Python : hints & cheats
Son propre fichier basique de log
import datetime
msg = "Test de log"
dt = datetime.datetime.now()
with open("monlogfile.log", 'a+') as f:
f.write('{:02}:{:02} - {}\n'.format(
dt.hour, dt.minute, msg))
Teaching Python
Découpages Python : formation
Demystifying Two Factor Auth
Python Open Source Projects of the Year
Thanks to Dan Bader
- Les bases de Pandas
- Envoyer un email avec Python
- Python et le calcul parallèle
- Introduction aux décorateurs Python
- Commprendre Null, « is », « != » et « None »
- Interfacing Python and C: Advanced “ctypes” Features: here
- Working with Random Numbers in Python » ici
- Introduction aux descripteurs Python
- Quand utiliser une « list comprehension » en Python
- Python REST API : Connexion, et SQLAlchemy – Part 4
- Construire une calculatrice en Python et PyQT
- Python, args et kwargs expliqués simplement (« démystifiés »)
- Exploration du code source de CPython
- Introduction à PySpark et la gestion de gros volumes de données (aka « big data processing »)
- Comment générer des données aléatoires en Python
- Comment gérer des pointeurs en Python (si c’est possible !)
- Comment gérer les logs en Python (la méthode correcte et propre !)
- Convertir une chaîne en entier (la meilleure méthode !)
- Face detection
- Différentes manières de tester plusieurs flags en même temps en Python :
x, y, z = 0, 1, 0
if x == 1 or y == 1 or z == 1:
print('ok')
if 1 in (x, y, z):
print('ok')
# si l'un d'eux n'est pas vide:
if x or y or z:
print('ok')
if any((x, y, z)):
print('ok') - Comment trier un dictionnaire Python par ses valeurs :
»»» xs = {'a': 4, 'b': 3, 'c': 2, 'd': 1}
»»» sorted(xs.items(), key=lambda x: x[1])
ou bien :
»»» import operator
»»» sorted(xs.items(), key=operator.itemgetter(1)) - Mesurer le temps d’exécution de petits morceaux de code Python :
»»» import timeit
»»» timeit.timeit('"-".join(str(n) for n in range(100))',
number=10000)
0.3412662749997253
»»» timeit.timeit('"-".join([str(n)
for n in range(100)])',
number=10000)
0.2996307989997149
»»» timeit.timeit('"-".join(map(str, range(100)))',
number=10000)
0.24581470699922647 - Comment utiliser la classe namedtuples :
# namedtup1e est une classe : »»» from collections import namedtuple »»» Car = namedtup1e('Car' , 'color mileage') # Our new "Car" class works as expected: »»» my_car = Car('red', 3812.4) »»» my_car.color 'red' »»» my_car.mileage 3812.4 # Une belle représentation repr avec : »»» my_car Car(color='red' , mileage=3812.4) # Comme les tuples, les namedtuples sont immuables : »»» my_car.color = 'blue' AttributeError: "can't set attribute" - «
is» vs «==» :»»» a = [1, 2, 3] »»» b = a »»» a is b True »»» a == b True »»» c = list(a) »»» a == c True »»» a is c False
–
isestTruesi deux variables pointent vers le même objet ;
–==estTruesi les variables contenues dans les objets sont identiques. - Titre Titre :
»»» Code code code
Learning Python in minutes
https://learnxinyminutes.com/docs/python3/
How to Send an Email With Python
https://dbader.org/blog/python-send-email
The Python range() Function
https://realpython.com/courses/python-range-function/
Python sleep(): How to Add Time Delays to Your Code
https://realpython.com/python-sleep/
Cool New Features in Python 3.8
https://realpython.com/python38-new-features/
Python Decorators From the Ground Up
https://pabloariasal.github.io/python-decorators-from-the-ground-up/
How — and why — you should use Python Generators
https://medium.freecodecamp.org/how-and-why-you-should-use-python-generators-f6fb56650888
Download information on all your gmail emails and the body text to either csv or json. I developed this to download my 100K + emails stored over several years on gmail.
https://teklern.blogspot.fr/2017/11/download-all-your-email-information.html
Memoization in Python: How to Cache Function Results
https://dbader.org/blog/python-memoization
Implementing a Neural Network from Scratch in Python – An Introduction
https://www.datasciencecentral.com/profiles/blogs/implementing-a-neural-network-from-scratch-in-python-an
—–
Mailtrap – Sending Emails
—–
Introduction to NumPy and Pandas – A Simple Tutorial
https://cloudxlab.com/blog/numpy-pandas-introduction
Fastest way to uniquify a list in Python >=3.6
https://www.peterbe.com/plog/fastest-way-to-uniquify-a-list-in-python-3.6
8 Python Modules For Files Handling
http://devarea.com/8-python-modules-for-files-handling/
How do async for loops work in Python? Using asynchronous for loops in Python
https://quentin.pradet.me/blog/using-asynchronous-for-loops-in-python.html
How to use Python and Flask to build a web app — an in-depth tutorial
https://medium.freecodecamp.org/how-to-use-python-and-flask-to-build-a-web-app-an-in-depth-tutorial-437dbfe9f1c6
Framework ultra simple pour faire des micro-services en Json
Falcon is a bare-metal Python web API framework for building very fast app backends and microservices.
http://falconframework.org
How to break a CAPTCHA system in 15 minutes with Machine Learning
https://medium.com/@ageitgey/how-to-break-a-captcha-system-in-15-minutes-with-machine-learning-dbebb035a710
Python Exceptions: An Introduction
https://realpython.com/python-exceptions/
Python Metaclasses
https://realpython.com/python-metaclasses/
Building a Simple Web App with Bottle, SQLAlchemy, and the Twitter API
https://realpython.com/blog/python/building-a-simple-web-app-with-bottle-sqlalchemy-twitter-api/
Python – Regular Expressions Practical Guide
http://devarea.com/python-regular-expressions-practical-guide/#.Wki2nN_iZhE
A fast high-level screen scraping and web crawling framework.
https://scrapy.org
A fast high-level screen scraping and web crawling framework.
https://pyfiddle.io/
Python Web scraping
http://scrapingauthority.com/python-scrapy-mysql-and-matplotlib-to-gain-web-data-insights/
Tips for writing extremely short Python programs
Extremely short Python programs (aka « golfing »)
Instagramming with Python for Data Analysis
The guide
Julien Danjou blog
Easy Python logging with daiquiri
The three things you need to know about packaging are:
- – Use pip to install your packages from PyPI
- – Use pbr to package your modules
- – Use PyPI to publish your package
A safe GitHub workflow with Pastamaker
The definitive guide to Python exceptions
How do you write your Python tests?
The unittest module in Python is the natural entry point to start writing test, and it’s really easy to use. It’s not really harder than using assert, and it will provide much nicer output when run. Once you get on that road, there’s a lot of other nice modules you can start using, such pytest, coverage, nose or mock. And if your project is hosted on places such as GitHub, it’s really easy to use services such as Travis to automate tests runs.
A simple filtering syntax tree in Python
Stop merging your pull requests manually
How I stopped merging broken code
More GitHub workflow automation
