DEPR: Deprecate DateRange

bench/bench_dense_to_sparse.py

@@ -2,7 +2,7 @@
 
 K = 100
 N = 100000
-rng = DateRange('1/1/2000', periods=N, offset=datetools.Minute())
+rng = DatetimeIndex('1/1/2000', periods=N, offset=datetools.Minute())
 
 rng2 = np.asarray(rng).astype('M8[us]').astype('i8')
 

bench/io_roundtrip.py

@@ -6,7 +6,7 @@
 import la
 import pandas
 from pandas.compat import range
-from pandas import datetools, DateRange
+from pandas import datetools, DatetimeIndex
 
 
 def timeit(f, iterations):
@@ -23,7 +23,7 @@ def rountrip_archive(N, K=50, iterations=10):
     arr = np.random.randn(N, K)
     # lar = la.larry(arr)
     dma = pandas.DataFrame(arr,
-                           DateRange('1/1/2000', periods=N,
+                           DatetimeIndex('1/1/2000', periods=N,
                                      offset=datetools.Minute()))
     dma[201] = 'bar'
 

doc/source/release.rst

@@ -180,6 +180,8 @@ Deprecations
 Prior Version Deprecations/Changes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+- Remove :class:`DateRange` in favor of :class:`DatetimeIndex` (:issue:`6816`)
+
 - Remove ``column`` keyword from ``DataFrame.sort`` (:issue:`4370`)
 
 Experimental Features

doc/source/v0.14.0.txt

@@ -326,6 +326,8 @@ Prior Version Deprecations/Changes
 
 Therse are prior version deprecations that are taking effect as of 0.14.0.
 
+- Remove :class:`DateRange` in favor of :class:`DatetimeIndex` (:issue:`6816`)
+
 - Remove ``column`` keyword from ``DataFrame.sort`` (:issue:`4370`)
 
 Deprecations

examples/regressions.py

@@ -3,13 +3,13 @@
 
 import numpy as np
 
-from pandas.core.api import Series, DataFrame, DateRange
+from pandas.core.api import Series, DataFrame, DatetimeIndex
 from pandas.stats.api import ols
 
 N = 100
 
 start = datetime(2009, 9, 2)
-dateRange = DateRange(start, periods=N)
+dateRange = DatetimeIndex(start, periods=N)
 
 
 def makeDataFrame():

pandas/core/api.py

@@ -28,7 +28,6 @@
 from pandas.tseries.period import Period, PeriodIndex
 
 # legacy
-from pandas.core.daterange import DateRange  # deprecated
 from pandas.core.common import save, load  # deprecated, remove in 0.13
 import pandas.core.datetools as datetools
 

pandas/core/daterange.py

@@ -1,48 +0,0 @@
-# pylint: disable=E1101,E1103
-
-from pandas.core.index import Index
-from pandas.tseries.index import DatetimeIndex
-import pandas.core.datetools as datetools
-
-
-#-----------------------------------------------------------------------------
-# DateRange class
-
-class DateRange(Index):
-
-    """Deprecated
-    """
-
-    offset = tzinfo = None
-
-    def __new__(cls, start=None, end=None, periods=None,
-                offset=datetools.bday, time_rule=None,
-                tzinfo=None, name=None, **kwds):
-
-        import warnings
-        warnings.warn("DateRange is deprecated, use DatetimeIndex instead",
-                      FutureWarning)
-
-        if time_rule is None:
-            time_rule = kwds.get('timeRule')
-        if time_rule is not None:
-            offset = datetools.get_offset(time_rule)
-
-        return DatetimeIndex(start=start, end=end,
-                             periods=periods, freq=offset,
-                             tzinfo=tzinfo, name=name, **kwds)
-
-    def __setstate__(self, aug_state):
-        """Necessary for making this object picklable"""
-        index_state = aug_state[:1]
-        offset = aug_state[1]
-
-        # for backwards compatibility
-        if len(aug_state) > 2:
-            tzinfo = aug_state[2]
-        else:  # pragma: no cover
-            tzinfo = None
-
-        self.offset = offset
-        self.tzinfo = tzinfo
-        Index.__setstate__(self, *index_state)

pandas/core/index.py

@@ -3888,26 +3888,6 @@ def _sanitize_and_check(indexes):
         return indexes, 'array'
 
 
-def _handle_legacy_indexes(indexes):
-    from pandas.core.daterange import DateRange
-    from pandas.tseries.index import DatetimeIndex
-
-    converted = []
-    for index in indexes:
-        if isinstance(index, DateRange):
-            if len(index) == 0:
-                kwds = dict(data=[], freq=index.offset, tz=index.tzinfo)
-            else:
-                kwds = dict(start=index[0], end=index[-1],
-                            freq=index.offset, tz=index.tzinfo)
-
-            index = DatetimeIndex(**kwds)
-
-        converted.append(index)
-
-    return converted
-
-
 def _get_consensus_names(indexes):
 
