Concatenate labels to an existing dataframe - pandas

Question

I want to use a list of names "headers" to create a new column in my dataframe. In the initial table, the name of each division is positioned above the results for each team in that division. I want to add that header to each row entry for each divsion to make the data more identifiable like this. I have the headers stored in the "header" object in my code. How can I multiply each division header by the number of rows that appear in the division and append to the dataset?
Edit: here is another snippet of what I want the get from the end product.
df3 = df.iloc[0:6]
df3.insert(0, 'Divisions', ['na','L5 Junior', 'L5 Junior', 'na',
'L5 Senior - Medium', 'L5 Senior - Medium'])
df3
.
import pandas as pd
import numpy as np
from bs4 import BeautifulSoup
import requests
Import HTML
scr = 'https://tv.varsity.com/results/7361971-2022-spirit-unlimited-battle-at-the-
boardwalk-atlantic-city-grand-ntls/31220'
scr1 = requests.get(scr)
soup = BeautifulSoup(scr1.text, "html.parser")
List of names to append
table_MN = pd.read_html(scr)
sp3 = soup.find(class_="full-content").find_all("h2")
headers = [elt.text for elt in sp3]
table_MN = pd.read_html(scr)
Extract text and header from division info
div = pd.DataFrame(headers)
div.columns = ["division"]
df = pd.concat(table_MN, ignore_index=True)
df.columns = df.iloc[0]
df

Answer 1

It is still not clear what is the output you are looking for. However, may I suggest the following, which accomplishes selecting common headers from tables in table_MN and the concatenating the results. If it is going in the right direction pls let me know, and indicate what else you want to extract from the resulting table:
tmn_1 = [tbl.T.set_index(0).T for tbl in table_MN]
pd.concat(tmn_1, axis=0, ignore_index = True)
output:
Rank Program Name Team Name Raw Score Deductions Performance Score Event Score
-- ------ --------------------------- ----------------- ----------- ------------ ------------------- -------------
0 1 Rockstar Cheer New Jersey City Girls 47.8667 0 95.7333 95.6833
1 2 Cheer Factor Xtraordinary 46.6667 0.15 93.1833 92.8541
2 1 Rockstar Cheer New Jersey City Girls 47.7667 0 95.5333 23.8833
3 2 Cheer Factor Xtraordinary 46.0333 0.2 91.8667 22.9667
4 1 Star Athletics Roar 47.5333 0.9 94.1667 93.9959
5 1 Prime Time All Stars Lady Onyx 43.9 1.35 86.45 86.6958
6 1 Prime Time All Stars Lady Onyx 44.1667 0.9 87.4333 21.8583
7 1 Just Cheer All Stars Jag 5 46.4333 0.15 92.7167 92.2875
8 1 Just Cheer All Stars Jag 5 45.8 0.6 91 22.75
9 1 Quest Athletics Black Ops 47.4333 0.45 94.4167 93.725
10 1 Quest Athletics Black Ops 46.5 1.35 91.65 22.9125
11 1 The Stingray Allstars X-Rays 45.3 0.95 89.65 88.4375
12 1 Vortex Allstars Lady Rays 45.7 0.5 90.9 91.1083
13 1 Vortex Allstars Lady Rays 45.8667 0 91.7333 22.9333
14 1 Upper Merion All Stars Citrus 46.4333 0 92.8667 92.7
15 2 Cheer Factor JUNIOR X 45.9 1.1 90.7 90.6542
16 3 NJ Premier All Stars Prodigy 44.6333 0.05 89.2167 89.8292
17 1 Upper Merion All Stars Citrus 46.1 0 92.2 23.05
18 2 NJ Premier All Stars Prodigy 45.8333 0 91.6667 22.9167
19 3 Cheer Factor JUNIOR X 45.7333 0.95 90.5167 22.6292
20 1 Virginia Royalty Athletics Dynasty 46.5 0 93 92.9
21 1 Virginia Royalty Athletics Dynasty 46.3 0 92.6 23.15
22 1 South Jersey Storm Lady Reign 47.2333 0 94.4667 93.4875
...