How to print top 10 elements of a dataframe column

Question

mask = df.groupby('prod_title')['trans_quantity'].sum().reset_index()    
mask = mask.sort_values(by='trans_quantity',ascending=False)    
top_num_sales = mask['prod_title'].head(10)

I want to print only the names from the top 10 elements from prod_title

I tried df['prod_title'] but it returns the index also I only want the names of the elements

df.head(20) data below

`

{'trans_id': {0: 10300097,
  1: 10300093,
  2: 10300093,
  3: 10300093,
  4: 10300093,
  5: 10300091,
  6: 10300096,
  7: 10300103,
  8: 10300100,
  9: 10300102,
  10: 10300101,
  11: 10300101,
  12: 10300098,
  13: 10300094,
  14: 10300099,
  15: 10300092,
  16: 10300095,
  17: 10300107,
  18: 10300113,
  19: 10300113},
 'prod_upc': {0: 719638485153,
  1: 73201504044,
  2: 719638485153,
  3: 441530839394,
  4: 733426809698,
  5: 344934101144,
  6: 717036112695,
  7: 242313721729,
  8: 100469015054,
  9: 287663658863,
  10: 441530839394,
  11: 287663658863,
  12: 242313721729,
  13: 374613020864,
  14: 832878954342,
  15: 469757173540,
  16: 73201504044,
  17: 374613020864,
  18: 100469015054,
  19: 469757173540},
 'cust_id': {0: 1001019,
  1: 1001015,
  2: 1001015,
  3: 1001015,
  4: 1001015,
  5: 1001012,
  6: 1001018,
  7: 1001025,
  8: 1001022,
  9: 1001024,
  10: 1001023,
  11: 1001023,
  12: 1001020,
  13: 1001016,
  14: 1001021,
  15: 1001014,
  16: 1001017,
  17: 1001028,
  18: 1001034,
  19: 1001034},
 'trans_timestamp': {0: Timestamp('2021-01-01 07:35:21.439873'),
  1: Timestamp('2021-01-01 09:33:37.499660'),
  2: Timestamp('2021-01-01 09:33:37.499660'),
  3: Timestamp('2021-01-01 09:33:37.499660'),
  4: Timestamp('2021-01-01 09:33:37.499660'),
  5: Timestamp('2021-01-01 10:08:32.241027'),
  6: Timestamp('2021-01-01 10:43:40.372766'),
  7: Timestamp('2021-01-01 11:05:32.193258'),
  8: Timestamp('2021-01-01 11:39:46.749952'),
  9: Timestamp('2021-01-01 11:44:27.467186'),
  10: Timestamp('2021-01-01 12:14:47.132247'),
  11: Timestamp('2021-01-01 12:14:47.132247'),
  12: Timestamp('2021-01-01 12:21:14.473565'),
  13: Timestamp('2021-01-01 12:40:29.169799'),
  14: Timestamp('2021-01-01 12:52:02.370864'),
  15: Timestamp('2021-01-01 13:10:33.543618'),
  16: Timestamp('2021-01-01 14:32:28.903947'),
  17: Timestamp('2021-01-02 03:58:22.059670'),
  18: Timestamp('2021-01-02 06:14:35.006901'),
  19: Timestamp('2021-01-02 06:14:35.006901')},
 'trans_year': {0: 2021,
  1: 2021,
  2: 2021,
  3: 2021,
  4: 2021,
  5: 2021,
  6: 2021,
  7: 2021,
  8: 2021,
  9: 2021,
  10: 2021,
  11: 2021,
  12: 2021,
  13: 2021,
  14: 2021,
  15: 2021,
  16: 2021,
  17: 2021,
  18: 2021,
  19: 2021},
 'trans_month': {0: 1,
  1: 1,
  2: 1,
  3: 1,
  4: 1,
  5: 1,
  6: 1,
  7: 1,
  8: 1,
  9: 1,
  10: 1,
  11: 1,
  12: 1,
  13: 1,
  14: 1,
  15: 1,
  16: 1,
  17: 1,
  18: 1,
  19: 1},
 'trans_day': {0: 1,
  1: 1,
  2: 1,
  3: 1,
  4: 1,
  5: 1,
  6: 1,
  7: 1,
  8: 1,
  9: 1,
  10: 1,
  11: 1,
  12: 1,
  13: 1,
  14: 1,
  15: 1,
  16: 1,
  17: 2,
