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As an experienced programmer in other languages, you may be looking to add Python to your toolbelt. The good news is that Python has a gentle learning curve and many features that make it an ideal language to learn for developers already proficient in other programming languages.

In this crash course, we‘ll cover everything you need to know to start being productive with Python quickly. We‘ll look at setting up your Python environment, basic Python syntax and semantics, powerful language features, and key differences between Python and other popular languages. By the end, you‘ll be ready to start using Python for your own projects.

Setting Up Your Python Environment

The first step to getting started with Python is installing it on your machine. You can download the latest version of Python from the official Python website at python.org. As of writing, the latest version is Python 3.9.5. It‘s recommended to install a Python 3.x version as Python 2 is no longer supported.

Once you have Python installed, you‘ll want to choose an Integrated Development Environment (IDE) or code editor to write your Python programs in. Some popular choices are:

• PyCharm: A full-featured Python IDE
• Visual Studio Code: A lightweight code editor with good Python support via extensions
• Vim/Emacs: If you‘re already proficient with one of these text editors, they work well for Python development

With your environment set up, you‘re ready to run your first Python program. Create a file called hello.py with the following contents:

print("Hello, World!")

Then run it from your terminal with:

python hello.py

Congrats, you just ran your first Python program! Let‘s dive into the syntax and semantics of the language now.

Basic Python Syntax and Semantics

Python has a clean, English-like syntax that makes it very readable, even for non-programmers. Let‘s go over the basics:

Variables and Data Types

Declaring variables in Python is simple:

x = 10
name = "Alice"

Python is dynamically and strongly typed, meaning you don‘t need to specify types, but objects have a fixed type that doesn‘t implicitly convert.

The basic data types are:

• int: Integers
• float: Floating point numbers
• str: Strings
• bool: Booleans (True/False)
• NoneType: The None object, represents a null value

Python also has several built-in compound data types:

• list: Ordered, mutable sequences
• tuple: Ordered, immutable sequences
• dict: Unordered mappings of keys to values
• set: Unordered collections of unique elements

Control Flow

Python uses indentation to delimit blocks of code. You don‘t need curly braces or keywords like begin/end.

If statements:

if x > 0:
print("Positive")
elif x < 0:
print("Negative")
else:
print("Zero")

Loops:

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

while x > 0:
print(x)
x -= 1

Functions

Defining functions is done using the def keyword:

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

Functions are first-class objects in Python and can be passed around like any other object. They can also be nested and returned from other functions.

Classes and Objects

Python is a multi-paradigm language and supports object-oriented programming. Defining classes is simple:

class Dog:

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

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

dog = Dog("Fido")
dog.bark() # prints "Fido says woof!"

Key OOP concepts like inheritance, encapsulation, and polymorphism are all supported.

This just scratches the surface of Python syntax, but you can see how simple and expressive it is. Let‘s look at some of Python‘s most powerful features next.

Powerful Python Language Features

Python packs a lot of punch into a simple syntax. Here are some of the most useful language features that allow you to be productive quickly:

Dynamic Typing

Not having to declare types means you can write programs more quickly and flexibly in Python. The interpreter does type checking at runtime, so you still get the safety of strong typing.

Built-in Data Structures

Python‘s built-in list, dict, and set types allow you to store and manipulate collections of data easily. They have a rich API and there are also many functions for working with these types in the standard library.

Batteries Included Standard Library

Python has a philosophy of "batteries included" – it comes with a large standard library with modules for common tasks like connecting to the web, reading and writing files, and working with data. This allows you to be productive without having to install many third-party libraries.

List Comprehensions

List comprehensions are a concise way to create lists in Python. For example, you can square the numbers from 1 to 10 like:

squares = [x**2 for x in range(1, 11)]

Decorators

Decorators are a way to modify or enhance functions without changing their definition. They‘re denoted by the @ symbol. For example, this decorator causes the wrapped function to run twice:

def double(func):
def wrapper():
func()
func()
return wrapper

@double
def hello():
print("Hello!")

hello() # prints "Hello!" twice

Decorators are a powerful way to write concise, reusable code in Python.

Context Managers

Context managers allow you to allocate and release resources precisely when you want to. The most common use is properly closing files:

with open("file.txt") as f:
data = f.read()

The with statement ensures that the file is properly closed, even if an exception is raised inside the block.

