Introduction
Python is one of the world’s most popular programming languages, noted for its simplicity and adaptability. Whether you’re learning Python for data research, web development, or automation, knowing the correct shortcuts can boost your productivity significantly.
If you’re a newbie, learning Python shortcuts will save you time, eliminate errors, and make coding more fun. In this essay, we’ll go over 20 key Python shortcuts that any novice should know. These will enable you to write cleaner code, debug faster, and work smarter, not harder!
20 Python Shortcuts Every Beginner Must Know to Code Faster & Smarter
1. Swap Two Variables Without a Temp Variable
Instead of using a temporary variable to swap two values, use Python’s elegant tuple unpacking:
x, y = y, x
This shortcut makes swapping variables faster and more readable.
2. List Comprehensions for Cleaner Code
Instead of using a loop to create a list, use list comprehensions:
squared_numbers = [x**2 for x in range(10)]
This one-liner is more efficient and readable.
3. Use Enumerate Instead of Range
When iterating through a list and needing the index, avoid using range(len()). Instead, use enumerate():
fruits = [‘apple’, ‘banana’, ‘cherry’]
for index, fruit in enumerate(fruits):
print(index, fruit)
This improves readability and eliminates errors from manual indexing.
4. Use zip() for Iterating Multiple Lists
If you need to loop through multiple lists simultaneously, zip() is the way to go:
names = [‘Alice’, ‘Bob’, ‘Charlie’]
ages = [25, 30, 35]
for name, age in zip(names, ages):
print(f”{name} is {age} years old.”)
This keeps your code clean and easy to understand.
5. Dictionary Comprehension
Just like list comprehensions, you can concisely create dictionaries:
squares = {x: x**2 for x in range(5)}
This makes dictionary creation faster and more readable.
Also Read – 10 Simple Python Project for Beginners to Boost Your Coding Skills
6. The get() Method for Safer Dictionary Access
Instead of checking if a key exists before accessing it, use get():
my_dict = {‘name’: ‘Alice’, ‘age’: 25}
print(my_dict.get(‘name’, ‘Unknown’))
This prevents key errors and allows for default values.
7. Using defaultdict to Avoid KeyErrors
Instead of manually checking and handling missing keys, use defaultdict from collections:
from collections import defaultdict
my_dict = defaultdict(int)
print(my_dict[‘new_key’]) # Returns 0 instead of an error
This is useful for counting occurrences or handling missing values.
8. The setdefault() Method
A handy shortcut for adding a key to a dictionary only if it doesn’t exist:
data = {}
data.setdefault(‘name’, ‘Unknown’)
This prevents unnecessary condition checks.
9. Joining Strings Efficiently
Instead of concatenating strings in loops, use join():
words = [‘Python’, ‘is’, ‘awesome’]
sentence = ‘ ‘.join(words)
This method is much faster and memory-efficient.
10. Using any() and all() for Quick Checks
To check if any or all elements in an iterable meet a condition:
numbers = [0, 1, 2, 3]
print(any(numbers)) # Returns True
print(all(numbers)) # Returns False
These functions replace multiple condition checks and loops.
11. Unpacking Function Arguments with *args and **kwargs
To make functions more flexible:
def print_numbers(*args):
print(args)
print_numbers(1, 2, 3)
This allows passing a variable number of arguments effortlessly.
12. Using map() for Efficient Iterations
Instead of loops, use map() to apply functions to an iterable:
numbers = [1, 2, 3]
squared = list(map(lambda x: x**2, numbers))
This makes transformations more concise.
13. Using filter() for Quick Filtering
To filter elements based on a condition:
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
This removes unnecessary loops and keeps the code clean.
14. Using sorted() with Custom Sorting
Sort lists with ease:
names = [‘Alice’, ‘Bob’, ‘Charlie’]
sorted_names = sorted(names, key=len)
This lets you customize sorting logic.
15. Using reversed() for Efficient Reversal
Instead of slicing, use:
numbers = [1, 2, 3, 4]
reversed_numbers = list(reversed(numbers))
This is more memory-efficient.
16. Using Counter to Count Elements
from collections import Counter
words = [‘apple’, ‘banana’, ‘apple’, ‘cherry’]
count = Counter(words)
This helps quickly count occurrences in lists.
17. Using isinstance() for Type Checking
Instead of type(), use:
print(isinstance(42, int))
This ensures proper type-checking.
18. Using try-except Instead of Checking First
Instead of:
if ‘key’ in my_dict:
value = my_dict[‘key’]
Use:
try:
value = my_dict[‘key’]
except KeyError:
value = ‘Default’
This follows the “Easier to Ask for Forgiveness than Permission” (EAFP) principle.
19. Using with open() for File Handling
Avoid forgetting to close files:
with open(‘file.txt’, ‘r’) as file:
content = file.read()
This ensures proper file closure.
20. Using timeit for Performance Testing
To measure execution time:
import timeit
print(timeit.timeit(“[x**2 for x in range(1000)]”, number=1000))
This helps optimise code performance.
Conclusion
Mastering these 20 Python shortcuts will allow you to produce better, faster, and cleaner code. Beginners who use these approaches can considerably enhance their coding efficiency. Keep practising, and these shortcuts will soon be second nature!
