Transcript#
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We got this great question asking about the difference between Numpy and Polars.
At the high level, Numpy is meant for matrices, and matrices require homogeneous data types, so everything in the grid must be the exact same type, so for example, floating point numbers. This makes Numpy incredibly fast for things like linear algebra and multidimensional math. Polars, on the other hand, is a data frame library. Data frames are built for tabular data, so think of like a spreadsheet, and with data frames, your columns can be of different types, so you can have strings, integers, dates, and so on in the same table.
In terms of their relationship, they both use structured data. Polars uses Apache Arrow and is built on Rust, and it also does something called lazy execution, so it makes it incredibly fast for querying massive tables. And so, in conclusion, Numpy for complex matrix math, and Polars for fast tabular data processing.
And so, in conclusion, Numpy for complex matrix math, and Polars for fast tabular data processing.
