Python Tutorial #1: Why Python? A Simple Guide for Developers

Python is the most popular programming language in the world right now. It has been the number one language on the TIOBE Index since 2021 and holds a record-breaking 26% rating in 2025-2026.

But why? What makes Python so popular? And should you learn it?

In this tutorial, we will answer these questions. By the end, you will understand what Python is, where it is used, and why it is worth learning in 2026.

What Is Python?

Python is a general-purpose programming language. General-purpose means you can use it for almost anything: websites, automation, data analysis, AI, scripts, and more.

Python was created by Guido van Rossum in 1991. He named it after the comedy show “Monty Python’s Flying Circus”, not the snake.

Here is what makes Python special:

• Easy to read. Python code looks almost like English.
• Easy to learn. You can write your first program in minutes.
• Huge ecosystem. Over 500,000 packages on PyPI (Python Package Index).
• Great community. Millions of developers, thousands of tutorials, active forums.

Let me show you. Here is a complete Python program:

print("Hello, World!")

That is it. One line. No class declarations. No semicolons. No import statements.

Compare that to Java:

public class Main {
    public static void main(String[] args) {
        System.out.println("Hello, World!");
    }
}

Or C++:

#include <iostream>
int main() {
    std::cout << "Hello, World!" << std::endl;
    return 0;
}

Python wins on simplicity. And this simplicity is not just for “Hello, World.” It stays simple as your programs get bigger.

Where Is Python Used?

Python is used in many different fields. Here are the biggest ones.

AI and Machine Learning

Python is the language of AI. Every major AI framework is written in Python or has a Python API:

• PyTorch and TensorFlow for deep learning
• LangChain and LangGraph for AI agents and LLM applications
• Hugging Face for NLP and pre-trained models
• scikit-learn for traditional machine learning

If you want to work with AI in 2026, you need Python. There is no way around it.

Web Development

Python has powerful web frameworks:

• FastAPI — modern, fast, growing quickly. Its adoption jumped from 29% to 38% among professional developers in one year.
• Django — full-featured framework. Instagram and Pinterest use it.
• Flask — lightweight and flexible.

Data Science and Analytics

Python is the standard tool for working with data:

• pandas for data manipulation
• Polars for faster data processing
• matplotlib and seaborn for visualization
• Jupyter Notebooks for interactive analysis

Automation and Scripting

Need to rename 1,000 files? Scrape a website? Send automated emails? Python makes it easy. A script that would take 50 lines in Bash takes 10 lines in Python.

DevOps and Infrastructure

Tools like Ansible, SaltStack, and many AWS Lambda functions are written in Python. If you work in DevOps, you will use Python scripts regularly.

Python vs Other Languages

Let me compare Python with a few popular languages. This is not about which language is “better.” Each language has its strengths.

Python vs JavaScript

Choose Python when you work with data, AI, or automation. Choose JavaScript when you build web frontends or full-stack web apps.

Python vs Java / Kotlin

Choose Python for quick scripts, data analysis, and AI. Choose Java/Kotlin for Android apps, enterprise systems, and applications where performance matters.

Python vs Rust

Choose Python when development speed matters more than execution speed. Choose Rust when you need maximum performance and memory safety.

“But Python Is Slow!”

You may have heard this. It is partially true.

Python is slower than C, Rust, Java, and Go for CPU-heavy tasks. This is because Python is an interpreted language with dynamic typing. The interpreter has to do extra work at runtime.

But here is the thing: it usually does not matter.

Most Python programs spend their time waiting. Waiting for network responses. Waiting for database queries. Waiting for file reads. For these I/O-bound tasks, Python is fast enough.

And when you do need speed, you have options:

• C extensions — NumPy and pandas use C under the hood. Your Python code calls fast C code.
• Cython — compile Python to C for a speed boost.
• Rust extensions — use pyo3 to write Python extensions in Rust.
• Multiprocessing — use multiple CPU cores.

Instagram serves over 2 billion users with Python (Django). If Python is fast enough for Instagram, it is probably fast enough for your project.

Who Uses Python?

Big companies use Python every day:

• Google — Python is one of their three “official” languages. YouTube backend uses Python.
• Netflix — uses Python for recommendation algorithms and infrastructure.
• Instagram — built on Django (Python web framework). Serves 2 billion+ users.
• Spotify — uses Python for data analysis and backend services.
• NASA — uses Python for scientific computing and data analysis.
• Uber — uses Python for data pipelines and machine learning.
• Amazon — uses Python for AWS Lambda, automation, and machine learning.

These are not toy projects. These are some of the largest systems in the world.

Why Learn Python in 2026?

Here are five reasons:

1. The AI Boom

AI is everywhere in 2026. And Python is the language of AI. Every LLM framework, every AI agent toolkit, and every machine learning library is Python-first. If you want to build AI applications, learning Python is not optional.

2. Job Market

Python developers are in high demand. Python is the most desired language in the Stack Overflow Developer Survey. Companies need Python developers for data engineering, AI, backend development, and automation.

3. Easy to Start

You can install Python in 5 minutes and write your first program immediately. No complex setup. No build tools. No IDE required. A text editor and a terminal are enough.

4. Huge Ecosystem

Whatever you need, there is probably a Python package for it. Web server? FastAPI. Data analysis? pandas. AI? PyTorch. PDF generation? reportlab. Email? smtplib. The list is endless.

5. Transferable Skills

Python teaches you programming concepts that apply to every language: variables, functions, loops, classes, error handling. Once you know Python, picking up JavaScript, Kotlin, or Rust is much easier.

Your First Python Code

Let me show you a few quick examples. Do not worry about understanding everything. We will cover each topic in detail in later tutorials.

Variables and printing

name = "Alex"
age = 25
print(f"My name is {name} and I am {age} years old.")

No type declarations. No semicolons. Just assign and print.

Lists and loops

numbers = [1, 2, 3, 4, 5]
doubled = [n * 2 for n in numbers]
print(doubled)  # Output: [2, 4, 6, 8, 10]

The second line is called a list comprehension. It creates a new list by doubling each number. This would take 4 lines in most other languages.

Dictionaries

user = {"name": "Sam", "age": 30, "city": "Berlin"}
print(f"{user['name']} lives in {user['city']}")

Dictionaries store key-value pairs. They are one of the most useful data structures in Python.

Dynamic typing

x = 42          # x is an integer
x = "hello"    # now x is a string
x = [1, 2, 3]  # now x is a list

In Python, variables do not have fixed types. You can assign any value to any variable. This makes Python flexible but also means you need to be careful.

The Zen of Python

Python has a set of design principles. You can see them by running this command:

python -c "import this"

Here are the most important ones:

1. Beautiful is better than ugly. Write clean code.
2. Simple is better than complex. Keep it simple.
3. Readability counts. Your code will be read more than it is written.
4. There should be one obvious way to do it. Python prefers one clear approach.
5. If the implementation is hard to explain, it is a bad idea. Simplicity wins.

These principles guide everything in Python. When you are unsure how to write something, choose the simpler option.

The Python Ecosystem

One of Python’s biggest strengths is its ecosystem. PyPI (the Python Package Index) has over 500,000 packages. Whatever you need to do, there is probably a package for it.

Here are some popular packages by category:

Web development: FastAPI, Django, Flask, Starlette Data science: pandas, Polars, NumPy, SciPy Machine learning: PyTorch, TensorFlow, scikit-learn, XGBoost AI/LLM: LangChain, LangGraph, Hugging Face Transformers HTTP requests: requests, httpx Testing: pytest, unittest Linting: ruff, mypy CLI tools: click, typer, argparse

You install packages with pip (Python’s package manager) or uv (the modern, faster alternative). We will cover package management in detail in Tutorial #8.

Python 2 vs Python 3

Python 2 reached end of life in January 2020. Always use Python 3. There is no reason to learn Python 2 in 2026.

When we say “Python” in this series, we mean Python 3.12 or newer. Some key differences between Python 2 and 3:

• print is a function in Python 3: print("hello") instead of print "hello"
• Integer division: 7 / 2 returns 3.5 in Python 3 (returns 3 in Python 2)
• Strings are Unicode by default in Python 3
• range() returns an iterator in Python 3 (not a list)

If you find old tutorials that use Python 2 syntax, skip them. They are outdated.

Common Misconceptions

“Python 2 vs Python 3”

Python 2 reached end of life in January 2020. Always use Python 3. In this tutorial series, we use Python 3.12 or newer. When we say “Python,” we mean Python 3.

“Python is only for beginners”

Not true. Python is used for complex AI systems, large-scale web applications, and scientific research. It is easy to start with, but it is also powerful enough for production systems.

“Python cannot do real applications”

Instagram. YouTube. Spotify. Uber. These are “real” applications.

What Will You Build?

In this tutorial series, you will learn Python from the ground up. Here is what we will cover:

Foundation (Tutorials 1-8): Variables, control flow, functions, data structures, strings, modules, and virtual environments. After this, you can write scripts and small programs.

Intermediate (Tutorials 9-17): Classes, dataclasses, error handling, file I/O, generators, decorators, and context managers. After this, you can write well-structured Python programs.

Advanced (Tutorials 18-21): Type hints, async/await, testing with pytest, and modern tooling. After this, you can contribute to professional Python projects.

Projects (Tutorials 22-25): Build real applications: a CLI tool, a REST API with FastAPI, a web scraper, and a data pipeline.

Every tutorial has working code examples you can run immediately. No theory without practice.

Source Code

You can find the code for this tutorial on GitHub:

kemalcodes/python-tutorial — tutorial-01-why-python

What’s Next?

In the next tutorial, we will install Python on your computer and write your first program. We will set up VS Code and learn how to run Python scripts.

Related Articles

• Python Cheat Sheet — quick reference for Python syntax
• Rust Tutorial Series — learn another modern language
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