remove unused time conversion funcs

pandas/_libs/index.pyx

@@ -22,20 +22,11 @@ from pandas._libs import tslib, algos, hashtable as _hash
 from pandas._libs.tslib import Timestamp, Timedelta
 from datetime import datetime, timedelta
 
-from datetime cimport (get_datetime64_value, _pydatetime_to_dts,
- pandas_datetimestruct)
-
 from cpython cimport PyTuple_Check, PyList_Check
 
-cdef extern from "datetime.h":
- bint PyDateTime_Check(object o)
- void PyDateTime_IMPORT()
-
 cdef int64_t iNaT = util.get_nat()
 
 
-PyDateTime_IMPORT
-
 cdef extern from "Python.h":
  int PySlice_Check(object)
 
@@ -415,12 +406,12 @@ cdef class DatetimeEngine(Int64Engine):
  if not self.is_unique:
  return self._get_loc_duplicates(val)
  values = self._get_index_values()
- conv = _to_i8(val)
+ conv = tslib.pydt_to_i8(val)
  loc = values.searchsorted(conv, side='left')
  return util.get_value_at(values, loc) == conv
 
  self._ensure_mapping_populated()
- return _to_i8(val) in self.mapping
+ return tslib.pydt_to_i8(val) in self.mapping
 
  cdef _get_index_values(self):
  return self.vgetter().view('i8')
@@ -435,12 +426,12 @@ cdef class DatetimeEngine(Int64Engine):
  # Welcome to the spaghetti factory
  if self.over_size_threshold and self.is_monotonic_increasing:
  if not self.is_unique:
- val = _to_i8(val)
+ val = tslib.pydt_to_i8(val)
  return self._get_loc_duplicates(val)
  values = self._get_index_values()
 
  try:
- conv = _to_i8(val)
+ conv = tslib.pydt_to_i8(val)
  loc = values.searchsorted(conv, side='left')
  except TypeError:
  self._date_check_type(val)
@@ -452,7 +443,7 @@ cdef class DatetimeEngine(Int64Engine):
 
  self._ensure_mapping_populated()
  if not self.unique:
- val = _to_i8(val)
+ val = tslib.pydt_to_i8(val)
  return self._get_loc_duplicates(val)
 
  try:
@@ -463,7 +454,7 @@ cdef class DatetimeEngine(Int64Engine):
  pass
 
  try:
- val = _to_i8(val)
+ val = tslib.pydt_to_i8(val)
  return self.mapping.get_item(val)
  except (TypeError, ValueError):
  self._date_check_type(val)
@@ -540,23 +531,6 @@ cpdef convert_scalar(ndarray arr, object value):
 
  return value
 
-cdef inline _to_i8(object val):
- cdef pandas_datetimestruct dts
- try:
- return val.value
- except AttributeError:
- if util.is_datetime64_object(val):
- return get_datetime64_value(val)
- elif PyDateTime_Check(val):
- tzinfo = getattr(val, 'tzinfo', None)
- # Save the original date value so we can get the utcoffset from it.
- ival = _pydatetime_to_dts(val, &dts)
- if tzinfo is not None and not is_utc(tzinfo):
- offset = get_utcoffset(tzinfo, val)
- ival -= tslib._delta_to_nanoseconds(offset)
- return ival
- return val
-
 
 cdef class MultiIndexObjectEngine(ObjectEngine):
  """

pandas/_libs/lib.pyx

@@ -45,8 +45,6 @@ cdef double NaN = <double> np.NaN
 cdef double nan = NaN
 cdef double NAN = nan
 
-from datetime import datetime as pydatetime
-
 # this is our tseries.pxd
 from datetime cimport (
  get_timedelta64_value, get_datetime64_value,
@@ -132,61 +130,6 @@ def memory_usage_of_objects(ndarray[object, ndim=1] arr):
  s += arr[i].__sizeof__()
  return s
 
-#----------------------------------------------------------------------
-# datetime / io related
-
-cdef int _EPOCH_ORD = 719163
-
-from datetime import date as pydate
-
-cdef inline int64_t gmtime(object date):
- cdef int y, m, d, h, mn, s, days
-
- y = PyDateTime_GET_YEAR(date)
- m = PyDateTime_GET_MONTH(date)
- d = PyDateTime_GET_DAY(date)
- h = PyDateTime_DATE_GET_HOUR(date)
- mn = PyDateTime_DATE_GET_MINUTE(date)
- s = PyDateTime_DATE_GET_SECOND(date)
-
- days = pydate(y, m, 1).toordinal() - _EPOCH_ORD + d - 1
- return ((<int64_t> (((days * 24 + h) * 60 + mn))) * 60 + s) * 1000
-
-
-cpdef object to_datetime(int64_t timestamp):
- return pydatetime.utcfromtimestamp(timestamp / 1000.0)
-
-
-cpdef object to_timestamp(object dt):
- return gmtime(dt)
-
-
-def array_to_timestamp(ndarray[object, ndim=1] arr):
- cdef int i, n
- cdef ndarray[int64_t, ndim=1] result
-
- n = len(arr)
- result = np.empty(n, dtype=np.int64)
-
- for i from 0 <= i < n:
- result[i] = gmtime(arr[i])
-
- return result
-
-
-def time64_to_datetime(ndarray[int64_t, ndim=1] arr):
- cdef int i, n
- cdef ndarray[object, ndim=1] result
-
- n = len(arr)
- result = np.empty(n, dtype=object)
-
- for i from 0 <= i < n:
- result[i] = to_datetime(arr[i])
-
- return result
-
-
 #----------------------------------------------------------------------
 # isnull / notnull related
 

pandas/_libs/tslib.pyx

@@ -3436,7 +3436,7 @@ def cast_to_nanoseconds(ndarray arr):
  return result
 
 
-def pydt_to_i8(object pydt):
+cpdef pydt_to_i8(object pydt):
  """
  Convert to int64 representation compatible with numpy datetime64; converts
  to UTC

pandas/io/pytables.py

@@ -25,7 +25,7 @@
 
 import numpy as np
 from pandas import (Series, DataFrame, Panel, Panel4D, Index,
- MultiIndex, Int64Index, isna, concat,
+ MultiIndex, Int64Index, isna, concat, to_datetime,
  SparseSeries, SparseDataFrame, PeriodIndex,
  DatetimeIndex, TimedeltaIndex)
 from pandas.core import config
@@ -4529,7 +4529,7 @@ def _unconvert_index(data, kind, encoding=None):
 def _unconvert_index_legacy(data, kind, legacy=False, encoding=None):
  kind = _ensure_decoded(kind)
  if kind == u('datetime'):
- index = lib.time64_to_datetime(data)
+ index = to_datetime(data)
  elif kind in (u('integer')):
  index = np.asarray(data, dtype=object)
  elif kind in (u('string')):