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For a Python list, use value in list_name. The expression returns True when the value is a member and False otherwise. Use not in to test for absence.
Check whether a value is in a Python list
The membership operators work directly in conditions and return a Boolean result:
values = [10, 42, 99]
if 42 in values:
print("found")
if 7 not in values:
print("7 is absent")Python’s language reference defines in and not in as membership tests. For built-in sequences such as lists and tuples, a value matches when an element is identical to it or equal to it. Python expression reference
What does in check for different containers?
The syntax is similar across built-in containers, but the membership question depends on the container type.
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| Container | Membership check | What it tests |
|---|---|---|
| List or tuple | value in items |
Whether an element is identical to or equal to value. |
| Set | value in items |
Whether the value is a set member. |
| Dictionary | value in mapping |
Whether the value is a key, not a dictionary value. |
To search dictionary values instead of keys, call values():
record = {"name": "Ada", "role": "engineer"}
"name" in record # True: checks keys
"Ada" in record.values() # True: checks valuesIf your program performs repeated membership checks, a set or dictionary may be a more suitable structure than a list when its semantics fit. This is a data-structure choice; the appropriate container depends on how the data is used.
How does membership work for custom containers?
A custom class can define its membership behavior with __contains__(). If it does not, Python tries iteration; if iteration is unavailable, it falls back to the legacy indexed-sequence protocol. The details are documented in Python’s data model reference.
Does in work with NumPy arrays?
NumPy supports scalar membership syntax: its ndarray.__contains__ method returns the Boolean result of key in self. For example, 42 in array_values asks whether the scalar value is present. NumPy ndarray reference
That is different from testing a condition against each element. A comparison such as array_values > 10 produces Boolean values; reduce them explicitly to ask whether any or all elements meet the condition:
(array_values > 10).any() # True if at least one element is greater than 10
(array_values > 10).all() # True if every element is greater than 10NumPy warns that using a multi-element array directly as a truth value is ambiguous and raises an error. Use .any() or .all() to specify which result you mean. NumPy ndarray reference
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