textacy is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. With the fundamentals --- tokenization, part-of-speech tagging, dependency parsing, etc. --- delegated to another library, textacy focuses primarily on the tasks that come before and follow after.

• Convenient entry points to working with one or many documents processed by spaCy, with functionality added via custom extensions
• Variety of downloadable datasets with both text content and metadata, from Congressional speeches to historical literature to Reddit comments
• Easy file I/O for streaming data to and from disk
• Cleaning, normalization, and exploration of raw text — before processing
• Flexible extraction of words, ngrams, noun chunks, entities, acronyms, key terms, and other elements of interest
• Tokenization and vectorization of documents, with functionality for training, interpreting, and visualizing topic models
• String, set, and document similarity comparison by a variety of metrics
• Calculations for common text statistics, including Flesch-Kincaid Grade Level and multilingual Flesch Reading Ease

... and more!

• PyPi project: https://pypi.org/project/textacy
• Source code: https://github.com/chartbeat-labs/textacy
• Documentation: https://chartbeat-labs.github.io/textacy

Howdy, y'all. 👋

• Burton DeWilde (burton@chartbeat.com)