This is going to be my first post in Dev.to

In this post we are basically going to cover 15 Python tips. 5 beginner, 5 intermediate and 5 advanced. You can try out the code present in this post here.

👶 5 Beginner Python Tips for Efficient Coding

👉 Tip 1: Use List Comprehensions

List comprehensions are a concise and Pythonic way to create lists. Instead of using a for loop and appending to a list, you can use a list comprehension to create a list in a single line of code. For example:

# Using a for loop
squares = []
for i in range(1, 6):
    squares.append(i ** 2)
print(squares)  # Output: [1, 4, 9, 16, 25]

# Using a list comprehension
squares = [i ** 2 for i in range(1, 6)]
print(squares)  # Output: [1, 4, 9, 16, 25]

🏃‍♂️💻 Run it yourself

✅ Use List Comprehensions When:

• You want to create a new list based on an existing list.
• You want to apply a function or operation to each element in a list.
• You want to filter a list based on a condition.

❌ Avoid Using List Comprehensions When:

• The expression in the comprehension is too complex or hard to read.
• The comprehension involves nested loops or conditions that are hard to understand.
• The resulting list would be very large and memory-intensive.

👉 Tip 2: Use Namedtuples for Readability

Namedtuples are a subclass of tuples that allow you to give names to each field. This can make your code more readable and self-documenting. For example:

from collections import namedtuple

# Defining a namedtuple
Person = namedtuple('Person', ['name', 'age', 'gender'])

# Creating an instance of the namedtuple
person = Person(name='John', age=30, gender='male')

# Accessing the fields of the namedtuple
print(person.name)  # Output: John
print(person.age)  # Output: 30
print(person.gender)  # Output: male

🏃‍♂️💻 Run it yourself

✅ Use namedtuple When:

• You have a simple object that consists of a fixed set of attributes or fields.
• You want to avoid defining a full-blown class with methods and inheritance.
• You want to create objects that are immutable and hashable.
• You want to use less memory than a regular object.

❌ Avoid Using namedtuple When:

• You need to define methods or properties for your objects.
• You need to customize behavior based on attributes or fields.
• You need to use inheritance or mixins.
• You need to create complex objects with many interdependent fields.

👉 Tip 3: Use Context Managers

Context managers are a way to manage resources, such as files, db or network connections, in a safe and efficient way. The with statement in Python can be used as a context manager. For example:

# Using a context manager to read a file
with open('file.txt', 'r') as f:
    data = f.read()
    print(data)

🏃‍♂️💻 Run it yourself

✅ Use Context Managers When:

• You need to manage resources that need to be cleaned up when you are done with them, such as files or network connections.
• You want to ensure that cleanup code is always executed, even if there are errors or exceptions.
• You want to encapsulate setup and teardown code in a reusable way.

❌ Avoid Using Context Managers When:

• You need to manage resources that are very lightweight or do not need to be cleaned up, such as integers or small strings.
• You need more fine-grained control over the setup and teardown process, such as calling multiple setup or teardown functions at different times.
• The cleanup process is very complex or requires a lot of custom code.

👉 Tip 4: Use the Zip Function

The zip function can be used to combine multiple lists into a single list of tuples. This can be useful when iterating over multiple lists in parallel. For example:

# Combining two lists using zip
names = ['John', 'Alice', 'Bob']
ages = [30, 25, 35]
for name, age in zip(names, ages):
    print(f'{name} is {age} years old.')

🏃‍♂️💻 Run it yourself

✅ Use zip() When:

• You need to combine multiple iterables element-wise, and the iterables are of the same length.
• You want to iterate over multiple iterables simultaneously, and perform some operation on the corresponding elements.
• You want to convert multiple iterables into a single iterable that can be easily passed as an argument to a function.

❌ Avoid Using zip() When:

• The iterables are of different lengths, and you do not want to truncate or pad the shorter iterables.
• You need to modify or update the original iterables, rather than just iterating over them simultaneously.
• You need to perform more complex operations on the elements of the iterables, such as filtering, mapping, or reducing.

👉 Tip 5: Use F-Strings for String Formatting

F-strings are a concise and Pythonic way to format strings. You can use curly braces {} to insert variables into a string, and you can also include expressions inside the curly braces. For example:

name = 'John'
age = 30
print(f'My name is {name} and I am {age} years old.')  # Output: My name is John and I am 30 years old.

🏃‍♂️💻 Run it yourself

✅ Use f-strings When:

• You need to format strings with expressions, variables, or literals.
• You want to embed Python expressions directly into the string, rather than concatenating them with the string using +.
• You want to simplify the syntax for formatting strings, and make the code more readable and maintainable.
• You are using Python 3.6 or later, which introduced f-strings as a new feature.

