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python3-jupyter-server-kernels-0.1.2-1.lbn36.noarch: Jupyter Server Kernels Jupyter Server Kernels is a Jupyter Server Extension providing support for kernels.

python3-jupyter-server-fileid-0.9.0-1.lbn36.noarch: jupyter_server_fileid A Jupyter Server extension providing an implementation of the File ID service. Requirements Jupyter Server Install To install the extension, execute: pip install jupyter_server_fileid Uninstall To remove the extension, execute: pip uninstall jupyter_server_fileid Troubleshoot If you are seeing the frontend extension, but it is not working, check that the server extension is enabled: jupyter server extension list Contributing Development install pip install -e . You can watch the source directory and run your Jupyter Server-based application at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension. For example, when running JupyterLab: jupyter lab --autoreload If your extension does not depend a part

python3-jupyter-server-2.14.1-1.lbn36.noarch: Jupyter Server The Jupyter Server provides the backend (i.e. the core services, APIs, and REST endpoints) for Jupyter web applications like Jupyter notebook, JupyterLab, and Voila. For more information, read our documentation here. Installation and Basic usage To install the latest release locally, make sure you have pip installed and run: pip install jupyter_server Jupyter Server currently supports Python>=3.6 on Linux, OSX and Windows. Versioning and Branches If Jupyter Server is a dependency of your project/application, it is important that you pin it to a version that works for your application. Currently, Jupyter Server only has minor and patch versions. Different minor versions likely include API-changes while patch versions do not change API. When a new minor version is released on PyPI, a branch for that version will be created in this repository, and the version of the main branch will be bumped to the next minor version number. That way, the main branch always reflects the

python3-jupyter-lsp-2.2.0-1.lbn36.noarch: jupyter-lspMulti-[Language Server][language-server] WebSocket proxy for your Jupyter notebook or lab server. For Python 3.6+.> See the parent of this repository, > [jupyterlab-lsp]( for the > reference client implementation for [JupyterLab][]. Language Serversjupyter-lsp does not come with any Language Servers! Learn more about installing and configuring [language servers][language servers...

python3-jupyter-jaeger-1.0.4-1.lbn36.noarch: This adds support for using the Jaeger distributed tracing tool with Jupyter. It facilitates the use case of tracking some process that starts in a kernel and is continued in a mime renderer. We are using it to profile and debug ibis-vega-transform which goes back and forth between the kernel and the frontend to interactively render charts with Altair. Installing this adds two Jupyter server extensions that start up the jaeger-all-in-one and jaeger-browser processes when you launch Jupyter. So to use it you must first instrument code in your kernel and/or in the frontend to record traces. It also provis a NPM Typescript plugin you can use to access the client from inside a JupyterLab extension.

python3-jupyter-events-0.10.0-1.lbn36.noarch: Jupyter Events An event system for Jupyter Applications and extensions. Jupyter Events enables Jupyter Python Applications (e.g. Jupyter Server, JupyterLab Server, JupyterHub, etc.) to emit events—structured data describing things happening inside the application. Other software (e.g. client applications like JupyterLab) can listen and respond to these events. Install Install Jupyter Events directly from PyPI: pip install jupyter_events or conda-forge: conda install -c conda-forge jupyter_events Documentation Documentation is available at jupyter-events.readthedocs.io. About the Jupyter Development Team The Jupyter Development Team is the set of all contributors to the Jupyter project. This includes all of the Jupyter subprojects. The core team that coordinates development on GitHub can be found here: https:/github.com/jupyter/. Our Copyright Policy Jupyter uses a shared copyright model. Each contributor maintains copyright over their contributions to Jupyter. But, it is important

python3-jupyter-cache-0.5.0-1.lbn36.noarch: jupyter-cache[![Github-CI][github-ci]][github-link] [![Coverage Status][codecov-badge]][codecov-link] [![Documentation Status][rtd-badge]][rtd- link] [![Code style: black][black-badge]][black-link] [![PyPI][pypi- badge]][pypi-link]A defined interface for working with a cache of jupyter notebooks. Why use jupyter-cache?If you have a number of notebooks whose execution outputs you want to...

python3-jupyter-c-kernel-1.2.2-14.fc36.noarch: Minimalistic C kernel for Jupyter

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-apache-airflow-providers-ydb-2.1.1-1.lbn36.noarch: Package apache-airflow-providers-ydb Release: 2.1.1 YDB

google-crc32c-1.1.2-11.lbn36.x86_64: This project collects a few CRC32C implementations under an umbrella that dispatches to a suitable implementation based on the host computer's hardware capabilities. CRC32C is specified as the CRC that uses the iSCSI polynomial in RFC 3720. The polynomial was introduced by G. Castagnoli, S. Braeuer and M. Herrmann. CRC32C is used in software such as Btrfs, ext4, Ceph and leveldb.

leveldb-1.23-4.fc36.x86_64: LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.

freetds-libs-1.3.3-2.fc36.x86_64: FreeTDS is a project to document and implement the TDS (Tabular DataStream) protocol. TDS is used by Sybase(TM) and Microsoft(TM) for client to database server communications. FreeTDS includes call level interfaces for DB-Lib, CT-Lib, and ODBC. This package contains the libraries for freetds.

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.

python3-sqlmodel-slim-0.0.22-1.lbn36.noarch: SQLModel is a library for interacting with SQL databases from Python code, with Python objects. It is designed to be intuitive, easy to use, highly compatible, and robust. SQLModel is based on Python type annotations, and powered by Pydantic and SQLAlchemy. The key features are: • Intuitive to write: Great editor support. Completion everywhere. Less time debugging. Designed to be easy to use and learn. Less time reading docs. • Easy to use: It has sensible defaults and does a lot of work underneath to simplify the code you write. • Compatible: It is designed to be compatible with FastAPI, Pydantic, and SQLAlchemy. • Extensible: You have all the power of SQLAlchemy and Pydantic underneath. • Short: Minimize code duplication. A single type annotation does a lot of work. No need to duplicate models in SQLAlchemy and Pydantic.

python3-sqlmodel-0.0.22-1.lbn36.noarch: SQLModel is a library for interacting with SQL databases from Python code, with Python objects. It is designed to be intuitive, easy to use, highly compatible, and robust. SQLModel is based on Python type annotations, and powered by Pydantic and SQLAlchemy. The key features are: • Intuitive to write: Great editor support. Completion everywhere. Less time debugging. Designed to be easy to use and learn. Less time reading docs. • Easy to use: It has sensible defaults and does a lot of work underneath to simplify the code you write. • Compatible: It is designed to be compatible with FastAPI, Pydantic, and SQLAlchemy. • Extensible: You have all the power of SQLAlchemy and Pydantic underneath. • Short: Minimize code duplication. A single type annotation does a lot of work. No need to duplicate models in SQLAlchemy and Pydantic.

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