It's a lovely idea to build pandas like functionality on top of NumPy's structured dtypes, but these benchmarks comparing PandaPy to Pandas are extremely misleading. The largest input dataset has 1258 rows and 9 columns, so basically all these tests shows is that PandaPy has less Python overhead.

For a more representative comparison, let's make everything 1000x larger, e.g., closing = np.concatenate(1000 * [closing])

Here's how a few representative benchmark change:

- describe: PandasPy was 5x faster, now 5x slower

- add: PandasPy was 2-3x faster than pandas, now ~15x slower

- concat: PandasPy was 25-70x faster, now 1-2x slower

- drop/rename: PandasPy is now ~1000x faster (NumPy can clearly do these operations without any data copies)

I couldn't test merge because it needs a sorted dataset, but hopefully you get the idea -- these benchmarks are meaningless, unless for some reason you only care about manipulating small datasets very quickly.

At large scale, pandas has two major advantages over NumPy/PandasPy:

- Pandas (often) uses a columnar data format, which makes it much faster to manipulate large datasets.

- Pandas has hash tables which it can rely upon for fast look-ups instead sorting.