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-rw-r--r--doc/source/release/1.21.0-notes.rst33
-rw-r--r--numpy/core/src/multiarray/dtypemeta.c13
-rw-r--r--numpy/core/tests/test_deprecations.py35
-rw-r--r--numpy/core/tests/test_half.py6
-rw-r--r--numpy/core/tests/test_numeric.py67
-rw-r--r--numpy/core/tests/test_regression.py4
-rw-r--r--numpy/lib/tests/test_regression.py3
7 files changed, 34 insertions, 127 deletions
diff --git a/doc/source/release/1.21.0-notes.rst b/doc/source/release/1.21.0-notes.rst
index ac65b8fd0..c0d283b72 100644
--- a/doc/source/release/1.21.0-notes.rst
+++ b/doc/source/release/1.21.0-notes.rst
@@ -82,39 +82,6 @@ The methods in question are:
Future Changes
==============
-Promotion of strings with numbers and bools is deprecated
----------------------------------------------------------
-Any promotion of numbers and strings is deprecated and will
-give a ``FutureWarning`` the main affected functionalities
-are:
-
-* `numpy.promote_types` and `numpy.result_type` which will raise
- an error in this case in the future.
-* `numpy.concatenate` will raise an error when concatenating a string
- and numeric array. You can use ``dtype="S"`` to explicitly request
- a string result.
-* `numpy.array` and related functions will start returning ``object``
- arrays because these functions use ``object`` as a fallback when
- no common dtype can be found. However, it may happen that future
- releases of NumPy will generally error in these cases.
-
-This will mainly affect code such as::
-
- np.asarray(['string', 0])
-
-and::
-
- np.concatenate((['string'], [0]))
-
-in both cases adding ``dtype="U"`` or ``dtype="S"`` will give the
-previous (string) result, while ``dtype=object`` will ensure an array with
-object dtype is returned.
-
-Comparisons, universal functions, and casting are not affected by this.
-
-(`gh-18116 <https://github.com/numpy/numpy/pull/18116>`__)
-
-
Expired deprecations
====================
diff --git a/numpy/core/src/multiarray/dtypemeta.c b/numpy/core/src/multiarray/dtypemeta.c
index 40ca9ee2a..4ee721964 100644
--- a/numpy/core/src/multiarray/dtypemeta.c
+++ b/numpy/core/src/multiarray/dtypemeta.c
@@ -415,19 +415,6 @@ string_unicode_common_dtype(PyArray_DTypeMeta *cls, PyArray_DTypeMeta *other)
Py_INCREF(Py_NotImplemented);
return (PyArray_DTypeMeta *)Py_NotImplemented;
}
- if (other->type_num != NPY_STRING && other->type_num != NPY_UNICODE) {
- /* Deprecated 2020-12-19, NumPy 1.21. */
- if (DEPRECATE_FUTUREWARNING(
- "Promotion of numbers and bools to strings is deprecated. "
- "In the future, code such as `np.concatenate((['string'], [0]))` "
- "will raise an error, while `np.asarray(['string', 0])` will "
- "return an array with `dtype=object`. To avoid the warning "
- "while retaining a string result use `dtype='U'` (or 'S'). "
- "To get an array of Python objects use `dtype=object`. "
- "(Warning added in NumPy 1.21)") < 0) {
- return NULL;
- }
- }
/*
* The builtin types are ordered by complexity (aside from object) here.
* Arguably, we should not consider numbers and strings "common", but
diff --git a/numpy/core/tests/test_deprecations.py b/numpy/core/tests/test_deprecations.py
index ffe0147b2..42e632e4a 100644
--- a/numpy/core/tests/test_deprecations.py
+++ b/numpy/core/tests/test_deprecations.py
@@ -1105,41 +1105,6 @@ class TestNoseDecoratorsDeprecated(_DeprecationTestCase):
self.assert_deprecated(_test_parametrize)
-class TestStringPromotion(_DeprecationTestCase):
- # Deprecated 2020-12-19, NumPy 1.21
- warning_cls = FutureWarning
- message = "Promotion of numbers and bools to strings is deprecated."
-
- @pytest.mark.parametrize("dtype", "?bhilqpBHILQPefdgFDG")
- @pytest.mark.parametrize("string_dt", ["S", "U"])
- def test_deprecated(self, dtype, string_dt):
- self.assert_deprecated(lambda: np.promote_types(dtype, string_dt))
-
- # concatenate has to be able to promote to find the result dtype:
- arr1 = np.ones(3, dtype=dtype)
- arr2 = np.ones(3, dtype=string_dt)
- self.assert_deprecated(lambda: np.concatenate((arr1, arr2), axis=0))
- self.assert_deprecated(lambda: np.concatenate((arr1, arr2), axis=None))
-
- self.assert_deprecated(lambda: np.array([arr1[0], arr2[0]]))
-
- @pytest.mark.parametrize("dtype", "?bhilqpBHILQPefdgFDG")
- @pytest.mark.parametrize("string_dt", ["S", "U"])
- def test_not_deprecated(self, dtype, string_dt):
- # The ufunc type resolvers run into this, but giving a futurewarning
- # here is unnecessary (it ends up as an error anyway), so test that
- # no warning is given:
- arr1 = np.ones(3, dtype=dtype)
- arr2 = np.ones(3, dtype=string_dt)
-
- # Adding two arrays uses result_type normally, which would fail:
- with pytest.raises(TypeError):
- self.assert_not_deprecated(lambda: arr1 + arr2)
- # np.equal uses a different type resolver:
- with pytest.raises(TypeError):
- self.assert_not_deprecated(lambda: np.equal(arr1, arr2))
-
-
class TestSingleElementSignature(_DeprecationTestCase):
# Deprecated 2021-04-01, NumPy 1.21
message = r"The use of a length 1"
diff --git a/numpy/core/tests/test_half.py b/numpy/core/tests/test_half.py
index 449a01d21..1b6fd21e1 100644
--- a/numpy/core/tests/test_half.py
+++ b/numpy/core/tests/test_half.py
@@ -71,10 +71,8 @@ class TestHalf:
def test_half_conversion_to_string(self, string_dt):
# Currently uses S/U32 (which is sufficient for float32)
expected_dt = np.dtype(f"{string_dt}32")
- with pytest.warns(FutureWarning):
- assert np.promote_types(np.float16, string_dt) == expected_dt
- with pytest.warns(FutureWarning):
- assert np.promote_types(string_dt, np.float16) == expected_dt
+ assert np.promote_types(np.float16, string_dt) == expected_dt
+ assert np.promote_types(string_dt, np.float16) == expected_dt
arr = np.ones(3, dtype=np.float16).astype(string_dt)
assert arr.dtype == expected_dt
diff --git a/numpy/core/tests/test_numeric.py b/numpy/core/tests/test_numeric.py
index aba90ece5..f5113150e 100644
--- a/numpy/core/tests/test_numeric.py
+++ b/numpy/core/tests/test_numeric.py
@@ -848,12 +848,10 @@ class TestTypes:
assert_equal(np.promote_types('<i8', '<i8'), np.dtype('i8'))
assert_equal(np.promote_types('>i8', '>i8'), np.dtype('i8'))
- with pytest.warns(FutureWarning,
- match="Promotion of numbers and bools to strings"):
- assert_equal(np.promote_types('>i8', '>U16'), np.dtype('U21'))
- assert_equal(np.promote_types('<i8', '<U16'), np.dtype('U21'))
- assert_equal(np.promote_types('>U16', '>i8'), np.dtype('U21'))
- assert_equal(np.promote_types('<U16', '<i8'), np.dtype('U21'))
+ assert_equal(np.promote_types('>i8', '>U16'), np.dtype('U21'))
+ assert_equal(np.promote_types('<i8', '<U16'), np.dtype('U21'))
+ assert_equal(np.promote_types('>U16', '>i8'), np.dtype('U21'))
+ assert_equal(np.promote_types('<U16', '<i8'), np.dtype('U21'))
assert_equal(np.promote_types('<S5', '<U8'), np.dtype('U8'))
assert_equal(np.promote_types('>S5', '>U8'), np.dtype('U8'))
@@ -901,37 +899,32 @@ class TestTypes:
S = string_dtype
- with pytest.warns(FutureWarning,
- match="Promotion of numbers and bools to strings") as record:
- # Promote numeric with unsized string:
- assert_equal(promote_types('bool', S), np.dtype(S+'5'))
- assert_equal(promote_types('b', S), np.dtype(S+'4'))
- assert_equal(promote_types('u1', S), np.dtype(S+'3'))
- assert_equal(promote_types('u2', S), np.dtype(S+'5'))
- assert_equal(promote_types('u4', S), np.dtype(S+'10'))
