you write df.head(), you are calling a method. When you write model.fit(X, y), you are calling a method on a scikit-learn estimator. OOP is the structure that makes all of these libraries work. You do not need to build classes every day - but recognising the pattern makes reading documentation dramatically easier.

7.2 pip and Virtual Environments

pip - Python's package installer

Python's standard library is powerful, but the data ecosystem lives in third-party packages. pip is the tool that installs them.

pip install pandas          # install a package
pip install pandas==2.1.0   # install a specific version
pip install pandas numpy matplotlib  # install multiple packages
pip uninstall pandas        # remove a package
pip list                    # list all installed packages
pip freeze                  # list installed packages in requirements format
pip freeze > requirements.txt  # save your environment's packages to a file
pip install -r requirements.txt  # install from a requirements file

Virtual environments

A virtual environment is an isolated Python installation for a specific project. This prevents packages from one project conflicting with another.

# Create a virtual environment
python -m venv myenv

# Activate it
# Windows:
myenv\Scripts\activate
# macOS / Linux:
source myenv/bin/activate

# Your terminal prompt will change to show (myenv)
# Now pip install goes into this environment only

# Deactivate when done
deactivate

Always create a virtual environment for each new project. This is standard professional practice.

7.3 A First Look at Key Libraries

Here is a brief, honest preview of the libraries you will master in your course. Do not try to learn these now - just get a feel for how the Python you know translates into powerful tools.

pandas - Data Analytics & Data Engineering

import pandas as pd

df = pd.read_csv("students.csv")      # load a CSV into a DataFrame
print(df.head())                      # first 5 rows
print(df.describe())                  # summary statistics
print(df["score"].mean())             # average score
filtered = df[df["score"] >= 70]      # filter rows
df["grade"] = df["score"].apply(lambda s: "Pass" if s >= 50 else "Fail")

matplotlib - Data Analytics

import matplotlib.pyplot as plt

scores = [85, 92, 78, 95, 88, 72, 96, 81]
plt.hist(scores, bins=5, color="steelblue", edgecolor="white")
plt.title("Score Distribution")
plt.xlabel("Score")
plt.ylabel("Frequency")
plt.savefig("scores.png")
plt.show()

numpy - Data Science & AI

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
print(arr * 2)          # [2 4 6 8 10] - vectorised operation
print(np.mean(arr))     # 3.0
print(np.std(arr))      # 1.4142...
matrix = np.zeros((3, 3))  # 3x3 matrix of zeros

requests - AI Workflow

import requests

response = requests.get("https://api.example.com/data")
if response.status_code == 200:
    data = response.json()
    print(data)

7.4 Capstone Project - Student Score Analyser

This project combines everything from Days 1–6. Build it step by step. Do not look at the solution until you have made a genuine attempt.

Specification

You will build a command-line programme that:

1. Reads student data from a CSV file (students_data.csv) with columns: name, subject, score.
2. Validates the data - skips rows where the score is not a valid number between 0 and 100.
3. Computes the following statistics for each student (a student may have multiple subject scores):
   • Average score
   • Highest score
   • Lowest score
   • Grade (A ≥ 90, B ≥ 75, C ≥ 60, D ≥ 40, F < 40) based on their average
4. Displays a formatted report to the terminal.
5. Saves the report to a file called report.txt.

Sample input file (students_data.csv)

name,subject,score
Alice,Maths,88
Alice,Python,95
Alice,Statistics,82
Bob,Maths,72
Bob,Python,68
Bob,Statistics,75
Charlie,Maths,55
Charlie,Python,invalid
Charlie,Statistics,48
Diana,Maths,92
Diana,Python,97
Diana,Statistics,94

Expected output

==================================================
         STUDENT SCORE ANALYSER REPORT
==================================================
Student       Avg    High   Low    Grade
--------------------------------------------------
Alice         88.3   95     82     B
Bob           71.7   75     68     C
Charlie       51.5   55     48     D    [1 invalid score skipped]
Diana         94.3   97     92     A
--------------------------------------------------
Class average : 76.5
Top student   : Diana (94.3)
==================================================
Report saved to: report.txt

Hints:

• Use a dictionary of dictionaries to group scores by student name.
• Wrap score conversion in a try/except ValueError to handle invalid values.
• Write a get_grade(average) function that returns the letter grade.
• Build the report as a list of strings, then join with "\n" to write to the file and print to screen.

7.5 What Comes Next - Your Course Roadmap

Completing this 7-day course means you now have the foundations. Here is how they connect to your programme:

Week 1 of your course

In Week 1, you will no longer be learning Python syntax - you will be using Python as a tool. Instructors will assume you know:

• Variables, types and basic operators ✅ Day 1–2
• Conditions and loops ✅ Day 3
• Lists and dictionaries ✅ Day 4
• How to write and call functions ✅ Day 5
• How to read a file and handle an error ✅ Day 6

The progression by programme

Recommended practice before Week 1

1. Re-do the exercises from memory, without looking at the solutions.
2. Extend the capstone project: add sorting by grade, a subject breakdown, or ask the user which file to load.
3. Explore one library from your programme - install it with pip and run the official “Getting Started” example.
4. Read Python style guidelines: search for PEP 8 - the official Python style guide. Your code quality will thank you.

📌 Day 7 Summary

🎓 Course Complete - Full Summary

You are ready. The Python you have learned this week is the same Python used by data engineers at FAANG companies, data scientists publishing research, and analysts at every level of industry. The syntax is the same - the difference is what you build with it. That is what your course is for.

See you in class.

© Luxley Digital College - Python Foundations Pre-Course · All rights reserved