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Pandas 2.0 将逐步适用 arrow 取代目前的 Numpy 来存储数据。读写性能及处理速度将大为提升。 https://datapythonista.me/blog/pandas-20-and-the-arrow-revolution-part-i #python#data

https://github.com/4Catalyzer/pykubectl A python bridge to kubectl providing additional functionalities useful for CD and #automation. #machine_learning

http://asyncio.readthedocs.io/en/latest/webscraper.html #Web#scraping means downloading multiple web pages, often from different #servers. Typically, there is a considerable waiting time between sending a request and receiving the answer. Using a client that always waits for the server to answer before sending the next request, can lead to spending most of time waiting. Here asyncio can help to send many requests without waiting for a response and collecting the answers later. The following examples show how a synchronous client spends most of the time waiting and how to use asyncio to write asynchronous client that can handle many requests concurrently.

http://zetcode.com/python/csv/ Python #CSV tutorial shows how to read and write CSV #data with Python csv module. #learn

http://www.csestack.org/python-libraries-for-data-science/ As per the DIKW Pyramid Model, #Data_Science job revolves around finding the information, knowledge from Raw Data. And it can be bundled into the stack of 4 entities: source of #data manage and store data analyze the data display analyzed output (#visualization, statistics)

Dash, announced this year, is an open source library for building web applications, especially those that make good use of #data visualization, in pure Python. It is built on top of #Flask, #Plotly.js and #React, and provides abstractions that free you from having to learn those frameworks and let you become productive quickly. #Dash is a #Python framework for building analytical web applications. No JavaScript required. https://plot.ly/products/dash/

http://robotframework.org/ #Robot#Framework is a generic test #automation framework for acceptance testing and acceptance test-driven development (ATDD). It has easy-to-use tabular test data syntax and it utilizes the keyword-driven testing approach. Its testing capabilities can be extended by test libraries implemented either with Python or Java, and users can create new higher-level keywords from existing ones using the same syntax that is used for creating test cases.

https://alysivji.github.io/mongodb-pipelines-in-scrapy.html #Scraping Websites into #MongoDB using Scrapy #Pipelines Summary Discuss advantages of using Scrapy framework Create #Reddit spider and scrape top posts from list of subreddits Implement Scrapy pipeline to send scraped data into MongoDB Sure, we could hack together a solution using #Requests and #Beautiful_Soup (bs4), but if we ever wanted to add features like following next page links or creating data validation pipelines, we would have to do a lot more work.

Maid - Mobile Artificial Intelligence Distribution Maid is a cross-platform free and an open-source application for interfacing with llama.cpp models locally, and remotely with Ollama, Mistral, Google Gemini and OpenAI models remotely. -Choose from A wide range of models that runs LOCALLY and access remote models via api key! -Text based output -Image Generation (Selected Models only) -No video or short clips generation yet -Voice generation on selected models (Not tested) -Setting model parameters -Setting system prompt (Making the model behave/generate output in a certain way). -And more. Get it on Github - https://github.com/Mobile-Artificial-Intelligence/maid/releases/latest Fdroid - https://f-droid.org/packages/com.danemadsen.maid/ Spystore - https://play.google.com/store/apps/details?id=com.danemadsen.maid *Don't clear CACHE OF THE APP AND EXCLUDE IT FROM SYSTEM'S AUTO CACHE CLEANING as app stores everything in device cache* Follow @nogoolag and @libreware for more #ai

Cherry Studio Cherry Studio is a desktop client for Windows, Mac and Linux, which supports many LLM providers, including large cloud services and local models. Among its main functions is the ability to work with more than 300 pre -designed #AI assistants, the creation of custom assistants, as well as support for various formats of documents, including text, images and office files. The application offers tools for global search, top management and translating, which significantly improves interaction with the user thanks to the cross -platform and many settings options. https://github.com/cherryhq/cherry-studio

LibreChat AI Open-source platform that allows users to chat and interact with various #AI models through a unified interface. You can use OpenAI, Gemini, Anthropic and other AI models using their API. You may also use Ollama as an endpoint and use LibreChat to interact with local LLMs. It can be installed locally or deployed on a server. LibreChat is designed to be highly customizable and supports a wide range of AI providers and services. Let me summarize its main features: Free and Open Source: Accessible to everyone without any costs. Customization: Offers extensive options to tailor the platform to individual preferences. Multi-AI Support: Integrates with numerous AI models and services. Unified Interface: Provides a consistent experience for interacting with different AI models. https://www.librechat.ai https://itsfoss.com/librechat-linux/

Jan.ai https://jan.ai A platform that enables you to run self-hosted local #AI. Jan provides an OpenAI-equivalent API server at localhost:1337 that can be used as a drop-in replacement with compatible apps. With Jan, you can: -Run open-source LLMs locally or connect to cloud AIs like ChatGPT or Google. -Search the web and databases. Integrate AI with everyday tools to work on your behalf (with permission). -Customize and add features with Extensions. Jan is opinionated software about what AI should be.