A Python dictionary is a data structure that stores values in key-value pairs. It is one of the most versatile and widely used data structures in Python. Dictionaries are unordered (as of Python 3.6+, they maintain insertion order), mutable, and indexed by keys, which must be immutable types (like strings, numbers, or tuples). Values in a dictionary can be of any data type and can be duplicated, whereas keys can’t be repeated and must be immutable.
Creating a Python Dictionary
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person = { "name": "Alice", "age": 30, "city": "New York" } print(person) |
This defines a dictionary with three key-value pairs. Keys are strings, and values are strings and integers.
Accessing Dictionary Items
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print(person["name"]) # Output: Alice print(person.get("age")) # Output: 30 |
Use square brackets to access values by key. get() is safer and won’t throw an error if the key doesn’t exist.
Adding and Updating Items
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person["email"] = "alice@example.com" person["age"] = 31 print(person) |
Assigning to a key adds it if it’s new, or updates the value if the key already exists.
Removing Items from a Python Dictionary
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person.pop("city") print(person) |
pop() removes a key and returns its value. You can also use it del person["name"] to delete a key-value pair.
Python Dictionary Methods
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print(person.keys()) # dict_keys(['name', 'age', 'email']) print(person.values()) # dict_values(['Alice', 31, 'alice@example.com']) print(person.items()) # dict_items([('name', 'Alice'), ('age', 31), ('email', 'alice@example.com')]) |
Use keys(), values(), and items() to retrieve dictionary contents.
Iterating Through a Python Dictionary
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for key, value in person.items(): print(f"{key}: {value}") |
Loop through key-value pairs using items(). This is commonly used for logging or printing structured data.
Checking if a Key Exists
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if "email" in person: print("Email is present") |
Use the in keyword to check for the presence of a key in the dictionary.
Dictionary Comprehension
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squares = {x: x*x for x in range(5)} print(squares) # Output: {0: 0, 1: 1, 2: 4, 3: 9, 4: 16} |
Dictionary comprehensions offer a concise way to create dictionaries using loops.
Nested Python Dictionaries
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students = { "001": {"name": "Alice", "score": 85}, "002": {"name": "Bob", "score": 90} } print(students["001"]["name"]) # Output: Alice |
You can store dictionaries inside dictionaries to represent structured data, like records or configurations.
Copying Dictionaries
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copy_dict = person.copy() print(copy_dict) |
copy() creates a shallow copy of the dictionary.
Clearing All Items
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person.clear() print(person) # Output: {} |
Use clear() to empty a dictionary without deleting the variable itself.
Merging Python Dictionaries
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dict1 = {"a": 1, "b": 2} dict2 = {"b": 3, "c": 4} merged = {**dict1, **dict2} print(merged) # Output: {'a': 1, 'b': 3, 'c': 4} |
This merges two dictionaries. If there are duplicate keys, the right-hand dictionary’s values overwrite the left’s.
Conclusion
Python dictionaries are an essential data structure for representing and managing key-value mappings. They are fast, dynamic, and highly versatile, making them a perfect choice for lookups, configuration settings, counters, JSON-like data, and more.
By mastering dictionary operations—creation, access, update, iteration, and comprehension—you can greatly enhance the efficiency and clarity of your Python programs.
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