I am using the tld python library to grab the first level domain from the proxy request logs using a apply function. When I run into a strange request that tld doesnt know how to handle like 'http:1 CON' or 'http:/login.cgi%00' I run into an error message like the following:
TldBadUrl: Is not a valid URL http:1 con!
TldBadUrlTraceback (most recent call last)
in engine
----> 1 new_fld_column = request_2['request'].apply(get_fld)
/usr/local/lib/python2.7/site-packages/pandas/core/series.pyc in apply(self, func, convert_dtype, args, **kwds)
   2353             else:
   2354                 values = self.asobject
-> 2355                 mapped = lib.map_infer(values, f, convert=convert_dtype)
   2356 
   2357         if len(mapped) and isinstance(mapped[0], Series):
pandas/_libs/src/inference.pyx in pandas._libs.lib.map_infer (pandas/_libs/lib.c:66440)()
/home/cdsw/.local/lib/python2.7/site-packages/tld/utils.pyc in get_fld(url, 
fail_silently, fix_protocol, search_public, search_private, **kwargs)
    385         fix_protocol=fix_protocol,
    386         search_public=search_public,
--> 387         search_private=search_private
    388     )
    389 
/home/cdsw/.local/lib/python2.7/site-packages/tld/utils.pyc in process_url(url, fail_silently, fix_protocol, search_public, search_private)
    289             return None, None, parsed_url
    290         else:
--> 291             raise TldBadUrl(url=url)
    292 
    293     domain_parts = domain_name.split('.')
To overcome this it was suggested to me to wrap the function in a try-except clause to determine the rows that error out by querying them with NaN:
import tld
from tld import get_fld
def try_get_fld(x):
    try: 
        return get_fld(x)
    except tld.exceptions.TldBadUrl: 
        return np.nan
This seems to work for some of the "requests" like "http:1 con" and "http:/login.cgi%00" but then fails for "http://urnt12.knhc..txt/" where I get another error message like the one above:
TldDomainNotFound: Domain urnt12.knhc..txt didn't match any existing TLD name!
This is what the dataframe looks like total of 240,000 "requests" in a dataframe called "request":
request
  request                                      count
0 https://login.microsoftonline.com            24521
1 https://dt.adsafeprotected.com               11521
2 https://googleads.g.doubleclick.net          6252
3 https://fls-na.amazon.com                    65225
4 https://v10.vortex-win.data.microsoft.com    7852222
5 https://ib.adnxs.com                         12
6 http:1 CON                                   6 
7 http:/login.cgi%00                           45822
8 http://urnt12.knhc..txt/                     1 
My code:
from tld import get_tld
from tld import get_fld
import pandas as pd
import numpy as np
#Read back into to dataframe
request = pd.read_csv('Proxy/Proxy_Analytics/Request_Grouped_By_Request_Count_12032018.csv')
#Remove rows where there were null values in the request column 
request = request[pd.notnull(request['request'])]
#Find the urls that contain IP addresses and exclude them from the new dataframe
request = request[~request.request.str.findall(r'[0-9]+(?:\.[0-9]+){3}').astype(bool)]
#Reset index
request = request.reset_index(drop=True)
import tld
from tld import get_fld
def try_get_fld(x):
    try: 
        return get_fld(x)
    except tld.exceptions.TldBadUrl: 
        return np.nan
request['flds'] = request['request'].apply(try_get_fld)
#faulty_url_df = request[request['flds'].isna()]
#print(faulty_url_df)
 
    