How to fill missing value based on other columns in Pandas dataframe?
Assuming three columns of your dataframe is a, b and c. This is what you want:

df['c'] = df.apply(
    lambda row: row['a']*row['b'] if np.isnan(row['c']) else row['c'],
    axis=1
)


Full code:

df = pd.DataFrame(
    np.array([[1, 2, 3], [4, 5, np.nan], [7, 8, 9], [3, 2, np.nan], [5, 6, np.nan]]), 
    columns=['a', 'b', 'c']
)
df['c'] = df.apply(
    lambda row: row['a']*row['b'] if np.isnan(row['c']) else row['c'],
    axis=1
)