Dictionaries & Sets

person = {"name": "Rahul", "age": 28, "city": "Noida"}
person["name"]         # "Rahul"
person.get("salary")   # None (no KeyError)
person.get("salary", 0)  # 0 (default value)

Always use .get() when the key might not exist — it returns None instead of raising a KeyError. Essential when parsing API responses or JSON files where keys are optional.

d = {"a": 1, "b": 2}
d["c"] = 3          # add new key
d["a"] = 10         # update existing key
del d["b"]          # delete key
d.pop("c")          # delete and return value

pop() is safer than del — it can accept a default value if the key does not exist: d.pop("x", None). Use this pattern when cleaning optional fields.

d = {"a": 1, "b": 2, "c": 3}
for key in d:               # iterates over keys
    print(key)

for k, v in d.items():      # iterates over key-value pairs
    print(k, v)

list(d.keys())    # ["a", "b", "c"]
list(d.values())  # [1, 2, 3]

.items() is the most common pattern — you need both key and value. In Pandas, dict iteration is used for column renaming: df.rename(columns={"old": "new"}).

cities = {"Delhi", "Mumbai", "Noida", "Delhi"}  # duplicates removed
print(cities)  # {"Delhi", "Mumbai", "Noida"}

# Set operations:
a = {1, 2, 3}
b = {2, 3, 4}
a & b   # intersection: {2, 3}
a | b   # union: {1, 2, 3, 4}
a - b   # difference: {1}

Sets automatically remove duplicates and support fast membership testing (O(1)). Use when you need unique values or to find common/different elements between two groups.

d1 = {"a": 1, "b": 2}
d2 = {"c": 3, "d": 4}

# Python 3.9+
merged = d1 | d2

# All Python 3:
merged = {**d1, **d2}

# update() — modifies d1 in place:
d1.update(d2)

The ** unpacking syntax is common in interviews. In data work, you merge config dicts, combine column rename maps, or merge API parameter dicts before making requests.