Compute row average in pandas

df.drop('Region', axis=1).apply(lambda x: x.mean())

Pandas drop index type columns

df.index = df.index.droplevel(1)

This strategy is also useful if you want to combine the names from both levels like in the example below where the bottom level contains two 'y's:

cols = pd.MultiIndex.from_tuples([("A", "x"), ("A", "y"), ("B", "y")])
df = pd.DataFrame([[1,2, 8 ], [3,4, 9]], columns=cols)

A B
x y y
0 1 2 8
1 3 4 9
Dropping the top level would leave two columns with the index 'y'. That can be avoided by joining the names with the list comprehension.

df.columns = ['_'.join(col) for col in df.columns]

A_x A_y B_y

0 1 2 8
1 3 4 9

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Dropping rows in pandas with .index

As explained in the documentation, you can use drop with index:

   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

df.drop([0, 1]) # Here 0 and 1 are the index of the rows

Output:

   A  B   C   D
2  8  9  10  11

In this case it will drop the first 2 rows. With .index in your example, you find the rows where Quantity=0and retrieve their index(and then use like in the documentation)