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# ask-ai
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Slackbot

02/08/2024, 11:17 AM
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Scout

02/08/2024, 11:17 AM
Certainly! Here's an example based on the documentation that demonstrates how to define a source asset representing a
.csv
file and then use it as an upstream dependency for a regular asset:
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python from dagster import SourceAsset, asset  # Define the source asset representing a .csv file csv_source_asset = SourceAsset(key="csv_data_file")  # Define a regular asset that depends on the source asset @asset(deps=[csv_source_asset]) def process_csv_data(csv_data_file):     # Here you would include the logic to process the .csv data     # For example, you might read the .csv file into a dataframe, perform transformations, etc.     # The variable `csv_data_file` represents the data from the source asset     processed_data = csv_data_file  # Replace with actual processing logic     return processed_data
In this example,
csv_source_asset
is a
SourceAsset
that represents an external
.csv
file. The
process_csv_data
asset is defined with a dependency on
csv_source_asset
. When you materialize the
process_csv_data
asset, Dagster will understand that it should first ensure the
csv_source_asset
is available. Please note that in a real-world scenario, you would include the actual logic for processing the
.csv
data within the
process_csv_data
function. The
csv_data_file
parameter in the
process_csv_data
function is a placeholder for the data that would be loaded from the
.csv
file represented by the source asset.
In Dagster, to specify the path to a
.csv
file and ensure it is accessible to your assets or operations, you would typically use an IO manager. An IO manager is responsible for handling the input and output of data within your Dagster jobs. It can load data from a file system, a cloud storage service, or any other data source, and it can also handle the storage of outputs. Here's an example of how you might define an IO manager that loads a
.csv
file from a specified path and then use it with an asset:
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python import pandas as pd from dagster import asset, io_manager, OutputContext, InputContext  # Define an IO manager that reads and writes .csv files @io_manager def csv_io_manager():     class CsvIOManager:         def handle_output(self, context: OutputContext, obj: pd.DataFrame):             # Write the DataFrame to a .csv file             obj.to_csv(context.get_output_path(), index=False)          def load_input(self, context: InputContext) -> pd.DataFrame:             # Read a DataFrame from a .csv file             return pd.read_csv(context.upstream_output.get_output_path())      return CsvIOManager()  # Define a source asset that represents a .csv file @asset(io_manager_key="csv_io_manager") def csv_source_asset():     # The path to the .csv file is specified here     # For example, it could be a path like "/path/to/my_data.csv"     # In this example, we'll just create a simple DataFrame to simulate reading from a .csv file     return pd.DataFrame({"column1": [1, 2, 3], "column2": ["a", "b", "c"]})  # Define a regular asset that depends on the source asset @asset(io_manager_key="csv_io_manager", deps={"csv_source_asset": csv_source_asset}) def process_csv_data(csv_source_asset):     # Process the .csv data     processed_data = csv_source_asset  # Replace with actual processing logic     return processed_data
In this example, the
csv_io_manager
is an IO manager that handles reading from and writing to
.csv
files. The
csv_source_asset
asset is defined to use this IO manager, and it represents the source
.csv
file. The
process_csv_data
asset depends on
csv_source_asset
and also uses the
csv_io_manager
. When you run a job that includes these assets, Dagster will use the IO manager to load the
.csv
file specified in the
csv_source_asset
and provide it as input to the
process_csv_data
asset. Please note that in a real-world scenario, you would need to implement the logic to specify the actual file path within the
csv_source_asset
function or configure the IO manager to dynamically determine the file path based on the context or configuration.