diff options
| -rw-r--r-- | numpy/core/_add_newdocs_scalars.py | 58 |
1 files changed, 57 insertions, 1 deletions
diff --git a/numpy/core/_add_newdocs_scalars.py b/numpy/core/_add_newdocs_scalars.py index c367c18ed..b9b151224 100644 --- a/numpy/core/_add_newdocs_scalars.py +++ b/numpy/core/_add_newdocs_scalars.py @@ -176,6 +176,62 @@ add_newdoc_for_scalar_type('object_', [], Any Python object. """) +add_newdoc_for_scalar_type('str_', ['unicode_'], + r""" + A unicode string. + + When used in arrays, this type strips trailing null codepoints. + + Unlike the builtin `str`, this supports the :ref:`python:bufferobjects`, exposing its + contents as UCS4: + + >>> m = memoryview(np.str_("abc")) + >>> m.format + '3w' + >>> m.tobytes() + b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00' + """) + +add_newdoc_for_scalar_type('bytes_', ['string_'], + r""" + A byte string. + + When used in arrays, this type strips trailing null bytes. + """) + +add_newdoc_for_scalar_type('void', [], + r""" + Either an opaque sequence of bytes, or a structure. + + >>> np.void(b'abcd') + void(b'\x61\x62\x63\x64') + + Structured `void` scalars can only be constructed via extraction from :ref:`structured_arrays`: + + >>> arr = np.array((1, 2), dtype=[('x', np.int8), ('y', np.int8)]) + >>> arr[()] + (1, 2) # looks like a tuple, but is `np.void` + """) + +add_newdoc_for_scalar_type('datetime64', [], + """ + A datetime stored as a 64-bit integer, counting from ``1970-01-01T00:00:00``. + + >>> np.datetime64(10, 'Y') + numpy.datetime64('1980') + >>> np.datetime64(10, 'D') + numpy.datetime64('1970-01-11') + + See :ref:`arrays.datetime` for more information. + """) + +add_newdoc_for_scalar_type('timedelta64', [], + """ + A timedelta stored as a 64-bit integer. + + See :ref:`arrays.datetime` for more information. + """) + # TODO: work out how to put this on the base class, np.floating for float_name in ('half', 'single', 'double', 'longdouble'): add_newdoc('numpy.core.numerictypes', float_name, ('as_integer_ratio', @@ -184,7 +240,7 @@ for float_name in ('half', 'single', 'double', 'longdouble'): Return a pair of integers, whose ratio is exactly equal to the original floating point number, and with a positive denominator. - Raise OverflowError on infinities and a ValueError on NaNs. + Raise `OverflowError` on infinities and a `ValueError` on NaNs. >>> np.{ftype}(10.0).as_integer_ratio() (10, 1) |