String types
The string data type is the appropriate choice for any data that represents text. Unless you are working in a purely scientific domain, chances are that strings will be prevalent throughout the data that you use.
In this recipe, we will highlight some of the additional features pandas provides when working with string data, most notably through the pd.Series.str accessor. This accessor helps to change cases, extract substrings, match patterns, and more.
As a technical note, before we jump into the recipe, strings starting in pandas 3.0 will be significantly overhauled behind the scenes, enabling an implementation that is more type-correct, much faster, and requires far less memory than what was available in the pandas 2.x series. To make this possible in 3.0 and beyond, users are highly encouraged to install PyArrow alongside their pandas installation. For users looking for an authoritative reference on the why and how of strings in pandas 3.0, you may reference...
