Filtering pandas df with level values

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I have the following pandas df:

df
                        price           max    maxperhour
Site  Commodity Type                        
Mid   Biomass   Stock     6.0  1.500000e+15  1.500000e+15
      CO2       Env       0.0  1.500000e+15  1.500000e+15
      Coal      Stock     7.0  1.500000e+15  1.500000e+15
      Elec      Demand    NaN           NaN           NaN
      Gas       Stock    27.0  1.500000e+15  1.500000e+15
      Hydro     SupIm     NaN           NaN           NaN
      Lignite   Stock     4.0  1.500000e+15  1.500000e+15
      Solar     SupIm     NaN           NaN           NaN
      Wind      SupIm     NaN           NaN           NaN

I would like to filter above mentioned df and create a list of Commodity items as a list, when Site == 'Mid' and Type == ('Stock' or 'Demand').

so following list should be created with some pandas filtering function:

df.somefunction()
['Biomass', 'Coal', 'Gas', 'Lignite', 'Elec']

How would I achieve this?

Lastly if it is possible I would like to have 'Elec' as the last element, what I mean is; when the list is created, 'Elec' would be probably the third element of the list like:

['Biomass', 'Coal', 'Elec', 'Gas', 'Lignite']

However, it would be best if I can get 'Elec' as the last element like:

['Biomass', 'Coal', 'Gas', 'Lignite', 'Elec']

since it is the only element with Type == 'Demand'

From @jezrael

df[(df.index.get_level_values('Site') == 'Mid') & (df.index.get_level_values('Type') == 'Stock')].index.remove_unused_levels().get_level_values('Commodity').tolist()
levels pandas filter list python
1 Answers

Solution with MultiIndex:

m1 = (df.index.get_level_values('Site') == 'Mid')
m2 = (df.index.get_level_values('Type') == 'Stock')
m3 = (df.index.get_level_values('Type') == 'Demand')

idx1 = df[m1 & m2].index.remove_unused_levels().get_level_values('Commodity')
idx2 = df[m1 & m3].index.remove_unused_levels().get_level_values('Commodity')

idx = idx1.append(idx2)
print (idx)
Index(['Biomass', 'Coal', 'Gas', 'Lignite', 'Elec'], dtype='object', name='Commodity')

Alternative with columns:

df1 = df.reset_index()
m1 = (df1['Site'] == 'Mid')
m2 = (df1['Type'] == 'Stock')
m3 = (df1['Type'] == 'Demand')

idx1 = df1.loc[m1 & m2, 'Commodity']
idx2 = df1.loc[m1 & m3, 'Commodity']

idx = idx1.append(idx2).tolist()
print (idx)
['Biomass', 'Coal', 'Gas', 'Lignite', 'Elec']
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