Python is a high-level, interpreted, and dynamically typed programming language designed for readability and rapid development. Its modular architecture, extensive standard library, and multi-paradigm support (procedural, object-oriented, functional, and asynchronous) make it adaptable to a wide spectrum of applications. The CPython interpreter compiles source code into bytecode executed by a virtual machine, with performance-critical tasks offloaded to optimized C/C++ extensions. Despite the Global Interpreter Lock (GIL), Python remains highly effective for parallel and distributed workloads through multiprocessing, native bindings, and frameworks like Dask, Ray, and PySpark. Its dominant ecosystem in data science, machine learning, web development, automation, and DevOps is supported by libraries such as NumPy, TensorFlow, Django, and FastAPI. Python’s combination of simplicity, extensibility, and rich tooling positions it as a foundational language for modern software engineering, scientific research, and AI-driven systems.

At the end of this tutorial series, you’ll also find a dedicated quiz to evaluate your understanding and measure your progress as you learn Python.

🔹 Tutorial 1 — Introduction to Python & Installation

What you will learn

• What Python is
• Different Python versions
• How to install Python
• Setting up VS Code or PyCharm
• Running your first Python script

Summary

Install Python from python.org → Install VS Code → Create a .py file → Write:

print("Hello, Python!")

Run it from VS Code or Terminal.

🔹 Tutorial 2 — Python Syntax, Variables & Data Types

Covered topics

• Basic syntax
• Variables
• Data types: int, float, str, bool
• Type casting
• Getting user input

Example

name = input("Enter your name: ")
age = int(input("Enter your age: "))
print(f"Hello {name}, you are {age} years old.")

🔹 Tutorial 3 — Operators in Python

Types of operators

• Arithmetic (+, -, *, /, %, //, **)
• Comparison (==, !=, >, <, >=, <=)
• Logical (and, or, not)
• Assignment operators (+=, -=, etc.)

Example

x = 10
y = 3
print(x ** y)   # exponent

🔹 Tutorial 4 — Conditional Statements (if, elif, else)

Example

score = 85

if score >= 90:
    print("Excellent")
elif score >= 70:
    print("Good")
else:
    print("Keep Trying")

🔹 Tutorial 5 — Loops (for and while)

For loop example

for i in range(5):
    print(i)

While loop example

n = 5
while n > 0:
    print(n)
    n -= 1

🔹 Tutorial 6 — Functions in Python

Defining a function

def greet(name):
    return f"Hello {name}"

print(greet("Mofid"))

Key concepts

• Parameters & return values
• Default parameters
• Scope of variables

🔹 Tutorial 7 — Lists, Tuples, Sets & Dictionaries

List example

fruits = ["apple", "banana", "orange"]

Dictionary example

person = {"name": "Ali", "age": 22}
print(person["name"])

🔹 Tutorial 8 — String Manipulation

Examples

text = "Python Tutorial"
print(text.lower())
print(text.split())

🔹 Tutorial 9 — File Handling

Read a file

with open("data.txt", "r") as f:
    print(f.read())

Write a file

with open("data.txt", "w") as f:
    f.write("Hello from Python!")

🔹 Tutorial 10 — Error Handling (try/except)

Example

try:
    x = int("abc")
except ValueError:
    print("Invalid number!")

🔹 Tutorial 11 — Modules & Packages

Using a module

import math
print(math.sqrt(16))

Creating your own module

Create mytools.py:

def add(a, b):
    return a + b

Import it:

import mytools
print(mytools.add(2, 3))

🔹 Tutorial 12 — Object-Oriented Programming (OOP)

Class example

class Car:
    def __init__(self, brand, model):
        self.brand = brand
        self.model = model

    def info(self):
        return f"{self.brand} {self.model}"

car1 = Car("Toyota", "Corolla")
print(car1.info())

Topics covered:

• Classes & objects
• Methods
• Inheritance
• Polymorphism

🔹 Tutorial 13 — Virtual Environments & pip

Commands

python -m venv myenv
source myenv/bin/activate  # Linux/Mac
myenv\Scripts\activate     # Windows

pip install numpy

🔹 Tutorial 14 — Working With External Libraries

Examples:

• NumPy (Mathematics)
• Pandas (Data analysis)
• Matplotlib (Charts and plots)

import numpy as np
arr = np.array([1,2,3])
print(arr.mean())

🔹 Tutorial 15 — Mini Project for Beginners

Choose one of these:

Project A: Calculator App

Project B: Student Grade Manager

Project C: To-Do List (CLI)

Project D: Simple Website Scraper

import requests
from bs4 import BeautifulSoup

res = requests.get("https://mofidtech.fr")
print(res.status_code)