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Apify

class Apify:
Apify is a platform that allows you to automate any web workflow. Parameters:
  • api_key (Optional[str]): API key for authenticating with the Apify API.

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init

def __init__(self, api_key: Optional[str] = None):

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run_actor

def run_actor(
    self,
    actor_id: str,
    run_input: Optional[dict] = None,
    content_type: Optional[str] = None,
    build: Optional[str] = None,
    max_items: Optional[int] = None,
    memory_mbytes: Optional[int] = None,
    timeout_secs: Optional[int] = None,
    webhooks: Optional[list] = None,
    wait_secs: Optional[int] = None
):
Run an actor on the Apify platform. Parameters:
  • actor_id (str): The ID of the actor to run.
  • run_input (Optional[dict]): The input data for the actor. Defaults to None.
  • content_type (str, optional): The content type of the input.
  • build (str, optional): Specifies the Actor build to run. It can be either a build tag or build number. By default, the run uses the build specified in the default run configuration for the Actor (typically latest).
  • max_items (int, optional): Maximum number of results that will be returned by this run. If the Actor is charged per result, you will not be charged for more results than the given limit.
  • memory_mbytes (int, optional): Memory limit for the run, in megabytes. By default, the run uses a memory limit specified in the default run configuration for the Actor.
  • timeout_secs (int, optional): Optional timeout for the run, in seconds. By default, the run uses timeout specified in the default run configuration for the Actor.
  • webhooks (list, optional): Optional webhooks (https://docs.apify.com/webhooks) associated with the Actor run, which can be used to receive a notification, e.g. when the Actor finished or failed. If you already have a webhook set up for the Actor, you do not have to add it again here.
  • wait_secs (int, optional): The maximum number of seconds the server waits for finish. If not provided, waits indefinitely.
Returns: Optional[dict]: The output data from the actor if successful.

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please use the ‘defaultDatasetId’ to get the dataset

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get_dataset_client

def get_dataset_client(self, dataset_id: str):
Get a dataset client from the Apify platform. Parameters:
  • dataset_id (str): The ID of the dataset to get the client for.
Returns: DatasetClient: The dataset client.

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get_dataset

def get_dataset(self, dataset_id: str):
Get a dataset from the Apify platform. Parameters:
  • dataset_id (str): The ID of the dataset to get.
Returns: dict: The dataset.

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update_dataset

def update_dataset(self, dataset_id: str, name: str):
Update a dataset on the Apify platform. Parameters:
  • dataset_id (str): The ID of the dataset to update.
  • name (str): The new name for the dataset.
Returns: dict: The updated dataset.

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get_dataset_items

def get_dataset_items(self, dataset_id: str):
Get items from a dataset on the Apify platform. Parameters:
  • dataset_id (str): The ID of the dataset to get items from.
Returns: list: The items in the dataset.

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get_datasets

def get_datasets(
    self,
    unnamed: Optional[bool] = None,
    limit: Optional[int] = None,
    offset: Optional[int] = None,
    desc: Optional[bool] = None
):
Get all named datasets from the Apify platform. Parameters:
  • unnamed (bool, optional): Whether to include unnamed key-value stores in the list
  • limit (int, optional): How many key-value stores to retrieve
  • offset (int, optional): What key-value store to include as first when retrieving the list
  • desc (bool, optional): Whether to sort the key-value stores in descending order based on their modification date
Returns: List[dict]: The datasets.