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python3-requests-mock-1.10.0-8.lbn36.noarch: requests-mock provides a building block to stub out the HTTP requests portions of your testing code. You should checkout the docs for more information.

python3-pydantic-settings-2.0.3-1.lbn36.noarch: pydantic-settings Settings management using Pydantic, this is the new official home of Pydantic's BaseSettings. This package was kindly donated to the Pydantic organisation by Daniel Daniels, see pydantic/pydantic#4492 for discussion. For the old "Hipster-orgazmic tool to mange application settings" package, see version 0.2.5. See documentation for more details.

python3-pydantic-extra-types-2.4.1-1.lbn36.noarch: Pydantic Extra Types A place for pydantic types that probably shouldn't exist in the main pydantic lib. See pydantic/pydantic#5012 for more info.

python3-pydantic-core-2.1.2-1.lbn36.x86_64: pydantic-core This package provides the core functionality for pydantic validation and serialization. Pydantic-core is currently around 17x faster than pydantic V1. See tests/benchmarks/ for details. Example of direct usage NOTE: You should not need to use pydantic-core directly; instead, use pydantic, which in turn uses pydantic-core. from pydantic_core import SchemaValidator, ValidationError v = SchemaValidator( { 'type': 'typed-dict', 'fields': { 'name': { 'type': 'typed-dict-field', 'schema': { 'type': 'str', }, }, 'age': { 'type': 'typed-dict-field', 'schema': { 'type': 'int', 'ge': 18, }, }, 'is_developer': { 'type': 'typed-dict-field', 'schema': { 'type': 'default', 'sc

python3-pydantic-2.0.2-1.lbn36.noarch: Data validation and settings management using python type hinting.

python3-plyvel-1.5.1-1.lbn36.x86_64: Plyvel is a fast and feature-rich Python interface to the LevelDB embedded database library. It has a rich feature set, high performance, and a friendly Pythonic API.

python3-scramp-1.4.5-1.lbn36.noarch: Scramp is a pure-Python implementation of the SCRAM authentication protocol.

python3-redshift-connector-2.1.3-1.lbn36.noarch: redshift_connector is the Amazon Redshift connector for Python. Easy integration with pandas and numpy, as well as support for numerous Amazon Redshift specific features help you get the most out of your data Supported Amazon Redshift features include: IAM authentication Identity provider (IdP) authentication Redshift specific data types This pure Python connector implements Python Database API Specification 2.0.

python3-pyathena+sqlalchemy-3.12.2-1.lbn36.noarch: This is a metapackage bringing in sqlalchemy extras requires for python3-pyathena. It makes sure the dependencies are installed.

python3-pyathena+pandas-3.12.2-1.lbn36.noarch: This is a metapackage bringing in pandas extras requires for python3-pyathena. It makes sure the dependencies are installed.

python3-pyathena+fastparquet-3.12.2-1.lbn36.noarch: This is a metapackage bringing in fastparquet extras requires for python3-pyathena. It makes sure the dependencies are installed.

python3-pyathena+arrow-3.12.2-1.lbn36.noarch: This is a metapackage bringing in arrow extras requires for python3-pyathena. It makes sure the dependencies are installed.

python3-pyathena-3.12.2-1.lbn36.noarch: PyAthena is a Python DB API 2.0 (PEP 249) client for Amazon Athena.

python3-orjson-3.8.10-1.fc36.x86_64: orjosn is a fast, correct Python JSON library supporting dataclasses, datetimes, and numpy

python3-ydb-dbapi-0.1.11-1.lbn36.noarch: YDB Python DBAPI Introduction Python DBAPI to YDB, which provides both sync and async drivers and complies with PEP249. Installation pip install ydb-dbapi Usage To establish a new DBAPI connection you should provide host, port and database: import ydb_dbapi connection = ydb_dbapi.connect( host="localhost", port="2136", database="/local" ) # sync connection async_connection = await ydb_dbapi.async_connect( host="localhost", port="2136", database="/local" ) # async connection Usage of connection: with connection.cursor() as cursor: cursor.execute("SELECT id, val FROM table") row = cursor.fetchone() rows = cursor.fetchmany(size=5) rows = cursor.fetchall() Usage of async connection: async with async_connection.cursor() as cursor: await cursor.execute("SELECT id, val FROM table") row = await cursor.fetchone() rows = await cursor.fetchmany(size=5) rows = await cursor.fetchall()

python3-pyxlsb-1.0.10-1.lbn36.noarch: |PyPI|pyxlsb is an Excel 2007-2010 Binary Workbook (xlsb) parser for Python. The library is currently extremely limited, but functional enough for basic data extraction.Install .. code:: sh pip install pyxlsbUsage The module exposes an open_workbook(name) method (similar to Xlrd and OpenPyXl) for opening XLSB files. The Workbook object representing the file is returned... code:: python from...

python3-trio-websocket-0.12.2-2.lbn36.noarch: This library implements both server and client aspects of the the WebSocket protocol, striving for safety, correctness, and ergonomics. It is based on the wsproto project, which is a Sans-IO state machine that implements the majority of the WebSocket protocol, including framing, codecs, and events. This library handles I/O using the Trio framework. This library passes the Autobahn Test Suite.

python3-trio-0.30.0-2.lbn36.noarch: The Trio project's goal is to produce a production-quality, permissively licensed, async/await-native I/O library for Python. Like all async libraries, its main purpose is to help you write programs that do multiple things at the same time with parallelized I/O. A web spider that wants to fetch lots of pages in parallel, a web server that needs to juggle lots of downloads and websocket connections at the same time, a process supervisor monitoring multiple subprocesses... that sort of thing. Compared to other libraries, Trio attempts to distinguish itself with an obsessive focus on usability and correctness. Concurrency is complicated; we try to make it easy to get things right.

python3-sqlparse-0.5.3-1.lbn36.noarch: sqlparse is a tool for parsing SQL strings. It can generate pretty-printed renderings of SQL in various formats. It is a python module, together with a command-line tool.

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