Python dictionaries — keys, values, and methods

Dictionaries

A dictionary stores key-value pairs. Keys must be hashable (strings, ints, tuples). Access is O(1) on average — faster than scanning a list.

user = {
    "name": "Alice",
    "age": 30,
    "active": True
}

print(user["name"])            # Alice
print(user.get("email", ""))   # "" — safe default

Modifying a Dict

user["age"] = 31               # update existing
user["email"] = "a@b.com"      # add new key
del user["active"]             # remove key
user.update({"city": "NY", "age": 32})  # merge

Iterating

for key in user:                  # keys only
    print(key, user[key])

for k, v in user.items():        # key-value pairs
    print(f"{k}: {v}")

keys   = list(user.keys())
values = list(user.values())

Dict Comprehension

squares = {n: n**2 for n in range(1, 6)}
# {1:1, 2:4, 3:9, 4:16, 5:25}

Use .get(key, default) instead of direct access when a key might be absent — it avoids a KeyError.

Dictionaries are the workhorses of Python programs — configuration, caches, frequency maps, lookup tables, and JSON data all end up as dicts. Since Python 3.7, dictionaries preserve insertion order, so iteration is predictable. The collections.defaultdict removes the need to check for key existence before appending — defaultdict(list) auto-creates an empty list for missing keys. When building lookup tables from large datasets, a dict is almost always the right structure: O(1) average-case access versus O(n) for scanning a list.