In [4]:
import os
import pandas as pd
In [6]:
df = pd.DataFrame()
for f in os.listdir('files/'):
    tmp = pd.read_csv('files/' + f)
    df = pd.concat([df, tmp])
    
df
Out[6]:
Order ID Product Quantity Ordered Price Each Order Date Purchase Address
0 176558 USB-C Charging Cable 2 11.95 04/19/19 08:46 917 1st St, Dallas, TX 75001
1 NaN NaN NaN NaN NaN NaN
2 176559 Bose SoundSport Headphones 1 99.99 04/07/19 22:30 682 Chestnut St, Boston, MA 02215
3 176560 Google Phone 1 600 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001
4 176560 Wired Headphones 1 11.99 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001
... ... ... ... ... ... ...
11681 259353 AAA Batteries (4-pack) 3 2.99 09/17/19 20:56 840 Highland St, Los Angeles, CA 90001
11682 259354 iPhone 1 700 09/01/19 16:00 216 Dogwood St, San Francisco, CA 94016
11683 259355 iPhone 1 700 09/23/19 07:39 220 12th St, San Francisco, CA 94016
11684 259356 34in Ultrawide Monitor 1 379.99 09/19/19 17:30 511 Forest St, San Francisco, CA 94016
11685 259357 USB-C Charging Cable 1 11.95 09/30/19 00:18 250 Meadow St, San Francisco, CA 94016

186850 rows × 6 columns

In [7]:
df.to_csv('result.csv', index=False)
In [ ]: