Numerical Computing with Python

Tidying when multiple variables are stored as column names

One particular flavor of messy data appears whenever the column names contain multiple different variables themselves. A common example of this scenario occurs when age and sex are concatenated together. To tidy datasets like this, we must manipulate the columns with the pandas str accessor, an attribute that contains additional methods for string processing.