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You'll note I said I was playing with RPython, not PyPy. In my case, I was playing with writing a small interpreter, and comparing the RPython toolchain with the Truffle/Graal framework.
Writing RPython code, even if one is not developing or contributing to PyPy, means writing within a subset of python 2.
> RPython ("Restricted Python") is a subset of Python 2
https://www.pypy.org/posts/2022/04/how-is-pypy-tested.html
And RPython's translator specifically uses pypy, and uses python 2 syntax:
https://github.com/pypy/pypy/blob/main/rpython/bin/rpython#L...
... so getting the RPython toolchain (even if one is intending to improve the PyPy 3+ interpreters) requires setting up a pypy 2 interpreter. Hence the question in my post.
https://data-apis.org/dataframe-protocol/latest/purpose_and_...
Practically, pd.DataFrame(,dtype_backend="arrow") may be a quick performance boost.
Jupyter kernels with Papermill or cron and templated parameters at the top of a notebook with a dated filename in a (repo2docker compatible) git repo also solve for interactive reports. To provision a temporary container for a user editing report notebooks, there's Voila on binderhub / jupyterhub, https://github.com/binder-examples/voila
or repo2jupyterlite in WASM with MathTex and Pyodide's NumPy/pandas/sympy: https://github.com/jupyterlite/repo2jupyterlite
But that's not a GUI, that's notebooks. For Jupyter integration, TIL pyqtgraph has jupyter_rfb, Remote Frame Buffer: https://github.com/vispy/jupyter_rfb
https://data-apis.org/dataframe-protocol/latest/purpose_and_...
Practically, pd.DataFrame(,dtype_backend="arrow") may be a quick performance boost.
Jupyter kernels with Papermill or cron and templated parameters at the top of a notebook with a dated filename in a (repo2docker compatible) git repo also solve for interactive reports. To provision a temporary container for a user editing report notebooks, there's Voila on binderhub / jupyterhub, https://github.com/binder-examples/voila
or repo2jupyterlite in WASM with MathTex and Pyodide's NumPy/pandas/sympy: https://github.com/jupyterlite/repo2jupyterlite
But that's not a GUI, that's notebooks. For Jupyter integration, TIL pyqtgraph has jupyter_rfb, Remote Frame Buffer: https://github.com/vispy/jupyter_rfb
https://data-apis.org/dataframe-protocol/latest/purpose_and_...
Practically, pd.DataFrame(,dtype_backend="arrow") may be a quick performance boost.
Jupyter kernels with Papermill or cron and templated parameters at the top of a notebook with a dated filename in a (repo2docker compatible) git repo also solve for interactive reports. To provision a temporary container for a user editing report notebooks, there's Voila on binderhub / jupyterhub, https://github.com/binder-examples/voila
or repo2jupyterlite in WASM with MathTex and Pyodide's NumPy/pandas/sympy: https://github.com/jupyterlite/repo2jupyterlite
But that's not a GUI, that's notebooks. For Jupyter integration, TIL pyqtgraph has jupyter_rfb, Remote Frame Buffer: https://github.com/vispy/jupyter_rfb
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