Pandas explode dictionary to rows

We can create a list of tuples of Name, Marks and Subjects by iterating over the values of dataframe inside a list comprehension, then we can create a new dataframe from this list of tuples

out = pd.DataFrame([(n, k, v) for (n, d) in df.values for k, v in d.items()])
out.columns = ['Name', 'Subject', 'Marks']

Alternative pandas based approach

m = pd.DataFrame([*df['Sub_Marks']], df.index).stack()\
      .rename_axis([None,'Subject']).reset_index(1, name='Marks')

out = df[['Name']].join(m)

>>> out

    Name  Subject  Marks
0    Tom    Maths     30
1    Tom  English     40
2    Tom  Science     35
3  Harry    Maths     35
4  Harry  English     30
5  Harry  Science     25
6   Nick    Maths     32
7   Nick  English     23
8   Nick  Science     20

You can extract only the values from the dictionaries and then expand it to multiple columns like this:

data = {'Name' : ['Tom', "Harry", "Nick"], "Sub_Marks" : [{'Maths': 30, 'English': 40, 'Science': 35},{'Maths': 35, 'English': 42, 'Science': 31},{'Maths': 20, 'English': 14, 'Science': 65}]}
df = pd.DataFrame(data)

df[['Maths','English', 'Science']] = df['Sub_Marks'].apply(pd.Series)
df.drop(columns=['Sub_Marks'], inplace=True)
df = df.set_index('Name').stack().reset_index()
df.columns = ['Name', 'Subject', 'Marks']

You can use .apply() with pd.Series() to 'explode' the dictionary into columns and then use .melt() to transform the columns into Subject and Marks columns, as follows:

(df.drop(columns='Sub_Marks')
   .join(df.apply(lambda x: pd.Series(x['Sub_Marks']), axis=1))
   .melt(id_vars='Name', value_vars=['Maths', 'English', 'Science'], var_name='Subject', value_name='Marks')
   .sort_values('Name')
).reset_index(drop=True)

You can also use pd.DataFrame() together with to_list() to 'explode' the dictionary:

(df.join(pd.DataFrame(df.pop('Sub_Marks').to_list()))
   .melt(id_vars='Name', value_vars=['Maths', 'English', 'Science'], var_name='Subject', value_name='Marks')
   .sort_values('Name')
).reset_index(drop=True)

Output:

    Name  Subject  Marks
0  Harry    Maths     35
1  Harry  English     30
2  Harry  Science     25
3   Nick    Maths     32
4   Nick  English     23
5   Nick  Science     20
6    Tom    Maths     30
7    Tom  English     40
8    Tom  Science     35

Alternate method via explode -

df['Marks'] = df['Sub_Marks'].apply(lambda x: x.values())
df = df.apply(pd.Series.explode).rename(columns = {'Sub_Marks': 'Subject'})

You might wanna use ast.literal_eval first (if above method doesn't work) -

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