GS-Calc Citadel5
winget install --id=Citadel5-JP.GS-Calc -e GS-Calc is a spreadsheet that can be used for various purposes thanks to its scalability - editing/cleaning CSV, text files with millions of rows, splitting/merging files in the XLSX/XLS, text and other file formats, building complex data models with millions of formulas and around 450 built-in functions. GS-Calc features fast binary lookup's with dynamic internal sorting when processing tens of gigabytes of data or more, 4GB+ workbook files, 32 million rows x 16,384 columns, a unique feature of organizing worksheets in a tree form, pivot tables with 32 million rows, up to 64 processor cores during calculations, built-in Monte Carlo Simulations, array/"spilling" formulas for any functions written by users in C/C++ as plain DLL libraries even to replace all built-in functions; Python integration with UDF functions. You can work in up to 100 synchronized panes for each open workbook to monitor results in various sheets and their regions. GS-Calc can be installed on any portable storage device and used without performing any registry modifications.
GS-Calc is a modern spreadsheet designed for efficient big data processing, enabling users to handle large datasets with ease and precision. It supports millions of rows and columns, making it ideal for tasks like editing CSV files, merging XLSX documents, and building complex data models with millions of formulas. Key features include pivot tables capable of handling up to 32 million rows, Monte Carlo simulations for risk analysis, Python integration for custom functions, and the ability to utilize up to 64 processor cores for enhanced performance. The tool also allows organizing worksheets in a tree structure for better organization.
Ideal for data analysts, engineers, finance professionals, and researchers, GS-Calc provides high-performance data processing without the need for internet access. It can be installed via winget, ensuring seamless integration into workflows. This tool is perfect for those requiring precision and efficiency in managing large datasets offline.