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Check for None with identity
None is Python’s singleton null object. The is operator tests whether two references point to the same object, so value is None directly answers whether value refers to that object.
if value is None:
print("no value was provided")
if value is not None:
use(value)PEP 8 says: “Comparisons to singletons like None should always be done with is or is not, never the equality operators.” It also recommends is not None rather than the less readable not ... is None. See PEP 8.
Why not use == None?
== asks whether two values are equal. A class can define its own equality behavior with __eq__, so value == None may invoke code that does not mean “is this the None singleton?” Identity operators cannot be customized. Python’s data model documentation describes these comparison behaviors.
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For a None check, is None makes the intended question explicit and avoids depending on an object’s equality implementation.
None is not the same as a false value
If you need to know whether an optional value was supplied, test it against None. A truthiness check asks a different question and skips valid values that evaluate to false, such as 0, False, an empty string, an empty list, or an empty dictionary.
# Accepts 0, False, and empty containers when they are valid values
if value is not None:
use(value)
# Runs only when value is truthy
if value:
use(value)Use if value: only when you want truthiness, not when you want to distinguish None from other values. PEP 8 explicitly cautions against confusing these intentions.
For pandas data, use its missing-value checks
is None checks for the Python None object; it does not test every missing-data marker used in libraries. pandas also supports sentinels such as NaN, NaT, and pd.NA, whose equality behavior is not a reliable general missingness test. For pandas data, use isna() or notna(); pandas documents that these methods also treat None as missing. See the pandas missing-data guide.
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