REF: Use more cython memoryviews (#58271)

* fix some cython 3 todos

* Another memoryview

* Add another memoryview

* Use more memoryview

* another memoryview

* more memoryview syntax in groupby

* Add a few more memoryviews

* Use Py_ssize_t

Author: mroeschke

pandas/_libs/algos.pyx

@@ -351,7 +351,7 @@ def nancorr(const float64_t[:, :] mat, bint cov=False, minp=None):
         Py_ssize_t i, xi, yi, N, K
         int64_t minpv
         float64_t[:, ::1] result
-        ndarray[uint8_t, ndim=2] mask
+        uint8_t[:, :] mask
         int64_t nobs = 0
         float64_t vx, vy, dx, dy, meanx, meany, divisor, ssqdmx, ssqdmy, covxy
 
@@ -407,7 +407,7 @@ def nancorr_spearman(ndarray[float64_t, ndim=2] mat, Py_ssize_t minp=1) -> ndarr
         Py_ssize_t i, xi, yi, N, K
         ndarray[float64_t, ndim=2] result
         ndarray[float64_t, ndim=2] ranked_mat
-        ndarray[float64_t, ndim=1] rankedx, rankedy
+        float64_t[::1] rankedx, rankedy
         float64_t[::1] maskedx, maskedy
         ndarray[uint8_t, ndim=2] mask
         int64_t nobs = 0
@@ -566,8 +566,8 @@ def get_fill_indexer(const uint8_t[:] mask, limit=None):
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def pad(
-    ndarray[numeric_object_t] old,
-    ndarray[numeric_object_t] new,
+    const numeric_object_t[:] old,
+    const numeric_object_t[:] new,
     limit=None
 ) -> ndarray:
     # -> ndarray[intp_t, ndim=1]
@@ -691,8 +691,8 @@ def pad_2d_inplace(numeric_object_t[:, :] values, uint8_t[:, :] mask, limit=None
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def backfill(
-    ndarray[numeric_object_t] old,
-    ndarray[numeric_object_t] new,
+    const numeric_object_t[:] old,
+    const numeric_object_t[:] new,
     limit=None
 ) -> ndarray:  # -> ndarray[intp_t, ndim=1]
     """
@@ -786,7 +786,7 @@ def backfill_2d_inplace(numeric_object_t[:, :] values,
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
-def is_monotonic(ndarray[numeric_object_t, ndim=1] arr, bint timelike):
+def is_monotonic(const numeric_object_t[:] arr, bint timelike):
     """
     Returns
     -------
@@ -1089,8 +1089,7 @@ 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,
@@ -1378,16 +1377,15 @@ 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"
+    const diff_t[:, :] arr,
     ndarray[out_t, ndim=2] 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) once arr is memoryview
+        bint f_contig = arr.is_f_contig()
         diff_t left, right
 
     # Disable for unsupported dtype combinations,

pandas/_libs/groupby.pyx

@@ -511,9 +511,9 @@ def group_shift_indexer(
 @cython.wraparound(False)
 @cython.boundscheck(False)
 def group_fillna_indexer(
-    ndarray[intp_t] out,
-    ndarray[intp_t] labels,
-    ndarray[uint8_t] mask,
+    Py_ssize_t[::1] out,
+    const intp_t[::1] labels,
+    const uint8_t[:] mask,
     int64_t limit,
     bint compute_ffill,
     int ngroups,
@@ -1179,13 +1179,13 @@ def group_ohlc(
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def group_quantile(
-    ndarray[float64_t, ndim=2] out,
+    float64_t[:, ::1] out,
     ndarray[numeric_t, ndim=1] values,
-    ndarray[intp_t] labels,
+    const intp_t[::1] labels,
     const uint8_t[:] mask,
     const float64_t[:] qs,
-    ndarray[int64_t] starts,
-    ndarray[int64_t] ends,
+    const int64_t[::1] starts,
+    const int64_t[::1] ends,
     str interpolation,
     uint8_t[:, ::1] result_mask,
     bint is_datetimelike,
@@ -1388,7 +1388,7 @@ cdef inline void _check_below_mincount(
     uint8_t[:, ::1] result_mask,
     Py_ssize_t ncounts,
     Py_ssize_t K,
-    int64_t[:, ::1] nobs,
+    const int64_t[:, ::1] nobs,
     int64_t min_count,
     mincount_t[:, ::1] resx,
 ) noexcept:
@@ -1435,14 +1435,12 @@ cdef inline void _check_below_mincount(
                         out[i, j] = 0
 
 
-# TODO(cython3): GH#31710 use memorviews once cython 0.30 is released so we can
-#  use `const numeric_object_t[:, :] values`
 @cython.wraparound(False)
 @cython.boundscheck(False)
 def group_last(
     numeric_object_t[:, ::1] out,
     int64_t[::1] counts,
-    ndarray[numeric_object_t, ndim=2] values,
+    const numeric_object_t[:, :] values,
     const intp_t[::1] labels,
     const uint8_t[:, :] mask,
     uint8_t[:, ::1] result_mask=None,
@@ -1502,14 +1500,12 @@ def group_last(
     )
 
 
-# TODO(cython3): GH#31710 use memorviews once cython 0.30 is released so we can
-#  use `const numeric_object_t[:, :] values`
 @cython.wraparound(False)
 @cython.boundscheck(False)
 def group_nth(
     numeric_object_t[:, ::1] out,
     int64_t[::1] counts,
-    ndarray[numeric_object_t, ndim=2] values,
+    const numeric_object_t[:, :] values,
     const intp_t[::1] labels,
     const uint8_t[:, :] mask,
     uint8_t[:, ::1] result_mask=None,

pandas/_libs/hashing.pyx

@@ -11,7 +11,6 @@ import numpy as np
 
 from numpy cimport (
     import_array,
-    ndarray,
     uint8_t,
     uint64_t,
 )
@@ -23,7 +22,7 @@ from pandas._libs.util cimport is_nan
 
 @cython.boundscheck(False)
 def hash_object_array(
-    ndarray[object] arr, str key, str encoding="utf8"
+    object[:] arr, str key, str encoding="utf8"
 ) -> np.ndarray[np.uint64]:
     """
     Parameters