diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index 5094f6f07d534..13bd95004445d 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -873,7 +873,7 @@ def get_level_sorter(
     """
     cdef:
         Py_ssize_t i, l, r
-        ndarray[intp_t, ndim=1] out = np.empty(len(codes), dtype=np.intp)
+        ndarray[intp_t, ndim=1] out = cnp.PyArray_EMPTY(1, codes.shape, cnp.NPY_INTP, 0)
 
     for i in range(len(starts) - 1):
         l, r = starts[i], starts[i + 1]
@@ -2255,11 +2255,11 @@ def maybe_convert_numeric(
         int status, maybe_int
         Py_ssize_t i, n = values.size
         Seen seen = Seen(coerce_numeric)
-        ndarray[float64_t, ndim=1] floats = np.empty(n, dtype='f8')
-        ndarray[complex128_t, ndim=1] complexes = np.empty(n, dtype='c16')
-        ndarray[int64_t, ndim=1] ints = np.empty(n, dtype='i8')
-        ndarray[uint64_t, ndim=1] uints = np.empty(n, dtype='u8')
-        ndarray[uint8_t, ndim=1] bools = np.empty(n, dtype='u1')
+        ndarray[float64_t, ndim=1] floats = cnp.PyArray_EMPTY(1, values.shape, cnp.NPY_FLOAT64, 0)
+        ndarray[complex128_t, ndim=1] complexes = cnp.PyArray_EMPTY(1, values.shape, cnp.NPY_COMPLEX128, 0)
+        ndarray[int64_t, ndim=1] ints = cnp.PyArray_EMPTY(1, values.shape, cnp.NPY_INT64, 0)
+        ndarray[uint64_t, ndim=1] uints = cnp.PyArray_EMPTY(1, values.shape, cnp.NPY_UINT64, 0)
+        ndarray[uint8_t, ndim=1] bools = cnp.PyArray_EMPTY(1, values.shape, cnp.NPY_UINT8, 0)
         ndarray[uint8_t, ndim=1] mask = np.zeros(n, dtype="u1")
         float64_t fval
         bint allow_null_in_int = convert_to_masked_nullable
@@ -2479,11 +2479,11 @@ def maybe_convert_objects(ndarray[object] objects,
 
     n = len(objects)
 
-    floats = np.empty(n, dtype='f8')
-    complexes = np.empty(n, dtype='c16')
-    ints = np.empty(n, dtype='i8')
-    uints = np.empty(n, dtype='u8')
-    bools = np.empty(n, dtype=np.uint8)
+    floats = cnp.PyArray_EMPTY(1, objects.shape, cnp.NPY_FLOAT64, 0)
+    complexes = cnp.PyArray_EMPTY(1, objects.shape, cnp.NPY_COMPLEX128, 0)
+    ints = cnp.PyArray_EMPTY(1, objects.shape, cnp.NPY_INT64, 0)
+    uints = cnp.PyArray_EMPTY(1, objects.shape, cnp.NPY_UINT64, 0)
+    bools = cnp.PyArray_EMPTY(1, objects.shape, cnp.NPY_UINT8, 0)
     mask = np.full(n, False)
 
     if convert_datetime:
@@ -2785,7 +2785,7 @@ cdef _infer_all_nats(dtype, ndarray datetimes, ndarray timedeltas):
     else:
         # ExtensionDtype
         cls = dtype.construct_array_type()
-        i8vals = np.empty(len(datetimes), dtype="i8")
+        i8vals = cnp.PyArray_EMPTY(1, datetimes.shape, cnp.NPY_INT64, 0)
         i8vals.fill(NPY_NAT)
         result = cls(i8vals, dtype=dtype)
     return result
@@ -2888,7 +2888,7 @@ def map_infer(
         object val
 
