42 Abu Dhabi

Python Piscine

Comprehensive Python Learning Path

2 weeks

Individual Project

PythonNumPyPandasMatplotlibOOP

A structured learning path through Python programming, organized into progressive modules covering essential Python concepts from basic syntax to advanced programming patterns including OOP and data-oriented design.

Live Demo Source Code

Key Features

Python Fundamentals

Comprehensive coverage of Python basics including variables, data types, control structures, string manipulation, and package creation.

Image Processing

NumPy arrays for image manipulation, loading, transforming images, and applying color filters and visual effects.

Data Analysis

Pandas mastery with DataFrame operations, CSV handling, statistical analysis of population and life expectancy trends.

Object-Oriented Programming

Class design with abstract base classes, inheritance, polymorphism, and design pattern implementation.

Decorators & Functional Programming

Advanced Python features including decorators, dataclasses, higher-order functions, and closures.

Data Visualization

Creating charts and graphs with matplotlib, visualizing statistical data and trends effectively.

Development Journey

Module 00

Python Starting

Python basics: Variables, data types, control structures, string manipulation, package creation, and error handling.

Module 01

Python Array

NumPy fundamentals with array operations, image processing, BMI calculations, and image filters.

Module 02

Python Data Table

Pandas mastery with DataFrame operations, data visualization, statistical analysis, and data correlation.

Module 03

Object-Oriented Programming

Class design with abstract base classes, inheritance, polymorphism, and calculator systems implementation.

Module 04

Data-Oriented Design

Decorators, statistical functions, dataclasses, and functional programming with higher-order functions.

Challenges & Solutions

Progressive Learning Path

Problem:

Structuring a comprehensive learning experience that builds upon previous concepts while introducing new material.

Solution:

Organized content into 5 progressive modules, each building on the previous one with practical exercises and real-world applications.

Data Processing at Scale

Problem:

Handling large datasets efficiently while learning data manipulation techniques.

Solution:

Utilized NumPy and Pandas for optimized array operations and DataFrame processing with proper memory management.

Design Pattern Implementation

Problem:

Understanding and applying object-oriented design principles in practical scenarios.

Solution:

Implemented abstract classes, inheritance hierarchies, and polymorphic behavior through calculator and class design exercises.

decorator_example.py

python

from functools import wraps
import time

def callLimit(limit: int):
    """Decorator to limit function calls"""
    count = 0
    
    def decorator(function):
        @wraps(function)
        def wrapper(*args, **kwargs):
            nonlocal count
            if count < limit:
                count += 1
                return function(*args, **kwargs)
            print(f"Error: {function} call too many times")
            return None
        return wrapper
    return decorator

@callLimit(3)
def my_function():
    print("Function called!")
    return True

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