In [1]:
import pandas as pd
In [2]:
df = pd.read_csv('result.csv')
df
Out[2]:
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
... ... ... ... ... ... ...
186845 259353 AAA Batteries (4-pack) 3 2.99 09/17/19 20:56 840 Highland St, Los Angeles, CA 90001
186846 259354 iPhone 1 700 09/01/19 16:00 216 Dogwood St, San Francisco, CA 94016
186847 259355 iPhone 1 700 09/23/19 07:39 220 12th St, San Francisco, CA 94016
186848 259356 34in Ultrawide Monitor 1 379.99 09/19/19 17:30 511 Forest St, San Francisco, CA 94016
186849 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 [3]:
df.info(memory_usage='deep')

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 186850 entries, 0 to 186849
Data columns (total 6 columns):
 #   Column            Non-Null Count   Dtype 
---  ------            --------------   ----- 
 0   Order ID          186305 non-null  object
 1   Product           186305 non-null  object
 2   Quantity Ordered  186305 non-null  object
 3   Price Each        186305 non-null  object
 4   Order Date        186305 non-null  object
 5   Purchase Address  186305 non-null  object
dtypes: object(6)
memory usage: 75.6 MB
In [4]:
186850 - 186305
Out[4]:
545
In [6]:
df.dropna(inplace=True)
In [7]:
df.info(memory_usage='deep')

<class 'pandas.core.frame.DataFrame'>
Int64Index: 186305 entries, 0 to 186849
Data columns (total 6 columns):
 #   Column            Non-Null Count   Dtype 
---  ------            --------------   ----- 
 0   Order ID          186305 non-null  object
 1   Product           186305 non-null  object
 2   Quantity Ordered  186305 non-null  object
 3   Price Each        186305 non-null  object
 4   Order Date        186305 non-null  object
 5   Purchase Address  186305 non-null  object
dtypes: object(6)
memory usage: 76.9 MB
In [8]:
df.to_csv('result.csv', index=False)
In [ ]: