[{'from': 'human', 'value': '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'}, {'from': 'gpt', 'value': "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:\n``` \nimport pandas as pd\nimport multiprocessing as mp\n\ndef process_dataframe(df, results, idx):\n    # Apply some function to the DataFrame\n    result = df.sum()\n    # Store the result in the shared list\n    results[idx] = result\n\nif __name__ == '__main__':\n    # Load the DataFrame from a file\n    df = pd.read_csv('data.csv')\n    # Split the DataFrame into chunks\n    chunk_size = len(df) // 4\n    chunks = [df[i:i + chunk_size] for i in range(0, len(df), chunk_size)]\n    # Create a shared list to store the results\n    manager = mp.Manager()\n    results = manager.list([None] * len(chunks))\n    # Start the worker threads\n    processes = []\n    for i, chunk in enumerate(chunks):\n        p = mp.Process(target=process_dataframe, args=(chunk, results, i))\n        processes.append(p)\n        p.start()\n    # Wait for the worker threads to finish\n    for p in processes:\n        p.join()\n    # Print the results\n    print(results)\n``` \nThis 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."}]