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Reposted by Maria Sosnovskaya

Posit @posit.co · 18/02/2026

The newest edition of posit::glimpse() is here! 🚀 Read a roundup of major #RStats & #Python updates: ggplot2 4.0.0, dplyr 1.2.0, ellmer 0.4.0, Positron native Jupyter Notebook support , and more. 🎟️ Plus, registration for posit::conf(2026) is officially OPEN! Find it here: posit.co/blog/posit-g...

posit.co

posit::glimpse() Newsletter – February 2026 - Posit

This edition covers major updates from Posit, including new versions of ggplot2, ellmer, and dplyr.

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Maxim Sakhno @maxim-sakhno.bsky.social · 12/02/2026

Testing #FastAPI vs #LiteStar with nested empty deps (0-5 levels). Both show smooth perf degradation, unlike LiteStar's flat deps with TaskGroup. Still, 5 deps = 28% drop for LiteStar — DI overhead is significant even when deps do nothing. #Python

A bar chart comparing the performance of LiteStar (orange bars) and FastAPI (green bars) in Requests Per Second (RPS) as the number of dependencies ('Num Deps') increases from 0 to 5. The data shows that LiteStar consistently maintains a higher throughput than FastAPI across all dependency counts. For example, at 0 dependencies, LiteStar reaches 45573 RPS versus FastAPI's 37004 RPS. At 5 dependencies, LiteStar holds 32877 RPS while FastAPI drops to 26463 RPS.

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Maxim Sakhno @maxim-sakhno.bsky.social · 10/02/2026

Quick perf test: changed #LiteStar dependency resolution from TaskGroup to await. RPS impact? Minimal vs 17% degradation with TaskGroup at 4 deps. Each request does 4 DB calls. TaskGroup overhead matters more than expected. More details in thread 👇 #Python #Backend

A bar chart comparing performance in Requests Per Second (RPS) between 'TaskGroup' (purple bars) and 'Await' (orange bars) based on the number of dependencies ('Num Deps'). For 1 dependency, TaskGroup achieves 4604 RPS and Await achieves 4628 RPS. For 2 dependencies, TaskGroup is at 4085 RPS while Await is at 4558 RPS. For 4 dependencies, TaskGroup drops to 3817 RPS, whereas Await maintains 4529 RPS, showing that Await scales better as dependency count increases.

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Maxim Sakhno @maxim-sakhno.bsky.social · 06/02/2026

My #PyCon RU 2025 talk (Best Speaker award! 🏆) is now on YouTube: youtu.be/MpQgrhLO6aE English subtitles available! 🇬🇧 I break down the Dependency Inversion Principle, compare popular #Python #DI frameworks, and show why they matter for building better applications.

youtu.be

Как в Python применяется инверсия зависимостей. Максим Сахно, Контур

YouTube video by Видео с мероприятий {speach!

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RS Web Solutions @rswebsols.bsky.social · 31/01/2026

Flask vs Django: Key Differences Every Python Developer Should Know #programming #webdesign ift.tt/Kldvhmr Flask vs Django: a concise guide for Python developers comparing lightweight flexibility with batteries-included completeness. Learn whe…

ift.tt

Flask vs Django: 6 Important Differences You Must Know

Compare Flask vs Django for Python projects. Explore benefits, use cases, and must-know differences. Read our quick expert comparison!

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Maria Sosnovskaya @sosnovskaya.bsky.social · 29/01/2026

Great news! Hope it will help to make litestar faster

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Maria Sosnovskaya @sosnovskaya.bsky.social · 28/01/2026

Ohh, I'm using litestar, but I haven't known about it!

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Olaren @olaren.dev · 25/01/2026

Here is what I've been doing this past week: git.olaren.dev/Olaren/mapto... A little python program that generates poster for cities! You might have seen stuff like this around lately, but I modified it to put an emphasis on rail infrastructure :3

A picture showing an example output of the program on the city of Montréal and the autumn color scheme. You can see a map of the city with all the roads, trains, subways and light rail. At the bottom there the poster is titled MONTRÉAL - QUÉBEC. The latitude and longitude are also written under the title.

A picture showing an example output of the program on the city of Paris and the emerald color scheme. You can see a map of the city with all the roads, trains, subways and light rail. At the bottom there the poster is titled PARIS - FRANCE. The latitude and longitude are also written under the title.

A picture showing an example output of the program on the city of Tokyo and the japanese ink color scheme. You can see a map of the city with all the roads, trains, subways and light rail. At the bottom there the poster is titled TOKYO - JAPAN. The latitude and longitude are also written under the title.

A picture showing an example output of the program on the city of Berlin and the noir color scheme. You can see a map of the city with all the roads, trains, subways and light rail. At the bottom there the poster is titled BERLIN - GERMANY. The latitude and longitude are also written under the title.

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Python Coding (CLCODING) @clcoding.bsky.social · 19/01/2026

One Trick That Can Rewrite Python’s Behavior: Monkey Patching

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Reuven M. Lerner @lernerpython.com · 18/01/2026

If a text column #Python #Pandas data frame uses lots of memory and repeats, make it a category: df['x'].memory_usage(deep=True) # result: 8,093,417 df['x'] = df['x'].astype('category') df['x'].memory_usage(deep=True) # result: 154,853 A 99% savings!

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Maria Sosnovskaya @sosnovskaya.bsky.social · 18/01/2026

Hi everyone! I'm Maria Sosnovskaya, Paython dev It's my first post :) I hope, I will find a lot of expert content here! #Python #IAmNewHere

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