[REF] Move comparison methods to EAMixins, share code

pandas/_libs/src/numpy_helper.h

@@ -50,7 +50,7 @@ PANDAS_INLINE PyObject* char_to_string(const char* data) {
 
 
 void set_array_not_contiguous(PyArrayObject* ao) {
-    ao->flags &= ~(NPY_C_CONTIGUOUS | NPY_F_CONTIGUOUS);
+    ao->flags &= ~(NPY_ARRAY_C_CONTIGUOUS | NPY_ARRAY_F_CONTIGUOUS);
 }
 
 #endif  // PANDAS__LIBS_SRC_NUMPY_HELPER_H_

pandas/_libs/tslibs/period.pyx

@@ -1859,21 +1859,40 @@ cdef int64_t _ordinal_from_fields(year, month, quarter, day,
                                   hour, minute, second, freq):
     base, mult = get_freq_code(freq)
     if quarter is not None:
-        year, month = _quarter_to_myear(year, quarter, freq)
+        year, month = quarter_to_myear(year, quarter, freq)
 
     return period_ordinal(year, month, day, hour,
                           minute, second, 0, 0, base)
 
 
-def _quarter_to_myear(year, quarter, freq):
-    if quarter is not None:
-        if quarter <= 0 or quarter > 4:
-            raise ValueError('Quarter must be 1 <= q <= 4')
+def quarter_to_myear(int year, int quarter, freq):
+    """
+    A quarterly frequency defines a "year" which may not coincide with
+    the calendar-year.  Find the calendar-year and calendar-month associated
+    with the given year and quarter under the `freq`-derived calendar.
+
+    Parameters
+    ----------
+    year : int
+    quarter : int
+    freq : DateOffset
+
+    Returns
+    -------
+    year : int
+    month : int
+
+    See Also
+    --------
+    Period.qyear
+    """
+    if quarter <= 0 or quarter > 4:
+        raise ValueError('Quarter must be 1 <= q <= 4')
 
-        mnum = MONTH_NUMBERS[get_rule_month(freq)] + 1
-        month = (mnum + (quarter - 1) * 3) % 12 + 1
-        if month > mnum:
-            year -= 1
+    mnum = MONTH_NUMBERS[get_rule_month(freq)] + 1
+    month = (mnum + (quarter - 1) * 3) % 12 + 1
+    if month > mnum:
+        year -= 1
 
     return year, month
 

pandas/core/arrays/__init__.py

@@ -3,4 +3,4 @@
 from .categorical import Categorical  # noqa
 from .datetimes import DatetimeArrayMixin  # noqa
 from .period import PeriodArrayMixin  # noqa
-from .timedelta import TimedeltaArrayMixin  # noqa
+from .timedeltas import TimedeltaArrayMixin  # noqa

pandas/core/arrays/datetimelike.py

@@ -10,19 +10,53 @@
     DIFFERENT_FREQ_INDEX, IncompatibleFrequency)
 
 from pandas.errors import NullFrequencyError, PerformanceWarning
+from pandas import compat
 
 from pandas.tseries import frequencies
 from pandas.tseries.offsets import Tick
 
 from pandas.core.dtypes.common import (
+    needs_i8_conversion,
+    is_list_like,
+    is_bool_dtype,
     is_period_dtype,
     is_timedelta64_dtype,
     is_object_dtype)
+from pandas.core.dtypes.generic import ABCSeries, ABCDataFrame, ABCIndexClass
 
 import pandas.core.common as com
 from pandas.core.algorithms import checked_add_with_arr
 
 
+def _make_comparison_op(op, cls):
+    # TODO: share code with indexes.base version?  Main difference is that
+    # the block for MultiIndex was removed here.
+    def cmp_method(self, other):
+        if isinstance(other, ABCDataFrame):
+            return NotImplemented
+
+        if isinstance(other, (np.ndarray, ABCIndexClass, ABCSeries)):
+            if other.ndim > 0 and len(self) != len(other):
+                raise ValueError('Lengths must match to compare')
+
+        if needs_i8_conversion(self) and needs_i8_conversion(other):
+            # we may need to directly compare underlying
+            # representations
+            return self._evaluate_compare(other, op)
+
+        # numpy will show a DeprecationWarning on invalid elementwise
+        # comparisons, this will raise in the future
+        with warnings.catch_warnings(record=True):
+            with np.errstate(all='ignore'):
+                result = op(self.values, np.asarray(other))
+
+        return result
+
+    name = '__{name}__'.format(name=op.__name__)
+    # TODO: docstring?
+    return compat.set_function_name(cmp_method, name, cls)
+
+
 class AttributesMixin(object):
 
