@@ -873,7 +873,7 @@ def get_level_sorter(
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"""
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cdef:
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Py_ssize_t i , l , r
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- ndarray[intp_t , ndim = 1 ] out = np.empty( len ( codes), dtype = np.intp )
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+ ndarray[intp_t , ndim = 1 ] out = cnp.PyArray_EMPTY( 1 , codes.shape, cnp.NPY_INTP, 0 )
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for i in range(len(starts ) - 1):
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l, r = starts[i], starts[i + 1 ]
@@ -2255,11 +2255,11 @@ def maybe_convert_numeric(
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int status, maybe_int
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Py_ssize_t i, n = values.size
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Seen seen = Seen(coerce_numeric)
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- ndarray[float64_t, ndim= 1 ] floats = np.empty(n, dtype = ' f8 ' )
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- ndarray[complex128_t, ndim= 1 ] complexes = np.empty(n, dtype = ' c16 ' )
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- ndarray[int64_t, ndim= 1 ] ints = np.empty(n, dtype = ' i8 ' )
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- ndarray[uint64_t, ndim= 1 ] uints = np.empty(n, dtype = ' u8 ' )
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- ndarray[uint8_t, ndim= 1 ] bools = np.empty(n, dtype = ' u1 ' )
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+ ndarray[float64_t, ndim= 1 ] floats = cnp.PyArray_EMPTY( 1 , values.shape, cnp.NPY_FLOAT64, 0 )
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+ ndarray[complex128_t, ndim= 1 ] complexes = cnp.PyArray_EMPTY( 1 , values.shape, cnp.NPY_COMPLEX128, 0 )
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+ ndarray[int64_t, ndim= 1 ] ints = cnp.PyArray_EMPTY( 1 , values.shape, cnp.NPY_INT64, 0 )
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+ ndarray[uint64_t, ndim= 1 ] uints = cnp.PyArray_EMPTY( 1 , values.shape, cnp.NPY_UINT64, 0 )
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+ ndarray[uint8_t, ndim= 1 ] bools = cnp.PyArray_EMPTY( 1 , values.shape, cnp.NPY_UINT8, 0 )
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ndarray[uint8_t, ndim= 1 ] mask = np.zeros(n, dtype = " u1" )
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float64_t fval
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bint allow_null_in_int = convert_to_masked_nullable
@@ -2479,11 +2479,11 @@ def maybe_convert_objects(ndarray[object] objects,
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n = len (objects)
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- floats = np.empty(n, dtype = ' f8 ' )
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- complexes = np.empty(n, dtype = ' c16 ' )
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- ints = np.empty(n, dtype = ' i8 ' )
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- uints = np.empty(n, dtype = ' u8 ' )
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- bools = np.empty(n, dtype = np.uint8 )
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+ floats = cnp.PyArray_EMPTY( 1 , objects.shape, cnp.NPY_FLOAT64, 0 )
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+ complexes = cnp.PyArray_EMPTY( 1 , objects.shape, cnp.NPY_COMPLEX128, 0 )
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+ ints = cnp.PyArray_EMPTY( 1 , objects.shape, cnp.NPY_INT64, 0 )
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+ uints = cnp.PyArray_EMPTY( 1 , objects.shape, cnp.NPY_UINT64, 0 )
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+ bools = cnp.PyArray_EMPTY( 1 , objects.shape, cnp.NPY_UINT8, 0 )
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mask = np.full(n, False )
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if convert_datetime:
@@ -2785,7 +2785,7 @@ cdef _infer_all_nats(dtype, ndarray datetimes, ndarray timedeltas):
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else :
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# ExtensionDtype
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cls = dtype.construct_array_type()
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- i8vals = np.empty( len ( datetimes), dtype = " i8 " )
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+ i8vals = cnp.PyArray_EMPTY( 1 , datetimes.shape, cnp.NPY_INT64, 0 )
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i8vals.fill(NPY_NAT)
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result = cls (i8vals, dtype = dtype)
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return result
@@ -2888,7 +2888,7 @@ def map_infer(
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object val
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n = len (arr)
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- result = np.empty(n, dtype = object )
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+ result = cnp.PyArray_EMPTY( 1 , arr.shape, cnp.NPY_OBJECT, 0 )
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for i in range(n ):
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if ignore_na and checknull(arr[i]):
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result[i] = arr[i]
@@ -3083,7 +3083,7 @@ cpdef ndarray eq_NA_compat(ndarray[object] arr, object key):
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key is assumed to have `not isna(key)`
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"""
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cdef:
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- ndarray[uint8_t, cast= True ] result = np.empty( len ( arr), dtype = bool )
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+ ndarray[uint8_t, cast= True ] result = cnp.PyArray_EMPTY( arr.ndim, arr.shape, cnp.NPY_BOOL, 0 )
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Py_ssize_t i
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object item
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