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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsPython’s behavior can seem surprising when a default list keeps its contents, loop-created lambdas return the same value, or a sorting call leaves a variable as None. These five examples explain the rule behind each result and show a small fix. They are common teaching examples, not a measured ranking of Python bugs.
1. Mutable default arguments can retain state
Python evaluates a function’s default argument expressions once, when it executes the def statement—not each time the function is called. If a default list or dictionary is then changed, later calls that omit that argument use the same object and see its updated contents. The Python 3.14 language reference documents this definition-time behavior.
For example, a function with def add_item(item, items=[]): will keep appending to that one default list. If each call should start with a fresh list, use None as a sentinel and create the list inside the function:
def add_item(item, items=None):
if items is None:
items = []
items.append(item)
return itemsA mutable default is not inherently wrong: deliberately shared state can be useful, but make that choice explicit. Use the sentinel pattern when independent calls are intended.
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2. Lambdas in a loop can all use the final value
A function created inside a loop can close over the loop variable rather than a separate snapshot of its value. The variable is looked up when the function runs, so several lambdas may all observe the loop’s final value. The functions are distinct; they refer to the same changing binding. The Python Programming FAQ explains this late lookup.
Bind the current value as a default argument if each function should retain its own value:
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functions = [lambda n=n: n * n for n in range(5)]Now each lambda’s n default is set when that lambda is created, so calling the functions returns the squares of 0 through 4. A helper function that takes the current loop value and returns a new function is another way to create a separate local binding.
3. is checks identity; == checks value
a is b asks whether a and b refer to the very same object. a == b asks whether their values compare equal. Two separately created strings or integers can compare equal without being the same object, so do not use is for ordinary value comparisons. Python’s Programming FAQ cautions that apparent identity for some such values is not a guarantee to rely on.
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Use == for value comparisons and is for identity checks, especially for singletons such as None:
if result is None:
...
if status == "ready":
...4. list.sort() changes a list and returns None
Some methods mutate an existing object rather than producing a replacement value. list.sort() sorts its list in place and returns None. As a result, items = items.sort() overwrites the list reference with None. The Python Sorting HOWTO documents the in-place behavior; the Programming FAQ explains why mutating methods commonly return None.
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- To sort the existing list, call
items.sort()on its own. - To create a separate sorted list, use
sorted_items = sorted(items).
Choose based on whether the original list should change, rather than assigning the result of an in-place method.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Floating-point values are not exact decimal fractions
Binary floating-point cannot represent many decimal fractions exactly. In Python, 0.1 + 0.1 + 0.1 == 0.3 is false, even though the values are displayed using familiar decimal notation. This is a representation issue, not evidence that the addition operation is broken. The Python tutorial’s floating-point explanation shows why.
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For approximate comparisons, use math.isclose() and choose tolerances appropriate to the application:
import math
math.isclose(0.1 + 0.1 + 0.1, 0.3)For work that requires exact decimal representation, such as decimal-sensitive accounting calculations, consider decimal. Rounding a value for display changes how it is shown; it does not make the stored float exact or establish a suitable comparison tolerance.
Bonus: avoid changing a list while iterating over it
Removing or inserting items in the list being traversed can make later iterations skip or encounter unexpected elements. When the goal is to keep only items that pass a condition, build a new list instead. The Python tutorial recommends constructing a filtered list as the simpler, safer approach:
kept = [item for item in items if should_keep(item)]This leaves items unchanged and places the selected elements in kept.
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