'''
Reshaping your data

Now that you have all the data combined into a single DataFrame, the next step is 
to reshape it into a tidy data format.

Currently, the gapminder DataFrame has a separate column for each year. What you 
want instead is a single column that contains the year, and a single column that 
represents the average life expectancy for each year and country. By having year in 
its own column, you can use it as a predictor variable in a later analysis.

You can convert the DataFrame into the desired tidy format by melting it.

INSTRUCTIONS
100 XP
Reshape gapminder by melting it. Keep 'Life expectancy' fixed by specifying it as 
an argument to the id_vars parameter.
Rename the three columns of the melted DataFrame to 'country', 'year', 
and 'life_expectancy' by passing them in as a list to gapminder_melt.columns.
Print the head of the melted DataFrame.
'''
