DataFrame

Why we included this project

Performance-sensitive C++ teams often hit a wall when Python's data stack is too slow. DataFrame gives them a native alternative: heterogeneous columns that can hold anything from built-in types to user-defined structs, with join, merge, group-by, slicing, and multi-column sorting built in. It also bundles a practical set of analysis routines, from moving averages and PCA to polynomial fits, FFTs, and trading indicators, so quant groups can skip wiring a second library for statistics. The library stays lean by depending only on the standard library and leaning on multithreading, which suits large in-memory datasets where speed is predictable. If you work with financial or statistical data in C++ and would rather not hand-roll your own columnar machinery, this is a maintained option worth evaluating.