❌ Avoid Using f-strings When:

• You need to format strings with complex expressions or multiple variables, and the code becomes hard to read or understand.
• You need to support older versions of Python that do not support f-strings.
• You are formatting a large number of strings and performance is a concern.

🧑‍💻 5 Intermediate Python Tips for Efficient Coding

👉 Tip 1: Use Decorators for Code Reuse

Decorators are a way to modify the behavior of a function without changing its source code. This can be useful for adding functionality to a function or for reusing code across multiple functions. For example:

# Defining a decorator
def my_decorator(func):
    def wrapper(*args, **kwargs):
        print('Before function')
        result = func(*args, **kwargs)
        print('After function')
        return result
    return wrapper

# Using the decorator
@my_decorator
def my_function():
    print('Inside function')

my_function()  # Output: Before function \n Inside function \n After function

🏃‍♂️💻 Run it yourself

✅ Use Decorators When:

• You need to add behavior or functionality to existing functions or classes without modifying their code directly.
• You have a common behavior or functionality that you want to apply to multiple functions or classes.
• You want to separate the concerns of a function or class, and extract cross-cutting concerns into separate decorators.
• You want to simplify the implementation of a function or class by using decorators to handle boilerplate code, such as error handling, logging, or authentication.

❌ Avoid Using Decorators When:

• You have only a few functions or classes, and the behavior or functionality is specific to each of them.
• You have a simple behavior or functionality that can be implemented directly in the code, without requiring a decorator.
• You have a behavior or functionality that is tightly coupled to the implementation of a function or class, and modifying it using a decorator would introduce unnecessary complexity or duplication.

👉 Tip 2: Use Generators for Memory Efficiency

Generators are a way to create iterators in a memory-efficient way. Instead of creating a list of all values, generators create values on the fly as needed. For example:

# Creating a generator function
def squares(n):
    for i in range(1, n+1):
        yield i ** 2

# Using the generator
for square in squares(5):
    print(square)  # Output: 1 4 9 16 25

🏃‍♂️💻 Run it yourself

✅ Use Generators When:

• You are working with large data sets that cannot fit in memory
• You need to process the data in a sequential manner, one value at a time.
• You want to avoid loading the entire data set into memory at once, to reduce memory usage and improve performance.
• You want to iterate over a sequence of values, but don't need random access to them.
• You want to generate an infinite sequence of values, such as a Fibonacci sequence or a stream of sensor data.

❌ Avoid Using Generators When:

• You need random access to the values in the sequence, or need to iterate over the values multiple times.
• You need to modify the sequence of values, or need to filter, sort, or transform the values in a non-sequential manner.
• You have a small data set that can fit in memory, and generating the values all at once would not cause memory issues.
• You are working with non-sequential data, such as images or audio files, that require random access to different parts of the data.

👉 Tip 3: Use Enumerations for Readability

Enumerations are a way to define named constants in Python. This can make your code more readable and self-documenting. For example:

from enum import Enum

# Defining an enumeration
class Color(Enum):
    RED = 1
    GREEN = 2
    BLUE = 3

# Using the enumeration
print(Color.RED)  # Output: Color.RED
print(Color.RED.value)  # Output: 1

🏃‍♂️💻 Run it yourself

✅ Use Enum When:

• You need a fixed set of constants with names that are easier to read and understand than raw integers or strings.
• You want to prevent errors from using incorrect values, by providing a set of valid options.
• You want to create a custom data type that can be used throughout your code, with its own methods and attributes.

❌ Avoid Using Enum When:

• You only need to define a few constants that are easily represented by integers or strings.
• You need to store more than just a name and a value for each constant, such as additional data or behavior.
• You need to create a set of related constants that can be used in different contexts, or that may change over time.

👉 Tip 4: Use Map and Filter for Data Manipulation

The map and filter functions can be used to transform and filter data in a concise and efficient way. For example:

# Using map to transform data
numbers = [1, 2, 3, 4, 5]
squares = map(lambda x: x ** 2, numbers)
print(list(squares))  # Output: [1, 4, 9, 16, 25]

# Using filter to filter data
numbers = [1, 2, 3, 4, 5]
evens = filter(lambda x: x % 2 == 0, numbers)
print(list(evens))  # Output: [2, 4]

🏃‍♂️💻 Run it yourself

✅ Use map() When:

• You need to apply the same function to every element of an iterable object, and transform the elements into a new iterable.
• You want to avoid writing a for loop to iterate over the elements of the iterable, and apply the function to each element.
• You want to create a new iterable with the transformed elements, without modifying the original iterable.

✅ Use filter() When:

• You need to filter the elements of an iterable object based on a certain condition or criteria.
• You want to avoid writing a for loop to iterate over the elements of the iterable, and filter the elements based on a condition.
• You want to create a new iterable with the filtered elements, without modifying the original iterable.

