CLN/TYP: assorted

pandas/_libs/algos.pyx

@@ -750,7 +750,7 @@ def is_monotonic(ndarray[numeric_object_t, ndim=1] arr, bint timelike):
     n = len(arr)
 
     if n == 1:
-        if arr[0] != arr[0] or (timelike and <int64_t>arr[0] == NPY_NAT):
+        if arr[0] != arr[0] or (numeric_object_t is int64_t and timelike and arr[0] == NPY_NAT):
             # single value is NaN
             return False, False, True
         else:

pandas/_libs/indexing.pyx

@@ -2,21 +2,24 @@ cdef class NDFrameIndexerBase:
     """
     A base class for _NDFrameIndexer for fast instantiation and attribute access.
     """
+    cdef:
+        Py_ssize_t _ndim
+
     cdef public:
         str name
-        object obj, _ndim
+        object obj
 
     def __init__(self, name: str, obj):
         self.obj = obj
         self.name = name
-        self._ndim = None
+        self._ndim = -1
 
     @property
     def ndim(self) -> int:
         # Delay `ndim` instantiation until required as reading it
         # from `obj` isn't entirely cheap.
         ndim = self._ndim
-        if ndim is None:
+        if ndim == -1:
             ndim = self._ndim = self.obj.ndim
             if ndim > 2:
                 raise ValueError(  # pragma: no cover

pandas/_libs/internals.pyx

@@ -544,7 +544,7 @@ cpdef update_blklocs_and_blknos(
     """
     cdef:
         Py_ssize_t i
-        cnp.npy_intp length = len(blklocs) + 1
+        cnp.npy_intp length = blklocs.shape[0] + 1
         ndarray[intp_t, ndim=1] new_blklocs, new_blknos
 
     # equiv: new_blklocs = np.empty(length, dtype=np.intp)

pandas/_libs/tslib.pyx

@@ -218,7 +218,7 @@ def array_with_unit_to_datetime(
     """
     cdef:
         Py_ssize_t i, j, n=len(values)
-        int64_t m
+        int64_t mult
         int prec = 0
         ndarray[float64_t] fvalues
         bint is_ignore = errors=='ignore'
@@ -242,7 +242,7 @@ def array_with_unit_to_datetime(
             )
         return result, tz
 
-    m, _ = precision_from_unit(unit)
+    mult, _ = precision_from_unit(unit)
 
     if is_raise:
         # try a quick conversion to i8/f8
@@ -254,7 +254,7 @@ def array_with_unit_to_datetime(
             # fill missing values by comparing to NPY_NAT
             mask = iresult == NPY_NAT
             iresult[mask] = 0
-            fvalues = iresult.astype("f8") * m
+            fvalues = iresult.astype("f8") * mult
             need_to_iterate = False
 
         if not need_to_iterate:
@@ -265,10 +265,10 @@ def array_with_unit_to_datetime(
                 raise OutOfBoundsDatetime(f"cannot convert input with unit '{unit}'")
 
             if values.dtype.kind in ["i", "u"]:
-                result = (iresult * m).astype("M8[ns]")
+                result = (iresult * mult).astype("M8[ns]")
 
             elif values.dtype.kind == "f":
-                fresult = (values * m).astype("f8")
+                fresult = (values * mult).astype("f8")
                 fresult[mask] = 0
                 if prec:
                     fresult = round(fresult, prec)

pandas/_libs/tslibs/conversion.pxd

@@ -19,9 +19,9 @@ cdef class _TSObject:
         bint fold
 
 
-cdef convert_to_tsobject(object ts, tzinfo tz, str unit,
-                         bint dayfirst, bint yearfirst,
-                         int32_t nanos=*)
+cdef _TSObject convert_to_tsobject(object ts, tzinfo tz, str unit,
+                                   bint dayfirst, bint yearfirst,
+                                   int32_t nanos=*)
 
 cdef _TSObject convert_datetime_to_tsobject(datetime ts, tzinfo tz,
                                             int32_t nanos=*)

pandas/_libs/tslibs/conversion.pyx

@@ -345,8 +345,8 @@ cdef class _TSObject:
         self.fold = 0
 
 
-cdef convert_to_tsobject(object ts, tzinfo tz, str unit,
-                         bint dayfirst, bint yearfirst, int32_t nanos=0):
+cdef _TSObject convert_to_tsobject(object ts, tzinfo tz, str unit,
+                                   bint dayfirst, bint yearfirst, int32_t nanos=0):
     """
     Extract datetime and int64 from any of:
         - np.int64 (with unit providing a possible modifier)