     n = len(arr)
-    result = np.empty(n, dtype=object)
+    result = cnp.PyArray_EMPTY(1, arr.shape, cnp.NPY_OBJECT, 0)
     for i in range(n):
         if ignore_na and checknull(arr[i]):
             result[i] = arr[i]
@@ -3083,7 +3083,7 @@ cpdef ndarray eq_NA_compat(ndarray[object] arr, object key):
     key is assumed to have `not isna(key)`
     """
     cdef:
-        ndarray[uint8_t, cast=True] result = np.empty(len(arr), dtype=bool)
+        ndarray[uint8_t, cast=True] result = cnp.PyArray_EMPTY(arr.ndim, arr.shape, cnp.NPY_BOOL, 0)
         Py_ssize_t i
         object item
 
diff --git a/pandas/_libs/tslib.pyx b/pandas/_libs/tslib.pyx
index c96a65cdff525..f4d495de26600 100644
--- a/pandas/_libs/tslib.pyx
+++ b/pandas/_libs/tslib.pyx
@@ -123,7 +123,7 @@ def format_array_from_datetime(
         ndarray[int64_t] consider_values
         bint show_ms = False, show_us = False, show_ns = False
         bint basic_format = False
-        ndarray[object] result = np.empty(N, dtype=object)
+        ndarray[object] result = cnp.PyArray_EMPTY(values.ndim, values.shape, cnp.NPY_OBJECT, 0)
         object ts, res
         npy_datetimestruct dts
 
@@ -349,7 +349,7 @@ def array_with_unit_to_datetime(
     # and are in ignore mode
     # redo as object
 
-    oresult = np.empty(n, dtype=object)
+    oresult = cnp.PyArray_EMPTY(values.ndim, values.shape, cnp.NPY_OBJECT, 0)
     for i in range(n):
         val = values[i]
 
@@ -668,7 +668,7 @@ cdef ndarray[object] ignore_errors_out_of_bounds_fallback(ndarray[object] values
         Py_ssize_t i, n = len(values)
         object val
 
-    oresult = np.empty(n, dtype=object)
+    oresult = cnp.PyArray_EMPTY(values.ndim, values.shape, cnp.NPY_OBJECT, 0)
 
     for i in range(n):
         val = values[i]
@@ -730,7 +730,7 @@ cdef _array_to_datetime_object(
 
     assert is_raise or is_ignore or is_coerce
 
-    oresult = np.empty(n, dtype=object)
+    oresult = cnp.PyArray_EMPTY(values.ndim, values.shape, cnp.NPY_OBJECT, 0)
 
     # We return an object array and only attempt to parse:
     # 1) NaT or NaT-like values
diff --git a/pandas/_libs/tslibs/period.pyx b/pandas/_libs/tslibs/period.pyx
index 3d04562cb73c3..c2637db23293e 100644
--- a/pandas/_libs/tslibs/period.pyx
+++ b/pandas/_libs/tslibs/period.pyx
@@ -1057,7 +1057,7 @@ def period_asfreq_arr(ndarray[int64_t] arr, int freq1, int freq2, bint end):
     cdef:
         Py_ssize_t n = len(arr)
         Py_ssize_t increment = arr.strides[0] // 8
-        ndarray[int64_t] result = np.empty(n, dtype=np.int64)
+        ndarray[int64_t] result = cnp.PyArray_EMPTY(arr.ndim, arr.shape, cnp.NPY_INT64, 0)
 
     _period_asfreq(
         <int64_t*>cnp.PyArray_DATA(arr),
@@ -1440,7 +1440,7 @@ def extract_ordinals(ndarray values, freq) -> np.ndarray:
     cdef:
         Py_ssize_t i, n = values.size
         int64_t ordinal
-        ndarray ordinals = np.empty((<object>values).shape, dtype=np.int64)
+        ndarray ordinals = cnp.PyArray_EMPTY(values.ndim, values.shape, cnp.NPY_INT64, 0)
         cnp.broadcast mi = cnp.PyArray_MultiIterNew2(ordinals, values)
         object p