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How to Create Generator Functions with yield in Python
Create a memory-efficient generator function using yield to produce a Fibonacci sequence up to a limit.
def fibonacci_sequence(limit):
"""Generate Fibonacci numbers up to a given limit."""
a, b = 0, 1
while a <= limit:
yield a
a, b = b, a + b
if __name__ == "__main__":
fib_gen = fibonacci_sequence(100)
for number in fib_gen:
print(number, end=" ")
print()How to measure function memory with sys.getsizeof in Python
Measure the memory footprint of Python functions (user-defined and built-in) using sys.getsizeof.
import sys
def sample_function(a, b, c):
return a + b - c
def measure_function_memory(func):
size = sys.getsizeof(func)
print(f"Memory size of {func.__name__}: {size} bytes")
if __name__ == "__main__":
measure_function_memory(sample_function)
measure_function_memory(print)
measure_function_m…Profile Python functions with cProfile
Profile a Python program with cProfile, capture the stats in memory, and print a sorted performance report.
import cProfile
import pstats
import io
def slow_function():
total = 0
for i in range(100000):
total += i ** 2
return total
def medium_function():
return sum(range(10000))
def fast_function():
return sum(range(100))
def main():
result1 = slow_function()
result2 = medium_func…Compress and Extract ZIP Files Programmatically in Python
Create a ZIP archive with in-memory files and extract its contents to a directory using Python's stdlib zipfile and pathlib modules.
import zipfile
from pathlib import Path
import tempfile
import os
def create_sample_zip(zip_path: str, files: dict) -> None:
"""Create a ZIP file containing the given files (name -> content mapping)."""
with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zf:
for filename, content in files.ite…Create an In-Memory SQLite Table and Query It in Python
This code creates an in-memory SQLite database, defines an employees table, inserts sample rows, and runs a filtered query with sorted results.
import sqlite3
conn = sqlite3.connect(":memory:")
cursor = conn.cursor()
cursor.execute("""
CREATE TABLE employees (
id INTEGER PRIMARY KEY,
name TEXT NOT NULL,
department TEXT NOT NULL,
salary REAL
)
""")
employees = [
(1, "Alice", "Engineering", 95000),
(2, "Bob", "…How to Compress a String to Gzip Bytes in Python
Compress a string into gzip-compressed bytes entirely in memory using the standard library gzip module.
import gzip
def compress_to_gzip_bytes(data: str, encoding: str = "utf-8") -> bytes:
"""Compress a string to gzip-compressed bytes in memory."""
return gzip.compress(data.encode(encoding))
if __name__ == "__main__":
original = "Hello, world! " * 10
compressed = compress_to_gzip_bytes(original)
pr…How to Serialize a Python Object to Pickle Bytes in Memory
Serialize a Python object to pickle bytes in memory with pickle.dumps, then deserialize it back with pickle.loads and verify the roundtrip.
import pickle
class Person:
def __init__(self, name, age, skills):
self.name = name
self.age = age
self.skills = skills
def main():
person = Person("Alice", 30, ["Python", "SQL", "Docker"])
# Serialize to bytes in memory
pickle_bytes = pickle.dumps(person)
print(…How to Write Simple XML Documents with ElementTree in Python
Create well-structured XML documents in memory using Python's built-in ElementTree module, complete with nested elements, attributes, and text content.
import xml.etree.ElementTree as ET
def create_xml_document():
# Create root element
root = ET.Element("catalog")
# Create a book element with attributes and children
book1 = ET.SubElement(root, "book", id="bk101")
ET.SubElement(book1, "author").text = "Gambardella, Matthew"
ET.SubElement(…How to Build an In-Memory CRUD Repository Class in Python
Define a Python Repository class that stores objects in a dictionary and supports create, read, update, delete, and list operations.
class Repository:
def __init__(self):
self._data = {}
def create(self, key, value):
self._data[key] = value
return key
def read(self, key):
return self._data.get(key)
def update(self, key, value):
if key not in self._data:
raise KeyError(f"Key '{ke…Slots Class: How to Reduce Memory Usage in Python
Use __slots__ to prevent dynamic attribute creation and reduce per-instance memory overhead, while keeping methods intact.
class SlotsDemo:
__slots__ = ("name", "age", "email")
def __init__(self, name, age, email):
self.name = name
self.age = age
self.email = email
def describe(self):
return f"{self.name}, {self.age}, {self.email}"
if __name__ == "__main__":
instance = SlotsDemo("Alice", …Build a lazy generator to read file lines in Python
Create a generator function that yields file lines one at a time, avoiding loading the entire file into memory, and demonstrate its lazy processing.
def lazy_lines(filepath):
"""Yield lines from a file one at a time without loading the whole file into memory."""
