Four data structures in Python
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List:
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Mutable: You can change, add, or remove items after the list creation.
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Ordered: The order of items is maintained, and items can be accessed by their index.
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Syntax: Created using square brackets [] or the list() function.
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Duplicates: Allows duplicate elements.
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Example: [1, 2, 3, 'apple', 'banana']
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Tuple:
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Immutable: Once created, you cannot change, add, or remove items.
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Ordered: Like lists, the order is maintained, and index access is possible.
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Syntax: Created using parentheses () or the tuple() function.
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Duplicates: Allows duplicate elements.
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Example: (1, 2, 3, 'apple', 'banana')
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Set:
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Mutable: You can add or remove items, but you cannot change individual items.
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Unordered: No index access because sets do not record element position.
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Syntax: Created using curly braces {} or the set() function.
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Duplicates: Does not allow duplicate elements; only unique items.
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Example: {1, 2, 3, 'apple', 'banana'}
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Dictionary:
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Mutable: You can change, add, or remove items, which are key-value pairs.
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Ordered: As of Python 3.7, dictionaries maintain insertion order.
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Syntax: Created using curly braces {} with key-value pairs or the dict() function.
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Duplicates: Keys must be unique, but values can be duplicated.
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Example: {1: 'apple', 2: 'banana', 3: 'cherry'}
Quick summary in code:
# List: Mutable, ordered, allows duplicates
my_list = [1, 2, 2, 'apple']
# Tuple: Immutable, ordered, allows duplicates
my_tuple = (1, 2, 2, 'apple')
# Set: Mutable, unordered, no duplicates
my_set = {1, 2, 'apple'}
# Dictionary: Mutable, ordered (since Python 3.7), unique keys
my_dict = {1: 'apple', 2: 'banana', 3: 'apple'}
Each structure has its own use-cases depending on the requirements of mutability, order, and uniqueness in your Python program.
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