Tidying when multiple variables are stored as column values

Tidy datasets must have a single column for each variable. Occasionally, multiple variable names are placed in a single column with their corresponding value placed in another. The general format for this kind of messy data is as follows:

In this example, the first and last three rows represent two distinct observations that should each be rows. The data needs to be pivoted such that it ends up like this:

Getting ready

In this recipe, we identify the column containing the improperly structured variables and pivot it to create tidy data.

How to do it...

1. Read in the restaurant inspections dataset, and convert the Date column data type to datetime64:

>>> inspections = pd.read_csv('data/restaurant_inspections.csv',
                              parse_dates=['Date'])
>>> inspections.head()

2. This dataset has two variables, Name and Date, that are each correctly contained in a single column. The Info column itself has five different...