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python3-yandexcloud-0.308.0-1.lbn36.noarch

python3-yandex-query-client-0.1.4-1.lbn36.noarch: Installation Install pip package: %pip install yandex-query-client --upgrade --quiet Example: simple select IAM_TOKEN="...." PROJECT="my_folder_id" config = YQHttpClientConfig(IAM_TOKEN, PROJECT) client = YQHttpClient(config) query_id = client.create_query(query_text="select 777", name="my sample query") result_set_count = client.wait_query_to_succeed(query_id) results = client.get_query_all_result_sets(query_id, result_set_count=result_set_count) print(f"results={results}")

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-ydb-3.21.0-1.lbn36.noarch: ydb

python3-xmlsec-1.3.14-1.lbn36.x86_64: Python bindings for the XML Security Library.

python3-wtforms-sqlalchemy-0.4.1-1.lbn36.noarch: WTForms-SQLAlchemy WTForms-SQLAlchemy is a fork of the wtforms.ext.sqlalchemy package from WTForms. The package has been renamed to wtforms_sqlalchemy but otherwise should function the same as wtforms.ext.sqlalchemy did.to install:: pip install WTForms-SQLAlchemyAn example using Flask is included in examples/flask.

python3-wtforms-jsonschema2-0.7.0-1.lbn36.noarch: WTForms JSONSchema 2 [![Build status]( JSONSchema 2 is a converter to turn forms made with WTForms into a OrderedDict following the JSONSchema syntax.It was developed independently of [wtforms_jsonschema]( Main differences are that it is unit tested, adds support for validators and is easier to extend. That being said, not all fields that are supported by wtforms_jsonschema are supported by...

python3-wtforms-json-0.3.5-1.lbn36.noarch: WTForms-JSON Adds smart json support for WTForms. Useful for when using WTForms with RESTful

python3-tokenizers-0.15.1-1.lbn36.x86_64: Tokenizers Provides an implementation of today's most used tokenizers, with a focus on performance and versatility. Bindings over the Rust implementation. If you are interested in the High-level design, you can go check it there. Otherwise, let's dive in! Main features: Train new vocabularies and tokenize using 4 pre-made tokenizers (Bert WordPiece and the 3 most common BPE versions). Extremely fast (both training and tokenization), thanks to the Rust implementation. Takes less than 20 seconds to tokenize a GB of text on a server's CPU. Easy to use, but also extremely versatile. Designed for research and production. Normalization comes with alignments tracking. It's always possible to get the part of the original sentence that corresponds to a given token. Does all the pre-processing: Truncate, Pad, add the special tokens your model needs.

python3-starlette-0.46.1-2.lbn36.noarch: Starlette is a lightweight ASGI framework/toolkit, which is ideal for building async web services in Python. It is production-ready, and gives you the following: • A lightweight, low-complexity HTTP web framework. • WebSocket support. • In-process background tasks. • Startup and shutdown events. • Test client built on requests. • CORS, GZip, Static Files, Streaming responses. • Session and Cookie support. • 100% test coverage. • 100% type annotated codebase. • Few hard dependencies. • Compatible with asyncio and trio backends. • Great overall performance against independent benchmarks.

python3-snowflake-sqlalchemy-1.5.0-1.lbn36.noarch: This package includes the Snowflake SQLAlchemy, which supports Snowsql dialects for SQLAlchemy < Documentation is available at: < code is also available at: < Release Notes- v1.4.4(Nov 16, 2022) - Fixed a bug that percent signs in a non- compiled statement should not be interpolated with emtpy sequence when executed.- v1.4.3(Oct 17, 2022) - Fixed a bug that SnowflakeDialect.normalize_name and...

python3-snowflake-snowpark-python-1.31.1-1.lbn36.noarch: Snowflake Snowpark Python and Snowpark pandas APIs The Snowpark library provides intuitive APIs for querying and processing data in a data pipeline. Using this library, you can build applications that process data in Snowflake without having to move data to the system where your application code runs.

python3-scrapegraph-py-1.12.0-1.lbn36.noarch: 🌐 ScrapeGraph Python SDK Official Python SDK for the ScrapeGraph API - Smart web scraping powered by AI. 🚀 Features 🤖 AI-powered web scraping and search 🔄 Both sync and async clients 📊 Structured output with Pydantic schemas 🔍 Detailed logging ⚡ Automatic retries 🔐 Secure authentication

python3-scour-0.38.2-12.lbn36.noarch: Scour is an SVG optimizer/cleaner written in Python that reduces the size of scalable vector graphics by optimizing structure and removing unnecessary data. It can be used to create streamlined vector graphics suitable for web deployment, publishing/sharing or further processing. The goal of Scour is to output a file that renders identically at a fraction of the size by removing a lot of redundant information created by most SVG editors. Optimization options are typically lossless but can be tweaked for more aggressive cleaning.

python3-scikit-learn-1.6.1-1.lbn36.x86_64: Scikit-learn integrates machine learning algorithms in the tightly-knit scientific Python world, building upon numpy, scipy, and matplotlib. As a machine-learning module, it provides versatile tools for data mining and analysis in any field of science and engineering. It strives to be simple and efficient, accessible to everybody, and reusable in various contexts.

python3-scikit-build-core-0.11.0-1.lbn36.noarch: A next generation Python CMake adapter and Python API for plugins

python3-scikit-build-0.18.1-2.lbn36.noarch: Improved build system generator for CPython C/C++/Fortran/Cython extensions. Better support is available for additional compilers, build systems, cross compilation, and locating dependencies and determining their build requirements. The scikit-build package is fundamentally just glue between the setup-tools Python module and CMake.

python3-rapidfuzz-3.12.2-1.lbn36.x86_64: RapidFuzz is a fast string matching library for Python and C++, which is using the string similarity calculations from FuzzyWuzzy. However there are a couple of aspects that set RapidFuzz apart from FuzzyWuzzy: - It is MIT licensed so it can be used whichever License you might want to choose for your project, while you're forced to adopt the GPL license when using FuzzyWuzzy - It provides many string_metrics like hamming or jaro_winkler, which are not included in FuzzyWuzzy - It is mostly written in C++ and on top of this comes with a lot of Algorithmic improvements to make string matching even faster, while still providing the same results. For detailed benchmarks check the documentation - Fixes multiple bugs in the partial_ratio implementation

python3-pydantic-graph-0.0.19-1.lbn36.noarch: pydantic-graph

python3-pydantic-ai-slim+vertexai-0.0.19-1.lbn36.noarch: This is a metapackage bringing in vertexai extras requires for python3-pydantic-ai-slim. It makes sure the dependencies are installed.

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