pandas/_libs/tslibs/dtypes.pxd

@@ -5,7 +5,7 @@ from pandas._libs.tslibs.np_datetime cimport NPY_DATETIMEUNIT
 
 cdef str npy_unit_to_abbrev(NPY_DATETIMEUNIT unit)
 cdef NPY_DATETIMEUNIT freq_group_code_to_npy_unit(int freq) nogil
-cdef int64_t periods_per_day(NPY_DATETIMEUNIT reso=*)
+cdef int64_t periods_per_day(NPY_DATETIMEUNIT reso=*) except? -1
 
 cdef dict attrname_to_abbrevs
 

pandas/_libs/tslibs/dtypes.pyx

@@ -307,7 +307,7 @@ cdef NPY_DATETIMEUNIT freq_group_code_to_npy_unit(int freq) nogil:
         return NPY_DATETIMEUNIT.NPY_FR_D
 
 
-cdef int64_t periods_per_day(NPY_DATETIMEUNIT reso=NPY_DATETIMEUNIT.NPY_FR_ns):
+cdef int64_t periods_per_day(NPY_DATETIMEUNIT reso=NPY_DATETIMEUNIT.NPY_FR_ns) except? -1:
     """
     How many of the given time units fit into a single day?
     """

pandas/_libs/tslibs/offsets.pyx

@@ -1910,7 +1910,7 @@ cdef class WeekOfMonthMixin(SingleConstructorOffset):
 
         shifted = shift_month(other, months, "start")
         to_day = self._get_offset_day(shifted)
-        return shift_day(shifted, to_day - shifted.day)
+        return _shift_day(shifted, to_day - shifted.day)
 
     def is_on_offset(self, dt: datetime) -> bool:
         if self.normalize and not _is_normalized(dt):
@@ -3132,7 +3132,7 @@ cdef class FY5253Quarter(FY5253Mixin):
             qtr_lens = self.get_weeks(norm)
 
             # check that qtr_lens is consistent with self._offset addition
-            end = shift_day(start, days=7 * sum(qtr_lens))
+            end = _shift_day(start, days=7 * sum(qtr_lens))
             assert self._offset.is_on_offset(end), (start, end, qtr_lens)
 
             tdelta = norm - start
@@ -3173,7 +3173,7 @@ cdef class FY5253Quarter(FY5253Mixin):
         # Note: we always have 0 <= n < 4
         weeks = sum(qtr_lens[:n])
         if weeks:
-            res = shift_day(res, days=weeks * 7)
+            res = _shift_day(res, days=weeks * 7)
 
         return res
 
@@ -3210,7 +3210,7 @@ cdef class FY5253Quarter(FY5253Mixin):
 
         current = next_year_end
         for qtr_len in qtr_lens:
-            current = shift_day(current, days=qtr_len * 7)
+            current = _shift_day(current, days=qtr_len * 7)
             if dt == current:
                 return True
         return False
@@ -3729,7 +3729,7 @@ cpdef to_offset(freq):
 # ----------------------------------------------------------------------
 # RelativeDelta Arithmetic
 
-def shift_day(other: datetime, days: int) -> datetime:
+cdef datetime _shift_day(datetime other, int days):
     """
     Increment the datetime `other` by the given number of days, retaining
     the time-portion of the datetime.  For tz-naive datetimes this is
@@ -3915,6 +3915,8 @@ cdef inline void _shift_quarters(const int64_t[:] dtindex,
         out[i] = dtstruct_to_dt64(&dts)
 
 
+@cython.wraparound(False)
+@cython.boundscheck(False)
 cdef ndarray[int64_t] _shift_bdays(const int64_t[:] i8other, int periods):
     """
     Implementation of BusinessDay.apply_offset.

pandas/_libs/tslibs/timedeltas.pxd

@@ -15,4 +15,5 @@ cdef class _Timedelta(timedelta):
         int64_t _d, _h, _m, _s, _ms, _us, _ns
 
     cpdef timedelta to_pytimedelta(_Timedelta self)
-    cpdef bint _has_ns(self)
+    cdef bint _has_ns(self)
+    cdef _ensure_components(_Timedelta self)

pandas/_libs/tslibs/timedeltas.pyx

@@ -154,7 +154,7 @@ def ints_to_pytimedelta(const int64_t[:] arr, box=False):
     cdef:
         Py_ssize_t i, n = len(arr)
         int64_t value
-        object[:] result = np.empty(n, dtype=object)
+        object[::1] result = np.empty(n, dtype=object)
 
     for i in range(n):
 