- assert_equal(promote_types('u8', S), np.dtype(S+'20'))
- assert_equal(promote_types('i1', S), np.dtype(S+'4'))
- assert_equal(promote_types('i2', S), np.dtype(S+'6'))
- assert_equal(promote_types('i4', S), np.dtype(S+'11'))
- assert_equal(promote_types('i8', S), np.dtype(S+'21'))
- # Promote numeric with sized string:
- assert_equal(promote_types('bool', S+'1'), np.dtype(S+'5'))
- assert_equal(promote_types('bool', S+'30'), np.dtype(S+'30'))
- assert_equal(promote_types('b', S+'1'), np.dtype(S+'4'))
- assert_equal(promote_types('b', S+'30'), np.dtype(S+'30'))
- assert_equal(promote_types('u1', S+'1'), np.dtype(S+'3'))
- assert_equal(promote_types('u1', S+'30'), np.dtype(S+'30'))
- assert_equal(promote_types('u2', S+'1'), np.dtype(S+'5'))
- assert_equal(promote_types('u2', S+'30'), np.dtype(S+'30'))
- assert_equal(promote_types('u4', S+'1'), np.dtype(S+'10'))
- assert_equal(promote_types('u4', S+'30'), np.dtype(S+'30'))
- assert_equal(promote_types('u8', S+'1'), np.dtype(S+'20'))
- assert_equal(promote_types('u8', S+'30'), np.dtype(S+'30'))
- # Promote with object:
- assert_equal(promote_types('O', S+'30'), np.dtype('O'))
-
- assert len(record) == 22 # each string promotion gave one warning
-
+ # Promote numeric with unsized string:
+ assert_equal(promote_types('bool', S), np.dtype(S+'5'))
+ assert_equal(promote_types('b', S), np.dtype(S+'4'))
+ assert_equal(promote_types('u1', S), np.dtype(S+'3'))
+ assert_equal(promote_types('u2', S), np.dtype(S+'5'))
+ assert_equal(promote_types('u4', S), np.dtype(S+'10'))
+ assert_equal(promote_types('u8', S), np.dtype(S+'20'))
+ assert_equal(promote_types('i1', S), np.dtype(S+'4'))
+ assert_equal(promote_types('i2', S), np.dtype(S+'6'))
+ assert_equal(promote_types('i4', S), np.dtype(S+'11'))
+ assert_equal(promote_types('i8', S), np.dtype(S+'21'))
+ # Promote numeric with sized string:
+ assert_equal(promote_types('bool', S+'1'), np.dtype(S+'5'))
+ assert_equal(promote_types('bool', S+'30'), np.dtype(S+'30'))
+ assert_equal(promote_types('b', S+'1'), np.dtype(S+'4'))
+ assert_equal(promote_types('b', S+'30'), np.dtype(S+'30'))
+ assert_equal(promote_types('u1', S+'1'), np.dtype(S+'3'))
+ assert_equal(promote_types('u1', S+'30'), np.dtype(S+'30'))
+ assert_equal(promote_types('u2', S+'1'), np.dtype(S+'5'))
+ assert_equal(promote_types('u2', S+'30'), np.dtype(S+'30'))
+ assert_equal(promote_types('u4', S+'1'), np.dtype(S+'10'))
+ assert_equal(promote_types('u4', S+'30'), np.dtype(S+'30'))
+ assert_equal(promote_types('u8', S+'1'), np.dtype(S+'20'))
+ assert_equal(promote_types('u8', S+'30'), np.dtype(S+'30'))
+ # Promote with object:
+ assert_equal(promote_types('O', S+'30'), np.dtype('O'))
@pytest.mark.parametrize(["dtype1", "dtype2"],
[[np.dtype("V6"), np.dtype("V10")],
diff --git a/numpy/core/tests/test_regression.py b/numpy/core/tests/test_regression.py
index dbfb75c9a..312d0683d 100644
--- a/numpy/core/tests/test_regression.py
+++ b/numpy/core/tests/test_regression.py
@@ -782,9 +782,7 @@ class TestRegression:
# Ticket #514
s = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
t = []
- with pytest.warns(FutureWarning,
- match="Promotion of numbers and bools to strings"):
- np.hstack((t, s))
+ np.hstack((t, s))
def test_arr_transpose(self):
# Ticket #516
diff --git a/numpy/lib/tests/test_regression.py b/numpy/lib/tests/test_regression.py
index 94fac7ef0..373226277 100644
--- a/numpy/lib/tests/test_regression.py
+++ b/numpy/lib/tests/test_regression.py
@@ -64,8 +64,7 @@ class TestRegression:
def test_mem_string_concat(self):
# Ticket #469
x = np.array([])
- with pytest.warns(FutureWarning):
- np.append(x, 'asdasd\tasdasd')
+ np.append(x, 'asdasd\tasdasd')
def test_poly_div(self):
# Ticket #553
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