move is_null_datetimelike to nattype for self-containment

pandas/_libs/missing.pxd

@@ -1,6 +1,11 @@
 # -*- coding: utf-8 -*-
 # cython: profile=False
 
-cdef bint is_null_datetimelike(object val)
+from tslibs.nattype cimport is_null_datetimelike
+
 cpdef bint checknull(object val)
 cpdef bint checknull_old(object val)
+
+cdef bint is_null_datetime64(v)
+cdef bint is_null_timedelta64(v)
+cdef bint is_null_period(v)

pandas/_libs/missing.pyx

@@ -15,29 +15,14 @@ cimport util
 
 from tslibs.np_datetime cimport get_timedelta64_value, get_datetime64_value
 from tslibs.nattype import NaT
+from tslibs.nattype cimport is_null_datetimelike
 
 cdef double INF = <double> np.inf
 cdef double NEGINF = -INF
 
 cdef int64_t NPY_NAT = util.get_nat()
 
 
-cdef inline bint is_null_datetimelike(object val):
-    # determine if we have a null for a timedelta/datetime (or integer
-    # versions)
-    if util._checknull(val):
-        return True
-    elif val is NaT:
-        return True
-    elif util.is_timedelta64_object(val):
-        return val.view('int64') == NPY_NAT
-    elif util.is_datetime64_object(val):
-        return val.view('int64') == NPY_NAT
-    elif util.is_integer_object(val):
-        return val == NPY_NAT
-    return False
-
-
 cdef inline bint _check_all_nulls(object val):
     """ utility to check if a value is any type of null """
     cdef bint res
@@ -308,3 +293,37 @@ cpdef bint isneginf_scalar(object val):
         return True
     else:
         return False
+
+
+cdef inline bint is_null_datetime64(v):
+    # determine if we have a null for a datetime (or integer versions),
+    # excluding np.timedelta64('nat')
+    if util._checknull(v):
+        return True
+    elif v is NaT:
+        return True
+    elif util.is_datetime64_object(v):
+        return v.view('int64') == NPY_NAT
+    return False
+
+
+cdef inline bint is_null_timedelta64(v):
+    # determine if we have a null for a timedelta (or integer versions),
+    # excluding np.datetime64('nat')
+    if util._checknull(v):
+        return True
+    elif v is NaT:
+        return True
+    elif util.is_timedelta64_object(v):
+        return v.view('int64') == NPY_NAT
+    return False
+
+
+cdef inline bint is_null_period(v):
+    # determine if we have a null for a Period (or integer versions),
+    # excluding np.datetime64('nat') and np.timedelta64('nat')
+    if util._checknull(v):
+        return True
+    elif v is NaT:
+        return True
+    return False

pandas/_libs/src/inference.pyx

@@ -28,6 +28,8 @@ cdef extern from "numpy/arrayobject.h":
         cdef object fields
         cdef tuple names
 
+from missing cimport is_null_datetime64, is_null_timedelta64, is_null_period
+
 from util cimport UINT8_MAX, UINT64_MAX, INT64_MAX, INT64_MIN
 
 # core.common import for fast inference checks
@@ -574,40 +576,6 @@ cpdef object infer_datetimelike_array(object arr):
     return 'mixed'
 
 
-cdef inline bint is_null_datetime64(v):
-    # determine if we have a null for a datetime (or integer versions),
-    # excluding np.timedelta64('nat')
-    if util._checknull(v):
-        return True
-    elif v is NaT:
-        return True
-    elif util.is_datetime64_object(v):
-        return v.view('int64') == iNaT
-    return False
-
-
-cdef inline bint is_null_timedelta64(v):
-    # determine if we have a null for a timedelta (or integer versions),
-    # excluding np.datetime64('nat')
-    if util._checknull(v):
-        return True
-    elif v is NaT:
-        return True
-    elif util.is_timedelta64_object(v):
-        return v.view('int64') == iNaT
-    return False
-
-
-cdef inline bint is_null_period(v):
-    # determine if we have a null for a Period (or integer versions),
-    # excluding np.datetime64('nat') and np.timedelta64('nat')
-    if util._checknull(v):
-        return True
-    elif v is NaT:
-        return True
-    return False
-
-
 cdef inline bint is_datetime(object o):
     return PyDateTime_Check(o)
 

pandas/_libs/tslibs/nattype.pxd

@@ -7,3 +7,4 @@ cdef int64_t NPY_NAT
 cdef bint _nat_scalar_rules[6]
 
 cdef bint checknull_with_nat(object val)
+cdef bint is_null_datetimelike(object val)

pandas/_libs/tslibs/nattype.pyx

@@ -16,6 +16,7 @@ cimport numpy as cnp
 from numpy cimport int64_t
 cnp.import_array()
 
