5 Python Basics That Often Confuse Beginners

You Thought Python Was Easy… Until These Tripped You Up

5 Python Basics That Often Confuse Beginners

Introduction: Python Is Simple — Until It Isn’t

Python has earned its reputation as the most beginner-friendly programming language. Clean syntax. English-like readability. A huge supportive community.

And yet, there comes a point for every learner when the “easy language” feels anything but.

Whether it’s the weird behavior of default arguments, or the mysterious difference between is and ==, Python has its share of gotchas — little traps that confuse even those with some coding experience.

In this article, I’ll walk you through 5 deceptively simple Python basics that tend to confuse beginners the most. I’ll explain why they trip you up, how they actually work, and how to never fall for them again.

Let’s clear the confusion.

1. The is vs == Dilemma

At first glance, both seem to compare values. So why is Python being weird?

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

What’s going on here?

• == checks for equality — whether the values are the same.
• is checks for identity — whether both variables point to the same object in memory.

Even trickier, Python sometimes reuses immutable objects (like small integers or short strings), so a is b may sometimes return True even if you didn’t expect it.

Use == for comparing values. Use is only when you want to check object identity, like:

if variable is None: 
    ...

2. Default Mutable Arguments: The Hidden Bug

This one has bitten every Python developer at some point.

def add_item(item, my_list=[]): 
    my_list.append(item) 
    return my_list 
 
print(add_item("apple"))  # ['apple'] 
print(add_item("banana"))  # ['apple', 'banana'] ???

Why did the second call remember the previous list?

In Python, default arguments are evaluated once, at function definition time — not each time the function is called.

So my_list=[] is created once, and reused on each call.

Use None as the default and create the list inside the function.

def add_item(item, my_list=None): 
    if my_list is None: 
        my_list = [] 
    my_list.append(item) 
    return my_list

3. The Mystery of Variable Scope in Loops and Comprehensions

Python’s scoping rules are surprisingly different from other languages.

Let’s say:

x = 10 
def foo(): 
    print(x) 
 
foo()  # 10

Now try this:

x = 10 
def foo(): 
    x = x + 1 
    print(x) 
 
foo()  # UnboundLocalError!

Why did this error happen?

Because Python thinks x is a local variable — since you’re assigning to it inside the function — but it's referenced before it’s assigned.

This scoping confusion also shows up in loop variables and comprehensions:

funcs = [] 
for i in range(3): 
    funcs.append(lambda: i) 
 
print([f() for f in funcs])  # [2, 2, 2] — not [0, 1, 2]

The lambda captures the reference to i, not its value at the time. By the time you call the function, i is already 2.

Use default arguments to capture the current value.

for i in range(3): 
    funcs.append(lambda i=i: i)

4. List Copying: Why Your Clone Isn’t a Clone

You copy a list. You change one. Both change. What just happened?

a = [1, 2, 3] 
b = a 
b.append(4) 
 
print(a)  # [1, 2, 3, 4]

Oops. That’s not a copy. That’s a reference.

Python lists are mutable. When you do b = a, both a and b point to the same list.

How to make a true copy:

• Shallow copy (top-level only):

b = a.copy() 
# or 
b = a[:]

• Deep copy (nested structures):

import copy 
b = copy.deepcopy(a)

Modifying complex or nested data structures without proper copying is a fast track to bugs.

5. Truthy and Falsy Values That Aren’t What They Seem

Python has its own logic when it comes to truth.

if []: 
    print("This is true.") 
else: 
    print("This is false.")  # This runs

Common Falsy values in Python:

• None
• False
• 0, 0.0
• '', [], {}, set()

Everything else is Truthy — including non-empty strings like "False" or numbers like -1.

Gotcha Example:

def is_valid(value): 
    return bool(value) 
 
print(is_valid(0))       # False 
print(is_valid("False")) # True

Just because a string looks like “False” doesn’t mean it evaluates as False.

Always be clear about what type you’re dealing with. If needed, do explicit checks:

if value is None: 
    ... 
elif value == 0: 
    ...

Bonus Confusion: Chained Comparisons

Python allows this:

x = 5 
print(1 < x < 10)  # True

This is not the same as:

print((1 < x) < 10)  # Though it evaluates the same here, the logic is different

Python reads 1 < x < 10 as:
→ 1 < x and x < 10

It’s a feature, not a bug — but worth knowing!

Final Thoughts: Embrace the Confusion, Master the Language

Getting confused is a sign of learning, not failure. Python’s elegance hides a lot of complexity beneath the surface — and these “simple” basics are where most of the pitfalls lie.

If you’ve stumbled over any of these before, know that you’re not alone. Every experienced Python developer has faced them too.

Here’s how to turn confusion into clarity:

• Experiment with code — break it intentionally.
• Read the docs, but don’t stop there.
• Ask why, not just what.

Python rewards curiosity. The more you dig, the cleaner your code will be — and the fewer silent bugs you’ll ship.

Keep Learning, Keep Coding.

Which of these Python confusions got you the most when you were starting out? Share your experience in the comments or give this article a clap if it helped clear something up.