'''
Reshaping your data using melt

Melting data is the process of turning columns of your data into rows of data. 
Consider the DataFrames from the previous exercise. In the tidy DataFrame, the 
variables Ozone, Solar.R, Wind, and Temp each had their own column. If, however, 
you wanted these variables to be in rows instead, you could melt the DataFrame. 
In doing so, however, you would make the data untidy! This is important to keep in 
mind: Depending on how your data is represented, you will have to reshape it 
differently.

In this exercise, you will practice melting a DataFrame using pd.melt(). There are 
two parameters you should be aware of: id_vars and value_vars. The id_vars 
represent the columns of the data you do not want to melt (i.e., keep it in its 
current shape), while the value_vars represent the columns you do wish to melt into 
rows. By default, if no value_vars are provided, all columns not set in the id_vars 
will be melted. This could save a bit of typing, depending on the number of columns 
that need to be melted.

The (tidy) DataFrame airquality has been pre-loaded. Your job is to melt its Ozone, 
Solar.R, Wind, and Temp columns into rows. Later in this chapter, you'll learn how 
to bring this melted DataFrame back into a tidy form.

INSTRUCTIONS
100 XP
Print the head of airquality.
Use pd.melt() to melt the Ozone, Solar.R, Wind, and Temp columns of airquality into 
rows. Do this by using id_vars to specify the columns you do not wish to melt: 
'Month' and 'Day'.
Print the head of airquality_melt.
'''
