Evaluating and cleaning string series data

There are many string cleaning methods in Python and pandas. This is a good thing. Given the great variety of data stored in strings, it is important to have a wide range of tools to call upon when performing string evaluation and manipulation: when selecting fragments of a string by position, when checking whether a string contains a pattern, when splitting a string, when testing a string's length, when joining two or more strings, when changing the case of a string, and so on. We'll explore some of the methods that are used most frequently for string evaluation and cleaning in this recipe.

Getting ready

We will work with the National Longitudinal Survey data in this recipe. (The NLS data was actually a little too clean for this recipe. To illustrate working with strings with trailing spaces, I added trailing spaces to the maritalstatus column values.)

How to do it...

In this recipe, we will perform some common string...