CLN: intp_t instead of int64_t for indexers in libs funcs

Author: jbrockmendel

pandas/_libs/algos.pyx

@@ -199,20 +199,23 @@ def groupsort_indexer(const int64_t[:] index, Py_ssize_t ngroups):
 
     Returns
     -------
-    tuple
-        1-d indexer ordered by groups, group counts.
+    ndarray[intp_t, ndim=1]
+        Indexer
+    ndarray[int64_t, ndim=1]
+        Group Counts
 
     Notes
     -----
     This is a reverse of the label factorization process.
     """
     cdef:
         Py_ssize_t i, loc, label, n
-        ndarray[int64_t] counts, where, result
+        ndarray[int64_t] counts, where
+        ndarray[intp_t] indexer
 
     counts = np.zeros(ngroups + 1, dtype=np.int64)
     n = len(index)
-    result = np.zeros(n, dtype=np.int64)
+    indexer = np.zeros(n, dtype=np.intp)
     where = np.zeros(ngroups + 1, dtype=np.int64)
 
     with nogil:
@@ -228,10 +231,10 @@ def groupsort_indexer(const int64_t[:] index, Py_ssize_t ngroups):
         # this is our indexer
         for i in range(n):
             label = index[i] + 1
-            result[where[label]] = i
+            indexer[where[label]] = i
             where[label] += 1
 
-    return result, counts
+    return indexer, counts
 
 
 @cython.boundscheck(False)

pandas/_libs/algos_take_helper.pxi.in

@@ -102,7 +102,7 @@ def take_2d_axis0_{{name}}_{{dest}}(const {{c_type_in}}[:, :] values,
 {{else}}
 def take_2d_axis0_{{name}}_{{dest}}(ndarray[{{c_type_in}}, ndim=2] values,
 {{endif}}
-                                    ndarray[int64_t] indexer,
+                                    ndarray[intp_t] indexer,
                                     {{c_type_out}}[:, :] out,
                                     fill_value=np.nan):
     cdef:
@@ -156,7 +156,7 @@ def take_2d_axis1_{{name}}_{{dest}}(const {{c_type_in}}[:, :] values,
 {{else}}
 def take_2d_axis1_{{name}}_{{dest}}(ndarray[{{c_type_in}}, ndim=2] values,
 {{endif}}
-                                    ndarray[int64_t] indexer,
+                                    ndarray[intp_t] indexer,
                                     {{c_type_out}}[:, :] out,
                                     fill_value=np.nan):
 

pandas/_libs/groupby.pyx

@@ -19,6 +19,7 @@ from numpy cimport (
     int16_t,
     int32_t,
     int64_t,
+    intp_t,
     ndarray,
     uint8_t,
     uint16_t,
@@ -141,6 +142,7 @@ def group_median_float64(ndarray[float64_t, ndim=2] out,
         Py_ssize_t i, j, N, K, ngroups, size
         ndarray[int64_t] _counts
         ndarray[float64_t, ndim=2] data
+        ndarray[intp_t] indexer
         float64_t* ptr
 
     assert min_count == -1, "'min_count' only used in add and prod"

pandas/_libs/join.pyx

@@ -10,6 +10,7 @@ from numpy cimport (
     int16_t,
     int32_t,
     int64_t,
+    intp_t,
     ndarray,
     uint8_t,
     uint16_t,
@@ -31,7 +32,8 @@ def inner_join(const int64_t[:] left, const int64_t[:] right,
                Py_ssize_t max_groups):
     cdef:
         Py_ssize_t i, j, k, count = 0
-        ndarray[int64_t] left_count, right_count, left_sorter, right_sorter
+        ndarray[intp_t] left_sorter, right_sorter
+        ndarray[int64_t] left_count, right_count
         ndarray[int64_t] left_indexer, right_indexer
         int64_t lc, rc
         Py_ssize_t loc, left_pos = 0, right_pos = 0, position = 0
@@ -82,8 +84,8 @@ def left_outer_join(const int64_t[:] left, const int64_t[:] right,
                     Py_ssize_t max_groups, bint sort=True):
     cdef:
         Py_ssize_t i, j, k, count = 0
-        ndarray[int64_t] left_count, right_count, left_sorter, right_sorter
-        ndarray rev
+        ndarray[int64_t] left_count, right_count
+        ndarray[intp_t] rev, left_sorter, right_sorter
         ndarray[int64_t] left_indexer, right_indexer
         int64_t lc, rc
         Py_ssize_t loc, left_pos = 0, right_pos = 0, position = 0
@@ -155,7 +157,8 @@ def full_outer_join(const int64_t[:] left, const int64_t[:] right,
                     Py_ssize_t max_groups):
     cdef:
         Py_ssize_t i, j, k, count = 0
-        ndarray[int64_t] left_count, right_count, left_sorter, right_sorter
+        ndarray[intp_t] left_sorter, right_sorter
+        ndarray[int64_t] left_count, right_count
         ndarray[int64_t] left_indexer, right_indexer
         int64_t lc, rc
         int64_t left_pos = 0, right_pos = 0
@@ -213,12 +216,16 @@ def full_outer_join(const int64_t[:] left, const int64_t[:] right,
             _get_result_indexer(right_sorter, right_indexer))
 
