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Description
hello,
While investigating a slowness in plotly, I have stumbled upon the to_scalar_or_list function (
After some tinkering, I came with the two following changes that vastly improves the performance:
-
move out of the function the lines 38/39 (
) with theget_moduleas 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