Pandas: how do I group a Data Frame by a set of ordinal values? - pandas

Question

I'm starting to learn about Python Pandas and want to generate a graph with the sum of arbitrary groupings of an ordinal value. It can be better explained with a simple example.
Suppose I have the following table of food consumption data:
And I have two groups of foods defined as two lists:
healthy = ['apple', 'brocolli']
junk = ['cheetos', 'coke']
Now I want to plot a graph with the evolution of consumption of junk and healthy food. I believe I must then process my data to get a DataFrame like:
Suppose the first table is already in a Dataframe called food, how do I transform it to get the second one?
I also welcome suggestions to reword my question to make it clearer, or for different approaches to generate the plot.

Answer 1

First create dictinary with lists and then swap keys with values.
Then groupby by mapped column food by dict and year, aggregate sum and last reshape by unstack:
healthy = ['apple', 'brocolli']
junk = ['cheetos', 'coke']
d1 = {'healthy':healthy, 'junk':junk}
##http://stackoverflow.com/a/31674731/2901002
d = {k: oldk for oldk, oldv in d1.items() for k in oldv}
print (d)
{'brocolli': 'healthy', 'cheetos': 'junk', 'apple': 'healthy', 'coke': 'junk'}
df1 = df.groupby([df.food.map(d), 'year'])['amount'].sum().unstack(0)
print (df1)
food healthy junk
year
2010 10 11
2011 17 10
2012 13 24
Another solution with pivot_table:
df1 = df.pivot_table(index='year', columns=df.food.map(d), values='amount', aggfunc='sum')
print (df1)
food healthy junk
year
2010 10 11
2011 17 10
2012 13 24