9c250ef0-6e7a-4455-b62d-939f8d02b4ac
prompter
Write a python code: using multiprocessing, each thread reads pandas dataframe, applies some function on it, and copies the result into python list that is accessible to all threads
0a13224f-1dd3-4aab-930f-96b9b81db2f4
9c250ef0-6e7a-4455-b62d-939f8d02b4ac
assistant
Here's an example of how you could use the multiprocessing module in Python to apply a function to a Pandas DataFrame in parallel using multiple threads:
``` 
import pandas as pd
import multiprocessing as mp

def process_dataframe(df, results, idx):
    # Apply some function to the DataFrame
    result = df.sum()
    # Store the result in the shared list
    results[idx] = result

if __name__ == '__main__':
    # Load the DataFrame from a file
    df = pd.read_csv('data.csv')
    # Split the DataFrame into chunks
    chunk_size = len(df) // 4
    chunks = [df[i:i + chunk_size] for i in range(0, len(df), chunk_size)]
    # Create a shared list to store the results
    manager = mp.Manager()
    results = manager.list([None] * len(chunks))
    # Start the worker threads
    processes = []
    for i, chunk in enumerate(chunks):
        p = mp.Process(target=process_dataframe, args=(chunk, results, i))
        processes.append(p)
        p.start()
    # Wait for the worker threads to finish
    for p in processes:
        p.join()
    # Print the results
    print(results)
``` 
This code will split the DataFrame into 4 chunks and apply the process_dataframe function to each chunk in parallel using 4 worker threads. The results of the function will be stored in a shared list results that can be accessed by all threads. The main thread will wait for all worker threads to finish before printing the final results.