You are here: Home

Modified items

All recently modified items, latest first.
RPMPackage python3-papermill-2.6.0-3.lbn36.noarch
papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. Papermill lets you: - parameterize notebooks - execute notebooks This opens up new opportunities for how notebooks can be used. For example: - Perhaps you have a financial report that you wish to run with different values on the first or last day of a month or at the beginning or end of the year, using parameters makes this task easier. - Do you want to run a notebook and depending on its results, choose a particular notebook to run next? You can now programmatically execute a workflow without having to copy and paste from notebook to notebook manually. Papermill takes an opinionated approach to notebook parameterization and execution based on our experiences using notebooks at scale in data pipelines.
RPMPackage python3-papermill-2.6.0-3.lbn36.noarch
papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. Papermill lets you: - parameterize notebooks - execute notebooks This opens up new opportunities for how notebooks can be used. For example: - Perhaps you have a financial report that you wish to run with different values on the first or last day of a month or at the beginning or end of the year, using parameters makes this task easier. - Do you want to run a notebook and depending on its results, choose a particular notebook to run next? You can now programmatically execute a workflow without having to copy and paste from notebook to notebook manually. Papermill takes an opinionated approach to notebook parameterization and execution based on our experiences using notebooks at scale in data pipelines.
RPMPackage python3-xmlsec-1.3.14-1.lbn36.x86_64
Python bindings for the XML Security Library.
RPMPackage python3-pooch-1.5.2-7.fc36.noarch
Pooch manages your Python library's sample data files: it automatically downloads and stores them in a local directory, with support for versioning and corruption checks.
RPMPackage python3-tomlkit-0.13.2-2.lbn36.noarch
TOML Kit is a 1.0.0-compliant TOML library. It includes a parser that preserves all comments, indentations, whitespace and internal element ordering, and makes them accessible and editable via an intuitive API. You can also create new TOML documents from scratch using the provided helpers. Part of the implementation has been adapted, improved and fixed from Molten.
RPMPackage python3-pywinrm-0.4.3-1.lbn36.noarch
pywinrm ======= pywinrm is a Python client for the Windows Remote Management (WinRM) service. It allows you to invoke commands on target Windows machines from any machine that can run Python. |License| |Test workflow| |Coverage| |PyPI| WinRM allows you to perform various management tasks remotely. These include, but are not limited to: running batch scripts, powershell scripts, and fetching...
RPMPackage python3-pyspnego+yaml-0.10.2-1.lbn36.noarch
This is a metapackage bringing in yaml extras requires for python3-pyspnego. It makes sure the dependencies are installed.
RPMPackage python3-pyspnego+kerberos-0.10.2-1.lbn36.noarch
This is a metapackage bringing in kerberos extras requires for python3-pyspnego. It makes sure the dependencies are installed.
RPMPackage python3-pyspnego-0.10.2-1.lbn36.noarch
Library to handle SPNEGO (Negotiate, NTLM, Kerberos) and CredSSP authentication. Also includes a packet parser that can be used to decode raw NTLM/SPNEGO/Kerberos tokens into a human readable format.
RPMPackage python3-pyspark-3.3.1-1.lbn36.noarch
Apache SparkSpark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX...
RPMPackage python3-pygithub-2.3.0-1.lbn36.noarch
PyGitHub PyGitHub is a Python library to access the GitHub REST API. This library enables you to manage GitHub resources such as repositories, user profiles, and organizations in your Python applications. Install pip install PyGithub Simple Demo from github import Github from github import Auth auth = Auth.Token("access_token") g = Github(auth=auth) g = Github(base_url="https:/{hostname}/api/v3", auth=auth) for repo in g.get_user().get_repos(): print(repo.name) g.close() Documentation More information can be found on the PyGitHub documentation site. Development Contributing Long-term discussion and bug reports are maintained via GitHub Issues. Code review is done via GitHub Pull Requests. For more information read CONTRIBUTING.md. Main
RPMPackage python3-std-uritemplate-2.0.3-1.lbn36.noarch
std-uritemplate This is a complete and maintained cross-language implementation of the Uri Template specification RFC 6570 Level 4. [!NOTE] Low activity is this repository is expected as long as there are no outstanding bug reports the implementations are considered stable and mature. Available implementations Language Complete Reviewed Published Java ✅ ✅ ✅ Python ✅ ❌ ✅ Typescript ✅ ✅ ✅ Go ✅ ✅ ✅ C# ✅ ✅ ✅ Ruby ✅ ❌ ✅ PHP ✅ ✅ ✅ Swift ✅ ❌ ✅ Dart ✅ ✅ ✅ Usage Java You can use the library as a Maven dependency: <dependency> <groupId>io.github.std-uritemplate</groupId> <artifactId>std-uritemplate</artifactId> <version>REPLACE-ME</version> </dependency> in Gradle: implementation 'io.github.std-uritemplate:std-uritemplate:REPLACE-ME' and use it in your project: import io.github.stduritemplate.StdUriTemplate; ... StdUriTemplate.expand(template, substitutions); Python Install the package with pip (or any alternative): pip install std-uritemp
RPMPackage python3-validators-0.34.0-2.lbn36.noarch
Python has all kinds of data validation tools, but every one of them seems to require defining a schema or form. I wanted to create a simple validation library where validating a simple value does not require defining a form or a schema.