+cimport util
 from util cimport (get_nat,
                    is_integer_object, is_float_object,
                    is_datetime64_object, is_timedelta64_object)
@@ -587,3 +588,28 @@ cdef inline bint checknull_with_nat(object val):
     """ utility to check if a value is a nat or not """
     return val is None or (
         PyFloat_Check(val) and val != val) or val is NaT
+
+
+cdef inline bint is_null_datetimelike(object val):
+    """
+    Determine if we have a null for a timedelta/datetime (or integer versions)
+
+    Parameters
+    ----------
+    val : object
+
+    Returns
+    -------
+    null_datetimelike : bool
+    """
+    if util._checknull(val):
+        return True
+    elif val is NaT:
+        return True
+    elif util.is_timedelta64_object(val):
+        return val.view('int64') == NPY_NAT
+    elif util.is_datetime64_object(val):
+        return val.view('int64') == NPY_NAT
+    elif util.is_integer_object(val):
+        return val == NPY_NAT
+    return False

pandas/_libs/tslibs/period.pyx

@@ -38,8 +38,6 @@ cdef extern from "../src/datetime/np_datetime.h":
 cimport util
 from util cimport is_period_object, is_string_object, INT32_MIN
 
-from pandas._libs.missing cimport is_null_datetimelike
-
 from timestamps import Timestamp
 from timezones cimport is_utc, is_tzlocal, get_utcoffset, get_dst_info
 from timedeltas cimport delta_to_nanoseconds
@@ -54,7 +52,7 @@ from frequencies cimport (get_freq_code, get_base_alias,
 from parsing import parse_time_string, NAT_SENTINEL
 from resolution import Resolution
 from nattype import nat_strings, NaT, iNaT
-from nattype cimport _nat_scalar_rules, NPY_NAT
+from nattype cimport _nat_scalar_rules, NPY_NAT, is_null_datetimelike
 
 from pandas.tseries import offsets
 from pandas.tseries import frequencies

setup.py

@@ -531,16 +531,6 @@ def pxd(name):
         'pyxfile': '_libs/ops',
         'pxdfiles': ['_libs/src/util',
                      '_libs/missing']},
-    '_libs.tslibs.period': {
-        'pyxfile': '_libs/tslibs/period',
-        'pxdfiles': ['_libs/src/util',
-                     '_libs/missing',
-                     '_libs/tslibs/ccalendar',
-                     '_libs/tslibs/timedeltas',
-                     '_libs/tslibs/timezones',
-                     '_libs/tslibs/nattype'],
-        'depends': tseries_depends + ['pandas/_libs/src/period_helper.h'],
-        'sources': np_datetime_sources + ['pandas/_libs/src/period_helper.c']},
     '_libs.properties': {
         'pyxfile': '_libs/properties',
         'include': []},
@@ -601,6 +591,15 @@ def pxd(name):
     '_libs.tslibs.parsing': {
         'pyxfile': '_libs/tslibs/parsing',
         'pxdfiles': ['_libs/src/util']},
+    '_libs.tslibs.period': {
+        'pyxfile': '_libs/tslibs/period',
+        'pxdfiles': ['_libs/src/util',
+                     '_libs/tslibs/ccalendar',
+                     '_libs/tslibs/timedeltas',
+                     '_libs/tslibs/timezones',
+                     '_libs/tslibs/nattype'],
+        'depends': tseries_depends + ['pandas/_libs/src/period_helper.h'],
+        'sources': np_datetime_sources + ['pandas/_libs/src/period_helper.c']},
     '_libs.tslibs.resolution': {
         'pyxfile': '_libs/tslibs/resolution',
         'pxdfiles': ['_libs/src/util',