python implementation of add label & tag operation

odue_df=df_train_stmt.loc[(df_train_stmt.AGE3>0)|(df_train_stmt.AGE4>0)|(df_train_stmt.AGE5>0)|(df_train_stmt.AGE6>0),['XACCOUNT']].drop_duplicates()
odue_df['label']=1
cust_df=df_acct[['CUSTR_NBR','XACCOUNT']].drop_duplicates()
# Do the merge
df_y=(cust_df,odue_df,how='left',on='XACCOUNT').groupby('CUSTR_NBR').agg({'label':max}).reset_index().fillna(0)

# Label Label
def label(row):
   if row['Date_received'] == 'null':
       return -1
   if row['Date'] != 'null':
       td = pd.to_datetime(row['Date'], format='%Y%m%d') - pd.to_datetime(row['Date_received'], format='%Y%m%d')
       if td <= (15, 'D'):
           return 1
   return 0
dfoff['label'] = (label, axis=1)
#Tagging to determine the number of days
def get_label(s):
    s = (':')
    if s[0]=='null':
        return 0
    elif (date(int(s[0][0:4]),int(s[0][4:6]),int(s[0][6:8]))-date(int(s[1][0:4]),int(s[1][4:6]),int(s[1][6:8]))).days<=15:
        return 1
    else:
        return -1
 = (get_label)

 
import re
import json 
import requests
from bs4 import BeautifulSoup
import 
from lxml import etree
 
result = ('/places/default/view/Algeria-4')
with open('', 'wb') as f:
    ()
# print(parse_regex())
test_data = """
        <div>
            <ul>
                 <li class="item-0"><a href="" rel="external nofollow"  rel="external nofollow"  >9,596,960first item</a></li>
                 <li class="item-1"><a href="" rel="external nofollow" >second item</a></li>
                 <li class="item-inactive"><a href="" rel="external nofollow" >third item</a></li>
                 <li class="item-1"><a href="" rel="external nofollow"  >fourth item</a></li>
                 <li class="item-0"><a href="" rel="external nofollow"  rel="external nofollow" >fifth item</a></li>
                 <li class="good-0"><a href="" rel="external nofollow"  rel="external nofollow" >fifth item</a></li>
             </ul>
             <book>
                    <title lang="aaengbb">Harry Potter</title>
                    <price >29.99</price>
            </book>
            <book>
                <title lang="zh">Learning XML</title>
                <price>39.95</price>
            </book>
            <book>
                <title>Python</title>
                <price>40</price>
            </book>
         </div>
        """
# //div/ul/li/a[@id] selects a tag with an id attribute in the a tag
# //div/ul/li/a Select all a tags
# //div/ul/li[2]/a
"""
/ Start with the root tag  Must have strict paternity
// Select from the current tag subsequent nodes containing
* wildcard character (computing)　Select all
//div/book[1]/title Select the title tag of the first book tag under the div
//div/book[1]/tittle[@lang="zh"] Select the title tag of the first book tag under the div and the title tag whose content is zh.
//div/book/title //book/title //title has the same result but with a different fetch path.
//book/title/@* selects all attribute values for title
//book/title/text() selects the content of the title, using the built-in function
//a[@href="" rel="external nofollow"  rel="external nofollow"  and @]
//div/book/[last()]/title/text() picks out the last book element
//div/book[price > 39]/title/text() selects out book sub-tags with price values greater than 39
//li[starts-with(@class,'item')] picks out the class attribute prefixed with item
//title[contains(@lang, "eng")] picks out tags whose title attribute lang contains the eng keyword
"""
html = (test_data)  # Load any string
html_data = ('//title[contains(@lang,"eng")]')  # xpath Find path
# print(dir(html_data[0])) # see what html_data has to offer
print(html_data)
for i in html_data:
    print()