How to implement multithreading for web scraping?

My problem seems simple and I don't know how to solve it.

I have a program that look like this:

def webscrape(url):
    soup = BeautifulSoup(requests.get(url).content, "html.parser")
    data = json.loads(data)
    df = pd.DataFrame(data['dataProvider'])
    return df

I would like to apply this function for multiple sites:

df = pd.DataFrame()
for url in urls:
    df = pd.concat([df, webscrape(url)], axis=1)

I would like to accelerate my program with multithreading but I don't know how I should start.

To accelerate your program with multithreading, you can use the concurrent.futures module in Python. This module provides a high-level interface for asynchronously executing callables (functions or methods) using threads or processes. In your case, you can use the ThreadPoolExecutor from concurrent.futures to parallelize the web scraping process.
Here's how you can modify your code to use multithreading:

import concurrent.futures
import pandas as pd
from bs4 import BeautifulSoup
import requests
import json

def webscrape(url):
    soup = BeautifulSoup(requests.get(url).content, "html.parser")
    data = json.loads(data)
    df = pd.DataFrame(data['dataProvider'])
    return df

def scrape_all_sites(urls):
    dfs = []
    with concurrent.futures.ThreadPoolExecutor() as executor:
        # Submit the tasks to the executor and store the future objects
        futures = [executor.submit(webscrape, url) for url in urls]

        # Gather the results from the completed futures
        for future in concurrent.futures.as_completed(futures):
            df = future.result()
            dfs.append(df)

    # Concatenate all dataframes into one
    result_df = pd.concat(dfs, axis=1)
    return result_df

# Assuming you have a list of URLs called 'urls'
result_df = scrape_all_sites(urls)

If you need add workers, Here's how you can set the max_workers option in the scrape_all_sites function:

def scrape_all_sites(urls, max_workers=None):
    dfs = []
    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
        # Rest of the code remains unchanged
        # ...

When calling the scrape_all_sites function, you can specify the max_workers argument to determine the number of threads:

# Assuming you have a list of URLs called 'urls'
result_df = scrape_all_sites(urls, max_workers=4)  # Set max_workers as desired