how to create a DF from a DF based on a condition

you can also try creating a dataframe as a reference , then mask where keyword matches with stack for dropping NaN:

keyword = 'DOSA'

m = pd.DataFrame(df['Combinations'].tolist(),index=df.index)
c = m.eq(keyword).any(1)
df[m.eq(keyword).any(1)].assign(Combinations=
                         m[c].where(m[c].ne(keyword)).stack().droplevel(1))

   Combinations  Count
6          IDLY   2020
9     CHAPPATHI   1297
13         VADA   1141

For string type, you can convert into tuple by:

import ast
df['Combinations'] = df['Combinations'].apply(ast.literal_eval)

In general, it's not ideal to have list, tuples, sets, etc inside a dataframe. It's better to have multiple records for each instance when needed.

You can use explode turn Combinations into this form and filter on that

keyword = 'DOSA'

s = df.explode('Combinations')

s.loc[s.Combinations.eq('keyword').groupby(level=0).transform('any') & s.Combinations.ne('keyword')]

Or chain the two commands with .loc[lambda ]:

(df.explode('Combinations')
   .loc[lambda x: x.Combinations.ne(keyword) & 
            x.Combinations.eq(keyword).groupby(level=0).transform('any')]
)

Output:

   Combinations  Count
6          IDLY   2020
9     CHAPPATHI   1297
13         VADA   1141

What I will do

x=df.explode('Combinations')
x=x.loc[x.index[x.Combinations=='DOSA']].query('Combinations !="DOSA"')
x
   Combinations  Count
6          IDLY   2020
9     CHAPPATHI   1297
13         VADA   1141

d = df[df['Combinations'].transform(lambda x: 'DOSA' in x)].copy()
d['Combinations'] = d['Combinations'].apply(lambda x: set(x).difference(['DOSA']).pop())
print(d)

Prints:

   ID Combinations  Count
1   6         IDLY   2020
2   9    CHAPPATHI   1297
5  13         VADA   1141