PERF: TimedeltaArray.__iter__

asv_bench/benchmarks/timeseries.py

@@ -3,7 +3,14 @@
 import dateutil
 import numpy as np
 
-from pandas import DataFrame, Series, date_range, period_range, to_datetime
+from pandas import (
+    DataFrame,
+    Series,
+    date_range,
+    period_range,
+    timedelta_range,
+    to_datetime,
+)
 
 from pandas.tseries.frequencies import infer_freq
 
@@ -121,12 +128,15 @@ def time_convert(self):
 
 class Iteration:
 
-    params = [date_range, period_range]
+    params = [date_range, period_range, timedelta_range]
     param_names = ["time_index"]
 
     def setup(self, time_index):
         N = 10 ** 6
-        self.idx = time_index(start="20140101", freq="T", periods=N)
+        if time_index is timedelta_range:
+            self.idx = time_index(start=0, freq="T", periods=N)
+        else:
+            self.idx = time_index(start="20140101", freq="T", periods=N)
         self.exit = 10000
 
     def time_iter(self, time_index):

pandas/core/arrays/datetimelike.py

@@ -469,7 +469,10 @@ def _box_values(self, values):
         return lib.map_infer(values, self._box_func)
 
     def __iter__(self):
-        return (self._box_func(v) for v in self.asi8)
+        if self.ndim > 1:
+            return (self[n] for n in range(len(self)))
+        else:
+            return (self._box_func(v) for v in self.asi8)
 
     @property
     def asi8(self) -> np.ndarray:

pandas/core/arrays/datetimes.py

@@ -558,19 +558,21 @@ def __iter__(self):
         ------
         tstamp : Timestamp
         """
-
-        # convert in chunks of 10k for efficiency
-        data = self.asi8
-        length = len(self)
-        chunksize = 10000
-        chunks = int(length / chunksize) + 1
-        for i in range(chunks):
-            start_i = i * chunksize
-            end_i = min((i + 1) * chunksize, length)
-            converted = ints_to_pydatetime(
-                data[start_i:end_i], tz=self.tz, freq=self.freq, box="timestamp"
-            )
-            yield from converted
+        if self.ndim > 1:
+            return (self[n] for n in range(len(self)))
+        else:
+            # convert in chunks of 10k for efficiency
+            data = self.asi8
+            length = len(self)
+            chunksize = 10000
+            chunks = int(length / chunksize) + 1
+            for i in range(chunks):
+                start_i = i * chunksize
+                end_i = min((i + 1) * chunksize, length)
+                converted = ints_to_pydatetime(
+                    data[start_i:end_i], tz=self.tz, freq=self.freq, box="timestamp"
+                )
+                yield from converted
 
     def astype(self, dtype, copy=True):
         # We handle

pandas/core/arrays/timedeltas.py

@@ -16,7 +16,11 @@
 )
 from pandas._libs.tslibs.conversion import precision_from_unit
 from pandas._libs.tslibs.fields import get_timedelta_field
-from pandas._libs.tslibs.timedeltas import array_to_timedelta64, parse_timedelta_unit
+from pandas._libs.tslibs.timedeltas import (
+    array_to_timedelta64,
+    ints_to_pytimedelta,
+    parse_timedelta_unit,
+)
 from pandas.compat.numpy import function as nv
 
 from pandas.core.dtypes.common import (
@@ -328,6 +332,21 @@ def astype(self, dtype, copy=True):
             return self
         return dtl.DatetimeLikeArrayMixin.astype(self, dtype, copy=copy)
 
+    def __iter__(self):
+        if self.ndim > 1:
+            return (self[n] for n in range(len(self)))
+        else:
+            # convert in chunks of 10k for efficiency
+            data = self.asi8
+            length = len(self)
+            chunksize = 10000
+            chunks = int(length / chunksize) + 1
+            for i in range(chunks):
+                start_i = i * chunksize
+                end_i = min((i + 1) * chunksize, length)
+                converted = ints_to_pytimedelta(data[start_i:end_i], box=True)
+                yield from converted
+
     # ----------------------------------------------------------------
     # Reductions
 

pandas/tests/arrays/test_datetimelike.py

@@ -310,6 +310,15 @@ def test_getitem_2d(self, arr1d):
         expected = arr1d[-1]
         assert result == expected
 
+    def test_iter_2d(self, arr1d):
+        data2d = arr1d._data[:3, np.newaxis]
+        arr2d = type(arr1d)._simple_new(data2d, dtype=arr1d.dtype)
+        result = list(arr2d)
+        for x in result:
+            assert isinstance(x, type(arr1d))
+            assert x.ndim == 1
+            assert x.dtype == arr1d.dtype
+
     def test_setitem(self):
         data = np.arange(10, dtype="i8") * 24 * 3600 * 10 ** 9
         arr = self.array_cls(data, freq="D")