Over the past two decades, the advent of Integral Field Spectroscopy (IFS) has revolutionized the field of astronomy, enabling comprehensive analysis of both the spatial and spectroscopic properties of extended objects. However, this technological leap has also introduced significant challenges in the reduction and processing of IFS data. The complex nature of IFS datasets requires meticulous correction and calibration steps, often demanding customized pipelines tailored to specific instruments.

To address these challenges, we present PyKOALA: a cutting-edge, open-source Python package designed to streamline the reduction of IFS data. Originally conceived as an expert pipeline component to complement the outputs of 2dfdr and enhance the data reduction process for the Kilofibre Optical AAT Lenslet Array (KOALA) Integral Field Unit (IFU), PyKOALA's vision has expanded over the past few years from a single-instrument focus to a versatile, multi-instrument framework. It now provides a modular and flexible framework, that allows astronomers to customize their reduction sequences and apply an arbitrary number of corrections across various IFS instruments. PyKOALA offers a streamlined interface that facilitates the ingestion of data from different IFUs, standardizing the fundamental properties of IFS data for consistent processing.

The first official release of PyKOALA is expected during early 2025, though its current beta version already features comprehensive documentation and a suite of Jupyter notebook tutorials to ease the learning curve. In this talk, I will showcase PyKOALA’s powerful capabilities, highlighting key features such as multi-instrument support and advanced correction modules, with examples from the initial results of the HI-KOALA IFS Dwarf Galaxy Survey (HI-KIDS).