Integration with data libraries

The Apify client for Python seamlessly integrates with data analysis libraries like Pandas. This allows you to load dataset items directly into a Pandas DataFrame for efficient manipulation and analysis. Pandas provides robust data structures and tools for handling large datasets, making it a powerful addition to your Apify workflows.

The following example demonstrates how to retrieve items from the most recent dataset of an Actor run and load them into a Pandas DataFrame for further analysis:

Async client

import asyncio

import pandas as pd

from apify_client import ApifyClientAsync

TOKEN = 'MY-APIFY-TOKEN'


async def main() -> None:
    # Initialize the Apify client
    apify_client = ApifyClientAsync(token=TOKEN)
    actor_client = apify_client.actor('apify/web-scraper')
    run_client = actor_client.last_run()
    dataset_client = run_client.dataset()

    # Load items from last dataset run
    dataset_data = await dataset_client.list_items()

    # Pass dataset items to Pandas DataFrame
    data_frame = pd.DataFrame(dataset_data.items)

    print(data_frame.info)


if __name__ == '__main__':
    asyncio.run(main())

Sync client

import pandas as pd

from apify_client import ApifyClient

TOKEN = 'MY-APIFY-TOKEN'


def main() -> None:
    # Initialize the Apify client
    apify_client = ApifyClient(token=TOKEN)
    actor_client = apify_client.actor('apify/web-scraper')
    run_client = actor_client.last_run()
    dataset_client = run_client.dataset()

    # Load items from last dataset run
    dataset_data = dataset_client.list_items()

    # Pass dataset items to Pandas DataFrame
    data_frame = pd.DataFrame(dataset_data.items)

    print(data_frame.info)


if __name__ == '__main__':
    main()