How to map True and False to 'Yes' and 'No' in a pandas data frame for columns of dtype bool only?

I have a pandas data frame (v 0.20.3):

df = pd.DataFrame({'coname1': ['Apple','Yahoo'], 'coname2':['Apple', 'Google']})
df['eq'] = df.apply(lambda row: row['coname1'] == row['coname2'], axis=1).astype(bool)

   coname1 coname2     eq
0    Apple   Apple   True
1    Yahoo  Google  False

If I would like to replace True/False to 'Yes'/'No', I could run this:

df.replace({
                True: 'Yes',
                False: 'No'
            })

   coname1 coname2   eq
0    Apple   Apple  Yes
1    Yahoo  Google   No

Which seems to get the job done. However, if a data frame is just one row with a value of 0/1 in a column, it will be also replaced as it's being treated as Boolean.

df1 = pd.DataFrame({'coname1': [1], 'coname2':['Google'], 'coname3':[777]})
df1['eq'] = True

   coname1 coname2  coname3    eq
0        1  Google      777  True

df1.replace({
                True: 'Yes',
                False: 'No'
            })

  coname1 coname2 coname3   eq
0     Yes  Google     777  Yes

I would like to map True/False to Yes/No for all columns in the data frame that are of dtype bool.

How do I tell pandas to run map True/False to arbitrary strings only for the columns that are of dtype bool without explicitly specifying the names of columns as I may not know them in advance?