import pandas as pd
df = pd.read_csv('result.csv')
df
Order_ID | Product | Quantity | Price | Order_Date | Address | |
---|---|---|---|---|---|---|
0 | 176558 | USB-C Charging Cable | 2 | 11.95 | 04/19/19 08:46 | 917 1st St, Dallas, TX 75001 |
1 | 176559 | Bose SoundSport Headphones | 1 | 99.99 | 04/07/19 22:30 | 682 Chestnut St, Boston, MA 02215 |
2 | 176560 | Google Phone | 1 | 600.00 | 04/12/19 14:38 | 669 Spruce St, Los Angeles, CA 90001 |
3 | 176560 | Wired Headphones | 1 | 11.99 | 04/12/19 14:38 | 669 Spruce St, Los Angeles, CA 90001 |
4 | 176561 | Wired Headphones | 1 | 11.99 | 04/30/19 09:27 | 333 8th St, Los Angeles, CA 90001 |
... | ... | ... | ... | ... | ... | ... |
185945 | 259353 | AAA Batteries (4-pack) | 3 | 2.99 | 09/17/19 20:56 | 840 Highland St, Los Angeles, CA 90001 |
185946 | 259354 | iPhone | 1 | 700.00 | 09/01/19 16:00 | 216 Dogwood St, San Francisco, CA 94016 |
185947 | 259355 | iPhone | 1 | 700.00 | 09/23/19 07:39 | 220 12th St, San Francisco, CA 94016 |
185948 | 259356 | 34in Ultrawide Monitor | 1 | 379.99 | 09/19/19 17:30 | 511 Forest St, San Francisco, CA 94016 |
185949 | 259357 | USB-C Charging Cable | 1 | 11.95 | 09/30/19 00:18 | 250 Meadow St, San Francisco, CA 94016 |
185950 rows × 6 columns
df['Total'] = df.Quantity * df.Price
df
Order_ID | Product | Quantity | Price | Order_Date | Address | Total | |
---|---|---|---|---|---|---|---|
0 | 176558 | USB-C Charging Cable | 2 | 11.95 | 04/19/19 08:46 | 917 1st St, Dallas, TX 75001 | 23.90 |
1 | 176559 | Bose SoundSport Headphones | 1 | 99.99 | 04/07/19 22:30 | 682 Chestnut St, Boston, MA 02215 | 99.99 |
2 | 176560 | Google Phone | 1 | 600.00 | 04/12/19 14:38 | 669 Spruce St, Los Angeles, CA 90001 | 600.00 |
3 | 176560 | Wired Headphones | 1 | 11.99 | 04/12/19 14:38 | 669 Spruce St, Los Angeles, CA 90001 | 11.99 |
4 | 176561 | Wired Headphones | 1 | 11.99 | 04/30/19 09:27 | 333 8th St, Los Angeles, CA 90001 | 11.99 |
... | ... | ... | ... | ... | ... | ... | ... |
185945 | 259353 | AAA Batteries (4-pack) | 3 | 2.99 | 09/17/19 20:56 | 840 Highland St, Los Angeles, CA 90001 | 8.97 |
185946 | 259354 | iPhone | 1 | 700.00 | 09/01/19 16:00 | 216 Dogwood St, San Francisco, CA 94016 | 700.00 |
185947 | 259355 | iPhone | 1 | 700.00 | 09/23/19 07:39 | 220 12th St, San Francisco, CA 94016 | 700.00 |
185948 | 259356 | 34in Ultrawide Monitor | 1 | 379.99 | 09/19/19 17:30 | 511 Forest St, San Francisco, CA 94016 | 379.99 |
185949 | 259357 | USB-C Charging Cable | 1 | 11.95 | 09/30/19 00:18 | 250 Meadow St, San Francisco, CA 94016 | 11.95 |
185950 rows × 7 columns
df2 = df[['Order_ID', 'Product', 'Quantity', 'Price', 'Total', 'Order_Date', 'Address']]
df2
Order_ID | Product | Quantity | Price | Total | Order_Date | Address | |
---|---|---|---|---|---|---|---|
0 | 176558 | USB-C Charging Cable | 2 | 11.95 | 23.90 | 04/19/19 08:46 | 917 1st St, Dallas, TX 75001 |
1 | 176559 | Bose SoundSport Headphones | 1 | 99.99 | 99.99 | 04/07/19 22:30 | 682 Chestnut St, Boston, MA 02215 |
2 | 176560 | Google Phone | 1 | 600.00 | 600.00 | 04/12/19 14:38 | 669 Spruce St, Los Angeles, CA 90001 |
3 | 176560 | Wired Headphones | 1 | 11.99 | 11.99 | 04/12/19 14:38 | 669 Spruce St, Los Angeles, CA 90001 |
4 | 176561 | Wired Headphones | 1 | 11.99 | 11.99 | 04/30/19 09:27 | 333 8th St, Los Angeles, CA 90001 |
... | ... | ... | ... | ... | ... | ... | ... |
185945 | 259353 | AAA Batteries (4-pack) | 3 | 2.99 | 8.97 | 09/17/19 20:56 | 840 Highland St, Los Angeles, CA 90001 |
185946 | 259354 | iPhone | 1 | 700.00 | 700.00 | 09/01/19 16:00 | 216 Dogwood St, San Francisco, CA 94016 |
185947 | 259355 | iPhone | 1 | 700.00 | 700.00 | 09/23/19 07:39 | 220 12th St, San Francisco, CA 94016 |
185948 | 259356 | 34in Ultrawide Monitor | 1 | 379.99 | 379.99 | 09/19/19 17:30 | 511 Forest St, San Francisco, CA 94016 |
185949 | 259357 | USB-C Charging Cable | 1 | 11.95 | 11.95 | 09/30/19 00:18 | 250 Meadow St, San Francisco, CA 94016 |
185950 rows × 7 columns
df2.to_csv('result.csv', index=False)