Accept constant memoryviews in HashTable.lookup (pandas-dev#21688)

ci/appveyor-27.yaml

@@ -24,7 +24,7 @@ dependencies:
   - xlsxwriter
   - xlwt
   # universal
-  - cython
+  - cython>=0.28.2
   - pytest
   - pytest-xdist
   - moto

ci/appveyor-36.yaml

@@ -22,6 +22,6 @@ dependencies:
   - xlsxwriter
   - xlwt
   # universal
-  - cython
+  - cython>=0.28.2
   - pytest
   - pytest-xdist

ci/circle-27-compat.yaml

@@ -4,7 +4,7 @@ channels:
   - conda-forge
 dependencies:
   - bottleneck=1.0.0
-  - cython=0.24
+  - cython=0.28.2
   - jinja2=2.8
   - numexpr=2.4.4 # we test that we correctly don't use an unsupported numexpr
   - numpy=1.9.2

ci/circle-35-ascii.yaml

@@ -2,7 +2,7 @@ name: pandas
 channels:
   - defaults
 dependencies:
-  - cython
+  - cython>=0.28.2
   - nomkl
   - numpy
   - python-dateutil

ci/circle-36-locale.yaml

@@ -4,7 +4,7 @@ channels:
   - conda-forge
 dependencies:
   - beautifulsoup4
-  - cython
+  - cython>=0.28.2
   - html5lib
   - ipython
   - jinja2

ci/circle-36-locale_slow.yaml

@@ -4,7 +4,7 @@ channels:
   - conda-forge
 dependencies:
   - beautifulsoup4
-  - cython
+  - cython>=0.28.2
   - gcsfs
   - html5lib
   - ipython

ci/environment-dev.yaml

@@ -3,7 +3,7 @@ channels:
   - defaults
   - conda-forge
 dependencies:
-  - Cython
+  - Cython>=0.28.2
   - NumPy
   - flake8
   - moto

ci/travis-27-locale.yaml

@@ -4,7 +4,7 @@ channels:
   - conda-forge
 dependencies:
   - bottleneck=1.0.0
-  - cython=0.24
+  - cython=0.28.2
   - lxml
   - matplotlib=1.4.3
   - numpy=1.9.2

ci/travis-27.yaml

@@ -5,7 +5,7 @@ channels:
 dependencies:
   - beautifulsoup4
   - bottleneck
-  - cython=0.24
+  - cython=0.28.2
   - fastparquet
   - feather-format
   - flake8=3.4.1

ci/travis-35-osx.yaml

@@ -4,7 +4,7 @@ channels:
 dependencies:
   - beautifulsoup4
   - bottleneck
-  - cython
+  - cython>=0.28.2
   - html5lib
   - jinja2
   - lxml

ci/travis-36-doc.yaml

@@ -6,7 +6,7 @@ channels:
 dependencies:
   - beautifulsoup4
   - bottleneck
-  - cython
+  - cython>=0.28.2
   - fastparquet
   - feather-format
   - html5lib

ci/travis-36-numpydev.yaml

@@ -4,7 +4,7 @@ channels:
 dependencies:
   - python=3.6*
   - pytz
-  - Cython
+  - Cython>=0.28.2
   # universal
   - pytest
   - pytest-xdist

ci/travis-36-slow.yaml

@@ -4,7 +4,7 @@ channels:
   - conda-forge
 dependencies:
   - beautifulsoup4
-  - cython
+  - cython>=0.28.2
   - html5lib
   - lxml
   - matplotlib

ci/travis-36.yaml

@@ -4,7 +4,7 @@ channels:
   - conda-forge
 dependencies:
   - beautifulsoup4
-  - cython
+  - cython>=0.28.2
   - dask
   - fastparquet
   - feather-format

ci/travis-37.yaml

@@ -5,7 +5,7 @@ channels:
   - c3i_test
 dependencies:
   - python=3.7
-  - cython
+  - cython>=0.28.2
   - numpy
   - python-dateutil
   - nomkl

doc/source/install.rst

@@ -253,7 +253,7 @@ Optional Dependencies
 ~~~~~~~~~~~~~~~~~~~~~
 
 * `Cython <http://www.cython.org>`__: Only necessary to build development
-  version. Version 0.24 or higher.
+  version. Version 0.28.2 or higher.
 * `SciPy <http://www.scipy.org>`__: miscellaneous statistical functions, Version 0.14.0 or higher
 * `xarray <http://xarray.pydata.org>`__: pandas like handling for > 2 dims, needed for converting Panels to xarray objects. Version 0.7.0 or higher is recommended.
 * `PyTables <http://www.pytables.org>`__: necessary for HDF5-based storage. Version 3.0.0 or higher required, Version 3.2.1 or higher highly recommended.

doc/source/whatsnew/v0.24.0.txt

@@ -410,12 +410,17 @@ Reshaping
 -
 -
 
+Build Changes
+^^^^^^^^^^^^^
+
+- Building pandas for development now requires ``cython >= 0.28.2`` (:issue:`21688`)
 -
 
 Other
 ^^^^^
 
 - :meth: `~pandas.io.formats.style.Styler.background_gradient` now takes a ``text_color_threshold`` parameter to automatically lighten the text color based on the luminance of the background color. This improves readability with dark background colors without the need to limit the background colormap range. (:issue:`21258`)
+- Require at least 0.28.2 version of ``cython`` to support read-only memoryviews (:issue:`21688`)
 -
 -
 -

pandas/_libs/hashtable_class_helper.pxi.in

@@ -120,7 +120,7 @@ cdef class {{name}}Vector:
 
         append_data_{{dtype}}(self.data, x)
 
-    cdef extend(self, {{arg}}[:] x):
+    cdef extend(self, const {{arg}}[:] x):
         for i in range(len(x)):
             self.append(x[i])
 
@@ -253,56 +253,10 @@ dtypes = [('Float64', 'float64', True, 'nan'),
           ('UInt64', 'uint64', False, 0),
           ('Int64', 'int64', False, 'iNaT')]
 
-def get_dispatch(dtypes):
-  for (name, dtype, float_group, default_na_value) in dtypes:
-    unique_template = """\
-        cdef:
-           Py_ssize_t i, n = len(values)
-           int ret = 0
-           {dtype}_t val
-           khiter_t k
-           bint seen_na = 0
-           {name}Vector uniques = {name}Vector()
-           {name}VectorData *ud
-
-        ud = uniques.data
-
-        with nogil:
-            for i in range(n):
-                val = values[i]
-                IF {float_group}:
-                  if val == val:
-                      k = kh_get_{dtype}(self.table, val)
-                      if k == self.table.n_buckets:
-                          kh_put_{dtype}(self.table, val, &ret)
-                          if needs_resize(ud):
-                              with gil:
-                                  uniques.resize()
-                          append_data_{dtype}(ud, val)
-                  elif not seen_na:
-                      seen_na = 1
-                      if needs_resize(ud):
-                          with gil:
-                              uniques.resize()
-                      append_data_{dtype}(ud, NAN)
-                ELSE:
-                  k = kh_get_{dtype}(self.table, val)
-                  if k == self.table.n_buckets:
-                      kh_put_{dtype}(self.table, val, &ret)
-                      if needs_resize(ud):
-                          with gil:
-                              uniques.resize()
-                      append_data_{dtype}(ud, val)
-        return uniques.to_array()
-      """
-
-    unique_template = unique_template.format(name=name, dtype=dtype, float_group=float_group)
-
-    yield (name, dtype, float_group, default_na_value, unique_template)
 }}
 
 
-{{for name, dtype, float_group, default_na_value, unique_template in get_dispatch(dtypes)}}
+{{for name, dtype, float_group, default_na_value in dtypes}}
 
 cdef class {{name}}HashTable(HashTable):
 
@@ -351,7 +305,7 @@ cdef class {{name}}HashTable(HashTable):
             raise KeyError(key)
 
     @cython.boundscheck(False)
-    def map(self, {{dtype}}_t[:] keys, int64_t[:] values):
+    def map(self, const {{dtype}}_t[:] keys, const int64_t[:] values):
         cdef:
             Py_ssize_t i, n = len(values)
             int ret = 0
@@ -379,7 +333,7 @@ cdef class {{name}}HashTable(HashTable):
                 self.table.vals[k] = i
 
     @cython.boundscheck(False)
-    def lookup(self, {{dtype}}_t[:] values):
+    def lookup(self, const {{dtype}}_t[:] values):
         cdef:
             Py_ssize_t i, n = len(values)
             int ret = 0
@@ -404,7 +358,7 @@ cdef class {{name}}HashTable(HashTable):
         return uniques.to_array(), labels
 
     @cython.boundscheck(False)
-    def get_labels(self, {{dtype}}_t[:] values, {{name}}Vector uniques,
+    def get_labels(self, const {{dtype}}_t[:] values, {{name}}Vector uniques,
                    Py_ssize_t count_prior, Py_ssize_t na_sentinel,
                    object na_value=None):
         cdef:
@@ -461,7 +415,7 @@ cdef class {{name}}HashTable(HashTable):
