pandas is a batteries included dataframe library, implementing hundreds of generic operations for tabular data, such as math or string operations, aggregations and window functions... In some case, domain specific code may benefit from user defined functions (UDFs) that implement some particular logic. These functions can sometimes be implemented using more basic pandas vectorized operations, and they will be reasonably fast, but in some others a Python function working with the individual values needs to be implemented, and those will execute orders of magnitude slower than their equivalent vectorized versions. In this tutorial we will see how to implement functions in Rust that can be used with dataframe values at the individual level, but run at the speed of vectorized code, and in some cases faster.