You’re staring at the terminal. The code worked fine ten minutes ago, but now Python is throwing a fit. TypeError: 'NoneType' object is not iterable. It’s one of those errors that feels personal. It doesn't just say your code is wrong; it says your code is trying to do something physically impossible, like trying to peel an orange that doesn't exist.
Basically, you told Python to loop over something—a list, a dictionary, a string—but that "something" turned out to be None. Python can’t iterate over nothingness. It panics.
It happens to everyone. From junior devs to senior engineers at places like Google or Meta, everyone hits this wall. It usually boils down to a function that forgot to return a value or an API call that went sideways.
The mechanics of the NoneType error
To understand why this happens, we have to look at what Python expects. When you write for item in my_list:, Python calls the __iter__ method on my_list. If my_list is actually a list, everything is cool. But if my_list is None, there is no __iter__ method. Python sees you trying to treat a void like a collection and shuts the whole operation down.
It’s often a silent failure that happens way before the error actually pops up.
Think about the .sort() method on a list. This is a classic trap. You might write my_list = [3, 1, 2].sort(). You’d expect my_list to be [1, 2, 3], right? Wrong. In Python, the .sort() method modifies the list in place and returns None. So, when you later try to do for x in my_list:, you get the dreaded TypeError: 'NoneType' object is not iterable. You’ve turned your data into a ghost.
The silent return value
Functions are the primary suspects. If you don't explicitly use a return statement in a Python function, it returns None by default.
def get_users(active_only):
users = ["Alice", "Bob"]
if active_only:
return users
# Missing else/default return!
for user in get_users(False):
print(user)
In the example above, if active_only is False, the function finishes without hitting a return. Python fills that gap with None. When the for loop tries to run, it crashes. This is why "Typechecking" has become such a massive deal in the Python community over the last few years. Tools like mypy or Pyright (which Microsoft uses for VS Code) are designed specifically to catch these "maybe None" scenarios before you even run the code.
Why your APIs are lying to you
External data is a mess. Honestly, most NoneType errors in production environments come from assuming an API or a database query always returns what you asked for.
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You might be using the requests library to grab a JSON list of products. If the server returns a 404 or a 500, and your logic doesn't catch it, you might end up passing a null response into your processing loop.
I’ve seen this happen in data science pipelines constantly. You’re using Pandas and you run a function that filters a DataFrame. If the filter is too aggressive and returns an empty object or a None result from a custom apply function, the next step in your pipeline will crumble.
The dangerous "Or" trick
Sometimes we try to be clever. We use the or operator to provide a default.items = get_items() or []
This is generally safe. It says "if the first thing is falsy (like None), use this empty list instead." But if get_items() returns an empty list [], and your logic depends on it being specifically None to trigger a different branch, you’ve just created a logic bug that is even harder to find than a TypeError.
Practical debugging steps
When you see the error, don't just start changing code. Use a debugger or, if you're old school, use print(f"DEBUG: {my_variable=}").
- Locate the line number. Python's traceback is actually very good. It tells you exactly where the iteration was attempted.
- Identify the variable. Which object is being iterated? Is it in a
forloop? A list comprehension? A star-expansion like*my_var? - Trace it backward. Go up the script. Where was this variable last assigned?
- Check the source. If it came from a function, look at every possible exit point of that function. Does it always return a list?
Guido van Rossum, the creator of Python, once noted that adding static typing (via type hints) was one of the most important evolutions for the language. This is precisely because None is so slippery.
Defensive coding patterns
You can stop this from happening by being a bit more pessimistic about your data. Don't trust that a function will do what it says.
Explicit checks
The most basic fix is the "if" check.
data = fetch_data()
if data is not None:
for item in data:
process(item)
else:
logger.error("No data received")
It’s a bit verbose, but it’s robust.
The Null Object Pattern
Instead of returning None, design your functions to return an empty version of the expected type. If a function is supposed to return a list of tags, and there are no tags, return []. An empty list is iterable. A loop over an empty list simply does nothing and moves on. No crash. No middle-of-the-night pager alerts.
Use Type Hints
If you aren't using type hints in 2026, you're making life harder for yourself. Use Optional[List[str]] or the newer list[str] | None syntax. This tells your editor to scream at you if you forget to handle the None case. It turns a runtime disaster into a squiggly red line that you fix in five seconds.
Dealing with Third-Party Libraries
Sometimes the error isn't in your code. It's in a library you're using.
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Take BeautifulSoup for web scraping. If you run soup.find('div').find_all('p'), and the div doesn't exist, the first find() returns None. Then you try to call find_all() on None. Boom.
In these cases, optional chaining (which Python doesn't have in the same way JavaScript does) has to be simulated. You have to break the chain.
target_div = soup.find('div', class_='content')
if target_div:
paragraphs = target_div.find_all('p')
It feels slower to write, but it's the difference between a script that finishes and a script that dies halfway through.
What about the "yield" keyword?
Generators are another common culprit. If you have a generator function (one that uses yield), it will always return a generator object, even if it yields nothing. However, if you accidentally return None inside a generator before any yield is hit, and you're treating the result of a call to that function as the iterable, things get weird.
Usually, a generator that doesn't yield anything just acts like an empty list. But if you have a wrapper function that returns a generator or None depending on a condition, you're back in NoneType territory.
Actionable Next Steps
To truly banish TypeError: 'NoneType' object is not iterable from your workflow, you need a system.
- Audit your returns: Go through your utility functions. Ensure every branch returns a collection (list, tuple, dict) rather than letting it fall through to a default
None. - Install a Linter: Use Ruff or Flake8. These tools are incredibly fast and catch uninitialized variables or missing return values almost instantly.
- Default Arguments: If you have a function that takes a list, use
def my_func(items=None):. Inside, checkif items is None: items = []. This prevents the "mutable default argument" bug while still giving you a safe iterable to work with. - Wrap your API calls: Create a "wrapper" or "adapter" for external data. This wrapper should be responsible for catching errors and returning an empty list instead of
Nonewhen things fail. - Use the 'get' method: When working with dictionaries,
my_dict.get('key', [])is your best friend. It guarantees that even if the key is missing, you get an empty list back instead of aNonethat crashes your next loop.
The goal isn't just to fix the error today, but to write code that is "None-safe" by design. Python gives you a lot of flexibility, but with that comes the responsibility of managing the gaps where data should be. Stop treating None as a valid state for a collection, and your code will suddenly become much more reliable.