diff --git a/pandas/_libs/algos.pyx b/pandas/_libs/algos.pyx index dd1f38ce3a842..5f3d946a1e024 100644 --- a/pandas/_libs/algos.pyx +++ b/pandas/_libs/algos.pyx @@ -1173,12 +1173,12 @@ ctypedef fused out_t: @cython.boundscheck(False) @cython.wraparound(False) -def diff_2d(ndarray[diff_t, ndim=2] arr, - ndarray[out_t, ndim=2] out, +def diff_2d(diff_t[:, :] arr, + out_t[:, :] out, Py_ssize_t periods, int axis): cdef: Py_ssize_t i, j, sx, sy, start, stop - bint f_contig = arr.flags.f_contiguous + bint f_contig = arr.is_f_contig() # Disable for unsupported dtype combinations, # see https://github.com/cython/cython/issues/2646 diff --git a/pandas/_libs/hashing.pyx b/pandas/_libs/hashing.pyx index 878da670b2f68..2d859db22ea23 100644 --- a/pandas/_libs/hashing.pyx +++ b/pandas/_libs/hashing.pyx @@ -5,7 +5,7 @@ import cython from libc.stdlib cimport malloc, free import numpy as np -from numpy cimport uint8_t, uint32_t, uint64_t, import_array +from numpy cimport ndarray, uint8_t, uint32_t, uint64_t, import_array import_array() from pandas._libs.util cimport is_nan @@ -15,7 +15,7 @@ DEF dROUNDS = 4 @cython.boundscheck(False) -def hash_object_array(object[:] arr, object key, object encoding='utf8'): +def hash_object_array(ndarray[object] arr, object key, object encoding='utf8'): """ Parameters ---------- diff --git a/pandas/_libs/tslibs/conversion.pyx b/pandas/_libs/tslibs/conversion.pyx index bf38fcfb6103c..57b4100fbceb0 100644 --- a/pandas/_libs/tslibs/conversion.pyx +++ b/pandas/_libs/tslibs/conversion.pyx @@ -152,7 +152,7 @@ def ensure_timedelta64ns(arr: ndarray, copy: bool=True): @cython.boundscheck(False) @cython.wraparound(False) -def datetime_to_datetime64(object[:] values): +def datetime_to_datetime64(ndarray[object] values): """ Convert ndarray of datetime-like objects to int64 array representing nanosecond timestamps. diff --git a/pandas/_libs/window/aggregations.pyx b/pandas/_libs/window/aggregations.pyx index f675818599b2c..80b9144042041 100644 --- a/pandas/_libs/window/aggregations.pyx +++ b/pandas/_libs/window/aggregations.pyx @@ -56,7 +56,7 @@ cdef: cdef inline int int_max(int a, int b): return a if a >= b else b cdef inline int int_min(int a, int b): return a if a <= b else b -cdef inline bint is_monotonic_start_end_bounds( +cdef bint is_monotonic_start_end_bounds( ndarray[int64_t, ndim=1] start, ndarray[int64_t, ndim=1] end ): return is_monotonic(start, False)[0] and is_monotonic(end, False)[0]