These are just a few examples of Python features that allow you to write concise, expressive code quickly. But coming from other languages, there are a few things that might trip you up, so let‘s look at some key differences next.

Key Differences Between Python and Other Programming Languages

If you‘re used to programming in languages like Java, C++, or JavaScript, there are a few things in Python that may surprise you:

Indentation Matters

In most programming languages, indentation is just a convention to make code more readable. But in Python, it‘s part of the syntax and used to delimit blocks of code. This forces a consistent style and makes code more readable overall.

Mutable vs Immutable Types

Some of Python‘s basic types like strings and tuples are immutable, meaning they can‘t be changed after creation. This is different from languages like JavaScript where all types are mutable by default. It‘s important to be aware of which types are mutable and immutable in Python.

Strongly Typed

Although you don‘t declare types in Python, once an object has a type, it‘s enforced by the interpreter. You can‘t perform operations between mismatched types without explicitly converting them. This is different than languages like JavaScript which do implicit type coercion.

Different Operators

Python uses the == operator for equality comparison and the is operator for identity comparison. It also uses and, or, not instead of &&, ||, ! like many C-based languages.

Different Ranges

In languages like C++ and Java, ranges include the start value up to but not including the end value. In Python, ranges include the start value up to and including the end value.

Errors vs Exceptions

Python uses exceptions to handle all errors, unlike C++ which uses error codes for some low-level errors. Getting used to handling exceptions and clean-up with finally blocks takes some practice.

Python 2 vs 3

Python 3.x introduced some backwards-incompatible changes from Python 2.x. It‘s important to be aware of which version you‘re using, as most new projects should use Python 3.

Being aware of these differences will help you avoid common pitfalls when first learning Python. But the best way to learn is by doing, so let‘s look at how you can continue your Python journey.

How to Continue Your Python Journey

With the basics under your belt, you‘ll want to start building real projects with Python. Here are some recommendations for going forward:

1. Work through the Python tutorial: https://docs.python.org/3/tutorial/index.html
2. Read the official Python documentation: https://docs.python.org/3/
3. Explore the standard library, especially these modules:
   • sys: System-specific parameters and functions
   • os: Operating system interfaces
   • collections: Container datatypes
   • itertools: Functions creating iterators for efficient looping
4. Explore third party packages. Some popular ones are:
   • numpy/scipy: Scientific computing
   • pandas: Data analysis
   • requests: HTTP requests
   • Django/Flask: Web frameworks
5. Read Python books:
   • "Python Crash Course" by Eric Matthes
   • "Automate the Boring Stuff with Python" by Al Sweigart
   • "Fluent Python" by Luciano Ramalho
6. Build projects you‘re interested in! The best way to learn is by building real applications to solve problems you care about.

As you‘re writing more Python code, it‘s also important to be aware of Python idioms and best practices, which we‘ll look at next.

Tips for Writing Pythonic Code

"Pythonic" code is idiomatic Python that follows the conventions of the language and standard library. Here are some tips for writing clean, Pythonic code:

1. Follow the PEP 8 style guide: Defines conventions for code formatting, naming, and more. https://www.python.org/dev/peps/pep-0008/

2. Be concise and explicit: Python values code that is short but readable. Avoid unnecessary verbosity.

3. Use built-in language features when possible: Python has many features like list comprehensions, context managers, and iterators that allow you to write concise, expressive code. Use them!

4. Don‘t repeat yourself: Python strongly values code reuse. Factor out duplicated code into functions or classes.

5. Ask forgiveness, not permission: It‘s often better to do something and handle any potential exceptions, rather than checking for all potential error conditions in advance.

6. Use with statements for resource management: with allows you to allocate and release resources like file handles cleanly and safely.

7. Use virtual environments: Virtual environments allow you to isolate project dependencies to avoid conflicts. Use a tool like venv to manage them.

Following these guidelines will help you write clean, maintainable Python code that other developers can easily understand.

Conclusion

Congratulations, you now have a solid foundation for being productive with Python! We covered all the basics from setting up your environment, to basic syntax and semantics, powerful language features, key differences from other languages, and tips for writing good Python code.

Remember that the best way to learn is by doing. Get out there and build some real projects with Python. Leverage your existing programming knowledge and embrace Python‘s unique features to write clean, concise code.

You‘ll be a Python pro in no time!