 
-cdef _get_result_indexer(ndarray[int64_t] sorter, ndarray[int64_t] indexer):
+cdef ndarray[int64_t] _get_result_indexer(
+    ndarray[intp_t] sorter, ndarray[int64_t] indexer
+):
     if len(sorter) > 0:
         # cython-only equivalent to
         #  `res = algos.take_nd(sorter, indexer, fill_value=-1)`
         res = np.empty(len(indexer), dtype=np.int64)
         take_1d_int64_int64(sorter, indexer, res, -1)
+        # FIXME: sorter is intp_t, not int64_t, opposite for indexer;
+        #  will this break on 32bit builds?
     else:
         # length-0 case
         res = np.empty(len(indexer), dtype=np.int64)

pandas/core/array_algos/take.py

@@ -117,10 +117,10 @@ def _take_nd_ndarray(
 ) -> np.ndarray:
 
     if indexer is None:
-        indexer = np.arange(arr.shape[axis], dtype=np.int64)
+        indexer = np.arange(arr.shape[axis], dtype=np.intp)
         dtype, fill_value = arr.dtype, arr.dtype.type()
     else:
-        indexer = ensure_int64(indexer, copy=False)
+        indexer = ensure_platform_int(indexer)
     indexer, dtype, fill_value, mask_info = _take_preprocess_indexer_and_fill_value(
         arr, indexer, out, fill_value, allow_fill
     )
@@ -320,7 +320,7 @@ def _get_take_nd_function(
     if func is None:
 
         def func(arr, indexer, out, fill_value=np.nan):
-            indexer = ensure_int64(indexer)
+            indexer = ensure_platform_int(indexer)
             _take_nd_object(
                 arr, indexer, out, axis=axis, fill_value=fill_value, mask_info=mask_info
             )
@@ -471,7 +471,7 @@ def wrapper(
 
 def _take_nd_object(
     arr: np.ndarray,
-    indexer: np.ndarray,
+    indexer: np.ndarray,  # np.ndarray[np.intp]
     out: np.ndarray,
     axis: int,
     fill_value,
@@ -547,4 +547,5 @@ def _take_preprocess_indexer_and_fill_value(
                 # to crash when trying to cast it to dtype)
                 dtype, fill_value = arr.dtype, arr.dtype.type()
 
+    indexer = ensure_platform_int(indexer)
     return indexer, dtype, fill_value, mask_info

pandas/core/arrays/categorical.py

@@ -1979,7 +1979,7 @@ def _reverse_indexer(self) -> Dict[Hashable, np.ndarray]:
         """
         categories = self.categories
         r, counts = libalgos.groupsort_indexer(
-            self.codes.astype("int64"), categories.size
+            self.codes.astype("int64", copy=False), categories.size
         )
         counts = counts.cumsum()
         _result = (r[start:end] for start, end in zip(counts, counts[1:]))

pandas/core/sorting.py

@@ -596,6 +596,7 @@ def get_group_index_sorter(
     )
     if do_groupsort:
         sorter, _ = algos.groupsort_indexer(ensure_int64(group_index), ngroups)
+        # sorter _should_ already be intp, but mypy is not yet able to verify
         return ensure_platform_int(sorter)
     else:
         return group_index.argsort(kind="mergesort")

pandas/tests/test_algos.py

@@ -2123,19 +2123,19 @@ def test_groupsort_indexer():
 
     # need to use a stable sort
     # np.argsort returns int, groupsort_indexer
-    # always returns int64
+    # always returns intp
     expected = np.argsort(a, kind="mergesort")
-    expected = expected.astype(np.int64)
+    expected = expected.astype(np.intp)
 
     tm.assert_numpy_array_equal(result, expected)
 
     # compare with lexsort
     # np.lexsort returns int, groupsort_indexer
-    # always returns int64
+    # always returns intp
     key = a * 1000 + b
     result = libalgos.groupsort_indexer(key, 1000000)[0]
     expected = np.lexsort((b, a))
-    expected = expected.astype(np.int64)
+    expected = expected.astype(np.intp)
 
     tm.assert_numpy_array_equal(result, expected)