CLN: Cython 3 cleanups (#54482)

* CLN: Update code for Cython 3

* clean more

* fix tests

pandas/_libs/algos.pyx

@@ -998,8 +998,7 @@ def rank_1d(
 
     N = len(values)
     if labels is not None:
-        # TODO(cython3): cast won't be necessary (#2992)
-        assert <Py_ssize_t>len(labels) == N
+        assert len(labels) == N
     out = np.empty(N)
     grp_sizes = np.ones(N, dtype=np.int64)
 
@@ -1088,8 +1087,7 @@ cdef void rank_sorted_1d(
     float64_t[::1] out,
     int64_t[::1] grp_sizes,
     const intp_t[:] sort_indexer,
-    # TODO(cython3): make const (https://github.com/cython/cython/issues/3222)
-    numeric_object_t[:] masked_vals,
+    const numeric_object_t[:] masked_vals,
     const uint8_t[:] mask,
     bint check_mask,
     Py_ssize_t N,
@@ -1144,108 +1142,7 @@ cdef void rank_sorted_1d(
     # array that we sorted previously, which gives us the location of
     # that sorted value for retrieval back from the original
     # values / masked_vals arrays
-    # TODO(cython3): de-duplicate once cython supports conditional nogil
-    if numeric_object_t is object:
-        with gil:
-            for i in range(N):
-                at_end = i == N - 1
-
-                # dups and sum_ranks will be incremented each loop where
-                # the value / group remains the same, and should be reset
-                # when either of those change. Used to calculate tiebreakers
-                dups += 1
-                sum_ranks += i - grp_start + 1
-
-                next_val_diff = at_end or are_diff(masked_vals[sort_indexer[i]],
-                                                   masked_vals[sort_indexer[i+1]])
-
-                # We'll need this check later anyway to determine group size, so just
-                # compute it here since shortcircuiting won't help
-                group_changed = at_end or (check_labels and
-                                           (labels[sort_indexer[i]]
-                                            != labels[sort_indexer[i+1]]))
-
-                # Update out only when there is a transition of values or labels.
-                # When a new value or group is encountered, go back #dups steps(
-                # the number of occurrence of current value) and assign the ranks
-                # based on the starting index of the current group (grp_start)
-                # and the current index
-                if (next_val_diff or group_changed or (check_mask and
-                                                       (mask[sort_indexer[i]]
-                                                        ^ mask[sort_indexer[i+1]]))):
-
-                    # If keep_na, check for missing values and assign back
-                    # to the result where appropriate
-                    if keep_na and check_mask and mask[sort_indexer[i]]:
-                        grp_na_count = dups
-                        for j in range(i - dups + 1, i + 1):
-                            out[sort_indexer[j]] = NaN
-                    elif tiebreak == TIEBREAK_AVERAGE:
-                        for j in range(i - dups + 1, i + 1):
-                            out[sort_indexer[j]] = sum_ranks / <float64_t>dups
-                    elif tiebreak == TIEBREAK_MIN:
-                        for j in range(i - dups + 1, i + 1):
-                            out[sort_indexer[j]] = i - grp_start - dups + 2
-                    elif tiebreak == TIEBREAK_MAX:
-                        for j in range(i - dups + 1, i + 1):
-                            out[sort_indexer[j]] = i - grp_start + 1
-
-                    # With n as the previous rank in the group and m as the number
-                    # of duplicates in this stretch, if TIEBREAK_FIRST and ascending,
-                    # then rankings should be n + 1, n + 2 ... n + m
-                    elif tiebreak == TIEBREAK_FIRST:
-                        for j in range(i - dups + 1, i + 1):
-                            out[sort_indexer[j]] = j + 1 - grp_start
-
-                    # If TIEBREAK_FIRST and descending, the ranking should be
-                    # n + m, n + (m - 1) ... n + 1. This is equivalent to
-                    # (i - dups + 1) + (i - j + 1) - grp_start
-                    elif tiebreak == TIEBREAK_FIRST_DESCENDING:
-                        for j in range(i - dups + 1, i + 1):
-                            out[sort_indexer[j]] = 2 * i - j - dups + 2 - grp_start
-                    elif tiebreak == TIEBREAK_DENSE:
-                        for j in range(i - dups + 1, i + 1):
-                            out[sort_indexer[j]] = grp_vals_seen
-
-                    # Look forward to the next value (using the sorting in
-                    # lexsort_indexer). If the value does not equal the current
-                    # value then we need to reset the dups and sum_ranks, knowing
-                    # that a new value is coming up. The conditional also needs
-                    # to handle nan equality and the end of iteration. If group
-                    # changes we do not record seeing a new value in the group
-                    if not group_changed and (next_val_diff or (check_mask and
-                                              (mask[sort_indexer[i]]
-                                               ^ mask[sort_indexer[i+1]]))):
-                        dups = sum_ranks = 0
-                        grp_vals_seen += 1
-
-                    # Similar to the previous conditional, check now if we are
-                    # moving to a new group. If so, keep track of the index where
-                    # the new group occurs, so the tiebreaker calculations can
-                    # decrement that from their position. Fill in the size of each
-                    # group encountered (used by pct calculations later). Also be
-                    # sure to reset any of the items helping to calculate dups
-                    if group_changed:
-
-                        # If not dense tiebreak, group size used to compute
-                        # percentile will be # of non-null elements in group
-                        if tiebreak != TIEBREAK_DENSE:
-                            grp_size = i - grp_start + 1 - grp_na_count
-
-                        # Otherwise, it will be the number of distinct values
-                        # in the group, subtracting 1 if NaNs are present
-                        # since that is a distinct value we shouldn't count
-                        else:
-                            grp_size = grp_vals_seen - (grp_na_count > 0)
-
-                        for j in range(grp_start, i + 1):
-                            grp_sizes[sort_indexer[j]] = grp_size
-
-                        dups = sum_ranks = 0
-                        grp_na_count = 0
-                        grp_start = i + 1
-                        grp_vals_seen = 1
-    else:
+    with gil(numeric_object_t is object):
         for i in range(N):
             at_end = i == N - 1
 
@@ -1474,16 +1371,18 @@ ctypedef fused out_t:
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def diff_2d(
-    ndarray[diff_t, ndim=2] arr,  # TODO(cython3) update to "const diff_t[:, :] arr"
-    ndarray[out_t, ndim=2] out,
+    # TODO: cython bug (post Cython 3) prevents update to "const diff_t[:, :] arr"
+    ndarray[diff_t, ndim=2] arr,
+    out_t[:, :] out,
     Py_ssize_t periods,
     int axis,
     bint datetimelike=False,
 ):
     cdef:
         Py_ssize_t i, j, sx, sy, start, stop
         bint f_contig = arr.flags.f_contiguous
-        # bint f_contig = arr.is_f_contig()  # TODO(cython3)
+        # TODO: change to this when arr becomes a memoryview
+        # bint f_contig = arr.is_f_contig()
         diff_t left, right
 
     # Disable for unsupported dtype combinations,

pandas/_libs/arrays.pyx

@@ -126,8 +126,7 @@ cdef class NDArrayBacked:
 
     @property
     def size(self) -> int:
-        # TODO(cython3): use self._ndarray.size
-        return cnp.PyArray_SIZE(self._ndarray)
+        return self._ndarray.size
 
     @property
     def nbytes(self) -> int: