Published: Sun 17 March 2024
By Keith Marston
In blog .
tags: Python
Python libraries
A collection I libraries I have seen that maybe useful in the future, or have used or just sound cool etc.
MQTT
This is one of the "main" Python client libraries for MQTT
https://pypi.org/project/paho-mqtt
pynput
Monitor and control user input devices
Simulate input from Mouse or keyboard or listen to mouse oir keyboard events
https://pypi.org/project/pynput/
https://pastebin.com/t2iyW2gc?fbclid=IwAR1ahUmQ_VAocUSbSX0k0wMes_Ylp-9RFUaRaIjZdmHzHlWM6LayGDzMYLo
uMQTT
Micro Python MQTT library
Functools
Data structures
https://levelup.gitconnected.com/3-data-structures-you-probably-didnt-know-already-existed-in-python-f4cc313c5520
Heap
A heap is a tree-based data structure that is used to implement an abstract data type called Priority Queue.
Double Ended Queue (Deque)
A Double-ended queue is a generalization of stacks and queues that allows append and pop operations from both ends.
It is preferable to a list as it has approximately O(1) time complexity for append and pop operations in either direction.
Array
An Array is a data structure that stores its elements in contiguous memory locations.
While lists can store heterogeneous data (data of different types), arrays store homogeneous items (requiring a type specification at initialization) which makes them more memory-efficient for large datasets of uniform data.
Packaging in Python / Debian
pyproject.toml
Define the meta data first - generally the main single source of truth for "modern" Python systems
Pybuilder
https://pybuilder.io/documentation/tutorial
Some decorators
https://levelup.gitconnected.com/top-10-python-decorators-every-developer-should-master-b78420574b9f
@retry - Resilience in Code
Handling errors gracefully is essential. The @retry decorator allows you to retry a function in case of exceptions, ensuring your data analysis pipelines keep running smoothly.
from tenacity import retry, stop_after_attempt @retry(stop=stop_after_attempt(3)) def process_data(): # Data processing code
8. @log_function_call - Debugging Friend
Struggling with debugging? The @log_function_call decorator logs every function call, helping you trace issues in your data analysis scripts.
def log_function_call(func): def wrapper(args, kwargs): print(f"Calling {func.name } with args: {args}, kwargs: {kwargs}") result = func( args, **kwargs) print(f"{func.name } returned: {result}") return result
9. @time_it - Performance Tracker
Optimize your code by measuring execution time. The @time_it decorator provides valuable insights into performance bottlenecks.
import time def time_it(func): def wrapper(args, *kwargs): start_time = time.time()