I think you need if is necessary convert MultiIndex to Index:

df.columns = df.columns.map(''.join)

Or if need remove level use droplevel:

df.columns = df.columns.droplevel(0)

If need access to values is possible use xs:

df = df.xs('CID', axis=1, level=1)

You can also check:

What is the difference between size and count in pandas?

EDIT:

For remove MultiIndex is another solution select by ['FID'].

df = df.groupby(by=['CID','FE'])['FID'].count().unstack().reset_index()

Samples (also added rename_axis for nicer output):

df = pd.DataFrame({'CID':[2,2,3],
                   'FE':[5,5,6],
                   'FID':[1,7,9]})

print (df)
   CID  FE  FID
0    2   5    1
1    2   5    7
2    3   6    9

df = df.groupby(by=['CID','FE'])['FID']
       .count()
       .unstack()
       .reset_index()
       .rename_axis(None, axis=1)

print (df)    
   CID    5    6
0    2  2.0  NaN
1    3  NaN  1.0

You can use MultiIndex.droplevel:

Copy>>> cols = pd.MultiIndex.from_tuples([("a", "b"), ("a", "c")])
>>> df = pd.DataFrame([[1,2], [3,4]], columns=cols)
>>> df
   a   
   b  c
0  1  2
1  3  4

[2 rows x 2 columns]
>>> df.columns = df.columns.droplevel()
>>> df
   b  c
0  1  2
1  3  4

[2 rows x 2 columns]

You can use MultiIndex.droplevel:

df.columns = df.columns.droplevel(0)

Another solution should be changed pivot_table, obviosly remove [] around ['purchase_count']