ENH: add is_leapyear property for datetime-like

doc/source/api.rst

@@ -472,6 +472,7 @@ These can be accessed like ``Series.dt.<property>``.
    Series.dt.is_quarter_end
    Series.dt.is_year_start
    Series.dt.is_year_end
+   Series.dt.is_leap_year
    Series.dt.daysinmonth
    Series.dt.days_in_month
    Series.dt.tz
@@ -1497,6 +1498,7 @@ Time/Date Components
    DatetimeIndex.is_quarter_end
    DatetimeIndex.is_year_start
    DatetimeIndex.is_year_end
+   DatetimeIndex.is_leap_year
    DatetimeIndex.inferred_freq
 
 Selecting

doc/source/timeseries.rst

@@ -560,6 +560,7 @@ There are several time/date properties that one can access from ``Timestamp`` or
     is_quarter_end,"Logical indicating if last day of quarter (defined by frequency)"
     is_year_start,"Logical indicating if first day of year (defined by frequency)"
     is_year_end,"Logical indicating if last day of year (defined by frequency)"
+    is_leap_year,"Logical indicating if the date belongs to a leap year"
 
 Furthermore, if you have a ``Series`` with datetimelike values, then you can access these properties via the ``.dt`` accessor, see the :ref:`docs <basics.dt_accessors>`
 

doc/source/whatsnew/v0.19.0.txt

@@ -347,6 +347,8 @@ API changes
 - ``astype()`` will now accept a dict of column name to data types mapping as the ``dtype`` argument. (:issue:`12086`)
 - The ``pd.read_json`` and ``DataFrame.to_json`` has gained support for reading and writing json lines with ``lines`` option see :ref:`Line delimited json <io.jsonl>` (:issue:`9180`)
 - ``pd.Timedelta(None)`` is now accepted and will return ``NaT``, mirroring ``pd.Timestamp`` (:issue:`13687`)
+- ``Timestamp``, ``Period``, ``DatetimeIndex``, ``PeriodIndex`` and ``.dt`` accessor have ``.is_leap_year`` property to check whether the date belongs to a leap year. (:issue:`13727`)
+
 
 .. _whatsnew_0190.api.tolist:
 
@@ -609,7 +611,9 @@ Deprecations
 - ``as_recarray`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13373`)
 - top-level ``pd.ordered_merge()`` has been renamed to ``pd.merge_ordered()`` and the original name will be removed in a future version (:issue:`13358`)
 - ``Timestamp.offset`` property (and named arg in the constructor), has been deprecated in favor of ``freq`` (:issue:`12160`)
-- ``pivot_annual`` is deprecated. Use ``pivot_table`` as alternative, an example is :ref:`here <cookbook.pivot>` (:issue:`736`)
+- ``pd.tseries.util.pivot_annual`` is deprecated. Use ``pivot_table`` as alternative, an example is :ref:`here <cookbook.pivot>` (:issue:`736`)
+- ``pd.tseries.util.isleapyear`` has been deprecated and will be removed in a subsequent release. Datetime-likes now have a ``.is_leap_year`` property. (:issue:`13727`)
+
 
 .. _whatsnew_0190.prior_deprecations:
 

pandas/src/period.pyx

@@ -913,6 +913,9 @@ cdef class _Period(object):
     property daysinmonth:
         def __get__(self):
             return self.days_in_month
+    property is_leap_year:
+        def __get__(self):
+            return bool(is_leapyear(self._field(0)))
 
     @classmethod
     def now(cls, freq=None):

pandas/tests/series/test_datetime_values.py

@@ -32,7 +32,7 @@ def test_dt_namespace_accessor(self):
         ok_for_base = ['year', 'month', 'day', 'hour', 'minute', 'second',
                        'weekofyear', 'week', 'dayofweek', 'weekday',
                        'dayofyear', 'quarter', 'freq', 'days_in_month',
-                       'daysinmonth']
+                       'daysinmonth', 'is_leap_year']
         ok_for_period = ok_for_base + ['qyear', 'start_time', 'end_time']
         ok_for_period_methods = ['strftime', 'to_timestamp', 'asfreq']
         ok_for_dt = ok_for_base + ['date', 'time', 'microsecond', 'nanosecond',

pandas/tseries/index.py

@@ -72,11 +72,14 @@ def f(self):
                                            self.freq.kwds.get('month', 12))
                         if self.freq else 12)
 
