Dataclasses and attrs: when and why
Python 3.7 introduced dataclasses (PEP557). Dataclasses can be a convenient way to generate classes whose primary goal is to contain values. The design of dataclasses is based on the pre-existing attr.s library. In fact Hynek Schlawack, the very same author of attrs, helped with the writing of PEP557. Basically dataclasses are a slimmed-down version of attrs. Whether this is an improvement or not
django-sabridge - SQLAlchemy access to Django models — django-sabridge v0.0 documentation
Motivation¶ Django’s ORM is wonderful and easy to use. When the standard ORM operations are insufficient for an application, Django provides multiple methods for more directly interacting with the database, including django.db.models.Manager.raw() and django.db.connection.cursor(). However, using these methods can easily lead to vendor lock-in. Additionally, programmatically building SQL is a diff
PyStruct - Structured Learning in Python — pystruct 0.2.4 documentation
PyStruct - Structured Learning in Python¶ PyStruct aims at being an easy-to-use structured learning and prediction library. Currently it implements only max-margin methods and a perceptron, but other algorithms might follow. The learning algorithms implemented in PyStruct have various names, which are often used loosely or differently in different communities. Common names are conditional random f
Passlib 1.7.1 documentation — Passlib v1.7.1 Documentation
Welcome¶ Passlib is a password hashing library for Python 2 & 3, which provides cross-platform implementations of over 30 password hashing algorithms, as well as a framework for managing existing password hashes. It’s designed to be useful for a wide range of tasks, from verifying a hash found in /etc/shadow, to providing full-strength password hashing for multi-user application. As a quick sample
Gensim: topic modelling for humans
✔ Train large-scale semantic NLP models ✔ Represent text as semantic vectors ✔ Find semantically related documents from gensim import corpora, models, similarities, downloader # Stream a training corpus directly from S3. corpus = corpora.MmCorpus("s3://path/to/corpus") # Train Latent Semantic Indexing with 200D vectors. lsi = models.LsiModel(corpus, num_topics=200) # Convert another corpus t
SZaru: Porting of excellent Sawzall aggregators.
Overview Example Install Document Source License Overview SZaru is a library to use Google's Sawzall aggregators in pure C++, Ruby and Python. Sawzall aggregators use memory efficient and one-pass algorithms to approximately compute popular statistics. For example, a simple algorithm of 'top N' computation requires O(K) memories where K means the number of unique elements. But SZaru requires only
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Flask-SeaSurf — SeaSurf 1.1.1 documentation
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Frontline compliance management for healthcare | Zipline
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