     @property
@@ -435,3 +469,85 @@ def _addsub_offset_array(self, other, op):
         if not is_period_dtype(self):
             kwargs['freq'] = 'infer'
         return type(self)(res_values, **kwargs)
+
+    # --------------------------------------------------------------
+    # Comparison Methods
+
+    def _evaluate_compare(self, other, op):
+        """
+        We have been called because a comparison between
+        8 aware arrays. numpy >= 1.11 will
+        now warn about NaT comparisons
+        """
+        # Called by comparison methods when comparing datetimelike
+        # with datetimelike
+
+        if not isinstance(other, type(self)):
+            # coerce to a similar object
+            if not is_list_like(other):
+                # scalar
+                other = [other]
+            elif lib.is_scalar(lib.item_from_zerodim(other)):
+                # ndarray scalar
+                other = [other.item()]
+            other = type(self)(other)
+
+        # compare
+        result = op(self.asi8, other.asi8)
+
+        # technically we could support bool dtyped Index
+        # for now just return the indexing array directly
+        mask = (self._isnan) | (other._isnan)
+
+        filler = iNaT
+        if is_bool_dtype(result):
+            filler = False
+
+        result[mask] = filler
+        return result
+
+    # TODO: get this from ExtensionOpsMixin
+    @classmethod
+    def _add_comparison_methods(cls):
+        """ add in comparison methods """
+        # DatetimeArray and TimedeltaArray comparison methods will
+        # call these as their super(...) methods
+        cls.__eq__ = _make_comparison_op(operator.eq, cls)
+        cls.__ne__ = _make_comparison_op(operator.ne, cls)
+        cls.__lt__ = _make_comparison_op(operator.lt, cls)
+        cls.__gt__ = _make_comparison_op(operator.gt, cls)
+        cls.__le__ = _make_comparison_op(operator.le, cls)
+        cls.__ge__ = _make_comparison_op(operator.ge, cls)
+
+
+DatetimeLikeArrayMixin._add_comparison_methods()
+
+
+# -------------------------------------------------------------------
+# Shared Constructor Helpers
+
+def validate_periods(periods):
+    """
+    If a `periods` argument is passed to the Datetime/Timedelta Array/Index
+    constructor, cast it to an integer.
+
+    Parameters
+    ----------
+    periods : None, float, int
+
+    Returns
+    -------
+    periods : None or int
+
+    Raises
+    ------
+    TypeError
+        if periods is None, float, or int
+    """
+    if periods is not None:
+        if lib.is_float(periods):
+            periods = int(periods)
+        elif not lib.is_integer(periods):
+            raise TypeError('periods must be a number, got {periods}'
+                            .format(periods=periods))
+    return periods

pandas/core/arrays/datetimes.py

@@ -13,21 +13,37 @@
 
 from pandas.util._decorators import cache_readonly
 from pandas.errors import PerformanceWarning
+from pandas import compat
 
 from pandas.core.dtypes.common import (
     _NS_DTYPE,
+    is_datetimelike,
     is_datetime64tz_dtype,
     is_datetime64_dtype,
     is_timedelta64_dtype,
     _ensure_int64)
 from pandas.core.dtypes.dtypes import DatetimeTZDtype
+from pandas.core.dtypes.missing import isna
+from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
 
+import pandas.core.common as com
 from pandas.core.algorithms import checked_add_with_arr
 
 from pandas.tseries.frequencies import to_offset, DateOffset
 from pandas.tseries.offsets import Tick
 
-from .datetimelike import DatetimeLikeArrayMixin
+from pandas.core.arrays import datetimelike as dtl
+
+
+def _to_m8(key, tz=None):
+    """
+    Timestamp-like => dt64
+    """
+    if not isinstance(key, Timestamp):
+        # this also converts strings
+        key = Timestamp(key, tz=tz)
+
+    return np.int64(conversion.pydt_to_i8(key)).view(_NS_DTYPE)
 
 
 def _field_accessor(name, field, docstring=None):
@@ -68,7 +84,58 @@ def f(self):
     return property(f)
 
