Python: How to delete rows ending in certain characters?

Question

I have a large data file and I need to delete rows that end in certain letters.

Here is an example of the file I'm using:

User Name     DN
MB212DA       CN=MB212DA,CN=Users,DC=prod,DC=trovp,DC=net
MB423DA       CN=MB423DA,OU=Generic Mailbox,DC=prod,DC=trovp,DC=net
MB424PL       CN=MB424PL,CN=Users,DC=prod,DC=trovp,DC=net
MBDA423       CN=MBDA423,OU=DNA,DC=prod,DC=trovp,DC=net
MB2ADA4       CN=MB2ADA4,OU=DNA,DC=prod,DC=trovp,DC=netenter code here

Code I am using:

from pandas import DataFrame, read_csv
import pandas as pd
f = pd.read_csv('test1.csv', sep=',',encoding='latin1') 
df = f.loc[~(~pd.isnull(f['User Name']) & f['UserName'].str.contains("DA|PL",))]

How do I use regular expression syntax to delete the words that end in "DA" and "PL" but make sure I do not delete the other rows because they contain "DA" or "PL" inside of them?

It should delete the rows and I end up with a file like this:

User Name     DN
MBDA423       CN=MBDA423,OU=DNA,DC=prod,DC=trovp,DC=net
MB2ADA4       CN=MB2ADA4,OU=DNA,DC=prod,DC=trovp,DC=net

First 3 rows are deleted because they ended in DA and PL.

Answer 1

You could use this expression

df = df[~df['User Name'].str.contains('(?:DA|PL)$')]

It will return all rows that don't end in either DA or PL.

The ?: is so that the brackets would not capture anything. Otherwise, you'd see pandas returning the following (harmless) warning:

UserWarning: This pattern has match groups. To actually get the groups, use str.extract.

Alternatively, using endswith() and without regular expressions, the same filtering could be achieved by using the following expression:

df = df[~df['User Name'].str.endswith(('DA', 'PL'))]

As expected, the version without regular expression will be faster. A simple test, consisting of big_df , which consists of 10001 copies of your original df :

# Create a larger DF to get better timing results
big_df = df.copy()

for i in range(10000):
    big_df = big_df.append(df)

print(big_df.shape)

>> (50005, 2)

# Without regular expressions
%%timeit
big_df[~big_df['User Name'].str.endswith(('DA', 'PL'))]

>> 10 loops, best of 3: 22.3 ms per loop

# With regular expressions
%%timeit
big_df[~big_df['User Name'].str.contains('(?:DA|PL)$')]

>> 10 loops, best of 3: 61.8 ms per loop

Answer 2

You can use a boolean mask whereby you check if the last two characters of User_Name are in not ( ~ ) in a set of two character endings:

>>> df[~df.User_Name.str[-2:].isin(['DA', 'PA'])]
  User_Name                                                 DN
2   MB424PL    CN=MB424PL, CN=Users, DC=prod, DC=trovp, DC=net
3   MBDA423      CN=MBDA423, OU=DNA, DC=prod, DC=trovp, DC=net
4   MB2ADA4  CN=MB2ADA4, OU=DNA, DC=prod, DC=trovp, DC=nete...

Answer 3

Instead of regular expressions , you can use the endswith() method to check if a string ends with a specific pattern.

Ie:

for row in rows:
    if row.endswith('DA') or row.endswith('PL'):
        #doSomething

You should create another df using the filtered data, and then use pd.to_csv() to save a clean version of your file.