Learn Python Programming – Everything You Need to Know (Free Book)

Python has exploded in popularity in recent years, becoming one of the most widely used and influential programming languages in the world. It consistently ranks as one of the top three most popular languages in various indices, and has seen a particularly sharp uptick in interest for data science and machine learning applications.

So what makes Python so appealing? In short, it hits a sweet spot of being both easy to learn and powerful enough to build just about anything with. Python code is renowned for being concise, readable, and almost English-like in its syntax. At the same time, it‘s an incredibly versatile language – you can use it for everything from simple scripting to building complex web applications to deep learning.

In this comprehensive guide, we‘ll walk through everything you need to know to become proficient in Python. We‘ll start with the basics of Python syntax and data types, then move on to more advanced concepts like object-oriented programming, importing libraries, and best practices for writing clean, performant Python code. We‘ll also explore Python‘s role in several key application areas, including web development, data science, and automation.

By the end of this article, you‘ll have a solid roadmap for your Python learning journey. We‘ll also link to a variety of free resources, including books, tutorials, and courses, to help you along the way. Let‘s dive in!

Why Learn Python?

Before we get into the technical details, let‘s discuss why you might want to learn Python in the first place. Here are a few key reasons:

1. It‘s beginner-friendly – Python is often recommended as the best first language to learn due to its simplicity and readability. Its syntax is straightforward and almost English-like, making it relatively easy to pick up for newcomers to coding.

2. It‘s versatile – Python is a general-purpose language, meaning it can be used for a wide variety of applications. "You can create web applications, analyze data, write utilities, and create business applications all with the same language," says Martin Chikilian, lead curriculum developer at Northeastern University.

3. It has a huge ecosystem – Python boasts an incredibly vast standard library and a massive collection of third-party packages. "Pythonistas," as Python developers are known, have built tools for just about anything you can think of. Whatever your Python project, chances are someone has built a package to help you do it.

4. It‘s in high demand – Python skills are highly sought after in the job market. It‘s widely used in scientific computing, data mining, and machine learning roles, which are some of the fastest-growing segments of the job market.

Here are some eye-opening statistics that demonstrate Python‘s popularity and influence:

• Python is the fourth most popular language globally as of 2022, behind only JavaScript, HTML/CSS, and SQL (Source)
• Python is the most wanted language according to Stack Overflow‘s 2022 Developer Survey, meaning developers who don‘t currently use it want to learn it (Source)
• The median Python developer salary in the US is $120,000 per year (Source)
• There are over 400,000 Python repositories on GitHub, making it the second most-used language on the platform (Source)

Major companies that use Python include Google, Netflix, Spotify, Dropbox, Reddit, and Lyft, among many others. It‘s used for a variety of applications at these companies, from recommendation algorithms to DevOps tools to data analysis pipelines.

Key Features of Python

Let‘s now look at some of the key features that make Python unique and contribute to its popularity:

1. Easy to learn and read – As we mentioned, Python‘s syntax is designed to be readable and concise. It uses English keywords like ‘if‘ and ‘in‘, and has fewer syntactical constructions than many other languages.

2. Interpreted – Python is an interpreted language, meaning the code is executed line by line at runtime. This makes for a faster development process, as there‘s no need to compile the code before running it.

3. Dynamically-typed – In Python, you don‘t need to specify a variable‘s type when you declare it (e.g., int, str, etc.). Python determines the type at runtime based on the value assigned. This reduces the amount of code you need to write.

4. Massive standard library – Python comes with a very extensive library of built-in functionality. "Python has a batteries-included philosophy," says Jake VanderPlas, author of the Python Data Science Handbook. "The standard library and the extended scientific computing stack have just about everything you need for most data science applications."

5. Interoperability – Python can interface with other languages like C and C++, allowing you to leverage their strengths. Libraries like NumPy and TensorFlow are actually mostly written in optimized C/C++ with a Python front-end.

6. Open source – Python is developed under an OSI-approved open source license, making it freely usable and distributable, even for commercial use. The language‘s development is driven by the community, with the Python Software Foundation providing leadership and direction.

Here‘s a table comparing Python to some other popular languages in terms of key attributes:

Language	Typing	Execution	Syntax	Standard Library	Open Source
Python	Dynamic	Interpreted	Indent-based	Extensive	Yes
JavaScript	Dynamic	Interpreted	Bracket-based	Moderate	Yes
Java	Static	Compiled	Bracket-based	Extensive	Mostly
C++	Static	Compiled	Bracket-based	Limited	Mostly

Python‘s Role in Key Application Areas

Python has made significant inroads in several key application areas in recent years. Let‘s explore a few of the most notable:

1. Web Development – Python has several popular web frameworks, including Django and Flask. Django follows a "batteries included" philosophy, providing an all-in-one solution. Flask is more minimalist and extensible. According to the 2020 JetBrains survey, 52% of Python developers use it for web development.

2. Data Science and Machine Learning – This is where Python has really shone in recent years. Libraries like NumPy, Pandas, Matplotlib, and scikit-learn have made Python the de facto standard for data science. And deep learning frameworks like TensorFlow and PyTorch have cemented its place in the machine learning world. A 2019 Kaggle survey found that 75% of data scientists and machine learning practitioners use Python.

3. Scripting and Automation – Python is often used as a scripting language to automate tasks, especially in DevOps and system administration roles. It‘s considered more readable and maintainable than traditional shell scripting. Tools like Ansible and OpenStack are written in Python.

4. Embedded Applications – Python is increasingly being used in embedded systems and internet of things devices. The Raspberry Pi, a popular single-board computer, has Python as its recommended language. MicroPython is a lean implementation of Python 3 that‘s optimized to run on microcontrollers.

Here‘s a table showing Python‘s popularity in various domains, according to the 2022 Stack Overflow Developer Survey:

Domain	% of Python Developers
Data analysis	51.2%
Machine learning	49.1%
Web development (backend)	44.0%
DevOps	38.7%
Web development (frontend)	23.7%

Object-Oriented Programming in Python

Python is a multi-paradigm language that fully supports object-oriented programming (OOP). In fact, in Python, nearly everything is an object – even numbers, strings, and functions.

The key concepts of OOP in Python include:

1. Classes and Objects – A class is a blueprint for creating objects. It defines the structure and behavior that the objects of that class will have. An object is an instance of a class – it‘s a concrete entity based on the class blueprint.

class Dog:
    def __init__(self, name):
        self.name = name

    def bark(self):
        print(f"{self.name} says woof!")

my_dog = Dog("Buddy")
my_dog.bark()  # Output: Buddy says woof!

2. Methods – Methods are functions defined within a class. They define the behavior of the objects of that class. There are three types of methods in Python:

   • Instance methods – The most common type. They take the instance (self) as the first argument.
   • Class methods – Marked with the @classmethod decorator. They take the class itself (cls) as the first argument.
   • Static methods – Marked with the @staticmethod decorator. They don‘t take any special first argument.

3. Inheritance – Inheritance allows a class to inherit attributes and methods from another class. The class that is being inherited from is called the parent or base class, and the class that is inheriting is called the child or derived class. Python supports both single and multiple inheritance.

class Animal:
    def __init__(self, name):
        self.name = name

    def speak(self):
        pass

class Cat(Animal):
    def speak(self):
        print(f"{self.name} says meow!")

class Dog(Animal):
    def speak(self):
        print(f"{self.name} says woof!")

4. Encapsulation – Encapsulation is the bundling of data and methods within a single unit, i.e., a class. Python doesn‘t have strict access modifiers like private or protected, but follows a convention of prefixing a single underscore (_) for "internal use" attributes and a double underscore (__) for "private" attributes (which triggers name mangling).

These are just the basics – Python‘s OOP system includes many more advanced features like properties, abstract base classes, and metaclasses.

Best Practices and Expert Tips

To wrap up, let‘s cover some best practices and expert tips for writing quality Python code:

1. Follow PEP 8 – PEP 8 is the official style guide for Python code. It covers naming conventions, whitespace usage, code layout, and more. Following PEP 8 makes your code more readable and maintainable.

2. Use virtual environments – Virtual environments allow you to isolate your Python project‘s dependencies from your system-wide Python installation. This prevents version conflicts and makes your project more portable. The standard library includes the venv module for creating virtual environments.

3. Write Pythonic code – "Pythonic" code is code that follows Python‘s idioms and best practices. It‘s concise, readable, and makes good use of Python‘s built-in features. Some examples:

   • Use list comprehensions and generator expressions instead of loops when possible
   • Use with statements for resource management (file I/O, database connections, etc.)
   • Use is for comparing to None, not ==
   • Use enumerate() instead of manually incrementing a counter in a loop

4. Profile and optimize when needed – Python provides built-in profiling tools like cProfile and timeit to identify performance bottlenecks. Focus on writing clear, correct code first, then optimize the parts that are actually slow.

5. Write tests – Automated testing helps ensure your code works as expected and prevents regressions. Python has several popular testing frameworks, including unittest (in the standard library) and pytest (a third-party package).

6. Document your code – Use docstrings to provide documentation for modules, classes, and functions. Follow the PEP 257 conventions for docstring format. For larger projects, consider using a documentation tool like Sphinx.

Here‘s a great quote that sums up the Pythonic philosophy:

"Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts." – Tim Peters, The Zen of Python

Learning Resources

Finally, here are some excellent free resources to aid your Python learning journey:

Books:

• "Automate the Boring Stuff with Python" by Al Sweigart
• "Think Python" by Allen B. Downey
• "Python for Everybody" by Dr. Charles Severance

Tutorials and Courses:

• The Official Python Tutorial
• MIT‘s Introduction to Computer Science and Programming in Python
• Google‘s Python Class
• Codecademy‘s Python Course

Coding Challenges:

• HackerRank
• Project Euler
• Codewars

Podcasts:

• Talk Python to Me
• Python Bytes
• Test & Code

Remember, the best way to learn is by doing. As you‘re learning, work on your own projects, contribute to open source, and engage with the Python community. With dedication and practice, you‘ll be well on your way to mastering Python.

Happy coding!