Solution: Summing and Swapping

def Sum_Swap(df):
    
    minm_r = np.min(df, axis = 1) # Get minimum elements from all rows
    
    maxm_r = np.max(df, axis = 1) # Get maximum elements from all rows
    
    df['row_sum'] = minm_r + maxm_r # Add the min & max values and assign them to new column
    minm_c = np.min(df, axis = 0) # Get minimum elements from all columns
    
    maxm_c = np.max(df, axis = 0) # Get maximum elements from all columns
    
    df.loc['col_sum'] = minm_c + maxm_c # Add the min & max values and assign them to new row
    a, b = df['row_sum'].copy(), df.loc['col_sum'].copy() # Store values of row and column in temparory variables
    
    df['row_sum'], df.loc['col_sum'] = b, a # Interchange the values
    
    return df
# Test Code
df = pd.DataFrame(np.random.randint(1, 100, 25).reshape(5, 5))
    
df_res = Sum_Swap(df.copy())
print(df_res)