Search Results for "machine"

pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase,...

High-performance xml pull reader/writer. The reader is almost zero-copy (use of Cow whenever possible) It is easy on memory allocation (the API provides a way to reuse buffers) It supports various encoding (with encoding feature), namespaces resolution, and special characters. Syntax is inspired by xml-rs. When using the serialize feature, quick-xml can be used with serde's Serialize/Deserialize traits. The mapping between XML and Rust types, and in particular the syntax that allows you to...

...Be a thin-as-reasonable layer on top of the underlying parsers, and don't attempt to fix behavioral differences between the parsers. "Native gems" contain pre-compiled libraries for a specific machine architecture. On supported platforms, this removes the need for compiling the C extension and the packaged libraries, or for system dependencies to exist. This results in much faster installation and more reliable installation, which as you probably know are the biggest headaches for Nokogiri users.

unfluff is a Node.js library designed to automatically extract the main content from an HTML document — stripping away navigation bars, ads, footers and other boilerplate to leave you with the “body content”, metadata (title, author, date) and other useful fields. It’s a tool very much aimed at content-analysis, web scraping, building datasets, or repurposing article text for downstream processing (like machine-learning or summarization). The API is simple: you feed in raw HTML and it returns a structured object with the extracted text and other fields. It supports caching internal representations to speed up repeated extractions. While its language support is best for English, it is still widely used in web-content-processing pipelines. ...

This tutorial tries to do what most Most Machine Learning tutorials available online do not. It is not a 30 minute tutorial that teaches you how to "Train your own neural network" or "Learn deep learning in under 30 minutes". It's a full pipeline which you would need to do if you actually work with machine learning - introducing you to all the parts, and all the implementation decisions and details that need to be made.