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| -rw-r--r-- | doc/neps/nep-0050-scalar-promotion.rst | 15 |
1 files changed, 9 insertions, 6 deletions
diff --git a/doc/neps/nep-0050-scalar-promotion.rst b/doc/neps/nep-0050-scalar-promotion.rst index 095bd3600..0fc601814 100644 --- a/doc/neps/nep-0050-scalar-promotion.rst +++ b/doc/neps/nep-0050-scalar-promotion.rst @@ -17,21 +17,24 @@ complex to implement and also made behavior hard to predict. There are two kinds of confusing results: -1. Value-based promotion means that values determine output types:: +1. Value-based promotion means that the value, for example of a Python integer, + can determine output type as found by ``np.result_type``:: np.result_type(np.int8, 1) == np.int8 np.result_type(np.int8, 255) == np.int16 + This logic arises because ``1`` can be represented by a ``uint8`` or + ``int8`` while ``255`` cannot be represented by an ``int8`` but only by + by a ``uint8`` or ``int16``. + This also holds when working with 0-D arrays (so-called "scalar arrays"):: int64_0d_array = np.array(1, dtype=np.int64) np.result_type(np.int8, int64_0d_array) == np.int8 - This logic arises because ``1`` can be represented by an ``int8`` while - ``255`` can be represented by an ``int16`` *or* ``uint8``. - - Because of this, the exact type is often ignored for 0-D arrays or - NumPy scalars. + Where the fact that ``int64_0d_array`` has an ``int64`` dtype has no + influence on the resulting dtype. The ``dtype=np.int64`` is effectively + ignored in this example since only its value matters. 2. For a Python ``int``, ``float``, or ``complex`` the value is inspected as previously shown. But surprisingly *not* when the NumPy object is a 0-D array |