-            result = tslib.get_start_end_field(
-                values, field, self.freqstr, month_kw)
+            result = tslib.get_start_end_field(values, field, self.freqstr,
+                                               month_kw)
         elif field in ['weekday_name']:
             result = tslib.get_date_name_field(values, field)
             return self._maybe_mask_results(result)
+        elif field in ['is_leap_year']:
+            # no need to mask NaT
+            return tslib.get_date_field(values, field)
         else:
             result = tslib.get_date_field(values, field)
 
@@ -227,7 +230,8 @@ def _join_i8_wrapper(joinf, **kwargs):
                          'daysinmonth', 'date', 'time', 'microsecond',
                          'nanosecond', 'is_month_start', 'is_month_end',
                          'is_quarter_start', 'is_quarter_end', 'is_year_start',
-                         'is_year_end', 'tz', 'freq', 'weekday_name']
+                         'is_year_end', 'tz', 'freq', 'weekday_name',
+                         'is_leap_year']
     _is_numeric_dtype = False
     _infer_as_myclass = True
 
@@ -1521,44 +1525,31 @@ def _set_freq(self, value):
                     doc="get/set the frequncy of the Index")
 
     year = _field_accessor('year', 'Y', "The year of the datetime")
-    month = _field_accessor(
-        'month', 'M', "The month as January=1, December=12")
+    month = _field_accessor('month', 'M',
+                            "The month as January=1, December=12")
     day = _field_accessor('day', 'D', "The days of the datetime")
     hour = _field_accessor('hour', 'h', "The hours of the datetime")
     minute = _field_accessor('minute', 'm', "The minutes of the datetime")
     second = _field_accessor('second', 's', "The seconds of the datetime")
-    microsecond = _field_accessor(
-        'microsecond',
-        'us',
-        "The microseconds of the datetime")
-    nanosecond = _field_accessor(
-        'nanosecond',
-        'ns',
-        "The nanoseconds of the datetime")
-    weekofyear = _field_accessor(
-        'weekofyear',
-        'woy',
-        "The week ordinal of the year")
+    microsecond = _field_accessor('microsecond', 'us',
+                                  "The microseconds of the datetime")
+    nanosecond = _field_accessor('nanosecond', 'ns',
+                                 "The nanoseconds of the datetime")
+    weekofyear = _field_accessor('weekofyear', 'woy',
+                                 "The week ordinal of the year")
     week = weekofyear
-    dayofweek = _field_accessor(
-        'dayofweek',
-        'dow',
-        "The day of the week with Monday=0, Sunday=6")
+    dayofweek = _field_accessor('dayofweek', 'dow',
+                                "The day of the week with Monday=0, Sunday=6")
     weekday = dayofweek
 
     weekday_name = _field_accessor(
         'weekday_name',
         'weekday_name',
         "The name of day in a week (ex: Friday)\n\n.. versionadded:: 0.18.1")
 
-    dayofyear = _field_accessor(
-        'dayofyear',
-        'doy',
-        "The ordinal day of the year")
-    quarter = _field_accessor(
-        'quarter',
-        'q',
-        "The quarter of the date")
+    dayofyear = _field_accessor('dayofyear', 'doy',
+                                "The ordinal day of the year")
+    quarter = _field_accessor('quarter', 'q', "The quarter of the date")
     days_in_month = _field_accessor(
         'days_in_month',
         'dim',
@@ -1588,6 +1579,10 @@ def _set_freq(self, value):
         'is_year_end',
         'is_year_end',
         "Logical indicating if last day of year (defined by frequency)")
+    is_leap_year = _field_accessor(
+        'is_leap_year',
+        'is_leap_year',
+        "Logical indicating if the date belongs to a leap year")
 
