Separate table row to 2 when scraping with pandas read_html

Question

Can't get the row format correct when using pandas read_html() . I'm looking for adjustments either to the method itself or the underlying html (scraped via bs4) to get the desired output.

Current output:

(note it is 1 row containing two types of data. ideally it should be separated to 2 rows as below)

Desired:

code to replicate the issue:

import requests
import pandas as pd
from bs4 import BeautifulSoup  # alternatively

url = "http://ufcstats.com/fight-details/bb15c0a2911043bd"

df = pd.read_html(url)[-1]  # last table
df.columns = [str(i) for i in range(len(df.columns))]

# to get the html via bs4
headers = {
    "Access-Control-Allow-Origin": "*",
    "Access-Control-Allow-Methods": "GET",
    "Access-Control-Allow-Headers": "Content-Type",
    "Access-Control-Max-Age": "3600",
    "User-Agent": "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:52.0) Gecko/20100101 Firefox/52.0",
}
req = requests.get(url, headers)
soup = BeautifulSoup(req.content, "html.parser")
table_html = soup.find_all("table", {"class": "b-fight-details__table"})[-1]

Answer 1

How to (quick) fix with beautifulsoup

You can create a dict with the headers from the table and then iterate over each td to append the list of values stored in the p :

data = {}

header = [x.text.strip() for x in table_html.select('tr th')]

for i,td in enumerate(table_html.select('tr:has(td) td')):
    data[header[i]] = [x.text.strip() for x in td.select('p')]

pd.DataFrame.from_dict(data)

Example

import requests
import pandas as pd
from bs4 import BeautifulSoup  # alternatively

url = "http://ufcstats.com/fight-details/bb15c0a2911043bd"

# to get the html via bs4
headers = {
    "Access-Control-Allow-Origin": "*",
    "Access-Control-Allow-Methods": "GET",
    "Access-Control-Allow-Headers": "Content-Type",
    "Access-Control-Max-Age": "3600",
    "User-Agent": "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:52.0) Gecko/20100101 Firefox/52.0",
}
req = requests.get(url, headers)
soup = BeautifulSoup(req.content, "html.parser")
table_html = soup.find_all("table", {"class": "b-fight-details__table"})[-1]

data = {}

header = [x.text.strip() for x in table_html.select('tr th')]

for i,td in enumerate(table_html.select('tr:has(td) td')):
    data[header[i]] = [x.text.strip() for x in td.select('p')]

pd.DataFrame.from_dict(data)

Output

Fighter	Sig. str	Sig. str.%	Head	Body	Leg	Distance	Clinch	Ground
Joanne Wood	27 of 68	39%	8 of 36	3 of 7	16 of 25	26 of 67	1 of 1	0 of 0
Taila Santos	30 of 60	50%	21 of 46	3 of 7	6 of 7	19 of 42	0 of 0	11 of 18

Answer 2

Similar idea to use enumerate to determine number of rows, but use :-soup-contains to target table, then nth-child selector to extract relevant row during list comprehension. pandas to convert resultant list of lists into a DataFrame. Assumes rows are added in same pattern as current 2.

from bs4 import BeautifulSoup as bs
import requests
import pandas as pd

r = requests.get('http://ufcstats.com/fight-details/bb15c0a2911043bd')
soup = bs(r.content, 'lxml')
table = soup.select_one(
    '.js-fight-section:has(p:-soup-contains("Significant Strikes")) + table')

df = pd.DataFrame(
    [[i.text.strip() for i in table.select(f'tr:nth-child(1) td p:nth-child({n+1})')]
     for n, _ in enumerate(table.select('tr:nth-child(1) > td:nth-child(1) > p'))], columns=[i.text.strip() for i in table.select('th')])

print(df)