The Beauty of Gradient

In a real-life example, let’s explore how this simple but powerful calculation can help you ...

https://towardsdatascience.com/the-beauty-of-gradient-7798f4f0dd40

Best of Both Worlds: Automated and Dynamic SQL Queries from Python

Bring automation to new heights with SQL and Python integrationContinue reading on Towards Data ...

https://towardsdatascience.com/best-of-both-worlds-automated-and-dynamic-sql-queries-from-python-5b74a24501b0

obss / sahi

A lightweight vision library for performing large scale object detection/ instance segmentation.

https://github.com/obss/sahi

Processing large JSON files in Python without running out of memory

If you need to process a large JSON file in Python, it’s very easy to run out of memory. One common solution is streaming parsing, aka lazy parsing, iterative parsing, or chunked processing. Let’s see how you can apply this technique to JSON processing.

https://pythonspeed.com/articles/json-memory-streaming/

Speed up your Pandas code

Face it, your pandas code is slow. Learn how to speed it up! In this video Rob discusses a key trick to making your code faster! Pandas is an essential tool for any python programmer and data scientist

https://www.youtube.com/watch?v=SAFmrTnEHLg

What did www.python.org look like from 1996 to 2021?

https://whatdiditlooklike.mementoweb.org/post/639415402106142720/what-did-httpswwwpythonorg-look-like-from

You Can Do Really Cool Things With Functions In Python

Here are a few not-so-common things you can do with functions in Python, including closures and partial function application. Functions are incredibly powerful and you can use them to write code that's really clean and often a lot shorter than when relying on classes and object-oriented programming.

https://www.youtube.com/watch?v=ph2HjBQuI8Y

How we parallelized 600+ pandas functions with Modin

Scaling up pandas is hard. With Modin, we took a first-principles approach to parallelizing the pandas API. Rather than focus on implementing what we knew was easy, we developed a theoretical basis for dataframes—the abstraction underlying pandas—and derived a dataframe algebra that can express the 600+ pandas operators in under 20 algebraic operators.

https://ponder.io/how-do-we-parallelized-600-pandas-functions-with-modin/

palahsu / DDoS-Ripper

DDos Ripper a Distributable Denied-of-Service (DDOS) attack server that cuts off targets or surrounding infrastructure in a flood of Internet traffic

https://github.com/palahsu/DDoS-Ripper

hpcaitech / ColossalAI

Colossal-AI: A Unified Deep Learning System for Large-Scale Parallel Training

https://github.com/hpcaitech/ColossalAI

Text Similarity w/ Levenshtein Distance in Python

Building a Plagiarism Detection Pipeline in Python.

https://t.co/sBwLcaclXt

Working with Image Data in Python

In this video I show how to work with image data in python! Using the popular python packages matplotlib and opencv you will learn how to open image data, how the data is formatted, some ways to manipulate the data and save it off in a different format.

https://www.youtube.com/watch?v=kSqxn6zGE0c

Running Python in WebAssembly

Python can now be compiled to Wasm. In this post, we show how to run cloud-side Python in a WebAssembly runtime.

https://www.fermyon.com/blog/python-wagi

Efficient Pandas Dataframes in Python

In this video Rob Mulla teaches how to make your pandas dataframes more efficient by casting dtypes correctly. This will make your code faster, use less memory and smaller when saving to disk or a database.

https://www.youtube.com/watch?v=u4_c2LDi4b8