1. Data remains stored in the excel file. The generated script pulls the raw data directly from the notebook - but it's a single read_xlsx call. So if you want to switch it out for an API call, db read, whatever - it's easy to do so.
2. We model data as primitive Python data types, or, if it's a table, as a pandas dataframe.
Currently, we detect at most one table per sheet, and it's gotta be contiguous. These are pretty huge limitations we'll be relaxing soon -- but we wanted to get something out as soon as it would have been useful to one person -- and in it's current state, this would have helped me with some of my larger Excel automation projects :)
Comments
Additional thoughts:
How are you modeling the input data? Are you using dataclasses and type annotations? something like https://docs.pydantic.dev/latest/ ?
How/Where is the data stored? Not inside the notebook, right?
Currently:
1. Data remains stored in the excel file. The generated script pulls the raw data directly from the notebook - but it's a single read_xlsx call. So if you want to switch it out for an API call, db read, whatever - it's easy to do so.
2. We model data as primitive Python data types, or, if it's a table, as a pandas dataframe.
Currently, we detect at most one table per sheet, and it's gotta be contiguous. These are pretty huge limitations we'll be relaxing soon -- but we wanted to get something out as soon as it would have been useful to one person -- and in it's current state, this would have helped me with some of my larger Excel automation projects :)