3D desktop Game Engine scriptable in Python

• Panda3D

  42 4,264 9.4 C++

  Powerful, mature open-source cross-platform game engine for Python and C++, developed by Disney and CMU

  

>> It'd have to be a situation like sklearn and numpy where all the actual functionality is implemented in c/c++ and you're just using it as a glue language

It sounds like this engine is not implemented in 100% python.

See also Panda3D https://www.panda3d.org/, which was used in commercial games back in the day, and is exactly what you are describing!

• Apache Arrow

  75 13,480 10.0 C++

  Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing

  

https://www.reddit.com/r/O3DE/comments/rdvxhx/why_python/ :

> Python is used for scripting the editor only, not in-game behaviors.

> For implementing entity behaviors the only out of box ways are C++, ScriptCanvas (visual scripting) or Lua. Python is currently not available for implementing game logic.

C++, Lua, and Python all implement CFFI (C Foreign Function Interface) for remote function and method calls.

"Using CFFI for embedding" https://cffi.readthedocs.io/en/latest/embedding.html :

> You can use CFFI to generate C code which exports the API of your choice to any C application that wants to link with this C code. This API, which you define yourself, ends up as the API of a .so/.dll/.dylib library—or you can statically link it within a larger application.

Apache Arrow already supports C, C++, Python, Rust, Go and has C GLib support Lua:

https://github.com/apache/arrow/tree/main/c_glib/example/lua :

> Arrow Lua example: All example codes use LGI to use Arrow GLib based bindings

pyarrow.from_numpy_dtype:

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• sklearn-pandas

  1 2,783 0.0 Python

  Pandas integration with sklearn

  

https://arrow.apache.org/docs/python/generated/pyarrow.from_...

https://github.com/scikit-learn-contrib/sklearn-pandas :

> Sklearn-pandas: This module provides a bridge between Scikit-Learn's machine learning methods and pandas-style Data Frames. In particular, it provides a way to map DataFrame columns to transformations, which are later recombined into features.

Pandas docs > PyArrow > I/O https://pandas.pydata.org/docs/user_guide/pyarrow.html#i-o-r... :

> By default, these functions and all other IO reader functions return NumPy-backed data. These readers can return PyArrow-backed data by specifying the parameter dtype_backend="pyarrow"

> [...] Several non-IO reader functions can also use the dtype_backend argument to return PyArrow-backed data including: to_numeric() , DataFrame.convert_dtypes() , Series.convert_dtypes()

  x = pandas.read_csv(".csv", dtype_backend="pyarrow")

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