Adding partitions to assets

In this section, you’ll update the assets in assets/trips.py to use the partitions.

Starting with taxi_trips_file, the asset code should currently look like this:

@asset
def taxi_trips_file() -> None:
    """
      The raw parquet files for the taxi trips dataset. Sourced from the NYC Open Data portal.
    """
    month_to_fetch = '2023-03'
    raw_trips = requests.get(
        f"https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_{month_to_fetch}.parquet"
    )

    with open(constants.TAXI_TRIPS_TEMPLATE_FILE_PATH.format(month_to_fetch), "wb") as output_file:
        output_file.write(raw_trips.content)

In this asset, month_to_fetch is set to 2023-03 to retrieve trip data for March 2023. Using the monthly_partition, you can update the asset to use the partition to retrieve three months of data.

To add the partition to the asset:

Import the monthly_partition from the partitions folder by adding the following to the top of assets/trips.py:
```
from ..partitions import monthly_partition
```
In the asset decorator (@asset), add a partitions_def parameter equal to monthly_partition:
```
@asset(
    partitions_def=monthly_partition
)
```
In Dagster, the context argument provides you with metadata about the current materialization. To access it, include it as the first argument in the asset definition function. You can enable type hinting for this by importing AssetExecutionContext from dagster and adding it to the function signature. For example, the updated asset definition should look like this:
```
from dagster import asset, AssetExecutionContext


@asset(
    partitions_def=monthly_partition
)
def taxi_trips_file(context: AssetExecutionContext) -> None:
```
Note: The context argument isn’t specific to partitions. However, this is the first time you've used it in Dagster University. The context argument provides information about how Dagster is running and materializing your asset. For example, you can use it to find out which partition Dagster is materializing, which job triggered the materialization, or what metadata was attached to its previous materializations.
In the original asset code, the logic was hard-coded to specifically fetch data for March 2023 ('2023-03'). Use the context argument’s partition_key property to dynamically fetch a specific partition’s month of data:
```
@asset(
    partitions_def=monthly_partition
)
def taxi_trips_file(context) -> None:
    partition_date_str = context.partition_key
```
In the NYC OpenData source system, the taxi trip files are structured in a YYYY-MM format. However, context.partition_key supplies the materializing partition’s date as a string in the YYYY-MM-DD format. Slice the string to make it match the format expected by our source system and replace our existing declaration of the month_to_fetch variable:
```
@asset(
    partitions_def=monthly_partition
)
def taxi_trips_file(context) -> None:
    partition_date_str = context.partition_key
    month_to_fetch = partition_date_str[:-3]
```

After following the steps above, the taxi_trips_file asset should look similar to the code snippet below:

from ..partitions import monthly_partition

@asset(
    partitions_def=monthly_partition
)
def taxi_trips_file(context) -> None:
  """
      The raw parquet files for the taxi trips dataset. Sourced from the NYC Open Data portal.
  """

  partition_date_str = context.partition_key
  month_to_fetch = partition_date_str[:-3]

  raw_trips = requests.get(
      f"https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_{month_to_fetch}.parquet"
  )

  with open(constants.TAXI_TRIPS_TEMPLATE_FILE_PATH.format(month_to_fetch), "wb") as output_file:
      output_file.write(raw_trips.content)