A useful skill for a data scientist trying to gather data is what is called 'web scraping'. Web scraping simply means you are collecting data from the internet. The most common Python library is called BeautifulSoup, but pandas also allows you to do some simple web scraping when downloading data from tables on various websites.
In this example, we would like to gather the list of tickers from the components of the CAC40. Here is the Wikipedia table:

Wikitable

Using Pandas

import pandas as pd

# pd.read_html returns a list of dataframes
list_df = pd.read_html('https://en.wikipedia.org/wiki/CAC_40')
# in our case, it is the third table:
df = list_df[3]

# You can also pass the 'id' of the table (by inspecting the web page)
list_df = pd.read_html('https://en.wikipedia.org/wiki/CAC_40', attrs={'id': 'constituents'})
# and take the first element of the list
df = list_df[0]

Using Beautiful Soup

BeautifulSoup is more versatile than pandas as you can see in the documentation Using the same example as above:

import requests
from bs4 import BeautifulSoup

response = requests.get('https://en.wikipedia.org/wiki/CAC_40')

if response.ok:
    soup = BeautifulSoup(response.text,'html.parser')
    elem = soup.find('table', id='constituents')
    # we use pd.read_html again, this time giving it an HTML string
    df = pd.read_html(str(elem))[0]