Lightweight DataFrame validation decorators for Pandas, Polars, Modin, and PyArrow. No custom types required.

pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).

Um pipeline de ETL modular para automação de mailing com deduplicação, enriquecimento e otimização de performance.

Polars, Pandas, Dask, and Modin

Lightweight, engine-agnostic dataframe validation

Modin: Scale your Pandas workflows by changing a single line of code

Generate data, load and download parquets to GCP and compare performance between Pandas, Polars and Modin with Python

Simple stats on SGE accounting data

Distributed XGBoost on Ray

A low-level execution library for analytic data processing.

Global Markets Options Pricing

A Bioinformatics demo in Python working with FASTQ files and using the Modin library

A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner

Python and Pandas are known to have issues around scalability and efficiency. You will learn how to use libraries such as Modin, Dask, Ray, Vaex etc to overcome the problems faced by Pandas.

AI Starter Kit to generate structured synthetic data using Intel® Distribution of Modin

oneAPI Hackathon: The LLM Challenge

Build a machine model to predict whether the freshwater is safe to drink or not.Based on the measures like pH, TDS, etc.

HHA507 / Data Science / Assignment 2 / Data Manipulation

Delve deeper into data manipulation using Python's prominent libraries. Explore the functionalities of Pandas and get a glimpse of alternatives like Polars, Dask, and Modin.