❌ Avoid Using map() and filter() When:

• The function you want to apply to the elements is complex or requires multiple arguments.
• The condition you want to use for filtering is complex or requires multiple conditions or criteria.
• The iterable object is very large or requires significant memory, as using map() and filter() can create new objects that require additional memory.

👉 Tip 5: Use Sets for Unique Values

Sets are a way to store unique values in Python. This can be useful for removing duplicates or for performing set operations such as union, intersection, and difference. For example:

# Creating a set
numbers = {1, 2, 3, 2, 1}
print(numbers)  # Output: {1, 2, 3}

# Performing set operations
set1 = {1, 2, 3}
set2 = {3, 4, 5}
print(set1.union(set2))  # Output: {1, 2, 3, 4, 5}
print(set1.intersection(set2))  # Output: {3}
print(set1.difference(set2))  # Output: {1, 2}

🏃‍♂️💻 Run it yourself

✅ Use Set When:

• You need to store a collection of unique elements and need to check for membership or intersection with other sets.
• You need to remove duplicates from a list or other iterable object, as sets automatically remove duplicates.
• You need to perform set operations such as union, intersection, and difference.

❌ Avoid Using Set When:

• You need to maintain the order of the elements, as sets are unordered.
• You need to access elements by index or position, as sets do not support indexing.
• You have duplicate elements that are important to the data or analysis, as sets automatically remove duplicates.

🚀 5 Expert Python Tips for Efficient Coding

👉 Tip 1: Use Decorators for Performance Optimization

Decorators can be used for more than just code reuse. They can also be used for performance optimization by caching the results of a function. This can be useful for expensive calculations or for functions that are called frequently. For example:

# Defining a memoization decorator
def memoize(func):
    cache = {}

    def wrapper(*args):
        if args in cache:
            return cache[args]
        result = func(*args)
        cache[args] = result
        return result

    return wrapper


# Using the decorator
@memoize
def fibonacci(n):
    if n in (0, 1):
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)


print(fibonacci(30))  # Output: 832040

🏃‍♂️💻 Run it yourself

👉 Tip 2: Use Asynchronous Programming for Concurrency

Asynchronous programming is a way to write concurrent code that can handle multiple tasks at once. It can improve the performance of I/O-bound tasks such as network requests and file operations. For example:

import asyncio
import aiohttp


async def fetch(session, url):
    async with session.get(url) as response:
        return await response.text()


async def main():
    async with aiohttp.ClientSession() as session:
        tasks = [
            asyncio.create_task(
                fetch(session, f"https://jsonplaceholder.typicode.com/todos/{i + 1}")
            )
            for i in range(10)
        ]
        responses = await asyncio.gather(*tasks)
        print(responses)


asyncio.run(main())

🏃‍♂️💻 Run it yourself

👉 Tip 3: Use Function Annotations to Improve Readability

Function annotations can be used to provide additional information about function arguments and return values, improving the readability of your code. For example:

def add(x: int, y: int) -> int:
    return x + y

More complex example:

from typing import Callable, TypeVar, Union

T = TypeVar('T')


def apply(func: Callable[[int, int], int], x: int, y: int) -> int:
    return func(x, y)


def get_first_item(items: list[T]) -> T:
    return items[0]


def greet(name: Union[str, None] = None) -> str:
    if name is None:
        return 'Hello, World!'
    else:
        return f'Hello, {name}!'


def repeat(func: Callable[[T], T], n: int, x: T) -> T:
    for i in range(n):
        x = func(x)
    return x


def double(x: int) -> int:
    return x * 2


def add(x: int, y: int) -> int:
    return x + y


numbers = [1, 2, 3, 4, 5]
result = apply(add, 3, 4)
first_number = get_first_item(numbers)
greeting = greet()
repeated_number = repeat(double, 3, 2)

print(result)  # Output: 7
print(first_number)  # Output: 1
print(greeting)  # Output: 'Hello, World!'
print(repeated_number)  # Output: 16

🏃‍♂️💻 Run it yourself

👉 Tip 4: Use collections.defaultdict for Default Values

If you need to initialize a dictionary with default values, you can use the collections.defaultdict class. For example:

from collections import defaultdict

d = defaultdict(int)
d['a'] += 1
print(d['a'])  # prints 1
print(d['b'])  # prints 0 (default value for int)

🏃‍♂️💻 Run it yourself

👉 Tip 5: Use contextlib.suppress to Suppress Exceptions

If you need to suppress a specific exception, you can use the contextlib.suppress context manager. For example:

import contextlib

with contextlib.suppress(FileNotFoundError):
    with open('file.txt') as f:
        # do something with f
        pas

🏃‍♂️💻 Run it yourself