How to filter column names from multiindex dataframe for a specific condition?

df1 = pd.DataFrame(
{
    "empid" : [1,2,3,4,5,6],
    "empname" : ['a', 'b','c','d','e','f'],
    "empcity" : ['aa','bb','cc','dd','ee','ff']
})
df1

df2 = pd.DataFrame(
{
    "empid" : [1,2,3,4,5,6],
    "empname" : ['a', 'b','m','d','n','f'],
    "empcity" : ['aa','bb','cc','ddd','ee','fff']
})
df2

df_all = pd.concat([df1.set_index('empid'),df2.set_index('empid')],axis='columns',keys=['first','second'])
df_all

df_final = df_all.swaplevel(axis = 'columns')[df1.columns[1:]]
df_final

orig = df1.columns[1:].tolist()
print (orig)
['empname', 'empcity']

df_final = (df_all.stack()
                  .assign(comparions=lambda x: x['first'].eq(x['second']))
                  .unstack()
                  .swaplevel(axis = 'columns')
                  .reindex(orig, axis=1, level=0))
print (df_final)

How to filter level[0] column name list where comparions = False from the dataframe df_final(consider there are more than 300 column like this at level 0)

Answer

First test if in level comparions are all Trues by DataFrame.xs with DataFrame.all:

s = df_final.xs('comparions', level=1, axis=1).all()

And then invert mask for test at least one False with filter indices:

L = s.index[~s].tolist()
print (L)
['empname', 'empcity']