Among programmers, Python has become one of the most popular programming languages in the world today. Python is a very popular programming language because of its simplicity & readability. When compared to other programming languages, code syntax is easy to understand & write. For these reasons, it is popular with new programmers and experienced professionals also. However, programming is not just writing code that runs. Programming can be writing elegant, efficient, professional code. The distinction between a beginner and a professional is not just an understanding of coding syntax, but also understanding some of the hidden features, best practices, and the other tricks.
This blog will go over some of the practical Python tricks that will help you on your way to becoming a better coder and to make your scripts shorter, more efficient, and professional.
1. Using List Comprehensions Instead of Loops
Many beginners heavily use for loops but professionals prefer list comprehensions for a more concise, faster approach to code.
Example:
# Traditional loop
squares = []
for i in range(10):
squares.append(i**2)
# List comprehension
squares = [i**2 for i in range(10)]
Why it’s pro level:
List comprehensions reduce lines of code, are easier for others to read, and often run faster than their for loop counterparts.
2. Unpacking multiple variable assignment
Instead of assigning variables one at a time, you can unpack and assign while initializing the variable.
Example:
# Traditional assignment
a = 10
b = 20
c = 30
# Pro trick
a, b, c = 10, 20, 30
This particular feature can be great for swapping values without a temporary variable:
3. Using enumerate() instead of Indexing by Range
Most beginner programmers tend to write range(len(…)) indexing which is not the prettiest solution.
Example:
# Beginner way
items = [“apple”, “banana”, “cherry”]
for i in range(len(items)):
print(i, items[i])
# Pro way
for index, value in enumerate(items):
print(index, value)
This solution is more Pythonic, cleaner and easier to read.
4. Dictionary Comprehension
Similar to lists, you can create dictionaries in a single line.
Example:
# Traditional way
squares = {}
for i in range(5):
squares[i] = i**2
# Pro way
squares = {i: i**2 for i in range(5)}
5. Using *args and **kwargs for Flexible Functions
Professional programmers will create functions that are capable of accepting an arbitrary number of arguments.
Example:
def pro_function(*args, **kwargs):
print(“Arguments:”, args)
print(“Keyword Arguments:”, kwargs)
pro_function(1, 2, 3, name=”Alice”, age=25)
This technique is especially useful when building reusable and flexible functions.
6. The Power of F-Strings
String formatting in Python used to depend on .format() or % formatting, then came f-strings, which are faster and cleaner, and were introduced in Python 3.6.
Example:
name = “Alice”
age = 25
# Old way
print(“My name is {} and I am {} years old”.format(name, age))
# Pro way
print(f”My name is {name} and I am {age} years old”)
7. Using zip() for Zip
When working with more than one list and you want to loop through them, use zip().
Example:
names = [“Alice”, “Bob”, “Charlie”]
scores = [85, 90, 95]
for name, score in zip(names, scores):
print(f”{name} scored {score}”)
8. Handling Missing Keys with defaultdict
Accessing keys in a dictionary that does not exist will raise errors. The solution for professionals is to use collections.defaultdict.
Example:
from collections import defaultdict
scores = defaultdict(int)
scores[“Alice”] += 10
scores[“Bob”] += 5
print(scores)
This avoids KeyError and initializes values automatically
9. Using Generators to Save Memory
Instead of making lists in memory, use generators to process large volumes of data.
Example:
# Generator expression
squares = (i**2 for i in range(10**6))
# Efficient iteration
for s in squares:
if s > 100:
break
print(s)
Using generators requires less memory usage and scale better.
10. Using Context Managers (with Statement)
Professionals avoid opening files and closing files. They use with, which handles cleanup automatically.
Example:
# Beginner way
file = open(“data.txt”, “r”)
content = file.read()
file.close()
# Pro way
with open(“data.txt”, “r”) as file:
content = file.read()
This is a safer way to open and close files and prevents memory leaks.
11. Sorting with lambda Functions
Sorting is not just restricted to numbers. You can sort using your own logic.
Example:
students = [(“Alice”, 25), (“Bob”, 20), (“Charlie”, 23)]
# Sort by age
students.sort(key=lambda x: x[1])
print(students)
12. Using any() and all()
Instead of writing a loop to check conditions, professionals use these built-in methods.
Example:
nums = [2, 4, 6, 8]
print(all(n % 2 == 0 for n in nums)) # True
print(any(n > 5 for n in nums)) # True
13. Unpacking in Function Calls
Unpacking is another option of passing in lists or dictionaries to functions.
Example:
def add(a, b, c):
return a + b + c
nums = [1, 2, 3]
print(add(*nums)) # 6
14. Using the collections.Counter for Frequency Counting
Although it is very time consuming to manually count all occurrences of elements of a list except if you are using Counter.
Example:
from collections import Counter
fruits = [“apple”, “banana”, “apple”, “cherry”, “banana”]
count = Counter(fruits)
print(count)
Conclusion:
Learning to leverage these Python tricks will allow you to write cleaner, faster, professional code. We want you to make your code look not only work, but look beautiful, and be efficient too! These habits will improve productivity and also be impressive to your developers and future employers.
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FAQs
1. Why should I use list comprehensions instead of loops?
List comprehensions are more readable, faster and are more ‘pythonic’ than loops.
2. What is the difference between *args and **kwargs?
*args takes variable positional arguments while **kwargs takes variable keyword arguments (name-value pairs).
3. Are f-strings better than .format()?
Yes. f-strings are not only more readable but also faster in execution compared to .format() and % formatting.
4. When should I use generators instead of lists?
Use generators when working with large datasets or when you don’t need to store the entire sequence in memory.
5. What makes code “Pythonic”?
“Pythonic” code emphasizes simplicity, readability, and efficient use of Python’s built-in features, rather than forcing other programming paradigms into Python.