with open(filepath, 'r', encoding='utf-8') as file:
for line in file:
yield line.rstrip('\n')
if __name__ == "__main__":
# Create a sample file to demonstrate
sample_c…Generator Function to Yield an Infinite Counter in Python
This code demonstrates a generator function that yields an infinite sequence of integers starting from a given value, allowing lazy, memory-efficient iteration.
def infinite_counter(start=0):
count = start
while True:
yield count
count += 1
if __name__ == "__main__":
counter = infinite_counter(5)
for _ in range(5):
print(next(counter))How to Create a Pairwise Generator with zip and tee in Python
Build a memory-efficient generator that yields successive overlapping pairs from any iterable using zip and tee.
from itertools import tee
def pairwise(iterable):
"""Yield successive overlapping pairs from iterable."""
a, b = tee(iterable)
next(b, None)
return zip(a, b)
if __name__ == "__main__":
values = [1, 2, 3, 4, 5]
print(list(pairwise(values)))
print(list(pairwise("hello")))How to Create an Infinite Arithmetic Sequence Generator in Python
Build a memory-efficient generator that yields an infinite arithmetic progression and extract the first N values with list comprehension.
"""Count generator infinite arithmetic progression"""
def arithmetic_counter(start=0, step=1):
"""Generate an infinite arithmetic sequence."""
current = start
while True:
yield current
current += step
if __name__ == "__main__":
counter = arithmetic_counter(1, 3)
result = [next(c…How to Generate Fibonacci Numbers in Python Without Recursion
Build an efficient infinite Fibonacci sequence using a generator function with O(1) memory and no recursion overhead.
def fib(n):
a, b = 0, 1
for _ in range(n):
yield a
a, b = b, a + b
if __name__ == "__main__":
count = 10
result = list(fib(count))
print(result)Memory efficient map over large file in Python
A generator-based streaming map that processes a large file line by line without loading the whole file into memory.
import sys
def process_lines(file_path):
"""Memory-efficient map over a large file: yields processed lines."""
with open(file_path, 'r') as f:
for line in f:
# Example mapping: strip whitespace and uppercase
yield line.strip().upper()
if __name__ == "__main__":
# Use a sma…Sum of Squares with a Generator Expression in Python
This code computes the sum of squares of integers from 1 to n using a generator expression, demonstrating a memory-efficient and concise way to aggregate a sequence.
def sum_of_squares(n):
return sum(x * x for x in range(1, n + 1))
if __name__ == "__main__":
print(f"Sum of squares from 1 to 5: {sum_of_squares(5)}")
print(f"Sum of squares from 1 to 10: {sum_of_squares(10)}")Cache LLM Completions by Hashing the Prompt in Python
A simple in-memory cache that stores LLM completions keyed by a SHA-256 hash of the prompt to avoid recomputing identical requests.
import hashlib
import json
class PromptCache:
def __init__(self):
self.cache = {}
def _hash_prompt(self, prompt: str) -> str:
return hashlib.sha256(prompt.encode("utf-8")).hexdigest()
def get(self, prompt: str) -> str | None:
key = self._hash_prompt(prompt)
return self.ca…How to Build an Agent Loop with Plan, Act, Observe in Python
Implements a simple plan-act-observe loop that an AI agent uses to iteratively complete a task in an environment while storing observations in memory.
class Agent:
def __init__(self, name):
self.name = name
self.memory = {}
def plan(self, task):
return f"Plan for {task}: step 1, step 2, step 3"
def act(self, plan, environment):
return f"Executing {plan} in {environment}"
def observe(self, action_result):
sel…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 Keep Last K Turns in a Memory Buffer in Python
A TurnBuffer class using deque with maxlen to keep only the most recent k conversation turns in memory for LLM context.
from collections import deque
class TurnBuffer:
def __init__(self, k):
self.k = k
self.turns = deque(maxlen=k)
def add(self, turn):
self.turns.append(turn)
def last_k(self):
return list(self.turns)
if __name__ == "__main__":
buffer = TurnBuffer(3)
buffer.add("tu…How to Monitor Process RSS Memory in Python
Poll the VmRSS field from /proc/PID/status to watch a process's resident memory and alert on growth.
import os
import time
import subprocess
import sys
def get_rss_mb(pid):
"""Return RSS memory in MB for a given process ID."""
try:
with open(f"/proc/{pid}/status", "r") as f:
for line in f:
if line.startswith("VmRSS:"):
return int(line.split()[1]) / 1024…Implement Exactly-Once Transaction Log in Python
A mock transaction log that deduplicates transaction IDs so each is recorded only once, with a dataclass for records and simple in-memory storage.
from dataclasses import dataclass
from typing import Dict, Optional
@dataclass
class TxnRecord:
txn_id: str
status: str
class ExactlyOnceTxnLog:
def __init__(self) -> None:
self._log: Dict[str, TxnRecord] = {}
self._processed_ids: set = set()
def record(self, txn_id: str, status: s…Browse by section
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