Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀

the portable Python dataframe library

Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

Lightweight and extensible compatibility layer between dataframe libraries!

Work with bioinformatic files using Arrow, Polars, and/or DuckDB

Command-line interface to quickly generate fake CSV and JSON data

Exploring Chicago crimes dataset with Jupyter notebooks, DuckDB, Malloy and new Panel/PyScript data and dashboard tools.

An open-source tool for reading OvertureMaps data with multiprocessing and additional Quality-of-Life features

Converts a whole subdirectory with a big (or small) volume of PDF documents to a dataset (pandas DataFrame) with error tracking and choice of features

db2ixf is a python package with a CLI that simplifies the parsing and processing of IBM Integration eXchange Format (IXF) files.

Type annotations for pyarrow

(PoC) A very memory-efficient way to read data from PostgreSQL

A web application for viewing Apache Parquet files . This is a Python + Flask application

Poor mans simple python api for creating a local or remote datalake based on several (pyarrow) datasets using duckdb

Reading both XLSX and XLSB files, fast and memory-safe, with Python, into PyArrow

Seamlessly switch Pandas DataFrame backend to PyArrow.

SQL2Arrow, short for 'SQL to Arrow,' is a Python library that provides convenient and high-performance methods to parse INSERT SQL statements into Arrow arrays. It is particularly useful for analyzing data dumped by mysqldump or other tools.

ibm_db extension to load a pyarrow table to db2

highspeed timeseries pandas dataframe database

A loose implementation of the deltalake protocol, written in Python on top of pyarrow, focused on extensibility, customizability, and distributed data.