5 Python Operators I Ignored for Years — And Why You Shouldn’t

You’re not using Python to its full power — and neither was I.

5 Python Operators I Ignored for Years — And Why You Shouldn’t

We all love discovering that one trick that makes us feel like we’ve leveled up as a developer.
For me, it wasn’t a fancy framework or a new library — it was the Python operators I had been sleeping on for years.

These are not obscure or deprecated. They’re right there in the documentation, but for some reason, I never used them — until I did, and everything changed.

Let me save you some years.

1. The Walrus Operator (:=)

First reaction: What even is that?
Second reaction (after using it): Why didn’t I learn this sooner?

The walrus operator, introduced in Python 3.8, lets you assign and return a value in a single expression. It shines in situations where you’re repeating function calls or conditions unnecessarily.

Example:

# Without walrus 
line = input("Enter something: ") 
while line != "quit": 
    print(f"You said: {line}") 
    line = input("Enter something: ") 
 
# With walrus 
while (line := input("Enter something: ")) != "quit": 
    print(f"You said: {line}")

Fewer lines

Cleaner loops

Smarter conditionals

Use it when your condition depends on a value you also need to use again.

2. The Bitwise NOT (~)

I always thought this was only for low-level bit manipulation. Turns out, it has real-world applications — especially when working with sets and boolean masks in libraries like Pandas.

Example:

# Logical NOT in Pandas using ~ 
df[~df["active"]]  # returns all rows where 'active' is False

Works seamlessly with boolean arrays

Faster and more idiomatic than df[df["active"] == False]

Great for data filtering

If you’re doing data science or web scraping, start thinking in ~.

3. The is vs == (Identity vs Equality)

Yes, I knew they existed — but I didn’t respect the difference until it bit me hard.

Quick rule of thumb:

• == checks value equality
• is checks object identity

a = [1, 2, 3] 
b = a 
c = [1, 2, 3] 
 
print(a == c)  # True 
print(a is c)  # False 
print(a is b)  # True

Using is to compare strings or numbers might give inconsistent results

Helps debug subtle bugs when caching or memoizing

Use is for None, not strings or integers.

4. The in Operator with Dictionaries

We all know in is great for lists, but did you know how powerful it is with dictionaries?

data = {"id": 101, "name": "Aashish", "verified": True} 
 
if "verified" in data: 
    print("Verified user!")

Cleaner and faster than dict.get() with None checking

Prevents KeyError

Excellent in if conditions and guard clauses

Also works with for key in dict: loops — Pythonic and readable.

5. The Chained Comparison (a < b < c)

This one feels like math — and I ignored it for years, opting for ugly and chains instead.

# Instead of this: 
if a < b and b < c: 
 
# You can do this: 
if a < b < c:

More expressive

Matches mathematical notation

Less prone to logic errors

Works with any comparable values — numbers, dates, even strings.

Final Thoughts: Don’t Wait Years Like I Did

It’s easy to get comfortable with the basics — loops, conditionals, functions. But Python is full of these small but mighty operators that make your code more elegant and expressive.

If you’re serious about writing clean, readable, and Pythonic code, these operators are your secret weapon.

Which of these operators have you been ignoring?
Drop a comment, or better — refactor one of your old scripts and feel the difference.