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Find First Duplicate Index in Python
Return the index of the first element that appears more than once in a list, using a dictionary for O(n) time.
def find_first_duplicate(arr):
seen = {}
for index, value in enumerate(arr):
if value in seen:
return index
seen[value] = index
return -1
if __name__ == "__main__":
test_array = [3, 5, 2, 8, 5, 1, 2]
result = find_first_duplicate(test_array)
print(f"Array: {test_arr…Find Maximum Distance Between Identical Elements in Python
Compute the maximum index distance between any two identical elements in a list using a dictionary to track first occurrences.
from collections import defaultdict
def max_distance_between_identical(nums):
first_occurrence = {}
max_dist = 0
for i, num in enumerate(nums):
if num in first_occurrence:
dist = i - first_occurrence[num]
max_dist = max(max_dist, dist)
else:
first_occur…How to Flatten List of Dict Values in Python
This code flattens the values of a list of dictionaries into a single list, handling both list values and scalar values.
def flatten_dict_values(dicts):
flattened = []
for d in dicts:
for value in d.values():
if isinstance(value, list):
flattened.extend(value)
else:
flattened.append(value)
return flattened
if __name__ == "__main__":
data = [
{"a": …Sort list by multiple keys with tuple ordering in Python
Sort a list of dictionaries by multiple criteria — surname, age, then score descending — using a tuple key and negation.
def sort_multi_key(data):
# Sorts by surname, then age, then score descending
return sorted(
data,
key=lambda person: (
person['surname'].lower(),
person['age'],
-person['score'] # negative to reverse sort by score
)
)
if __name__ == "__main__"…Batch Rows in Chunks with a Generator in Python
Group a list of row dicts into fixed-size chunks using a generator that yields one slice per call.
from typing import Iterator, List
def batch_rows(rows: List[dict], batch_size: int) -> Iterator[List[dict]]:
for i in range(0, len(rows), batch_size):
yield rows[i:i + batch_size]
if __name__ == "__main__":
sample_rows = [
{"id": 1, "name": "Alice"},
{"id": 2, "name": "Bob"},
…Convert Data in Python with Comprehensions and Generators
Convert mixed data to integers, filter and transform numbers, and extract fields from dicts using list comprehensions and generator expressions.
def convert_numbers(data):
"""Convert a list of mixed values into integers using a comprehension."""
return [int(item) for item in data if item is not None]
def double_even_numbers(numbers):
"""Double only even numbers using a generator expression."""
return (n * 2 for n in numbers if n % 2 == 0)
d…Count Data in Python with Comprehensions and Generators
Count list items with a dict comprehension and generate squares lazily with a generator expression, printing both results.
from collections import Counter
data = ["apple", "banana", "apple", "cherry", "banana", "apple"]
counts = {item: data.count(item) for item in set(data)}
square_gen = (x * x for x in range(5))
squares = list(square_gen)
if __name__ == "__main__":
print("Manual count:", counts)
print("Counter:", dict(Counter…Dict Comprehension to Map Keys to Lengths in Python
Build a dictionary that maps each word to its character count using a dictionary comprehension.
words = ["apple", "banana", "cherry", "date", "elderberry"]
word_lengths = {word: len(word) for word in words}
print(word_lengths)Generate Data with Python Comprehensions and Generators
Shows list, dict compregensions and generator expressions plus a Fibonacci generator to produce data lazily.
# Data generation helpers using comprehensions and generators
from itertools import islice
def fibonacci(limit):
"""Generate Fibonacci numbers up to a limit."""
a, b = 0, 1
while a <= limit:
yield a
a, b = b, a + b
def main():
# List comprehension: squares of even numbers
square…How to Group Data in Python with defaultdict and Comprehensions
Group a list of items by a computed key using a defaultdict-based generator helper and an alternative dictionary comprehension approach.
from collections import defaultdict
def group_by(data, key_func):
"""Group items in data by the value returned by key_func."""
result = defaultdict(list)
for item in data:
result[key_func(item)].append(item)
return dict(result)
def group_by_comprehension(data, key_func):
"""Same grouping …How to Parse CSV Rows as Generator Dicts in Python
Reads a CSV file and yields each row as a dictionary one at a time using a generator, so the file is processed lazily.
import csv
from pathlib import Path
def csv_to_dicts(filepath):
with open(filepath, mode="r", newline="", encoding="utf-8") as file:
reader = csv.DictReader(file)
for row in reader:
yield row
if __name__ == "__main__":
sample_csv = Path("sample_data.csv")
sample_csv.write_text…How to Parse Data with Generators and Comprehensions in Python
This code demonstrates using a generator expression to filter active users and a dictionary comprehension to aggregate scores by name.
def parse_data_helper(raw_records):
"""Extract active users' names and scores from raw records."""
parsed = (
(record["name"], record["score"])
for record in raw_records
if record["active"] and record["score"] >= 0
)
return list(parsed)
def aggregate_scores(parsed_data):
"…How to Use Comprehensions and Generators in Python
Demonstrate list, set, and dictionary comprehensions plus generator expressions and generator functions in one beginner-friendly script.
