significant speedup for "to_scalar_or_list"

hello,

While investigating a slowness in plotly, I have stumbled upon the to_scalar_or_list function (https://github.com/plotly/plotly.py/blob/abd86092e048d5c8b02da65123824b75e4311838/packages/python/plotly/_plotly_utils/basevalidators.py#L30) that was taking much time.
After some tinkering, I came with the two following changes that vastly improves the performance:

• move out of the function the lines 38/39 (https://github.com/plotly/plotly.py/blob/abd86092e048d5c8b02da65123824b75e4311838/packages/python/plotly/_plotly_utils/basevalidators.py#L38) with the get_module as it is slow and run each time the function is called (when handling a list of 10k elements, 10k calls) ==> can this be done once in plotly instead of dynamically in each function ? (I see the get_module is also used in many other places in the package)

• move the simplest case (v is a basic type) first as for the case of an iterable of size N, it will first do lot of complex tests for the iterable and then N times also all the complex tests for each items.

So at the end, it looks like

np = get_module("numpy", should_load=False)
pd = get_module("pandas", should_load=False)
# Utility functions
# -----------------
def to_scalar_or_list(v):
    # Handle the case where 'v' is a non-native scalar-like type,
    # such as numpy.float32. Without this case, the object might be
    # considered numpy-convertable and therefore promoted to a
    # 0-dimensional array, but we instead want it converted to a
    # Python native scalar type ('float' in the example above).
    # We explicitly check if is has the 'item' method, which conventionally
    # converts these types to native scalars.

    # check first for the simple case
    if isinstance(v,(int,float,str)):
        return v
    if np and np.isscalar(v) and hasattr(v, "item"):
        return v.item()
    if isinstance(v, (list, tuple)):
        return [to_scalar_or_list(e) for e in v]
    elif np and isinstance(v, np.ndarray):
        if v.ndim == 0:
            return v.item()
        return [to_scalar_or_list(e) for e in v]
    elif pd and isinstance(v, (pd.Series, pd.Index)):
        return [to_scalar_or_list(e) for e in v]
    elif is_numpy_convertable(v):
        return to_scalar_or_list(np.array(v))
    else:
        return v

Commencez dans packages/python/plotly/_plotly_utils/basevalidators.py, au niveau de to_scalar_or_list, et examinez l’utilisation voisine de get_module et is_numpy_convertable. Comparez le comportement et les performances pour les scalaires natifs, les listes ou tuples, les tableaux NumPy et les valeurs Series ou Index de pandas. Le travail est terminé lorsque les cas proposés conservent leurs conversions existantes tout en évitant les traitements répétés inutiles.