5 Python Libraries You absolutely need

@baniascodes

3.0K followers · usually 1.5K views a post

Views

6.1K

Likes

312

Comments

5

Shares

26

SoundTikTok sound · original sound - dst.aa

Promoted product

asyncpg

A fast PostgreSQL Database Client Library for Python/asyncio. - MagicStack/asyncpg

Why it worked

The post provides a curated list of useful Python libraries, directly addressing a common need for developers. The clear, concise presentation of each library's function and benefits, combined with visually appealing graphics, makes it highly shareable and informative.

Summary

This post highlights five essential Python libraries for developers. It details each library with a brief description and its primary function, covering areas like database interaction, text-to-speech, foundation models, web scraping, and vector search.

Structure

1Introduction of 5 Python libraries
2asyncpg: Async Postgres client
3pocket-tts: Fast TTS on CPU
4TabFM: Foundation Models outperforming XGBoost
5

Original caption

5 Python libraries you really need to use in your next projects. [python, ai engineering, data science, ml, deep learning, tts, postgres, web scraping]

More from @baniascodes

Product placement

The video showcases five Python libraries: asyncpg, pocket-tts, TabFM, Scrapling, and turbovec. Each library is presented with its name, a brief description, and a visual representation of its logo or interface.

On-screen text

5 Python Libraries You absolutely need 1. asyncpg Async postgres client and very fast asyncpg -- A fast PostgresQL Database Client Library for Python/asyncio 2. pocket-tts Fast TTS on CPU Pocket TTS A lightweight text-to-speech (TTS) application designed to run efficiently on CPUs. Forget about the hassle of using GPUs and web APIs serving TTS models. With Kyutai's Pocket TTS, generating audio is just a pip install and a function call away. 3. TabFM Foundation Models outperforming XGBoost TabFM: Tabular Foundation Models TabFM (Tabular Foundation Model) is a scikit-learn compatible tabular foundation model. It allows you to perform zero-shot classification and regression on tabular datasets with mixed column types out-of-the-box. At inference time, TabFM does not require training parameters on your dataset; instead, it leverages in-context learning by reading your training data as "context" to make instant predictions on new test samples. 4. Scrapling The best web scraper Scrapling Effortless Web Scraping for the Modern Web #1 Repository Of The Day 5. turbovec Fast and memory-efficient vector search turbovec Google's TurboQuant for vector search license MIT py v0.8.0 crates.io v0.9.0 paper arXiv A 10 million document corpus takes 31 GB of RAM as float32. turbovec fits in 4 GB - and searches it faster than FAISS. turbovec is a Rust vector index with Python bindings, built on Google Research's TurboQuant algorithm - a data-oblivious quantizer with near-optimal distortion and no separate training phase.