Issue #566

NumPy for Speed, Multiprocessing Performance, XML to YAML, and More

Feb. 28, 2023

NumPy for Speed, Multiprocessing Performance, XML to YAML, and More
#566 – FEBRUARY 28, 2023 VIEW IN BROWSER
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Using NumPy and Linear Algebra for Faster Python Code
Are you still using loops and lists to process your data in Python? Have you heard of a Python library with optimized data structures and built-in operations that can speed up your data science code? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, returns to share secrets for harnessing linear algebra and NumPy for your projects.
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Python’s Multiprocessing Performance Problem
While multiprocessing allows Python to scale to multiple CPUs, it has some performance overhead compared to threading. This article details why processes have performance issues that threads don’t, ways to work around it, and a sample bad solution.
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Simple Observability Tool for Peace of Mind: TelemetryHub by Scout APM
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Convert XML to YAML in Python
XML and YAML are two of the most popular text based data formats. This article teaches you how to use third-party Python libraries to convert from one to the other.
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Attack on PyPI Attempting to Deliver Rust Executable
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Django 4.2 Beta 1 Released
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Top 6 Text Annotation Tools
Text annotation is the process of reading natural language data and adding additional information to it in a way your program can use it. This info can be used to train models or help process the data. This article describes 6 different tools that can help you annotate your text data.
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One Does Not Simply pip install
Find your favorite package and turn to the readme to get it installed - it seems dead simple just a ‘pip install’ away. Nothing could possibly go wrong. Right? If you’re used to it, it is easy to forget almost all the instructions are skipping a step: using a virtual environment.
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The New Notification Stack for Python Developers
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How to Iterate Over Rows in Pandas, and Why You Shouldn’t
In this tutorial, you’ll learn how to iterate over a pandas DataFrame’s rows, but you’ll also understand why looping is against the way of the panda. You’ll understand vectorization, see how to choose vectorized methods, and compare the performance of iteration against pandas.
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App Fiddle: Learn Flask/SQLAlchemy “In Action”
App Fiddle is to apps what JSFiddle is to JavaScript. Use this instance to learn Flask/SQLAlchemy, running an app in Codespaces. You can browse and explore using VSCode on the web, customize, and debug a complete project, including a database.
APILOGICSERVER.GITHUB.IO • Shared by Val Huber

An Opinionated Python Boilerplate
This is Duarte’s take on what tools and practices to use for a new Python project. Includes samples for pyproject.toml, details on using pip-tools, and even the occasional Makefile.
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Understanding Python Metaclasses
Metaclasses are part of the darker corners of Python and many developers avoid them. This article dives deep into how you can use them to reduce boilerplate code and build APIs.
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How to Control Crowds with Python, OpenCV and InfluxDB
In this quick training, learn how to build a face recognition application using open source tools like OpenCV (Open Source Computer Vision Library) and InfluxDB time series platform. Github repository included.
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Build a CRUD REST API in Python
This step-by-step guide shows you how to build a REST API with Create, Read, Update, and Delete methods using Flask, SQLAlchemy, Postgres, and Docker.
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GPT in 60 Lines of NumPy
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Projects & Code


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Portable Django: Django and Python in an Executable Zip
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