     @property
     def time(self):

pandas/tseries/period.py

@@ -165,7 +165,8 @@ class PeriodIndex(DatelikeOps, DatetimeIndexOpsMixin, Int64Index):
                          'weekofyear', 'week', 'dayofweek', 'weekday',
                          'dayofyear', 'quarter', 'qyear', 'freq',
                          'days_in_month', 'daysinmonth',
-                         'to_timestamp', 'asfreq', 'start_time', 'end_time']
+                         'to_timestamp', 'asfreq', 'start_time', 'end_time',
+                         'is_leap_year']
     _is_numeric_dtype = False
     _infer_as_myclass = True
 
@@ -509,17 +510,22 @@ def to_datetime(self, dayfirst=False):
     second = _field_accessor('second', 7, "The second of the period")
     weekofyear = _field_accessor('week', 8, "The week ordinal of the year")
     week = weekofyear
-    dayofweek = _field_accessor(
-        'dayofweek', 10, "The day of the week with Monday=0, Sunday=6")
+    dayofweek = _field_accessor('dayofweek', 10,
+                                "The day of the week with Monday=0, Sunday=6")
     weekday = dayofweek
-    dayofyear = day_of_year = _field_accessor(
-        'dayofyear', 9, "The ordinal day of the year")
+    dayofyear = day_of_year = _field_accessor('dayofyear', 9,
+                                              "The ordinal day of the year")
     quarter = _field_accessor('quarter', 2, "The quarter of the date")
     qyear = _field_accessor('qyear', 1)
-    days_in_month = _field_accessor(
-        'days_in_month', 11, "The number of days in the month")
+    days_in_month = _field_accessor('days_in_month', 11,
+                                    "The number of days in the month")
     daysinmonth = days_in_month
 
+    @property
+    def is_leap_year(self):
+        """ Logical indicating if the date belongs to a leap year """
+        return tslib._isleapyear_arr(self.year)
+
     @property
     def start_time(self):
         return self.to_timestamp(how='start')

pandas/tseries/tests/test_period.py

@@ -1514,6 +1514,22 @@ def test_asfreq_mult(self):
             self.assertEqual(result.ordinal, expected.ordinal)
             self.assertEqual(result.freq, expected.freq)
 
+    def test_is_leap_year(self):
+        # GH 13727
+        for freq in ['A', 'M', 'D', 'H']:
+            p = Period('2000-01-01 00:00:00', freq=freq)
+            self.assertTrue(p.is_leap_year)
+            self.assertIsInstance(p.is_leap_year, bool)
+
+            p = Period('1999-01-01 00:00:00', freq=freq)
+            self.assertFalse(p.is_leap_year)
+
+            p = Period('2004-01-01 00:00:00', freq=freq)
+            self.assertTrue(p.is_leap_year)
+
+            p = Period('2100-01-01 00:00:00', freq=freq)
+            self.assertFalse(p.is_leap_year)
+
 
 class TestPeriodIndex(tm.TestCase):
     def setUp(self):
@@ -3130,16 +3146,25 @@ def test_fields(self):
     def _check_all_fields(self, periodindex):
         fields = ['year', 'month', 'day', 'hour', 'minute', 'second',
                   'weekofyear', 'week', 'dayofweek', 'weekday', 'dayofyear',
-                  'quarter', 'qyear', 'days_in_month']
+                  'quarter', 'qyear', 'days_in_month', 'is_leap_year']
 
         periods = list(periodindex)
+        s = pd.Series(periodindex)
 
         for field in fields:
             field_idx = getattr(periodindex, field)
             self.assertEqual(len(periodindex), len(field_idx))
             for x, val in zip(periods, field_idx):
                 self.assertEqual(getattr(x, field), val)
 
+            if len(s) == 0:
+                continue
+
+            field_s = getattr(s.dt, field)
+            self.assertEqual(len(periodindex), len(field_s))
+            for x, val in zip(periods, field_s):
+                self.assertEqual(getattr(x, field), val)
+
     def test_is_full(self):
         index = PeriodIndex([2005, 2007, 2009], freq='A')
         self.assertFalse(index.is_full)
@@ -4569,6 +4594,7 @@ def test_get_period_field_array_raises_on_out_of_range(self):
         self.assertRaises(ValueError, _period.get_period_field_arr, -1,
                           np.empty(1), 0)
 
