How to scrape table using beautifulsoup only summary and width?

I am trying to scrape a table from this site.:http://wayback.archive-it.org/7993/20170110233205/http://www.fda.gov/Safety/Recalls/ArchiveRecalls/2015/default.htm

This table has no id or class and only contains summary and width. Is there any way to scrape this table?
Perhaps xpath?

I heard that xpath is not compatible with beautifulsoup and hope that is wrong.

<table width="100%" cellpadding="3" border="1" summary="Layout showing RecallTest table with 6 columns: Date,Brand Name,Product Description,Reason/Problem,Company,Details/Photo" style="margin-bottom:28px">
		  <thead>
			<tr>
					<th scope="col" data-type="numeric" data-toggle="true"> Date </th>
			</tr>
		  </thead>
		  <tbody>

Here is my code:

import requests
from bs4 import BeautifulSoup
link = 'http://wayback.archive-it.org/7993/20170110233205/http://www.fda.gov/Safety/Recalls/ArchiveRecalls/2015/default.htm'
page = 15
pdf = []
for p in range(1,page+1):
   l = link + '?page='+str(p)
	# Downloading contents of the web page
	data = requests.get(l).text
	# Creating BeautifulSoup object
	soup = BeautifulSoup(data, 'html.parser')
	tables = soup.find_all('table')
	table = soup.find('table', INSERT XPATH EXPRESSION)
	df = pd.DataFrame(columns = ['date','brand','descr','reason','company'])
	for row in table.tbody.find_all('tr'):    
		# Find all data for each column
		columns = row.find_all('td')
		if columns != []:
			date = columns[0].text.strip()

Scraping tables it is best practice to use pandas.read_html() that covers 95% of all cases. Simply iterate the sites and concat the dataframes:

import pandas as pd
url = 'http://wayback.archive-it.org/7993/20170110233205/http://www.fda.gov/Safety/Recalls/ArchiveRecalls/2015/default.htm'
pd.concat(
[pd.read_html(url+'?page='+str(i))[0]for i in range(1,16)],
ignore_index=True
)

Note, that you can also include links via extract_links='body'

This will result in:

Based on your manually approach simply select the first table, iterate over the rows and store information in a list of dicts , that could be simply converted into a dataframe:

import requests
from bs4 import BeautifulSoup
url = 'http://wayback.archive-it.org/7993/20170110233205/http://www.fda.gov/Safety/Recalls/ArchiveRecalls/2015/default.htm'
data = []
for i in range(1,16):
soup = BeautifulSoup(requests.get(url+'?page='+str(i)).text)
for e in soup.table.select('tr:has(td)'):
data.append({
'date': e.td.text,
'any other': 'column',
'link': e.a.get('href')
})
data

There is just two line of code to get the table from website

import requests
import pandas as pd
url = "http://wayback.archive-it.org/7993/20170110233205/http://www.fda.gov/Safety/Recalls/ArchiveRecalls/2015/default.htm"
tables = pd.read_html(requests.get(url).text)
print(tables[0])

You have to use two modules requests and pandas

you can read more about pandas.read_html function from Here