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All of the python 3.9 standard library

For a version without the collapsible details sections (so you can search the whole thing in your browser), click here.

abc

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

"Build tools for others that you want to be built for you." - Kenneth Reitz
"Simplicity is alway better than functionality." - Pieter Hintjens

Following on from a meeting with Simon, Ed, and Bryan. It seems like we’re trying to answer two questions:

Where are we overloaded?
What aren't we using? (That we should look to find or fit work to.)

Different contexts:

Managing current load
Always starts with released shop orders.

Table Of Content

Skip to the relevant sections if needed.

2-min tutorial to do it the quick-and-dirty-way
Concepts for resolving Git conflicts
Setting up different editors / tool for using git mergetool
- Finding out what mergetool editors are supported
mergetool simple code example for vimdiff
- Resolving conflict from a Git pull
Other great references and tutorials

Moved to git repository: https://github.com/denji/nginx-tuning

NGINX Tuning For Best Performance

For this configuration you can use web server you like, i decided, because i work mostly with it to use nginx.

Generally, properly configured nginx can handle up to 400K to 500K requests per second (clustered), most what i saw is 50K to 80K (non-clustered) requests per second and 30% CPU load, course, this was 2 x Intel Xeon with HyperThreading enabled, but it can work without problem on slower machines.

You must understand that this config is used in testing environment and not in production so you will need to find a way to implement most of those features best possible for your servers.

	import os; import psutil; import timeit
	from nlp import load_dataset

	mem_before = psutil.Process(os.getpid()).memory_info().rss >> 20
	wiki = load_dataset("wikipedia", "20200501.en", split='train')
	mem_after = psutil.Process(os.getpid()).memory_info().rss >> 20
	print(f"RAM memory used: {(mem_after - mem_before)} MB")

	s = """batch_size = 1000
	for i in range(0, len(wiki), batch_size):

	"""
	Example of a Streamlit app for an interactive Prodigy dataset viewer that also lets you
	run simple training experiments for NER and text classification.

	Requires the Prodigy annotation tool to be installed: https://prodi.gy
	See here for details on Streamlit: https://streamlit.io.
	"""
	import streamlit as st
	from prodigy.components.db import connect
	from prodigy.models.ner import EntityRecognizer, merge_spans, guess_batch_size

	"""
	Générateur de jours ouvrés français en python
	"""

	def easter_date(year):
	"""
	Calcule la date du jour de Pâques d'une année donnée
	Voir https://github.com/dateutil/dateutil/blob/master/dateutil/easter.py

	:return: datetime

	import matplotlib
	import matplotlib.pyplot as plt
	import pandas as pd
	import squarify

	# qualtities plotted
	# squarre area is the town surface area (superf)
	# color scale is the town population in 2011 (p11_pop)

	# read data from csv file