+
 if __name__ == '__main__':
     import nose
     nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],

pandas/tseries/tests/test_timeseries.py

@@ -969,13 +969,20 @@ def test_nat_vector_field_access(self):
 
         fields = ['year', 'quarter', 'month', 'day', 'hour', 'minute',
                   'second', 'microsecond', 'nanosecond', 'week', 'dayofyear',
-                  'days_in_month']
+                  'days_in_month', 'is_leap_year']
+
         for field in fields:
             result = getattr(idx, field)
-            expected = [getattr(x, field) if x is not NaT else np.nan
-                        for x in idx]
+            expected = [getattr(x, field) for x in idx]
             self.assert_numpy_array_equal(result, np.array(expected))
 
+        s = pd.Series(idx)
+
+        for field in fields:
+            result = getattr(s.dt, field)
+            expected = [getattr(x, field) for x in idx]
+            self.assert_series_equal(result, pd.Series(expected))
+
     def test_nat_scalar_field_access(self):
         fields = ['year', 'quarter', 'month', 'day', 'hour', 'minute',
                   'second', 'microsecond', 'nanosecond', 'week', 'dayofyear',
@@ -4761,6 +4768,25 @@ def test_timestamp_compare_series(self):
             result = right_f(Timestamp('nat'), s_nat)
             tm.assert_series_equal(result, expected)
 
+    def test_is_leap_year(self):
+        # GH 13727
+        for tz in [None, 'UTC', 'US/Eastern', 'Asia/Tokyo']:
+            dt = Timestamp('2000-01-01 00:00:00', tz=tz)
+            self.assertTrue(dt.is_leap_year)
+            self.assertIsInstance(dt.is_leap_year, bool)
+
+            dt = Timestamp('1999-01-01 00:00:00', tz=tz)
+            self.assertFalse(dt.is_leap_year)
+
+            dt = Timestamp('2004-01-01 00:00:00', tz=tz)
+            self.assertTrue(dt.is_leap_year)
+
+            dt = Timestamp('2100-01-01 00:00:00', tz=tz)
+            self.assertFalse(dt.is_leap_year)
+
+        self.assertFalse(pd.NaT.is_leap_year)
+        self.assertIsInstance(pd.NaT.is_leap_year, bool)
+
 
 class TestSlicing(tm.TestCase):
     def test_slice_year(self):

pandas/tseries/tests/test_util.py

@@ -25,7 +25,9 @@ def test_daily(self):
             annual = pivot_annual(ts, 'D')
 
         doy = ts.index.dayofyear
-        doy[(~isleapyear(ts.index.year)) & (doy >= 60)] += 1
+
+        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+            doy[(~isleapyear(ts.index.year)) & (doy >= 60)] += 1
 
         for i in range(1, 367):
             subset = ts[doy == i]
@@ -51,7 +53,9 @@ def test_hourly(self):
         grouped = ts_hourly.groupby(ts_hourly.index.year)
         hoy = grouped.apply(lambda x: x.reset_index(drop=True))
         hoy = hoy.index.droplevel(0).values
-        hoy[~isleapyear(ts_hourly.index.year) & (hoy >= 1416)] += 24
+
+        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+            hoy[~isleapyear(ts_hourly.index.year) & (hoy >= 1416)] += 24
         hoy += 1
 
         with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
@@ -100,6 +104,16 @@ def test_period_daily(self):
     def test_period_weekly(self):
         pass
 
+    def test_isleapyear_deprecate(self):
+        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+            self.assertTrue(isleapyear(2000))
+
+        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+            self.assertFalse(isleapyear(2001))
+
+        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+            self.assertTrue(isleapyear(2004))
+
 
 def test_normalize_date():
     value = date(2012, 9, 7)

pandas/tseries/util.py

@@ -95,6 +95,10 @@ def isleapyear(year):
     year : integer / sequence
         A given (list of) year(s).
     """
+
+    msg = "isleapyear is deprecated. Use .is_leap_year property instead"
+    warnings.warn(msg, FutureWarning)
+
     year = np.asarray(year)
     return np.logical_or(year % 400 == 0,
                          np.logical_and(year % 4 == 0, year % 100 > 0))