     # find the non-none names, need to tupleify to make

pandas/core/internals.py

@@ -12,8 +12,7 @@
                                 _NS_DTYPE, _TD_DTYPE, ABCSeries, is_list_like,
                                 ABCSparseSeries, _infer_dtype_from_scalar,
                                 _values_from_object, _is_null_datelike_scalar)
-from pandas.core.index import (Index, MultiIndex, _ensure_index,
-                               _handle_legacy_indexes)
+from pandas.core.index import Index, MultiIndex, _ensure_index
 from pandas.core.indexing import (_maybe_convert_indices, _length_of_indexer)
 import pandas.core.common as com
 from pandas.sparse.array import _maybe_to_sparse, SparseArray
@@ -2369,7 +2368,6 @@ def __setstate__(self, state):
         ax_arrays, bvalues, bitems = state[:3]
 
         self.axes = [_ensure_index(ax) for ax in ax_arrays]
-        self.axes = _handle_legacy_indexes(self.axes)
 
         blocks = []
         for values, items in zip(bvalues, bitems):

pandas/core/series.py

@@ -26,7 +26,7 @@
                                 _ensure_object, SettingWithCopyError)
 
 from pandas.core.index import (Index, MultiIndex, InvalidIndexError,
-                               _ensure_index, _handle_legacy_indexes)
+                               _ensure_index)
 from pandas.core.indexing import (
     _check_bool_indexer,
     _is_index_slice, _maybe_convert_indices)
@@ -426,7 +426,6 @@ def _unpickle_series_compat(self, state):
             index, name = own_state[0], None
             if len(own_state) > 1:
                 name = own_state[1]
-            index = _handle_legacy_indexes([index])[0]
 
             # recreate
             self._data = SingleBlockManager(data, index, fastpath=True)

pandas/tseries/tests/test_timeseries.py

@@ -15,7 +15,6 @@
                     isnull, date_range, Timestamp, Period, DatetimeIndex,
                     Int64Index, to_datetime, bdate_range, Float64Index)
 
-from pandas.core.daterange import DateRange
 import pandas.core.datetools as datetools
 import pandas.tseries.offsets as offsets
 import pandas.tseries.tools as tools

pandas/tseries/tests/test_timeseries_legacy.py

@@ -13,7 +13,6 @@
                     isnull, date_range, Timestamp, DatetimeIndex,
                     Int64Index, to_datetime, bdate_range)
 
-from pandas.core.daterange import DateRange
 import pandas.core.datetools as datetools
 import pandas.tseries.offsets as offsets
 import pandas.tseries.frequencies as fmod
@@ -284,14 +283,6 @@ def test_inferTimeRule(self):
         self.assertRaises(Exception, inferTimeRule, index1[:2])
         self.assertRaises(Exception, inferTimeRule, index3)
 
-    def test_time_rule(self):
-        result = DateRange('1/1/2000', '1/30/2000', time_rule='WEEKDAY')
-        result2 = DateRange('1/1/2000', '1/30/2000', timeRule='WEEKDAY')
-        expected = date_range('1/1/2000', '1/30/2000', freq='B')
-
-        self.assert_(result.equals(expected))
-        self.assert_(result2.equals(expected))
-
     def tearDown(self):
         sys.stderr = sys.__stderr__
 

pandas/tseries/tests/test_timezones.py

@@ -12,7 +12,6 @@
 
 from pandas import DatetimeIndex, Int64Index, to_datetime, NaT
 
-from pandas.core.daterange import DateRange
 import pandas.core.datetools as datetools
 import pandas.tseries.offsets as offsets
 from pandas.tseries.index import bdate_range, date_range

scripts/bench_join.py

@@ -12,8 +12,8 @@
 a = np.arange(n, dtype=np.int64)
 b = np.arange(n * pct_overlap, n * (1 + pct_overlap), dtype=np.int64)
 
-dr1 = DateRange('1/1/2000', periods=n, offset=datetools.Minute())
-dr2 = DateRange(
+dr1 = DatetimeIndex('1/1/2000', periods=n, offset=datetools.Minute())
+dr2 = DatetimeIndex(
     dr1[int(pct_overlap * n)], periods=n, offset=datetools.Minute(2))
 
 aobj = a.astype(object)

scripts/groupby_speed.py

@@ -1,12 +1,12 @@
 from __future__ import print_function
 from pandas import *
 
-rng = DateRange('1/3/2011', '11/30/2011', offset=datetools.Minute())
+rng = DatetimeIndex('1/3/2011', '11/30/2011', offset=datetools.Minute())
 
 df = DataFrame(np.random.randn(len(rng), 5), index=rng,
                columns=list('OHLCV'))
 
