Exploring in Jupyter and then copying to Streamlit
Another option is to utilize the extremely popular Jupyter data science product to write and test out the Streamlit app’s code before placing it in the necessary script and formatting it correctly. This can be useful for exploring new functions that will live in the Streamlit app, but it has serious downsides.
Pros:
- The lightning-fast feedback loop makes it easier to experiment with very large apps.
- Users may be more familiar with Jupyter.
- The full app does not have to be run to get results, as Jupyter can be run in individual cells.
Cons:
- Jupyter may provide deceptive results if run out of order.
- “Copying” code over from Jupyter is time-consuming.
- Python versioning may be different between Jupyter and Streamlit.
My recommendation here is to develop Streamlit apps inside the environment where they are going to be run (that is, a Python file)....
