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How to Generate Text Helper Functions in Python
Three simple Python functions that repeat, join, and count characters in strings for beginners.
def repeat_text(text, times):
"""Repeat a string a given number of times."""
return text * times
def join_words(words, separator=" "):
"""Join a list of words into a single string."""
return separator.join(words)
def count_characters(text):
"""Count character occurrences in a string."""
ret…Repeat a string n times with a separator in Python
Repeats a string a given number of times, joining the repetitions with an optional separator, with a guard for non-positive counts.
def repeat_string_with_separator(s, n, sep=''):
"""
Repeats a string n times, joining with a separator.
Args:
s (str): The string to repeat.
n (int): Number of repetitions.
sep (str): Separator between repetitions (default: '').
Returns:
str: The repeated strin…Cache expensive function with lru_cache in Python
Use functools.lru_cache to memoize an expensive recursive function and show the dramatic speedup on repeated calls.
from functools import lru_cache
import time
@lru_cache(maxsize=128)
def expensive_operation(n):
"""Simulate an expensive Fibonacci-like calculation."""
if n < 2:
return n
return expensive_operation(n - 1) + expensive_operation(n - 2)
if __name__ == "__main__":
# First call (uncached) - take…How to Implement Memoized Fibonacci in Python with functools.cache
Use functools.cache to memoize a recursive Fibonacci function, avoiding repeated computation and dramatically speeding up the calculation.
from functools import cache
@cache
def fibonacci(n: int) -> int:
"""Return the n-th Fibonacci number (0-indexed)."""
if n < 2:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
if __name__ == "__main__":
for i in range(10):
print(f"fibonacci({i}) = {fibonacci(i)}")
print(f"Cache…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 swap dict keys and values in Python when values are unique
Swap dict keys and values using a dict comprehension, with a guard that raises an error when values repeat.
def swap_dict_keys_values(d):
"""Swap keys and values in a dict, assuming values are unique."""
if len(set(d.values())) != len(d.values()):
raise ValueError("Values must be unique to swap keys and values")
return {v: k for k, v in d.items()}
if __name__ == "__main__":
original = {"a": 1, "b": …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))How to Sample Random Items Without Replacement in Python
Select k random unique items from a sequence using random.sample for uniform, non-repeating selection.
import random
def sample_without_replacement(population, k):
"""Return k random items from population without replacement."""
if k > len(population):
raise ValueError("k cannot exceed population size")
# Use random.sample for O(k) time, no mutation of the original
return random.sample(populati…Cycle an iterable forever in Python
Define a generator that repeatedly yields items from an iterable, cycling back to the beginning infinitely.
def cycle_generator(iterable):
"""Yield items from iterable forever, cycling back to the start."""
items = list(iterable) # Convert to list so it can restart
index = 0
while True:
yield items[index]
index = (index + 1) % len(items)
if __name__ == "__main__":
colors = ["red", "gre…How to Generate a Collatz Sequence in Python
Generate the Collatz sequence for a given positive integer by repeatedly applying the 3n+1 rule until reaching 1.
def collatz_sequence(n):
if n <= 0:
raise ValueError("n must be a positive integer")
sequence = [n]
while n != 1:
if n % 2 == 0:
n = n // 2
else:
n = 3 * n + 1
sequence.append(n)
return sequence
if __name__ == "__main__":
start = 7
result…How to Repeat a Generator Cycle Single Value in Python
Build a generator that repeats a single value across multiple cycles, each cycle adding an extra repetition to mark its completion.
def repeat_with_cycle(value, cycle_limit, repetitions):
"""
Repeats a single value until reaching a cycle limit,
then yields the value one more time to demonstrate a full cycle.
Args:
value: The single value to repeat.
cycle_limit: Number of repetitions per cycle.
repetitio…How to Memoize Async Functions with lru_cache in Python
Cache async function results with functools.lru_cache to avoid repeated expensive awaits, cutting total execution from ~0.4s to ~0.2s in this example.
from functools import lru_cache
import asyncio
@lru_cache(maxsize=128)
async def fetch_data(user_id: int) -> str:
# Simulate expensive async operation
await asyncio.sleep(0.1)
return f"Data for user {user_id}"
async def main():
start = asyncio.get_event_loop().time()
# First calls (miss cach…How to Memoize Pure Functions with functools.lru_cache in Python
Use functools.lru_cache to memoize a pure Fibonacci function and avoid recomputing repeated values.
from functools import lru_cache
@lru_cache(maxsize=128)
def fibonacci(n: int) -> int:
"""Return the nth Fibonacci number (0-indexed) using memoization."""
if n < 2:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
if __name__ == "__main__":
for i in range(10):
print(f"fibonacci({…How to Use functools.cache for Unbounded Memoization in Python
Speed up repeated recursive calls by memoizing function results with Python's built-in functools.cache decorator.
```python
import functools
import time
@functools.cache
def fib(n):
if n < 2:
return n
return fib(n - 1) + fib(n - 2)
if __name__ == "__main__":
start = time.perf_counter()
result = fib(30)
elapsed = time.perf_counter() - start
print(f"fib(30) = {result}")
print(f"computed in {…How to memoize a function in Python with lru_cache
Use functools.lru_cache to memoize a recursive Fibonacci function, caching results for a fixed number of calls to avoid repeated computation.
from functools import lru_cache
@lru_cache(maxsize=128)
def fibonacci(n):
if n < 2:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
if __name__ == "__main__":
for i in range(10):
print(f"fib({i}) = {fibonacci(i)}")
print(f"Cache info: {fibonacci.cache_info()}")Simple Redis Cache Helper in Python
Build a minimal Redis-backed cache with TTL, JSON serialization, and automated fetching to speed up repeated expensive lookups.
import time
import redis
import json
class SimpleCache:
def __init__(self, host="localhost", port=6379, db=0, default_ttl=60):
self.client = redis.Redis(host=host, port=port, db=db, decode_responses=True)
self.default_ttl = default_ttl
def get(self, key):
value = self.client.get(key)…How to Speed Up Column Lookups with DataFrame Index in Python
Use pandas set_index to make repeated column value lookups O(1)-style fast instead of scanning the whole DataFrame each time.
import pandas as pd
# Mock dataset with duplicate customer IDs
data = {"customer_id": [101, 102, 103, 101, 104, 102],
"order_amount": [250.0, 85.5, 300.0, 175.25, 420.0, 95.75]}
df = pd.DataFrame(data)
df = df.set_index("customer_id")
# Simulated lookup request
search_id = 102
# Fast index-based lookup (no…Browse by section
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Guide: free Python code samples library
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