for loops in Python don't have indexes.
This small distinction makes for some big differences in the way we loop in Python.
It's tempting to reach for indexes when working with tuples, lists, and other sequences, but if we know the shape of the tuple we're working with, we can unpack it instead.
Tuple unpacking (aka "multiple assignment" or "iterable unpacking") is often underutilized by new Python programmers.
In Python it's very common to build up new lists while looping over old lists. Partly this is because we don't mutate lists very often while looping over them.
Because we build up new lists from old ones so often, Python has a special syntax to help us with this very common operation: list comprehensions.
Python has 4 scopes: local, enclosing, global, and built-ins. Python's "global" variables are only global to the module they're in. The only truly universal variables are the built-ins.
Decorators are functions that accept functions and return functions. They're weird but powerful.
Reading from and writing to text files (and sometimes binary files) is an important skill for most Python programmers.
List comprehensions make new lists. Generator expressions make new generator objects. Generators are iterators, which are lazy single-use iterables. Unlike lists, generators aren't data structures. Instead they do work as you loop over them.
We don't use getter and setter methods in Python. Instead we make properties.
Properties allow us to customize what happens when you access an attribute and what happens when you assign to an attribute.
Generator functions look like regular functions but they have one or more
yield statements within them. Unlike regular functions, the code within a generator function isn't run when you call it! Calling a generator function returns a generator object, which is a lazy iterable.
Exceptions happens! When exceptions happen, how should interpret the traceback for an exception? And how, when, and where should you catch exceptions?
You can overload many operators, protocols, and bits of functionality on your Python objects by implementing dunder methods.
Python's variables aren't buckets that contain things; they're pointers that reference objects.
The way Python's variables work can often confuse folks new to Python, both new programmers and folks moving from other languages like C++ or Java.
Python, like many programming languages, has functions. A function is a block of code you can call to run that code.
Python's functions have a lot of "wait I didn't know that" features. Functions can define default argument values, functions can be called with keyword arguments, and functions can be written to accept any number of arguments.
Classes are a way to bundle functionality and state together.
The terms "type" and "class" are interchangeable:
bool are all classes.
You'll certainly use quite a few classes in Python (remember types are classes) but you may not need to create your own often.
Modules are the tool we use for breaking up our code into multiple files in Python.
When you write a
.py file, you're making a Python module.
You can import your own modules, modules included in the Python standard library, or modules in third-party packages.
.py file can be used as a module or as a "script" which is run from your operating system's command-line/terminal. Python is a great programming language for making command-line scripts.
Regardless of what you're doing in Python, you almost certainly use strings all the time. A string is usually the default tool we reach for when we don't have a more specific way to represent our data.
These topics are commonly overlooked by new Python programmers.
Python has an
* prefix operator and a
** prefix operator that can be used in many different ways.
The below screencasts & articles explain each of the many uses of the
** operators in Python.
These articles aren't tagged yet.
My name is Trey Hunner. I do corporate Python training for teams and I teach Python online through Python Morsels.
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