How to Remove None Values from a List in Python

To remove None values from a list in Python, use a list comprehension with is not None. It returns a new list without None and keeps zeros and empty strings:

clean = [x for x in items if x is not None]

Every output below came from a real run on Python 3.12.5 and pandas 3.0.6 in the Windows Command Prompt.

Remove None from a list with a list comprehension

Here’s a week of store sales where two days never reported:

# Daily sales, Mon Oct 5 to Sun Oct 11, 2026 (None = register didn't report)
daily_sales = [1250.00, None, 980.50, 0.0, None, 1430.25, 615.75]

clean = [amount for amount in daily_sales if amount is not None]
print(clean)
print(f"Week total: ${sum(clean):,.2f}")
print(daily_sales)

Output:

[1250.0, 980.5, 0.0, 1430.25, 615.75]
Week total: $4,276.50
[1250.0, None, 980.5, 0.0, None, 1430.25, 615.75]

I like this form because 0.0 survives; a real $0.00 day isn’t missing data. If you want an else branch in the expression, see list comprehensions with if-else in Python.

Why shouldn’t you filter None with a plain if x or filter(None)?

A bare if v tests truthiness, not None, so it also drops 0, "", False and empty lists. filter(None, values) does the same. A lambda with is not None is the safe filter() form:

values = [0, None, 12.5, "", False, "Austin", [], None]

print("is not None: ", [v for v in values if v is not None])
print("filter lambda:", list(filter(lambda v: v is not None, values)))
print("truthy only: ", [v for v in values if v])
print("filter(None):", list(filter(None, values)))

Output:

is not None:  [0, 12.5, '', False, 'Austin', []]
filter lambda: [0, 12.5, '', False, 'Austin', []]
truthy only:  [12.5, 'Austin']
filter(None): [12.5, 'Austin']

Use the shorter form only when every falsy value should go. The two None tests are compared in is None vs == None in Python.

How do you remove None from the same list in place?

A comprehension makes a new list. If other code holds the old one, assign through a slice with items[:] = ... so every name sees the change:

orders = ["A-101", None, "A-102", None, "A-103"]
report = orders                       # a second name for the same list
orders[:] = [o for o in orders if o is not None]
print(orders, report, orders is report)

queue = ["TX", None, "CA", None]
while None in queue:
    queue.remove(None)
print(queue)

Output:

['A-101', 'A-102', 'A-103'] ['A-101', 'A-102', 'A-103'] True
['TX', 'CA']

The while loop works too, but it rescans the list each pass, so it’s slow on big lists. The same ideas apply to removing all instances of an element from a list.

Remove None from nested lists and dictionaries

For nested lists or JSON-style data, a small recursive function cleans every level:

def drop_none(data):
    if isinstance(data, list):
        return [drop_none(x) for x in data if x is not None]
    if isinstance(data, dict):
        return {k: drop_none(v) for k, v in data.items() if v is not None}
    return data

print(drop_none([[72, None, 75], [None, None], [68, None]]))
customer = {"name": "Emily Carter", "phone": None,
            "orders": [1001, None, 1002], "zip": "02134"}
print(drop_none(customer))

Output:

[[72, 75], [], [68]]
{'name': 'Emily Carter', 'orders': [1001, 1002], 'zip': '02134'}

The all-None row becomes [] and the phone key disappears. If you’d rather end up with one flat list, flatten the list of lists after cleaning it.

Replace None instead of removing it, or use pandas

Dropping values shifts positions, which matters when the index means a date. And sum() fails on None:

import pandas as pd

temps = [71.5, None, 73.0, None, 69.5]
try:
    print(sum(temps))
except TypeError as err:
    print("TypeError:", err)

print([t if t is not None else 0.0 for t in temps])
print(round(pd.Series(temps).mean(), 2))   # pandas skips None

Output:

TypeError: unsupported operand type(s) for +: 'float' and 'NoneType'
[71.5, 0.0, 73.0, 0.0, 69.5]
71.33

pandas treats None as missing. For NaN itself, see removing NaN values from a list in Python, and for tables, converting a list to a pandas DataFrame.

Want more list practice? Try these next:

• Initialize a list of size N in Python
• Remove duplicates from a list in Python
• Find the closest value in a list using Python
• Sort a list in Python without sort()

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