Dictionaries & sets
Key–value maps, uniqueness, counting, grouping, and fast lookups.
Build a defaultdict histogram of categories in Python
Count occurrences of each category in a list using collections.defaultdict(int) for automatic initialization.
from collections import defaultdict
def build_category_histogram(items):
"""Count occurrences of each category in a list of items."""
histogram = defaultdict(int)
for item in items:
histogram[item] += 1
return dict(histogram)
if __name__ == "__main__":
categories = ["fruit", "vegetable", …
Build an OrderedDict insertion order demo in Python 3
Demonstrate how OrderedDict preserves insertion order, how updates keep position, and how re-insertion moves keys to the end.
from collections import OrderedDict
def demo_ordered_dict():
# Create an OrderedDict and insert items in a specific order
ordered = OrderedDict()
ordered['banana'] = 3
ordered['apple'] = 2
ordered['cherry'] = 5
ordered['date'] = 1
print("Insertion order preserved:")
for key, value in …
Convert namedtuple to dict with asdict in Python
Convert a namedtuple instance into an ordinary dictionary using the asdict function from the collections module's namedtuple utility.
from collections import namedtuple, asdict
def main():
# Define a namedtuple for a person
Person = namedtuple("Person", ["name", "age", "city"])
person = Person(name="Alice", age=30, city="New York")
# Convert namedtuple to dict
person_dict = asdict(person)
print("Original namedtuple…
Count Word Frequency in Python with dict
Count how often each word appears in a text using Python's collections.Counter and regular expressions.
from collections import Counter
import re
def count_word_frequency(text):
"""Count frequency of each word in text (case-insensitive)."""
words = re.findall(r"\b\w+\b", text.lower())
return dict(Counter(words))
if __name__ == "__main__":
sample_text = "The quick brown fox jumps over the lazy dog. The …
How to Convert a Counter to a Plain Dict with Sorted Items in Python
This code converts a collections.Counter into a regular dictionary with items sorted by key, useful for stable, readable output.
from collections import Counter
def counter_to_sorted_dict(counter):
"""Convert a Counter to a plain dict with sorted items."""
return dict(sorted(counter.items()))
if __name__ == "__main__":
# Example usage
data = Counter(['apple', 'banana', 'apple', 'cherry', 'banana', 'date', 'apple'])
print("…
How to Count Tags with Sets and Dictionaries in Python
Count tag frequencies and collect unique tags from a list of dictionaries using Counter and sets in Python.
from collections import Counter
import json
def count_tags(entries):
"""Count tag frequencies across a list of entry dicts, using sets/dicts."""
tag_counter = Counter()
all_tags = set()
for entry in entries:
tags = set(entry["tags"])
all_tags.update(tags)
tag_counter.update(ta…
How to Count Word Frequencies in Python with Counter and Sets
This code processes a text string by lowercasing, splitting into words, counting frequencies with Counter, and extracting unique and sorted word lists using sets.
from collections import Counter
def process_text(text):
words = text.lower().split()
word_counts = Counter(words)
unique_words = set(words)
sorted_words = sorted(unique_words)
return {
"total_words": len(words),
"unique_words": len(unique_words),
"word_frequencies": di…
How to Count Words and Find Common Words in Python with Dictionaries and Sets
Build a simple text processor that counts unique words with dictionaries and finds common words across text halves using sets.
def process_text(text):
"""Process text: count unique words with counts, find common words."""
words = text.lower().replace(",", "").replace(".", "").split()
word_counts = {}
for word in words:
word_counts[word] = word_counts.get(word, 0) + 1
total_words = len(words)
unique_wo…
How to Subtract Counters in Python for Bag Differences
Use the Counter class's subtraction operator to compute bag differences, removing items and counts that appear in one multiset but not the other.
from collections import Counter
def subtract_counters(bag1, bag2):
"""Return the difference of two Counters (bag1 - bag2)."""
return bag1 - bag2
if __name__ == "__main__":
inventory = Counter(apples=10, bananas=5, oranges=3)
sold = Counter(apples=4, bananas=2, grapes=2)
remaining = subtract_count…
How to Use ChainMap for Layered Config Lookup in Python
This code demonstrates using collections.ChainMap to combine multiple dictionaries into a single layered lookup, where earlier maps override later ones.
from collections import ChainMap
defaults = {"theme": "light", "lang": "en", "debug": False}
user = {"lang": "de", "auto_save": True}
runtime = {"debug": True}
config = ChainMap(runtime, user, defaults)
if __name__ == "__main__":
print("theme:", config["theme"])
print("lang:", config["lang"])
print("deb…
How to Use Counter for Most Common Elements in Python
This code demonstrates how to find the most frequent elements in a list using Python's Counter class from the collections module.
from collections import Counter
def most_common_elements(items, n=1):
"""Return the n most common elements and their counts."""
counter = Counter(items)
return counter.most_common(n)
if __name__ == "__main__":
data = ["apple", "banana", "apple", "orange", "banana", "apple", "grape"]
print(most_co…
How to Use defaultdict(list) to Group Words by First Letter in Python
This code groups a list of words by their first letter using a defaultdict with a list factory, then prints each group sorted by initial.
from collections import defaultdict
def group_by_initial(words):
groups = defaultdict(list)
for word in words:
groups[word[0].upper()].append(word)
return dict(groups)
if __name__ == "__main__":
words = ["apple", "banana", "apricot", "blueberry", "cherry"]
result = group_by_initial(words)…
Multiset with Counter update and elements in Python
Demonstrates using collections.Counter as a multiset: updating counts with update() and iterating elements() to get repeated items.
from collections import Counter
multiset = Counter(['apple', 'banana', 'apple'])
multiset.update(['banana', 'cherry', 'apple'])
print("Elements after update:", sorted(multiset.elements()))
print("Counts:", dict(multiset))
print("Most common:", multiset.most_common(2))
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