 
-class DatetimeArrayMixin(DatetimeLikeArrayMixin):
+def _dt_array_cmp(opname, cls):
+    """
+    Wrap comparison operations to convert datetime-like to datetime64
+    """
+    nat_result = True if opname == '__ne__' else False
+
+    def wrapper(self, other):
+        meth = getattr(dtl.DatetimeLikeArrayMixin, opname)
+
+        if isinstance(other, (datetime, np.datetime64, compat.string_types)):
+            if isinstance(other, datetime):
+                # GH#18435 strings get a pass from tzawareness compat
+                self._assert_tzawareness_compat(other)
+
+            other = _to_m8(other, tz=self.tz)
+            result = meth(self, other)
+            if isna(other):
+                result.fill(nat_result)
+        else:
+            if isinstance(other, list):
+                other = type(self)(other)
+            elif not isinstance(other, (np.ndarray, ABCIndexClass, ABCSeries)):
+                # Following Timestamp convention, __eq__ is all-False
+                # and __ne__ is all True, others raise TypeError.
+                if opname == '__eq__':
+                    return np.zeros(shape=self.shape, dtype=bool)
+                elif opname == '__ne__':
+                    return np.ones(shape=self.shape, dtype=bool)
+                raise TypeError('%s type object %s' %
+                                (type(other), str(other)))
+
+            if is_datetimelike(other):
+                self._assert_tzawareness_compat(other)
+
+            result = meth(self, np.asarray(other))
+            result = com._values_from_object(result)
+
+            # Make sure to pass an array to result[...]; indexing with
+            # Series breaks with older version of numpy
+            o_mask = np.array(isna(other))
+            if o_mask.any():
+                result[o_mask] = nat_result
+
+        if self.hasnans:
+            result[self._isnan] = nat_result
+
+        return result
+
+    return compat.set_function_name(wrapper, opname, cls)
+
+
+class DatetimeArrayMixin(dtl.DatetimeLikeArrayMixin):
     """
     Assumes that subclass __new__/__init__ defines:
         tz
@@ -222,6 +289,18 @@ def __iter__(self):
     # -----------------------------------------------------------------
     # Comparison Methods
 
+    @classmethod
+    def _add_comparison_methods(cls):
+        """add in comparison methods"""
+        cls.__eq__ = _dt_array_cmp('__eq__', cls)
+        cls.__ne__ = _dt_array_cmp('__ne__', cls)
+        cls.__lt__ = _dt_array_cmp('__lt__', cls)
+        cls.__gt__ = _dt_array_cmp('__gt__', cls)
+        cls.__le__ = _dt_array_cmp('__le__', cls)
+        cls.__ge__ = _dt_array_cmp('__ge__', cls)
+        # TODO: Some classes pass __eq__ while others pass operator.eq;
+        # standardize this.
+
     def _has_same_tz(self, other):
         zzone = self._timezone
 
@@ -335,7 +414,7 @@ def _add_delta(self, delta):
         The result's name is set outside of _add_delta by the calling
         method (__add__ or __sub__)
         """
-        from pandas.core.arrays.timedelta import TimedeltaArrayMixin
+        from pandas.core.arrays.timedeltas import TimedeltaArrayMixin
 
         if isinstance(delta, (Tick, timedelta, np.timedelta64)):
             new_values = self._add_delta_td(delta)
@@ -1021,3 +1100,6 @@ def to_julian_date(self):
                  self.microsecond / 3600.0 / 1e+6 +
                  self.nanosecond / 3600.0 / 1e+9
                  ) / 24.0)
+
+
+DatetimeArrayMixin._add_comparison_methods()

pandas/core/arrays/period.py

@@ -8,7 +8,7 @@
 from pandas._libs.tslib import NaT, iNaT
 from pandas._libs.tslibs.period import (
     Period, IncompatibleFrequency, DIFFERENT_FREQ_INDEX,
-    get_period_field_arr, period_asfreq_arr, _quarter_to_myear)
+    get_period_field_arr, period_asfreq_arr)
 from pandas._libs.tslibs import period as libperiod
 from pandas._libs.tslibs.timedeltas import delta_to_nanoseconds
 from pandas._libs.tslibs.fields import isleapyear_arr
@@ -26,7 +26,7 @@
 from pandas.tseries import frequencies
 from pandas.tseries.offsets import Tick, DateOffset
 
-from .datetimelike import DatetimeLikeArrayMixin
+from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
 
 
 def _field_accessor(name, alias, docstring=None):
@@ -466,7 +466,7 @@ def _range_from_fields(year=None, month=None, quarter=None, day=None,
 
         year, quarter = _make_field_arrays(year, quarter)
         for y, q in compat.zip(year, quarter):
-            y, m = _quarter_to_myear(y, q, freq)
+            y, m = libperiod.quarter_to_myear(y, q, freq)
             val = libperiod.period_ordinal(y, m, 1, 1, 1, 1, 0, 0, base)
             ordinals.append(val)
     else: