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docu #17

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Oct 7, 2024
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docu #17

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13 changes: 8 additions & 5 deletions README.md
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Expand Up @@ -52,12 +52,15 @@ for i in range(1000):
> ```

### Lightning fast - really?
For aggregated point queries NanoCube are up to 100x or even 1,000x times faster than Pandas.
For this special purpose, NanoCube is even faster than other DataFrame oriented libraries,
like Spark, Polars, Modin, Dask or Vaex. If such libraries are a drop-in replacements for Pandas,
then you should be able to accelerate them with NanoCube too. Try it and let me know.
For aggregated point queries NanoCube are up to 100x or even 1,000x times faster than Pandas.
When proper sorting is applied to your DataFrame, the performance might improve even further.

NanoCube is beneficial only if multiple point queries (> 10) need to be executed, as the
For the special purpose of aggregative point queries, NanoCube is even by factors faster than other
DataFrame oriented libraries, like Spark, Polars, Modin, Dask or Vaex. If such libraries are
a drop-in replacements for Pandas, then you should be able to accelerate them with NanoCube too.
Try it and let me know.

NanoCube is beneficial only if some point queries (> 5) need to be executed, as the
initialization time for the NanoCube needs to be taken into consideration.
The more point query you run, the more you benefit from NanoCube.

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