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python3-ibm-cos-sdk-core-2.14.0-1.lbn36.noarch: ibm-cos-sdk-python-coreA low-level interface to IBM Cloud Object Storage based on the ibm_botocore package. This core package is the foundation for the [ibm- cos-sdk-python]( package. DocumentationDocumentation for ibm-cos-sdk-python- core can be found [here]( Getting HelpFeel free to use GitHub issues for tracking bugs and feature requests, but for help please use one of the following...

python3-ibm-cos-sdk-2.14.0-1.lbn36.noarch: IBM Cloud Object Storage - Python SDKThis package allows Python developers to write software that interacts with [IBM Cloud Object Storage]( It is a fork of the [boto3 library]( and can stand as a drop-in replacement if the application needs to connect to object storage using an S3-like API and does not make use of other AWS services. NoticeIBM has added a [Language Support Policy](language-...

python3-huggingface-hub-0.30.2-1.lbn36.noarch: The huggingface_hub library allows you to interact with the Hugging Face Hub, a machine learning platform for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the hundreds of machine learning apps hosted on the Hub. You can also create and share your own models and datasets with the community. The huggingface_hub library provides a simple way to do all these things with Python.

python3-groq-0.24.0-1.lbn36.noarch: Groq Python API library The Groq Python library provides convenient access to the Groq REST API from any Python 3.8+ application. The library includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx. It is generated with Stainless. Documentation The REST API documentation can be found on console.groq.com. The full API of this library can be found in api.md. Usage The full API of this library can be found in api.md. import os from groq import Groq client = Groq( api_key=os.environ.get("GROQ_API_KEY"), # This is the default and can be omitted ) chat_completion = client.chat.completions.create( messages=[ { "role": "user", "content": "Explain the importance of low latency LLMs", } ], model="llama3-8b-8192", ) print(chat_completion.choices[0].message.content)

python3-griffe-1.7.3-1.lbn36.noarch: Griffe Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API. Griffe, pronounced "grif" (/ɡʁif/), is a french word that means "claw", but also "signature" in a familiar way. "On reconnaît bien là sa griffe." Installation With pip: pip install griffe With pipx: python3.8 -m pip install --user pipx pipx install griffe Usage On the command line, pass the names of packages to the griffe dump command: $ griffe dump httpx fastapi { "httpx": { "name": "httpx", ... }, "fastapi": { "name": "fastapi", ... } } See the Dumping data section for more examples. Or pass a relative path to the griffe check command: $ griffe check mypackage --verbose mypackage/mymodule.py:10: MyClass.mymethod(myparam): Parameter kind was changed: Old: positional or keyword New: keyword-only For src layouts: $ griffe check --search src mypackage --verbose src/mypackage/

python3-websockets-14.1-2.lbn36.x86_64: websockets is a library for developing WebSocket servers and clients in Python. It implements RFC 6455 with a focus on correctness and simplicity. It passes the Autobahn Testsuite. Built on top of Python’s asynchronous I/O support introduced in PEP 3156, it provides an API based on coroutines, making it easy to write highly concurrent applications.

python3-google-genai-1.14.0-1.lbn36.noarch: Google Gen AI SDK Documentation: https:/googleapis.github.io/python-genai/ Google Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications. It supports the Gemini Developer API and Vertex AI APIs. Installation pip install google-genai Imports from google import genai from google.genai import types Create a client Please run one of the following code blocks to create a client for different services (Gemini Developer API or Vertex AI). client = genai.Client(api_key='GEMINI_API_KEY') client = genai.Client( vertexai=True, project='your-project-id', location='us-central1' ) (Optional) Using environment variables: You can create a client by configuring the necessary environment variables. Configuration setup instructions depends on whether you're using the Gemini Developer API or the Gemini API in Vertex AI. Gemini Developer A

python3-pandas-gbq-0.19.1-1.lbn36.noarch: pandas-gbq |preview| |pypi| |versions| **pandas-gbq** is a package providing an interface to the Google BigQuery API from pandas.- Library Documentation_ - Product Documentation_.. |previe .. |pyp .. |version .. _Library Documentation:

python3-pandas-gbq-0.19.1-1.lbn36.noarch: pandas-gbq |preview| |pypi| |versions| **pandas-gbq** is a package providing an interface to the Google BigQuery API from pandas.- Library Documentation_ - Product Documentation_.. |previe .. |pyp .. |version .. _Library Documentation:

python3-pandas-datareader-0.10.0-2.fc36.noarch: Data readers extracted from the pandas codebase, should be compatible with recent pandas versions.

python3-pandas-2.2.3-1.lbn36.x86_64: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

python3-pandas-2.2.3-1.lbn36.x86_64: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

python3-pandas-2.2.3-1.lbn36.x86_64: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

python3-maturin-1.4.0-1.lbn36.x86_64: There are four main commands: maturin new creates a new cargo project with maturin configured. maturin publish builds the crate into python packages and publishes them to pypi. maturin build builds the wheels and stores them in a folder (target/wheels by default), but doesn't upload them. It's possible to upload those with twine or maturin upload. maturin develop builds the crate and installs it as a python module directly in the current virtualenv. Note that while maturin develop is faster, it doesn't support all the feature that running pip install after maturin build supports. pyo3 and rust-cpython bindings are automatically detected, for cffi or binaries you need to pass -b cffi or -b bin. maturin doesn't need extra configuration files and doesn't clash with an existing setuptools-rust or milksnake configuration. You can even integrate it with testing tools such as tox. There are examples for the different bindings in the test-crates folder. The name of the package will be the name of the cargo project, i.e. the name field in the [package] section of Cargo.toml. The name of the module, which you are using when importing, will be the name value in the [lib] section (which defaults to the name of the package). For binaries, it's simply the name of the binary generated by cargo.

python3-maturin-1.4.0-1.lbn36.x86_64: There are four main commands: maturin new creates a new cargo project with maturin configured. maturin publish builds the crate into python packages and publishes them to pypi. maturin build builds the wheels and stores them in a folder (target/wheels by default), but doesn't upload them. It's possible to upload those with twine or maturin upload. maturin develop builds the crate and installs it as a python module directly in the current virtualenv. Note that while maturin develop is faster, it doesn't support all the feature that running pip install after maturin build supports. pyo3 and rust-cpython bindings are automatically detected, for cffi or binaries you need to pass -b cffi or -b bin. maturin doesn't need extra configuration files and doesn't clash with an existing setuptools-rust or milksnake configuration. You can even integrate it with testing tools such as tox. There are examples for the different bindings in the test-crates folder. The name of the package will be the name of the cargo project, i.e. the name field in the [package] section of Cargo.toml. The name of the module, which you are using when importing, will be the name value in the [lib] section (which defaults to the name of the package). For binaries, it's simply the name of the binary generated by cargo.

python3-matplotlib-tk-3.10.0-3.lbn36.x86_64: Tk backend for python3-matplotlib

python3-matplotlib-tk-3.10.0-3.lbn36.x86_64: Tk backend for python3-matplotlib

python3-matplotlib-tk-3.10.0-3.lbn36.x86_64: Tk backend for python3-matplotlib

python3-matplotlib-inline-0.1.7-2.lbn36.noarch: Inline Matplotlib backend for Jupyter

python3-matplotlib-data-fonts-3.10.0-3.lbn36.noarch: Fonts used by python-matplotlib

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