Python Weekly

Welcome to issue 538 of Python Weekly. Let's get straight to the links this week.

Articles, Tutorials and Talks

Your First NLP Tutorial in Python - Spam Classifier 

In this video, we implement an email spam classifier using NLTK (natural language processing toolkit) in Python. We use the bag of words (BOW) approach to building the model, after performing tokenizing, lemmatization / stemming, and removing stop words. You'll learn a ton about NLP in just 20 minutes!

Upcoming Python Features Brought to You by Python Enhancement Proposals

A look at recent Python Enhancement Proposals (PEPs) and all the exciting new features, changes and improvements they might bring.

Faster Python calculations with Numba: 2 lines of code, 13× speed-up

Python-based calculations, especially those that use NumPy, can run much faster by using the Numba library.

How to write better scientific code in Python?

Few tips on how to write better code for data science using python and numpy. We use a custom case to show good patterns.

Understanding Attributes, Dicts and Slots in Python

Python classes can have arbitrary attributes. How are they stored and how do they impact performance?

Writing Python Lambdas for AWS Cloud?

SonarQube 9.3 just expanded taint analysis to include could-native DB operations. Request your 14-day free trial of Developer Edition to see this in action. 

SPONSORServer-Sent Events: the alternative to WebSockets you should be using When developing real-time web applications, WebSockets might be the first thing that comes to your mind. However, Server Sent Events (SSE) are a simpler alternative that is often superior.Tutorial: Indexing DataFrames in PandasIn this tutorial, we are going to discuss what indexing pandas dataframes means, why we need it, what kinds of dataframe indexing exist, and what syntax should be used for selecting different subsets.K-Nearest Neighbor (KNN) Algorithm in PythonIn this tutorial, you’ll learn how all you need to know about the K-Nearest Neighbor algorithm and how it works using Scikit-Learn in Python. The K-Nearest Neighbor algorithm in this tutorial will focus on classification problems, though many of the principles will work for regression as well.Interactive Loop OptimizationWhat does it mean to listen on a port?How To Train Custom Named Entity Recognition[NER] Model With SpaCy v3Interesting Projects, Tools and LibrariesSSortTool for sorting top level statements in python files.imgreriteA command-line tool to hide and reveal information inside images.YOPOYOPO is an ~0 Code interactive dashboard that generates various standard plots. you can create various graphs and charts with a click of a button.EvoJAXEvoJAX is a scalable, general purpose, hardware-accelerated neuroevolution toolkit. Built on top of the JAX library, this toolkit enables neuroevolution algorithms to work with neural networks running in parallel across multiple TPU/GPUs.TSFEL TSFEL is an intuitive library to extract features from time series with over 60 different features on the statistical, temporal and spectral domains.LurnbyA tool for active reading and personal knowledge management.pzEasily handle day to day CLI operation via Python instead of regular Bash programs.master_librarianA simple tool to audit Unix/*BSD/Linux system libraries to find public security vulnerabilities.nangoStreamlining Django forms to provide all the wins of single-page-applications without the pain.python-webrtcA Python extension that provides bindings to WebRTC M92.NATSpeechA Non-Autoregressive Text-to-Speech Framework.EquiBindGeometric Deep Learning for Drug Binding Structure Prediction.Upcoming Events and WebinarsVirtual: San Diego Python Meetup February 2022There will be following talks Creating a REST API with Django Rest Framework Django rest with ML Designing as a Developer, with Python examplesPyData Tel Aviv February 2022There will be following talks ML with humans: Integrating experts into the learning process Harnessing Data to Improve Healthcare What's new in PyMongoArrowVirtual: PyData Chicago February 2022There will be a talk, Python-based ML and HPC workflows in the Cloud for science and engineering.  

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