8000 MAINT: Create rec and char submodules · numpy/numpy@ddf19d4 · GitHub
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MAINT: Create rec and char submodules
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‎doc/source/glossary.rst

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@@ -442,7 +442,7 @@ Glossary
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record array
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A :term:`structured array` with allowing access in an attribute style
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(``a.field``) in addition to ``a['field']``. For details, see
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:doc:`numpy.recarray. <reference/generated/numpy.recarray>`
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:doc:`numpy.rec.recarray. <reference/generated/numpy.rec.recarray>`
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row-major

‎doc/source/reference/arrays.classes.rst

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@@ -501,7 +501,7 @@ data-type. However, a chararray can also be created using the
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:toctree: generated/
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char.chararray
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core.defchararray.array
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char.array
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Another difference with the standard ndarray of str data-type is
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that the chararray inherits the feature introduced by Numarray that
@@ -526,7 +526,7 @@ scalar data type object :class:`record`.
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.. autosummary::
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:toctree: generated/
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recarray
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rec.recarray
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record
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.. note::

‎doc/source/user/basics.io.genfromtxt.rst

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@@ -323,7 +323,7 @@ Validating names
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----------------
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NumPy arrays with a structured dtype can also be viewed as
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:class:`~numpy.recarray`, where a field can be accessed as if it were an
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:class:`~numpy.rec.recarray`, where a field can be accessed as if it were an
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attribute. For that reason, we may need to make sure that the field name
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doesn't contain any space or invalid character, or that it does not
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correspond to the name of a standard attribute (like ``size`` or

‎doc/source/user/basics.rec.rst

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@@ -630,8 +630,8 @@ Record Arrays
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=============
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As an optional convenience numpy provides an ndarray subclass,
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:class:`numpy.recarray` that allows access to fields of structured arrays by
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attribute instead of only by index.
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:class:`numpy.rec.recarray` that allows access to fields of structured arrays
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by attribute instead of only by index.
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Record arrays use a special datatype, :class:`numpy.record`, that allows
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field access by attribute on the structured scalars obtained from the array.
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The ``numpy.rec`` module provides functions for creating recarrays from
@@ -697,7 +697,7 @@ array if the field has a structured type but as a plain ndarray otherwise. ::
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>>> type(recordarr.foo)
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<class 'numpy.ndarray'>
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>>> type(recordarr.bar)
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<class 'numpy.recarray'>
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<class 'numpy.rec.recarray'>
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Note that if a field has the same name as an ndarray attribute, the ndarray
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attribute takes precedence. Such fields will be inaccessible by attribute but

‎numpy/__init__.py

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@@ -129,8 +129,7 @@
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atleast_1d, atleast_2d, atleast_3d, base_repr, binary_repr,
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bitwise_and, bitwise_not, bitwise_or, bitwise_xor, block, bool_,
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broadcast, busday_count, busday_offset, busdaycalendar, byte, bytes_,
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can_cast, cbrt, cdouble, ceil, char, character,
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choose, clip, clongdouble,
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can_cast, cbrt, cdouble, ceil, character, choose, clip, clongdouble,
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complexfloating, compress, concatenate, conj, conjugate, convolve,
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copysign, copyto, correlate, cos, cosh, count_nonzero, cross, csingle,
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cumprod, cumproduct, cumsum, datetime64, datetime_as_string,
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negative, nested_iters, newaxis, nextafter, nonzero, not_equal,
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number, object_, ones, ones_like, outer, partition,
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pi, positive, power, printoptions, prod, product, promote_types,
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ptp, put, putmask, rad2deg, radians, ravel, rec, reciprocal,
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ptp, put, putmask, rad2deg, radians, ravel, reciprocal,
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record, remainder, repeat, require, reshape, resize, result_type,
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right_shift, rint, roll, rollaxis, round,
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searchsorted, set_printoptions,
@@ -243,7 +242,7 @@
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__numpy_submodules__ = {
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"linalg", "fft", "dtypes", "random", "polynomial", "ma",
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"exceptions", "lib", "ctypeslib", "testing", "typing",
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"f2py", "test"
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"f2py", "test", "rec", "char"
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}
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# We build warning messages for former attributes
@@ -350,6 +349,12 @@ def __getattr__(attr):
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elif attr == "typing":
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import numpy.typing as typing
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return typing
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elif attr == "rec":
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import numpy.rec as rec
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return rec
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elif attr == "char":
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import numpy.char as char
357+
return char
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elif attr == "array_api":
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import numpy.array_api as array_api
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return array_api

‎numpy/__init__.pyi

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@@ -210,6 +210,8 @@ from numpy import (
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version as version,
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exceptions as exceptions,
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dtypes as dtypes,
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rec as rec,
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char as char,
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)
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from numpy.core import defchararray, records

‎numpy/char/__init__.py

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from numpy.core.defchararray import __all__, __doc__
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from numpy.core.defchararray import *

