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<@ULVA73B9P> How do I cache a Pandas Dataframe?

*Edited by <@U02GMEZU18B>*: Ah, caching, the art of storing results for future use. In Prefect, you can cache a task's output, such as a Pandas DataFrame, by using the `cache_key_fn` function paired with `cache_expiration`. This allows you to define arbitrary caching mechanisms. Here's an example of how to cache a task's output:

```
from datetime import timedelta
from prefect import task
from prefect.tasks import task_input_hash

@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(minutes=1))
def my_dataframe_task():
    # Your code to generate a Pandas DataFrame
    pass
```

In this example, the `cache_key_fn` function is set to use the task's inputs as the cache key, and the `cache_expiration` is set to expire the cache after one minute. You can customize the `cache_key_fn` function and `cache_expiration` duration according to your needs.

For more details on caching, you can refer to the following resources:
- [Prefect 2 Migration Guide](<https://docs.prefect.io/latest/guides/migration-guide/>)
- [Prefect Flow and Task Configuration Tutorial](<https://docs.prefect.io/latest/tutorial/flow-task-config/>)

I hope this helps you with your caching endeavors. Remember, a cached DataFrame is a happy DataFrame. Or something like that.

<@ULVA73B9P> What if I’m downloading a large file and returning a Pandas Dataframe? I want the flow to run daily but for the large file to be downloaded once per week.

*Edited by <@U02GMEZU18B>*: Ah, the age-old conundrum of balancing data freshness and resource efficiency. In Prefect, you can achieve this by using a separate task for downloading the large file and setting a custom cache expiration for that task. Here's an example:

```python
from datetime import timedelta
from prefect import flow, task
from prefect.tasks import task_input_hash

@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(days=7))
def download_large_file():
    # Your code to download the large file
    pass

@task
def process_large_file(file):
    # Your code to process the large file and return a Pandas DataFrame
    pass

@flow
def my_flow():
    large_file = download_large_file()
    dataframe = process_large_file(large_file)

if __name__ == "__main__":
   my_flow()
```

In this example, the `download_large_file` task is set to cache its output for 7 days. This means that when the flow runs daily, it will reuse the cached output of the `download_large_file` task for a week before downloading the file again.

Remember, with great data comes great responsibility... or something like that.

:no_good: - this is prefect 1 stuff, hold on one min

if im understanding your use case, i think marvin had the general idea right, just with prefect 1 syntax. edited now!

```@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(days=7))
def download_large_file(driver):

    # selenium webdriver download the file
    driver.find_element(By.XPATH, "XPATH").click()

    # wait for download to complete
    while not os.path.exists("BigFile.zip"):
        time.sleep(60)

    # unzip file
    with zipfile.ZipFile("BigFile.zip", "r") as zip_ref:
        ...

    # dbf file path
    df = pd.DataFrame("BigFile.csv")

    return df```

Heres some pseudocode of what im trying to accomplish

Will give it a try to split this up into two tasks!

If you want a file based cache I wrote a cache function for that! See this issue and the repo there: <https://github.com/PrefectHQ/prefect/issues/9644>