djangoproject

http://www.kdnuggets.com/2017/09/essential-data-science-machine-learning-deep-learning-cheat-sheets.html #Cheat_Sheet, #Data_Science, #Deep_Learning, #Machine_Learning, #Neural_Networks, #Probability, #Python, R, #SQL, #Statistics This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools

http://krondo.com/an-introduction-to-asynchronous-programming-and-twisted/ Twisted Introduction This multi-part series introduces #Asynchronous Programming and the Twisted networking framework. #Twisted is an event-driven networking engine written in #Python and licensed under the open source ​MIT license. Twisted runs on Python 2 and an ever growing subset also works with Python 3. #network#learn

https://engineering.instagram.com/web-service-efficiency-at-instagram-with-python-4976d078e366 Web Service Efficiency at Instagram with Python #Instagram currently features the world’s largest deployment of the #Django web framework, which is written entirely in #Python. We initially chose to use Python because of its reputation for simplicity and practicality, which aligns well with our philosophy of “do the simple thing first.” But simplicity can come with a tradeoff: efficiency...

https://developer.mozilla.org/en-US/docs/Learn/Server-side/Django #Django is an extremely popular and fully featured server-side #web#framework, written in #Python. The module shows you why Django is one of the most popular web server frameworks, how to set up a development environment, and how to get started with using it to create your own web #applications. Prerequisites Before starting this module you don't need to have any knowledge of Django. You will need to understand what server-side web programming and web frameworks are, ideally by reading the topics in our Server-side website programming first steps module. A general knowledge of programming concepts and Python is recommended, but not essential to understanding the core concepts.

https://machinelearningmastery.com/setup-python-environment-machine-learning-deep-learning-anaconda/ It can be difficult to install a #Python#machine_learning environment on some platforms. Python itself must be installed first and then there are many packages to install, and it can be confusing for beginners. In this tutorial, you will discover how to set up a Python machine learning development environment using #Anaconda.

http://www.bogotobogo.com/python/python_unit_testing.php #python#unit_test In this chapter, we're going to write and debug a set of functions to convert to and from Hexa numbers. We can start mapping out what a hexa.py module should do. It will have two main functions, toHexa() and fromHexa(). The toHexa() function should take an integer from 1 to 65536 and return the hexa numeral representation as a string.

https://docs.python.org/3/library/gettext.html The #gettext module provides internationalization (#I18N) and localization (#L10N) services for your #Python modules and applications. It supports both the #GNU gettext message catalog #API and a higher level, class-based API that may be more appropriate for Python files. The interface described below allows you to write your module and application messages in one natural #language, and provide a catalog of #translated messages for running under different natural languages. Some hints on localizing your Python modules and applications are also given.

# In Python 3.4+ you can use # contextlib.suppress() to selectively # ignore specific exceptions: import contextlib with contextlib.suppress(FileNotFoundError): os.remove('somefile.tmp') # This is equivalent to: try: os.remove('somefile.tmp') except FileNotFoundError: pass # contextlib.suppress docstring: # # "Return a context manager that suppresses any # of the specified exceptions if they occur in the body # of a with statement and then resumes execution with # the first statement following the end of # the with statement." #python#learn

https://docs.python.org/3/library/subprocess.html The #subprocess module allows you to spawn new #processes, connect to their input/output/error pipes, and obtain their return codes. This module intends to replace several older #modules and #functions. #python

#AI#Artificial_Intelligence #aiohttp #AngularJS #API #AWS #asyncio #audio #automated_testing #automation #atexit #BeeWare #button #client #concurrency #Coroutine #cron #curl #data_analysis #data_mining #data_processing #database #Deep_Learning #Debian #decorator #dict #dispatch #django #django_cms #dropdownbox #Docker #event #Firefox #form #Generator #GeoDjango #git #Google #GPU #Gym #learn #Image_processing #intelligence #input #IOT #lambda #learn #lists #machine_learning #Magenta #map #Metaprogramming #Micro_services #mind #monitoring #MongoDB #Mozilla #Multipart #multi_touch_apps #multiprocessing #Nodes #NoSQL #numeric_computation #numerical #NumPy #OAuth #object_serialization #OCR #overloading #package #parallel #pipeline #protocols #PostGIS #pyAudioAnalysis #pycon #Pyflakes #PyInstaller #PySide #PyTorch #pytest #python #Pyvideo_archives #Qt #React #Redis #random #request #REST #satellite #scrapy #scikit_learn #SciPy #searching #submit #selectbox #Selenium #serialization #server #socket #task #telegram #TensorFlow #test #text_boxes #text #tuples #unicode #Universe #Unix #urllib #upload #Web

http://www.blopig.com/blog/2016/08/processing-large-files-using-python/ Oxford Protein Informatics Group (OPIG) Processing large files using python In the last year or so, and with my increased focus on ribo-seq data, I have come to fully appreciate what the term #big_data means. The ribo-seq studies in their raw forms can easily reach into hundreds of GBs, which means that processing them in both a timely and efficient manner requires some thought. In this blog post, and hopefully those following, I want to detail some of the methods I have come up (read: pieced together from multiple stack exchange posts), that help me take on data of this magnitude. Specifically I will be detailing methods for #python and R, though some of the methods are transferrable to other languages.

# #Python 3.3+ has a std # lib module for displaying # #tracebacks even when Python # "dies", e.g with a #segfault: import faulthandler faulthandler.enable() # Can also be enabled with # "python -X faulthandler" # from the command line. # Learn more here: # https://docs.python.org/3/library/faulthandler.html