Python 101: Learn the 5 Must-Know
Concepts
5 Important Python Concepts Every
Developer Should Know
If you're interested in becoming a developer who writes any
type of code in Python, then you need to understand these five
important Python concepts. These are what I see most
beginner and intermediate Python programmers making a ton
of mistakes with and misunderstanding when they're reading
through production code. The goal of this blog is to make sure
that when you're reading through production Python code, you
understand what's happening. You know the concept, and then
you can reproduce that code and write your own pull requests
and own features using Python code that other developers will
understand and expect. So with that said, let's get into the
concepts.
Mutable vs Immutable Types
An immutable type is something that cannot change, while a
mutable type is something that can change. Examples of
immutable types in Python include strings, integers, floats,
booleans, bytes, and tuples. Examples of mutable types include
lists, sets, and dictionaries. It's important to understand the
difference between these types because it can affect how your
code behaves. For example, when you make changes to a
mutable object, those changes will be reflected in all variables
that reference that object.
List Comprehensions
List comprehensions are a way to create a new list from an
existing iterable. They allow you to write a for loop inside of a
list and can help simplify code. For example, you can use a list
comprehension to create a list of all even numbers from 0 to
10:
x = [i for i in range(10) if i % 2 == 0]
This will create a list containing the numbers 0, 2, 4, 6, and 8.
Decorator Functions
A decorator function is a function that takes another function
as input and returns a new function. Decorators can be used to
, add functionality to an existing function without modifying its
code directly. For example, you can use a decorator to log the
input and output of a function:
def logger(func): def inner(*args, **kwargs):
print("Input:", args, kwargs) output =
func(*args, **kwargs) print("Output:",
output) return output return
inner@loggerdef add(x, y): return x + y
Here, the @logger decorator is applied to the add function.
When you call add(1, 2) , it will log the input ( 1, 2 ) and
output ( 3 ) of the function.
Generators
A generator is a type of iterator that allows you to iterate over
a sequence of values without creating the entire sequence in
memory. This can be useful when dealing with large datasets
that would otherwise be too large to fit in memory. Generators
are created using the yield keyword instead of return . For
example, you can use a generator to create a sequence of
Fibonacci numbers:
def fibonacci(): a, b = 0, 1 while True:
yield a a, b = b, a + bfor i in fibonacci():
if i > 100: break print(i)
This will print out all Fibonacci numbers less than 100.
Error Handling
Error handling is an important concept in Python because it
allows you to gracefully handle errors that might occur during
the execution of your code. You can use a try / except block
to catch and handle exceptions. For example, you can use
error handling to handle the case where a file you're trying to
open doesn't exist:
Concepts
5 Important Python Concepts Every
Developer Should Know
If you're interested in becoming a developer who writes any
type of code in Python, then you need to understand these five
important Python concepts. These are what I see most
beginner and intermediate Python programmers making a ton
of mistakes with and misunderstanding when they're reading
through production code. The goal of this blog is to make sure
that when you're reading through production Python code, you
understand what's happening. You know the concept, and then
you can reproduce that code and write your own pull requests
and own features using Python code that other developers will
understand and expect. So with that said, let's get into the
concepts.
Mutable vs Immutable Types
An immutable type is something that cannot change, while a
mutable type is something that can change. Examples of
immutable types in Python include strings, integers, floats,
booleans, bytes, and tuples. Examples of mutable types include
lists, sets, and dictionaries. It's important to understand the
difference between these types because it can affect how your
code behaves. For example, when you make changes to a
mutable object, those changes will be reflected in all variables
that reference that object.
List Comprehensions
List comprehensions are a way to create a new list from an
existing iterable. They allow you to write a for loop inside of a
list and can help simplify code. For example, you can use a list
comprehension to create a list of all even numbers from 0 to
10:
x = [i for i in range(10) if i % 2 == 0]
This will create a list containing the numbers 0, 2, 4, 6, and 8.
Decorator Functions
A decorator function is a function that takes another function
as input and returns a new function. Decorators can be used to
, add functionality to an existing function without modifying its
code directly. For example, you can use a decorator to log the
input and output of a function:
def logger(func): def inner(*args, **kwargs):
print("Input:", args, kwargs) output =
func(*args, **kwargs) print("Output:",
output) return output return
inner@loggerdef add(x, y): return x + y
Here, the @logger decorator is applied to the add function.
When you call add(1, 2) , it will log the input ( 1, 2 ) and
output ( 3 ) of the function.
Generators
A generator is a type of iterator that allows you to iterate over
a sequence of values without creating the entire sequence in
memory. This can be useful when dealing with large datasets
that would otherwise be too large to fit in memory. Generators
are created using the yield keyword instead of return . For
example, you can use a generator to create a sequence of
Fibonacci numbers:
def fibonacci(): a, b = 0, 1 while True:
yield a a, b = b, a + bfor i in fibonacci():
if i > 100: break print(i)
This will print out all Fibonacci numbers less than 100.
Error Handling
Error handling is an important concept in Python because it
allows you to gracefully handle errors that might occur during
the execution of your code. You can use a try / except block
to catch and handle exceptions. For example, you can use
error handling to handle the case where a file you're trying to
open doesn't exist:
