PythonHub Logo Python Hub Weekly Digest for 2022-03-27

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💖 Most Popular

Python Design Patterns

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

DASH101 — Part 1: Introduction to Dash layout
Learn to create beautiful custom dashboards in PythonContinue reading on Towards Data Science ...

Building a blog from scratch in 2022 using Hugo, Docker and a bit of Python

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


📖 Articles

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 ...

Atomos – Atomic Primitives for Python

Security and Django

Iterable, Ordered, Mutable, and Hashable Python Objects Explained
Discussing what each of these terms really means and implies, their main nuances, and some useful ...

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

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

Python finally offloads some batteries

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

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.

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.

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.


⚙️ Projects



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