# 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
`groupby`

operation - calculating the size of groups in a
`groupby`

operation - 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.