This will grab the data in the table named 'constituents'.
# find a specific table by table count
import pandas as pd
import requests
from bs4 import BeautifulSoup
res = requests.get("https://en.wikipedia.org/wiki/List_of_S%26P_500_companies")
soup = BeautifulSoup(res.content,'lxml')
table = soup.find_all('table')[0]
df = pd.read_html(str(table))
print(df[0].to_json(orient='records'))
Result:
[{"Symbol":"MMM","Security":"3M Company","SEC filings":"reports","GICS Sector":"Industrials","GICS Sub-Industry":"Industrial Conglomerates","Headquarters Location":"St. Paul, Minnesota","Date first added":"1976-08-09","CIK":66740,"Founded":"1902"},{"Symbol":"ABT","Security":"Abbott Laboratories","SEC filings":"reports","GICS Sector":"Health Care","GICS Sub-Industry":"Health Care Equipment","Headquarters Location":"North Chicago, Illinois","Date first added":"1964-03-31","CIK":1800,"Founded":"1888"},{"Symbol":"ABBV","Security":"AbbVie Inc.","SEC filings":"reports","GICS Sector":"Health Care","GICS Sub-Industry":"Pharmaceuticals","Headquarters Location":"North Chicago, Illinois","Date first added":"2012-12-31","CIK":1551152,"Founded":"2013 (1888)"},{"Symbol":"ABMD","Security":"Abiomed","SEC filings":"reports","GICS Sector":"Health Care","GICS Sub-Industry":"Health Care Equipment","Headquarters Location":"Danvers, Massachusetts","Date first added":"2018-05-31","CIK":815094,"Founded":"1981"},{"Symbol":"ACN","Security":"Accenture","SEC filings":"reports","GICS Sector":"Information Technology","GICS Sub-Industry":"IT Consulting & Other Services","Headquarters Location":"Dublin, Ireland","Date first added":"2011-07-06","CIK":1467373,"Founded":"1989"},{"Symbol":"ATVI","Security":"Activision Blizzard","SEC filings":"reports","GICS Sector":"Communication Services","GICS Sub-Industry":"Interactive Home Entertainment","Headquarters Location":"Santa Monica, California","Date first added":"2015-08-31","CIK":718877,"Founded":"2008"},{"Symbol":"ADBE","Security":"Adobe Inc.","SEC filings":"reports","GICS Sector":"Information Technology","GICS Sub-Industry":"Application Software","Headquarters Location":"San Jose, California","Date first added":"1997-05-05","CIK":796343,"Founded":"1982"},
Etc., etc., etc.
That's JSON. If you want a table, kind of like what you would use in Excel, simply print the df
.
Result:
[ Symbol Security SEC filings GICS Sector \
0 MMM 3M Company reports Industrials
1 ABT Abbott Laboratories reports Health Care
2 ABBV AbbVie Inc. reports Health Care
3 ABMD Abiomed reports Health Care
4 ACN Accenture reports Information Technology
.. ... ... ... ...
500 YUM Yum! Brands Inc reports Consumer Discretionary
501 ZBRA Zebra Technologies reports Information Technology
502 ZBH Zimmer Biomet reports Health Care
503 ZION Zions Bancorp reports Financials
504 ZTS Zoetis reports Health Care
GICS Sub-Industry Headquarters Location \
0 Industrial Conglomerates St. Paul, Minnesota
1 Health Care Equipment North Chicago, Illinois
2 Pharmaceuticals North Chicago, Illinois
3 Health Care Equipment Danvers, Massachusetts
4 IT Consulting & Other Services Dublin, Ireland
.. ... ...
500 Restaurants Louisville, Kentucky
501 Electronic Equipment & Instruments Lincolnshire, Illinois
502 Health Care Equipment Warsaw, Indiana
503 Regional Banks Salt Lake City, Utah
504 Pharmaceuticals Parsippany, New Jersey
Date first added CIK Founded
0 1976-08-09 66740 1902
1 1964-03-31 1800 1888
2 2012-12-31 1551152 2013 (1888)
3 2018-05-31 815094 1981
4 2011-07-06 1467373 1989
.. ... ... ...
500 1997-10-06 1041061 1997
501 2019-12-23 877212 1969
502 2001-08-07 1136869 1927
503 2001-06-22 109380 1873
504 2013-06-21 1555280 1952
[505 rows x 9 columns]]
Alternatively, you can export the df to a CSV file.
df.to_csv('constituents.csv')