Tidying variable values as column names with stack

To help understand the differences between tidy and messy data, let's take a look at a table that may or may not be in tidy form:

>>> import pandas as pd
>>> import numpy as np
>>> state_fruit = pd.read_csv('data/state_fruit.csv', index_col=0)
>>> state_fruit
         Apple  Orange  Banana
Texas       12      10      40
Arizona      9       7      12
Florida      0      14     190

There does not appear to be anything messy about this table, and the information is easily consumable. However, according to the tidy principles, it isn't tidy. Each column name is the value of a variable. In fact, none of the variable names are even present in the DataFrame. One of the first steps to transform a messy dataset into tidy data is to identify all of the variables. In this particular dataset, we have variables for state and fruit. There's also the numeric data that wasn&apos...