         return np.asarray(labels)
 
     @cython.boundscheck(False)
-    def get_labels_groupby(self, {{dtype}}_t[:] values):
+    def get_labels_groupby(self, const {{dtype}}_t[:] values):
         cdef:
             Py_ssize_t i, n = len(values)
             int64_t[:] labels
@@ -506,20 +460,46 @@ cdef class {{name}}HashTable(HashTable):
         return np.asarray(labels), arr_uniques
 
     @cython.boundscheck(False)
-    def unique(self, ndarray[{{dtype}}_t, ndim=1] values):
-        if values.flags.writeable:
-          # If the value is writeable (mutable) then use memview
-          return self.unique_memview(values)
+    def unique(self, const {{dtype}}_t[:] values):
+        cdef:
+           Py_ssize_t i, n = len(values)
+           int ret = 0
+           {{dtype}}_t val
+           khiter_t k
+           bint seen_na = 0
+           {{name}}Vector uniques = {{name}}Vector()
+           {{name}}VectorData *ud
 
-        # We cannot use the memoryview version on readonly-buffers due to
-        # a limitation of Cython's typed memoryviews. Instead we can use
-        # the slightly slower Cython ndarray type directly.
-        # see https://github.com/cython/cython/issues/1605
-{{unique_template}}
+        ud = uniques.data
 
-    @cython.boundscheck(False)
-    def unique_memview(self, {{dtype}}_t[:] values):
-{{unique_template}}
+        with nogil:
+            for i in range(n):
+                val = values[i]
+                {{if float_group}}
+                if val == val:
+                    k = kh_get_{{dtype}}(self.table, val)
+                    if k == self.table.n_buckets:
+                        kh_put_{{dtype}}(self.table, val, &ret)
+                        if needs_resize(ud):
+                            with gil:
+                                uniques.resize()
+                        append_data_{{dtype}}(ud, val)
+                elif not seen_na:
+                    seen_na = 1
+                    if needs_resize(ud):
+                        with gil:
+                            uniques.resize()
+                    append_data_{{dtype}}(ud, NAN)
+                {{else}}
+                k = kh_get_{{dtype}}(self.table, val)
+                if k == self.table.n_buckets:
+                    kh_put_{{dtype}}(self.table, val, &ret)
+                    if needs_resize(ud):
+                        with gil:
+                            uniques.resize()
+                    append_data_{{dtype}}(ud, val)
+                {{endif}}
+        return uniques.to_array()
 
 {{endfor}}
 

pandas/conftest.py

@@ -138,6 +138,14 @@ def compression_only(request):
     return request.param
 
 
+@pytest.fixture(params=[True, False])
+def writable(request):
+    """
+    Fixture that an array is writable
+    """
+    return request.param
+
+
 @pytest.fixture(scope='module')
 def datetime_tz_utc():
     from datetime import timezone

pandas/tests/test_algos.py

@@ -1077,15 +1077,19 @@ class TestGroupVarFloat32(GroupVarTestMixin):
 
 class TestHashTable(object):
 
-    def test_lookup_nan(self):
+    def test_lookup_nan(self, writable):
         xs = np.array([2.718, 3.14, np.nan, -7, 5, 2, 3])
+        # GH 21688 ensure we can deal with readonly memory views
+        xs.setflags(write=writable)
         m = ht.Float64HashTable()
         m.map_locations(xs)
         tm.assert_numpy_array_equal(m.lookup(xs), np.arange(len(xs),
                                                             dtype=np.int64))
 
-    def test_lookup_overflow(self):
+    def test_lookup_overflow(self, writable):
         xs = np.array([1, 2, 2**63], dtype=np.uint64)
+        # GH 21688 ensure we can deal with readonly memory views
+        xs.setflags(write=writable)
         m = ht.UInt64HashTable()
         m.map_locations(xs)
         tm.assert_numpy_array_equal(m.lookup(xs), np.arange(len(xs),
@@ -1096,12 +1100,14 @@ def test_get_unique(self):
         exp = np.array([1, 2, 2**63], dtype=np.uint64)
         tm.assert_numpy_array_equal(s.unique(), exp)
 
-    def test_vector_resize(self):
+    def test_vector_resize(self, writable):
         # Test for memory errors after internal vector
         # reallocations (pull request #7157)
 
         def _test_vector_resize(htable, uniques, dtype, nvals, safely_resizes):
             vals = np.array(np.random.randn(1000), dtype=dtype)
+            # GH 21688 ensure we can deal with readonly memory views
+            vals.setflags(write=writable)
             # get_labels may append to uniques
             htable.get_labels(vals[:nvals], uniques, 0, -1)
             # to_array() set an external_view_exists flag on uniques.

setup.py

@@ -32,7 +32,7 @@ def is_platform_mac():
     return sys.platform == 'darwin'
 
 
-min_cython_ver = '0.24'
+min_cython_ver = '0.28.2'
 try:
     import Cython
     ver = Cython.__version__