We have the following DataFrames that store lists of musical instruments that we can buy in a music store, the identifier of their category, and average prices:
keyboard_instruments = pd.DataFrame({'cat_id': ['001', '002', '003'],
'Instrument': ['Acoustic piano', 'Electric piano', 'Synthesizer'],
'Average price': ['$10,000', '$5,000', '$1,200']},
index=[1, 2, 3])
string_instruments = pd.DataFrame({'cat_id': ['004', '005', '006'],
'Instrument': ['Acoustic guitar', 'Cello', 'Violin'],
'Average price': ['$2,000', '$1,500', '$2,000']},
index=[1, 2, 3])After concatenation, we got the following dataset:
+----+----------+-----------------+-----------------+
| | cat_id | Instrument | Average price |
|----+----------+-----------------+-----------------|
| 0 | 001 | Acoustic piano | $10,000 |
| 1 | 002 | Electric piano | $5,000 |
| 2 | 003 | Synthesizer | $1,200 |
| 3 | 004 | Acoustic guitar | $2,000 |
| 4 | 005 | Cello | $1,500 |
| 5 | 006 | Violin | $2,000 |
+----+----------+-----------------+-----------------+Which parameters did we adjust to create this DataFrame? Write the body of the function concatenate_data() that will process two given datasets in the same way and return the resultant combined object. You do NOT need to call the function or print any results.
