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Python 3 library for use of the Automatic Certificate Management Environment protocol as defined by the IETF. It's used by the Let's Encrypt project.
Environmental Acquisiton This package implements "environmental acquisiton" for Python, as proposed in the OOPSLA96_ paper by Joseph Gil and David H. Lorenz: We propose a new programming paradigm, environmental acquisition in the context of object aggregation, in which objects acquire behaviour from their current containers at runtime. The key idea is that the behaviour of a component may...
This library, ADAL for Python, will no longer receive new feature improvements. Instead, use the new library MSAL for Python. If you are starting a new project, you can get started with the MSAL Python docs for details about the scenarios, usage, and relevant concepts. If your application is using the previous ADAL Python library, you can follow this migration guide to update to MSAL Python. Existing applications relying on ADAL Python will continue to work. Microsoft Azure Active Directory Authentication Library (ADAL) for Python master branch dev branch Reference Docs Getting Started Docs Python Samples Support Feedback The ADAL for Python library enables python applications to authenticate with Azure AD and get tokens to access Azure AD protected web resources. You can learn in detail about ADAL Python functionality and usage documented in the Wiki. Installation and Usage You can find the steps to install and basic usage of the library under ADAL Basics page in
Filesystem interface to Azure-Datalake Gen1 and Gen2 Storage Quickstart This package can be installed using: pip install adlfs or conda install -c conda-forge adlfs The adl:/ and abfs:/ protocols are included in fsspec's known_implementations registry in fsspec > 0.6.1, otherwise users must explicitly inform fsspec about the supported adlfs protocols. To use the Gen1 filesystem: import dask.dataframe as dd storage_options={'tenant_id': TENANT_ID, 'client_id': CLIENT_ID, 'client_secret': CLIENT_SECRET} dd.read_csv('adl:/{STORE_NAME}/{FOLDER}/*.csv', storage_options=storage_options) To use the Gen2 filesystem you can use the protocol abfs or az: import dask.dataframe as dd storage_options={'account_name': ACCOUNT_NAME, 'account_key': ACCOUNT_KEY} ddf = dd.read_csv('abfs:/{CONTAINER}/{FOLDER}/*.csv', storage_options=storage_options) ddf = dd.read_parquet('az:/{CONTAINER}/folder.parquet', storage_options=storage_options) Accepted protocol / uri formats include: 'PROTOCOL:/container
aeb43 is a parser for AEB43 files.Nutshell >>> import os >>> from aeb43 import AEB43Instantiate:: >>> aeb43 AEB43('aeb43/AEB43.txt')The accounts:: >>> len(aeb43.accounts) >>> account aeb43.accounts[0] >>> account.number '0001414452' >>> account.start_date datetime.date(2018, 3, 18) >>> account.end_date datetime.date(2018, 3, 20) >>> account.initial_balance Decimal('3005') >>>...
The aggdraw module implements the basic WCK 2D Drawing Interface on top of the AGG library. This library provides high-quality drawing, with anti-aliasing and alpha compositing, while being fully compatible with the WCK renderer.
Async client for amazon services using botocore and aiohttp/asyncio. This library is a mostly full featured asynchronous version of botocore. Basic Example import asyncio from aiobotocore.session import get_session AWS_ACCESS_KEY_ID = "xxx" AWS_SECRET_ACCESS_KEY = "xxx" async def go(): bucket = 'dataintake' filename = 'dummy.bin' folder = 'aiobotocore' key = '{}/{}'.format(folder, filename) session = get_session() async with session.create_client('s3', region_name='us-west-2', aws_secret_access_key=AWS_SECRET_ACCESS_KEY, aws_access_key_id=AWS_ACCESS_KEY_ID) as client: data = b'x01'*1024 resp = await client.put_object(Bucket=bucket, Key=key, Body=data) print(resp)
Asynchronous CSV reading and writing. InstallationPython 3.6+ is required. pip3 install aiocsv AsyncReader & AsyncDictReader accept any object that has a read(size: int) coroutine, which should return a string.AsyncWriter & AsyncDictWriter accept any object that has a write(b: str) coroutine.Reading is implemented using a custom CSV parser, which should behave exactly like the CPython...