‎numpy/char/__init__.pyi

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from numpy.core.defchararray import (
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equal as equal,
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not_equal as not_equal,
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greater_equal as greater_equal,
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less_equal as less_equal,
6+
greater as greater,
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less as less,
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str_len as str_len,
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add as add,
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multiply as multiply,
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mod as mod,
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capitalize as capitalize,
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center as center,
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count as count,
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decode as decode,
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encode as encode,
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endswith as endswith,
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expandtabs as expandtabs,
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find as find,
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index as index,
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isalnum as isalnum,
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isalpha as isalpha,
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isdigit as isdigit,
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islower as islower,
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isspace as isspace,
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istitle as istitle,
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isupper as isupper,
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join as join,
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ljust as ljust,
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lower as lower,
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lstrip as lstrip,
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partition as partition,
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replace as replace,
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rfind as rfind,
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rindex as rindex,
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rjust as rjust,
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rpartition as rpartition,
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rsplit as rsplit,
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rstrip as rstrip,
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split as split,
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splitlines as splitlines,
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startswith as startswith,
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strip as strip,
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swapcase as swapcase,
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title as title,
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translate as translate,
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upper as upper,
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zfill as zfill,
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isnumeric as isnumeric,
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isdecimal as isdecimal,
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array as array,
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asarray as asarray,
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compare_chararrays as compare_chararrays,
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chararray as chararray
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)

‎numpy/core/__init__.py

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@@ -76,10 +76,7 @@
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from .numeric import *
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from . import fromnumeric
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from .fromnumeric import *
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from . import defchararray as char
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from . import records
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from . import records as rec
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from .records import record, recarray, format_parser
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from .records import record
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# Note: module name memmap is overwritten by a class with same name
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from .memmap import *
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from . import function_base
@@ -105,7 +102,7 @@
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from . import _dtype
106103
from . import _methods
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108-
__all__ = ['char', 'rec', 'memmap', 'record', 'abs']
105+
__all__ = ['memmap', 'record', 'abs']
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__all__ += numeric.__all__
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__all__ += function_base.__all__
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__all__ += getlimits.__all__

‎numpy/core/defchararray.py

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@@ -35,7 +35,7 @@
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'replace', 'rfind', 'rindex', 'rjust', 'rpartition', 'rsplit',
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'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase',
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'title', 'translate', 'upper', 'zfill', 'isnumeric', 'isdecimal',
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'array', 'asarray', 'compare_chararrays'
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'array', 'asarray', 'compare_chararrays', 'chararray'
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]
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19191919
return _vec_string(a, bool_, 'isdecimal')
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1922+
@set_module("numpy.char")
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class chararray(ndarray):
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"""
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chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0,

‎numpy/core/records.py

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# All of the functions allow formats to be a dtype
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__all__ = [
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'record', 'recarray', 'format_parser', 'fromarrays', 'fromrecords',
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'fromstring', 'fromfile', 'array',
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'fromstring', 'fromfile', 'array', 'find_duplicate',
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]
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numfmt = nt.sctypeDict
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@set_module('numpy.rec')
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def find_duplicate(list):
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"""Find duplication in a list, return a list of duplicated elements"""
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return [
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# If byteorder is given it forces a particular byteorder on all
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# the fields (and any subfields)
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310+
@set_module("numpy.rec")
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class recarray(ndarray):
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"""Construct an ndarray that allows field access using attributes.
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# This should only happen if the user is playing
536539
# strange games with dtypes.
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prefix = "array("
538-
fmt = 'array(%s,%sdtype=%s).view(numpy.recarray)'
541+
fmt = 'array(%s,%sdtype=%s).view(numpy.rec.recarray)'
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# get data/shape string. logic taken from numeric.array_repr
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if self.size > 0 or self.shape == (0,):

‎numpy/lib/recfunctions.py

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@@ -596,7 +596,7 @@ def _rec_drop_fields_dispatcher(base, drop_names):
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@array_function_dispatch(_rec_drop_fields_dispatcher)
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def rec_drop_fields(base, drop_names):
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"""
599-
Returns a new numpy.recarray with fields in `drop_names` dropped.
599+
Returns a new numpy.rec.recarray with fields in `drop_names` dropped.
600600
"""
601601
return drop_fields(base, drop_names, usemask=False, asrecarray=True)
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@@ -958,7 +958,7 @@ def structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe'):
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If true, always return a copy. If false, a view is returned if
959959
possible, such as when the `dtype` and strides of the fields are
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suitable and the array subtype is one of `numpy.ndarray`,
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`numpy.recarray` or `numpy.memmap`.
961+
`numpy.rec.recarray` or `numpy.memmap`.
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.. versionchanged:: 1.25.0
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A view can now be returned if the fields are separated by a

‎numpy/ma/mrecords.py

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""":mod:`numpy.ma..mrecords`
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Defines the equivalent of :class:`numpy.recarrays` for masked arrays,
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Defines the equivalent of :class:`numpy.rec.recarrays` for masked arrays,
44
where fields can be accessed as attributes.
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Note that :class:`numpy.ma.MaskedArray` already supports structured datatypes
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and the masking of individual fields.

‎numpy/meson.build

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'polynomial',
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'testing',
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'tests',
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'typing'
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'typing',
297+
'rec',
298+
'char'
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]
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if py.version().version_compare('<3.12')
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pure_subdirs += 'distutils'

‎numpy/rec/__init__.py

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from numpy.core.records import __all__, __doc__
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from numpy.core.records import *

‎numpy/rec/__init__.pyi

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from numpy.core.records import (
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record as record,
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recarray as recarray,
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format_parser as format_parser,
5+
fromarrays as fromarrays,
6+
fromrecords as fromrecords,
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fromstring as fromstring,
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fromfile as fromfile,
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array as array
10+
)

‎numpy/typing/__init__.py

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Record array dtypes
136136
~~~~~~~~~~~~~~~~~~~
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138-
The dtype of `numpy.recarray`, and the `numpy.rec` functions in general,
138+
The dtype of `numpy.rec.recarray`, and the `numpy.rec` functions in general,
139139
can be specified in one of two ways:
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141141
* Directly via the ``dtype`` argument.

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