@Marvin, what do you think of pandas.DataFrames as input for flows, especially as deployment runs?

Using

pandas.DataFrame

as input for Prefect flows is quite feasible and is a common practice for handling data processing tasks. Here's a concise overview: 1. Using

pandas.DataFrame

in Flows: You can easily incorporate

pandas.DataFrame

within your Prefect flow functions. For example, you can define a flow that creates and manipulates a DataFrame:

python
   from prefect import flow
   import pandas as pd

   @flow(log_prints=True)
   def uses_pandas():
       df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
       print(df)

   if __name__ == "__main__":
       uses_pandas()

This example demonstrates a simple flow that prints a DataFrame. You can expand this to perform more complex data manipulations. 2. Deployment Considerations: When deploying flows that use

pandas.DataFrame

, ensure that your execution environment has the necessary dependencies installed, such as the

pandas

library. Prefect's deployment capabilities allow you to specify environments and dependencies, so this should be straightforward. 3. Handling Large DataFrames: Be cautious with very large DataFrames, as they can consume significant memory. Consider using Prefect's result storage options to handle large datasets efficiently by persisting intermediate results. For more detailed information, you can explore examples like this one. If you have more specific requirements or scenarios in mind, feel free to ask!

@Marvin I rather meant whether I can use pd.DataFrames as input parameters for that flow.

Using

pandas.DataFrame

as input parameters for Prefect flows is possible, but there are some considerations to keep in mind: 1. Serialization: Prefect requires that all input parameters to a flow be serializable.

pandas.DataFrame

objects are not inherently serializable with standard Python serialization libraries like

pickle

or

json

. To work around this, you can convert the DataFrame to a serializable format, such as a CSV string or a dictionary, before passing it as a parameter. Here's an example:

python
   from prefect import flow
   import pandas as pd

   @flow
   def process_data(data: dict):
       df = pd.DataFrame(data)
       print(df)

   if __name__ == "__main__":
       df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
       process_data(data=df.to_dict())

2. Handling Large DataFrames: If you're dealing with large DataFrames, consider using a storage system to store the data and pass a reference (e.g., a file path or a database ID) to the flow instead. This can help manage memory usage and improve performance. 3. Flow Input Types: Prefect's documentation on flow inputs emphasizes matching input types between sender and receiver. Ensure that your input serialization strategy aligns with these requirements. For more details on handling inputs in Prefect, you can refer to the Prefect documentation on inputs.