-rng5 = DateRange('1/3/2011', '11/30/2011', offset=datetools.Minute(5))
+rng5 = DatetimeIndex('1/3/2011', '11/30/2011', offset=datetools.Minute(5))
 gp = rng5.asof
 grouped = df.groupby(gp)
 

scripts/hdfstore_panel_perf.py

@@ -6,7 +6,7 @@
 
 panel = Panel(np.random.randn(i, j, k),
               items=[rands(10) for _ in range(i)],
-              major_axis=DateRange('1/1/2000', periods=j,
+              major_axis=DatetimeIndex('1/1/2000', periods=j,
                                    offset=datetools.Minute()),
               minor_axis=[rands(10) for _ in range(k)])
 

scripts/preepoch_test.py

@@ -7,7 +7,7 @@ def panda_test():
     # generate some data
     data = np.random.rand(50, 5)
     # generate some dates
-    dates = DateRange('1/1/1969', periods=50)
+    dates = DatetimeIndex('1/1/1969', periods=50)
     # generate column headings
     cols = ['A', 'B', 'C', 'D', 'E']
 

vb_suite/index_object.py

@@ -11,7 +11,7 @@
 # intersection, union
 
 setup = common_setup + """
-rng = DateRange('1/1/2000', periods=10000, offset=datetools.Minute())
+rng = DatetimeIndex('1/1/2000', periods=10000, offset=datetools.Minute())
 if rng.dtype == object:
     rng = rng.view(Index)
 else:

vb_suite/panel_ctor.py

@@ -11,7 +11,7 @@
 
 setup_same_index = common_setup + """
 # create 100 dataframes with the same index
-dr = np.asarray(DateRange(datetime(1990,1,1), datetime(2012,1,1)))
+dr = np.asarray(DatetimeIndex(datetime(1990,1,1), datetime(2012,1,1)))
 data_frames = {}
 for x in xrange(100):
    df = DataFrame({"a": [0]*len(dr), "b": [1]*len(dr),
@@ -27,7 +27,7 @@
 setup_equiv_indexes = common_setup + """
 data_frames = {}
 for x in xrange(100):
-   dr = np.asarray(DateRange(datetime(1990,1,1), datetime(2012,1,1)))
+   dr = np.asarray(DatetimeIndex(datetime(1990,1,1), datetime(2012,1,1)))
    df = DataFrame({"a": [0]*len(dr), "b": [1]*len(dr),
                    "c": [2]*len(dr)}, index=dr)
    data_frames[x] = df

vb_suite/plotting.py

@@ -7,7 +7,7 @@
     from pandas import date_range
 except ImportError:
     def date_range(start=None, end=None, periods=None, freq=None):
-        return DateRange(start, end, periods=periods, offset=freq)
+        return DatetimeIndex(start, end, periods=periods, offset=freq)
 
 """
 

vb_suite/reindex.py

@@ -18,7 +18,7 @@
 #----------------------------------------------------------------------
 
 setup = common_setup + """
-rng = DateRange('1/1/1970', periods=10000, offset=datetools.Minute())
+rng = DatetimeIndex('1/1/1970', periods=10000, offset=datetools.Minute())
 df = DataFrame(np.random.rand(10000, 10), index=rng,
                columns=range(10))
 df['foo'] = 'bar'

vb_suite/replace.py

@@ -9,7 +9,7 @@
 try:
     rng = date_range('1/1/2000', periods=N, freq='min')
 except NameError:
-    rng = DateRange('1/1/2000', periods=N, offset=datetools.Minute())
+    rng = DatetimeIndex('1/1/2000', periods=N, offset=datetools.Minute())
     date_range = DateRange
 
 ts = Series(np.random.randn(N), index=rng)

vb_suite/timeseries.py

@@ -8,9 +8,9 @@
 try:
     rng = date_range('1/1/2000', periods=N, freq='min')
 except NameError:
-    rng = DateRange('1/1/2000', periods=N, offset=datetools.Minute())
+    rng = DatetimeIndex('1/1/2000', periods=N, offset=datetools.Minute())
     def date_range(start=None, end=None, periods=None, freq=None):
-        return DateRange(start, end, periods=periods, offset=freq)
+        return DatetimeIndex(start, end, periods=periods, offset=freq)
 
 if hasattr(Series, 'convert'):
     Series.resample = Series.convert