def demonstrate_comprehensions_generators():
# List comprehension: transform and filter in one line
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
squares = [num ** 2 for num in numbers if num % 2 == 0]
print(f"Square of even numbers (list comprehension): {squares}")
# Set comprehension: unique values
…How to Use Comprehensions and Generators to Check Data in Python
A beginner-friendly helper that filters numeric values, computes squares and cubes with comprehensions and a generator, and returns a summary dictionary.
def check_data(iterable):
"""Return a summary of numeric data using comprehensions and a generator."""
values = [item for item in iterable if isinstance(item, (int, float))]
squares = [x ** 2 for x in values if x > 0]
cubes = (x ** 3 for x in values if x > 0)
cube_list = list(cubes)
return {
…How to Use List Comprehensions and Generators to Format Data in Python
A beginner-friendly helper that formats dictionaries into strings using a list comprehension and generates squared numbers lazily with a generator.
def format_data(items):
"""Format a list of dictionaries into readable strings."""
formatted = [
f"{item.get('name', 'Unknown')}: {item.get('value', 0)} units"
for item in items
if item.get('value', 0) > 0
]
return formatted if formatted else ["No positive values found"]
def g…How to Validate Data with Python Comprehensions and Generators
Use list, generator, and dictionary comprehensions to filter and transform data for quick validation in Python.
def validate_integer(data):
return [item for item in data if isinstance(item, int)]
def validate_positive(numbers):
return (num for num in numbers if num > 0)
def validate_string_lengths(data, min_length=3):
return {item: len(item) for item in data if isinstance(item, str) and len(item) >= min_length}
i…Merge Data with Comprehension and Generator in Python
Merge user and order data using a dictionary comprehension for lookups and a generator expression to filter and transform orders.
def merge_data(users, orders):
"""
Merge user and order data using a dictionary comprehension
and a generator expression for filtering.
"""
# Build a lookup: user_id -> user name
user_map = {user["id"]: user["name"] for user in users}
# Generator: yield orders with user names attached
…Python Comprehensions and Generators for Beginners
Learn list, dict, and set comprehensions plus generator expressions and generator functions with clear, runnable examples.
# Demonstrates list comprehensions, dict comprehensions, set comprehensions, and generators
def demonstrate_comprehensions():
# List comprehension: squares of even numbers
numbers = range(1, 11)
even_squares = [n ** 2 for n in numbers if n % 2 == 0]
# Dict comprehension: number to its factorial
…Write Data Helpers with Comprehensions and Generators in Python
Demonstrates list, dict, and set comprehensions plus generator expressions and generator functions for building concise data helpers.
# Basic comprehensions and generators demo
# List comprehension: squares of evens
squares = [x * x for x in range(10) if x % 2 == 0]
print("List comp:", squares)
# Dictionary comprehension: char -> count
text = "hello"
char_counts = {c: text.count(c) for c in set(text)}
print("Dict comp:", char_counts)
# Set compre…How to Build an Entity Memory Dict to Store Facts in Python
Store and recall facts about entities using nested dictionaries with remember, recall, and forget functions in Python.
facts = {}
def remember(entity, attribute, value):
if entity not in facts:
facts[entity] = {}
facts[entity][attribute] = value
def recall(entity, attribute):
return facts.get(entity, {}).get(attribute, None)
def forget(entity, attribute=None):
if attribute is None:
facts.pop(entity, …How to Build an In-Memory Vector Store in Python
Build a lightweight in-memory vector store using a Python dict and cosine similarity for fast nearest-neighbor searches.
import math
from typing import Dict, List, Optional
class InMemoryVectorStore:
def __init__(self) -> None:
self.vectors: Dict[str, List[float]] = {}
self.index: Dict[str, List[str]] = {} # query -> list of ids sorted by similarity
def add(self, vector_id: str, vector: List[float]) -> None:
…How to Convert Data to JSON and Back in Python
Convert a Python dict into a JSON string with indentation, then parse it back into a dict, demonstrating a common round-trip conversion for beginners.
import json
from datetime import datetime
def convert_data(data):
"""Convert a dict into a JSON string and back to dict."""
json_str = json.dumps(data, indent=2)
parsed = json.loads(json_str)
return json_str, parsed
def main():
sample_data = {
"user": "alice",
"message": "hello",
…How to Parse Chat Completion JSON in Python
Parse a mock OpenAI chat completion JSON response into a clean dictionary with content, finish reason, and model.
import json
def parse_chat_response(raw: str) -> dict:
data = json.loads(raw)
choice = data["choices"][0]
return {
"content": choice["message"]["content"],
"finish_reason": choice["finish_reason"],
"model": data["model"],
}
if __name__ == "__main__":
mock_response = '''
…How to Parse an LLM Response in Python
This code parses a JSON string from an LLM response, stripping code fences and handling common issues like whitespace, returning a Python dictionary.
import json
from typing import Any, Dict, List
def parse_llm_response(response: str) -> Dict[str, Any]:
"""Parse a JSON string from an LLM response, handling common edge cases."""
# Remove code fences if present
cleaned = response.strip()
if cleaned.startswith("Browse by section
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