Polars Expressions
Polars has a powerful concept called expressions that is central to its very fast performance.
Expressions are at the core of many data science operations:
- taking a sample of rows from a column
- multiplying values in a column
- extracting a column of years from dates
- convert a column of strings to lowercase
- and so on!
However, expressions are also used within other operations:
- taking the mean of a group in a
groupbyoperation - calculating the size of groups in a
groupbyoperation - taking the sum horizontally across columns
Polars performs these core data transformations very quickly by:
- automatic query optimization on each expression
- automatic parallelization of expressions on many columns
Polars expressions are a mapping from a series to a series (or mathematically Fn(Series) -> Series). As expressions have a Series as an input and a Series as an output then it is straightforward to do a sequence of expressions (similar to method chaining in Pandas).
This has all been a bit abstract, so let's start with some examples.