@@ -892,10 +892,10 @@ cdef class _Timedelta(timedelta):
 
         return cmp_scalar(self.value, ots.value, op)
 
-    cpdef bint _has_ns(self):
+    cdef bint _has_ns(self):
         return self.value % 1000 != 0
 
-    def _ensure_components(_Timedelta self):
+    cdef _ensure_components(_Timedelta self):
         """
         compute the components
         """
@@ -1160,7 +1160,10 @@ cdef class _Timedelta(timedelta):
         converted : string of a Timedelta
 
         """
-        cdef object sign, seconds_pretty, subs, fmt, comp_dict
+        cdef:
+            str sign, fmt
+            dict comp_dict
+            object subs
 
         self._ensure_components()
 

pandas/_libs/tslibs/timestamps.pxd

@@ -6,17 +6,18 @@ from numpy cimport int64_t
 
 from pandas._libs.tslibs.base cimport ABCTimestamp
 from pandas._libs.tslibs.np_datetime cimport npy_datetimestruct
+from pandas._libs.tslibs.offsets cimport BaseOffset
 
 
-cdef object create_timestamp_from_ts(int64_t value,
-                                     npy_datetimestruct dts,
-                                     tzinfo tz, object freq, bint fold)
+cdef _Timestamp create_timestamp_from_ts(int64_t value,
+                                         npy_datetimestruct dts,
+                                         tzinfo tz, BaseOffset freq, bint fold)
 
 
 cdef class _Timestamp(ABCTimestamp):
     cdef readonly:
         int64_t value, nanosecond
-        object _freq
+        BaseOffset _freq
 
     cdef bint _get_start_end_field(self, str field, freq)
     cdef _get_date_name_field(self, str field, object locale)

pandas/_libs/tslibs/timestamps.pyx

@@ -82,6 +82,7 @@ from pandas._libs.tslibs.np_datetime cimport (
 from pandas._libs.tslibs.np_datetime import OutOfBoundsDatetime
 
 from pandas._libs.tslibs.offsets cimport (
+    BaseOffset,
     is_offset_object,
     to_offset,
 )
@@ -113,9 +114,9 @@ _no_input = object()
 # ----------------------------------------------------------------------
 
 
-cdef inline object create_timestamp_from_ts(int64_t value,
-                                            npy_datetimestruct dts,
-                                            tzinfo tz, object freq, bint fold):
+cdef inline _Timestamp create_timestamp_from_ts(int64_t value,
+                                                npy_datetimestruct dts,
+                                                tzinfo tz, BaseOffset freq, bint fold):
     """ convenience routine to construct a Timestamp from its parts """
     cdef _Timestamp ts_base
     ts_base = _Timestamp.__new__(Timestamp, dts.year, dts.month,

pandas/_libs/tslibs/timezones.pyx

@@ -230,10 +230,10 @@ cdef object _get_utc_trans_times_from_dateutil_tz(tzinfo tz):
     return new_trans
 
 
-cdef int64_t[:] unbox_utcoffsets(object transinfo):
+cdef int64_t[::1] unbox_utcoffsets(object transinfo):
     cdef:
         Py_ssize_t i, sz
-        int64_t[:] arr
+        int64_t[::1] arr
 
     sz = len(transinfo)
     arr = np.empty(sz, dtype='i8')

pandas/_libs/tslibs/tzconversion.pyx

@@ -610,7 +610,7 @@ cdef int64_t _tz_localize_using_tzinfo_api(
     int64_t val, tzinfo tz, bint to_utc=True, bint* fold=NULL
 ) except? -1:
     """
-    Convert the i8 representation of a datetime from a general-cast timezone to
+    Convert the i8 representation of a datetime from a general-case timezone to
     UTC, or vice-versa using the datetime/tzinfo API.
 
     Private, not intended for use outside of tslibs.tzconversion.

pandas/core/arrays/datetimes.py

@@ -2006,10 +2006,10 @@ def sequence_to_datetimes(data, require_iso8601: bool = False) -> DatetimeArray:
 def _sequence_to_dt64ns(
     data,
     dtype=None,
-    copy=False,
+    copy: bool = False,
     tz=None,
-    dayfirst=False,
-    yearfirst=False,
+    dayfirst: bool = False,
+    yearfirst: bool = False,
     ambiguous="raise",
     *,
     allow_mixed: bool = False,