Dictionaries & sets
Key–value maps, uniqueness, counting, grouping, and fast lookups.
How to Use Dictionaries and Sets in Python for Beginners
Introduces Python dictionaries and sets with practical examples including creating, modifying, and performing set operations, plus a word-frequency counter.
def demonstrate_dict_sets():
# Create a dictionary with basic info
person = {
"name": "Alice",
"age": 30,
"city": "New York"
}
print("Dictionary:", person)
# Access and modify dictionary values
person["age"] = 31
person["email"] = "alice@example.com"
print("Afte…
How to Use a Frozenset as a Dict Key in Python
Demonstrates using an immutable frozenset as a hashable dictionary key, including equality and lookup with differently-ordered elements.
frozen = frozenset({"a", "b", "c"})
mapping = {frozen: "set as hashable key"}
other_frozen = frozenset(["c", "b", "a"])
print(f"Are keys equal? {frozen == other_frozen}")
print(f"Lookup with different order: {mapping[other_frozen]}")
print(f"Hash matches: {hash(frozen) == hash(other_frozen)}")
How to Use defaultdict(set) in Python to Group Unique Values
Group key-value pairs into a dictionary of sets, automatically creating a new set for each key using defaultdict.
from collections import defaultdict
def track_groups(pairs):
groups = defaultdict(set)
for key, value in pairs:
groups[key].add(value)
return groups
if __name__ == "__main__":
data = [
("fruit", "apple"),
("fruit", "banana"),
("fruit", "apple"),
("veg", "carrot…
How to Validate Text and Count Words in Python
Count word frequencies, find unique and repeated words in a text using Python dictionaries and sets for beginner text validation.
def validate_text(text):
words = text.lower().split()
word_counts = {}
for word in words:
cleaned = word.strip('.,!?;:"\'')
if cleaned:
word_counts[cleaned] = word_counts.get(cleaned, 0) + 1
unique_words = set(word_counts.keys())
repeated_words = {word for word…
How to count words and find unique words in Python
Build a beginner-friendly text processor that counts word frequencies, finds unique words, and identifies words with vowels using dictionaries and sets.
def text_processor(text):
words = text.lower().replace(",", "").replace(".", "").split()
word_count = {}
for word in words:
word_count[word] = word_count.get(word, 0) + 1
unique_words = set(words)
vowels = set("aeiou")
words_with_vowels = {word for word in unique_words if vowe…
How to merge dictionaries and sets in Python
Merges multiple dictionaries with the ** unpacking operator and combines sets using union operations into a single structure.
def merge_dictionaries_and_sets(school_dict, teacher_dict, course_dict, student_sets):
"""
Merges multiple dictionaries and sets into a single combined structure.
Demonstrates dict unpacking and set union operations.
"""
# Merge all dictionaries using the unpacking operator (Python 3.9+)
merged…
Text Processor with Dictionaries and Sets in Python
Build a simple text processor that counts word frequencies with a dictionary and tracks unique words with a set.
def analyze_text(text):
words = text.lower().split()
word_freq = {}
unique_words = set()
for word in words:
clean_word = word.strip('.,!?;:')
if clean_word:
word_freq[clean_word] = word_freq.get(clean_word, 0) + 1
unique_words.add(clean_word)
return…
Validate dictionary data with sets in Python
Validate a dictionary against required keys and allowed value sets, returning a list of validation errors.
def validate_data(data, required_keys, allowed_values=None):
"""
Validate a dictionary against required keys and optional allowed value sets.
Returns a list of validation errors (empty list if valid).
"""
errors = []
# Check for missing required keys
missing = set(required_keys) - set(…
Browse by section
Each section groups closely related Python snippets.
Dictionaries & sets — Python code examples
What you will find here
This page collects dictionaries & sets snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.