RPMPackage python3-propcache-0.2.0-4.lbn36.x86_64
Module for fast property caching.
RPMPackage python3-weaviate-client-4.14.0-1.lbn36.noarch
A python native client for easy interaction with a Weaviate instance. The client is tested for python 3.8 and higher. Visit the official Weaviate website for more information about the Weaviate and how to use it in production. Articles Here are some articles on Weaviate: Semantic Search Queries Return More Informed Results Getting Started with Weaviate Python Library A sub-50ms neural search with DistilBERT and Weaviate Documentation Weaviate Python client overview. Weaviate documentation. Additional reference documentation Support Use our Forum for support or any other question. Use our Slack Channel for discussions or any other question. Use the weaviate tag on StackOverflow for questions. For bugs or problems, submit a GitHub issue. Contributing To contribute, read How to Contribute.
RPMPackage python3-xmltodict-0.13.0-4.lbn36.noarch
xmltodict is a Python module that makes working with XML feel like you are working with JSON. It's very fast (Expat-based) and has a streaming mode with a small memory footprint, suitable for big XML dumps like Discogs or Wikipedia.
RPMPackage python3-portalocker-2.10.0-1.lbn36.noarch
Library to provide an easy API to file locking
RPMPackage python3-pinecone-plugin-inference-0.4.1-1.lbn36.noarch
Inference API plugin for python SDK Installation The plugin is distributed separately from the core python sdk. pip install pinecone-client pip install pinecone-plugin-inference Usage Interact with Pinecone's Inference APIs, e.g. create embeddings (currently in preview). Models currently supported: multilingual-e5-large Generate embeddings The following example highlights how to use an embedding model to generate embeddings for a list of documents and a user query, with the ultimate goal of retrieving similar documents from a Pinecone index. from pinecone import Pinecone pc = Pinecone(api_key="<<PINECONE_API_KEY>>") model = "multilingual-e5-large" text = [ "Turkey is a classic meat to eat at American Thanksgiving.", "Many people enjoy the beautiful mosques in Turkey.", ] text_embeddings = pc.inference.embed( model=model, inputs=text, parameters={"input_
RPMPackage python3-pinecone-client-4.1.2-1.lbn36.noarch
Pinecone Python Client The official Pinecone Python client. For more information, see the docs at https:/www.pinecone.io/docs/ Documentation If you are upgrading from a 2.2.x version of the client, check out the v3 Migration Guide. Reference Documentation Example code Many of the brief examples shown in this README are using very small vectors to keep the documentation concise, but most real world usage will involve much larger embedding vectors. To see some more realistic examples of how this client can be used, explore some of our many Jupyter notebooks in the examples repository. Prerequisites The Pinecone Python client is compatible with Python 3.8 and greater. Installation There are two flavors of the Pinecone python client. The default client installed from PyPI as pinecone-client has a minimal set of dependencies and interacts with Pinecone via HTTP requests. If you are aiming to maximimize performance, you can install additional gRPC dependencies to access an alternate clie
RPMPackage python3-qdrant-client-1.10.1-1.lbn36.noarch
Python Client library for the Qdrant vector search engine. Python Qdrant Client Client library and SDK for the Qdrant vector search engine. Python Client API Documentation is available here. Library contains type definitions for all Qdrant API and allows to make both Sync and Async requests. Client allows calls for all Qdrant API methods directly. It also provides some additional helper methods for frequently required operations, e.g. initial collection uploading. See QuickStart for more details! Installation pip install qdrant-client Features Type hints for all API methods Local mode - use same API without running server REST and gRPC support Minimal dependencies Extensive Test Coverage Local mode Python client allows you to run same code in local mode without running Qdrant server. Simply initialize client like this: from qdrant_client import QdrantClient client = QdrantClient(":memory:") client = QdrantClient(path="path/to/db") # Persists changes to disk Local mo