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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.