  18: 2,
  19: 2},
 'trans_hour': {0: 1,
  1: 1,
  2: 1,
  3: 1,
  4: 1,
  5: 1,
  6: 1,
  7: 1,
  8: 1,
  9: 1,
  10: 1,
  11: 1,
  12: 1,
  13: 1,
  14: 1,
  15: 1,
  16: 1,
  17: 2,
  18: 2,
  19: 2},
 'trans_quantity': {0: 1,
  1: 1,
  2: 1,
  3: 2,
  4: 1,
  5: 1,
  6: 1,
  7: 1,
  8: 1,
  9: 1,
  10: 1,
  11: 1,
  12: 2,
  13: 1,
  14: 1,
  15: 1,
  16: 1,
  17: 1,
  18: 1,
  19: 3},
 'cust_age': {0: 20,
  1: 34,
  2: 34,
  3: 34,
  4: 34,
  5: 26,
  6: 26,
  7: 24,
  8: 27,
  9: 35,
  10: 29,
  11: 29,
  12: 28,
  13: 29,
  14: 19,
  15: 32,
  16: 31,
  17: 21,
  18: 29,
  19: 29},
 'cust_state': {0: 'New York',
  1: 'New York',
  2: 'New York',
  3: 'New York',
  4: 'New York',
  5: 'New Jersey',
  6: 'Pennsylvania',
  7: 'New Jersey',
  8: 'New York',
  9: 'Connecticut',
  10: 'Connecticut',
  11: 'Connecticut',
  12: 'Pennsylvania',
  13: 'New York',
  14: 'Pennsylvania',
  15: 'New Jersey',
  16: 'Connecticut',
  17: 'Connecticut',
  18: 'Pennsylvania',
  19: 'Pennsylvania'},
 'prod_price': {0: 72.99,
  1: 18.95,
  2: 72.99,
  3: 28.45,
  4: 18.95,
  5: 24.95,
  6: 60.99,
  7: 65.99,
  8: 18.95,
  9: 9.95,
  10: 28.45,
  11: 9.95,
  12: 65.99,
  13: 10.97,
  14: 45.99,
  15: 35.99,
  16: 18.95,
  17: 10.97,
  18: 18.95,
  19: 35.99},
 'prod_title': {0: 'Cat Cave',
  1: 'Purrfect Puree',
  2: 'Cat Cave',
  3: 'Ball and String',
  4: 'Yum Fish-Dish',
  5: 'Fetch Blaster',
  6: 'Reddy Beddy',
  7: 'Reddy Beddy',
  8: 'Tuna Tasties',
  9: 'All Veggie Yummies',
  10: 'Ball and String',
  11: 'All Veggie Yummies',
  12: 'Reddy Beddy',
  13: 'Chewie Dental',
  14: 'Snoozer Hammock',
  15: 'Kitty Climber',
  16: 'Purrfect Puree',
  17: 'Chewie Dental',
  18: 'Tuna Tasties',
  19: 'Kitty Climber'},
 'prod_category': {0: 'bedding',
  1: 'treat',
  2: 'bedding',
  3: 'toy',
  4: 'food',
  5: 'toy',
  6: 'bedding',
  7: 'bedding',
  8: 'treat',
  9: 'treat',
  10: 'toy',
  11: 'treat',
  12: 'bedding',
  13: 'treat',
  14: 'bedding',
  15: 'toy',
  16: 'treat',
  17: 'treat',
  18: 'treat',
  19: 'toy'},
 'prod_animal_type': {0: 'cat',
  1: 'cat',
  2: 'cat',
  3: 'cat',
  4: 'cat',
  5: 'dog',
  6: 'dog',
  7: 'dog',
  8: 'cat',
  9: 'dog',
  10: 'cat',
  11: 'dog',
  12: 'dog',
  13: 'dog',
  14: 'cat',
  15: 'cat',
  16: 'cat',
  17: 'dog',
  18: 'cat',
  19: 'cat'},
 'total_sales': {0: 72.99,
  1: 18.95,
  2: 72.99,
  3: 56.9,
  4: 18.95,
  5: 24.95,
  6: 60.99,
  7: 65.99,
  8: 18.95,
  9: 9.95,
  10: 28.45,
  11: 9.95,
  12: 131.98,
  13: 10.97,
  14: 45.99,
  15: 35.99,
  16: 18.95,
  17: 10.97,
  18: 18.95,
  19: 107.97}}

`

Answer 1

You're looking for help on indexing and selecting data. This should work for your specific problem

df = pd.DataFrame({
    'prod_title' : np.random.permutation([l for l in 'abcdefghijklmnopqrstuvwxyz']),
    'total_sales' : np.random.randint(low=10000, high=400000, size=26)
})

df.sort_values(by='total_sales')['prod_title'].values[:10]

>>> array(['f', 'u', 'x', 's', 'y', 'l', 'e', 'q', 'n', 'c'], dtype=object)