PERF: perf improvements in timedelta conversions from integer dtypes

doc/source/release.rst

@@ -208,6 +208,7 @@ Improvements to existing features
 - Performance improvement when converting ``DatetimeIndex`` to floating ordinals
   using ``DatetimeConverter`` (:issue:`6636`)
 - Performance improvement for  ``DataFrame.shift`` (:issue:`5609`)
+- Performance improvements in timedelta conversions for integer dtypes (:issue:`6754`)
 
 .. _release.bug_fixes-0.14.0:
 

pandas/core/common.py

@@ -2130,6 +2130,16 @@ def is_timedelta64_dtype(arr_or_dtype):
     return issubclass(tipo, np.timedelta64)
 
 
+def is_timedelta64_ns_dtype(arr_or_dtype):
+    if isinstance(arr_or_dtype, np.dtype):
+        tipo = arr_or_dtype.type
+    elif isinstance(arr_or_dtype, type):
+        tipo = np.dtype(arr_or_dtype).type
+    else:
+        tipo = arr_or_dtype.dtype.type
+    return tipo == _TD_DTYPE
+
+
 def needs_i8_conversion(arr_or_dtype):
     return (is_datetime64_dtype(arr_or_dtype) or
             is_timedelta64_dtype(arr_or_dtype))

pandas/tseries/tests/test_timedeltas.py

@@ -173,6 +173,36 @@ def conv(v):
         expected = np.timedelta64(timedelta(seconds=1))
         self.assertEqual(result, expected)
 
+        # arrays of various dtypes
+        arr = np.array([1]*5,dtype='int64')
+        result = to_timedelta(arr,unit='s')
+        expected = Series([ np.timedelta64(1,'s') ]*5)
+        tm.assert_series_equal(result, expected)
+
+        arr = np.array([1]*5,dtype='int64')
+        result = to_timedelta(arr,unit='m')
+        expected = Series([ np.timedelta64(1,'m') ]*5)
+        tm.assert_series_equal(result, expected)
+
+        arr = np.array([1]*5,dtype='int64')
+        result = to_timedelta(arr,unit='h')
+        expected = Series([ np.timedelta64(1,'h') ]*5)
+        tm.assert_series_equal(result, expected)
+
+        arr = np.array([1]*5,dtype='timedelta64[s]')
+        result = to_timedelta(arr)
+        expected = Series([ np.timedelta64(1,'s') ]*5)
+        tm.assert_series_equal(result, expected)
+
+        arr = np.array([1]*5,dtype='timedelta64[D]')
+        result = to_timedelta(arr)
+        expected = Series([ np.timedelta64(1,'D') ]*5)
+        tm.assert_series_equal(result, expected)
+
+        # these will error
+        self.assertRaises(ValueError, lambda : to_timedelta(['1h']))
+        self.assertRaises(ValueError, lambda : to_timedelta(['1m']))
+
     def test_to_timedelta_via_apply(self):
         _skip_if_numpy_not_friendly()
 

pandas/tseries/timedeltas.py

@@ -8,7 +8,7 @@
 import numpy as np
 import pandas.tslib as tslib
 from pandas import compat, _np_version_under1p7
-from pandas.core.common import (ABCSeries, is_integer, is_timedelta64_dtype,
+from pandas.core.common import (ABCSeries, is_integer, is_integer_dtype, is_timedelta64_dtype,
                                 _values_from_object, is_list_like, isnull)
 
 repr_timedelta = tslib.repr_timedelta64
@@ -23,7 +23,7 @@ def to_timedelta(arg, box=True, unit='ns'):
     arg : string, timedelta, array of strings (with possible NAs)
     box : boolean, default True
         If True returns a Series of the results, if False returns ndarray of values
-    unit : unit of the arg (D,s,ms,us,ns) denote the unit, which is an integer/float number
+    unit : unit of the arg (D,h,m,s,ms,us,ns) denote the unit, which is an integer/float number
 
     Returns
     -------
@@ -32,18 +32,22 @@ def to_timedelta(arg, box=True, unit='ns'):
     if _np_version_under1p7:
         raise ValueError("to_timedelta is not support for numpy < 1.7")
 
-    def _convert_listlike(arg, box):
+    def _convert_listlike(arg, box, unit):
 
         if isinstance(arg, (list,tuple)):
             arg = np.array(arg, dtype='O')
 
         if is_timedelta64_dtype(arg):
-            if box:
-                from pandas import Series
-                return Series(arg,dtype='m8[ns]')
-            return arg
+            value = arg.astype('timedelta64[ns]')
+        elif is_integer_dtype(arg):
+            # these are shortcutable
+            value = arg.astype('timedelta64[{0}]'.format(unit)).astype('timedelta64[ns]')
+        else:
+            try:
+                value = tslib.array_to_timedelta64(_ensure_object(arg),unit=unit)
+            except:
+                value = np.array([ _coerce_scalar_to_timedelta_type(r, unit=unit) for r in arg ])
 
-        value = np.array([ _coerce_scalar_to_timedelta_type(r, unit=unit) for r in arg ])
         if box:
             from pandas import Series
             value = Series(value,dtype='m8[ns]')
@@ -53,10 +57,10 @@ def _convert_listlike(arg, box):
         return arg
     elif isinstance(arg, ABCSeries):
         from pandas import Series
-        values = _convert_listlike(arg.values, box=False)
+        values = _convert_listlike(arg.values, box=False, unit=unit)
         return Series(values, index=arg.index, name=arg.name, dtype='m8[ns]')
     elif is_list_like(arg):
-        return _convert_listlike(arg, box=box)
+        return _convert_listlike(arg, box=box, unit=unit)
 
     # ...so it must be a scalar value. Return scalar.
     return _coerce_scalar_to_timedelta_type(arg, unit=unit)
@@ -139,7 +143,7 @@ def convert(r=None, unit=None, m=m):
         return convert
 
     # no converter
-    raise ValueError("cannot create timedelta string converter")
+    raise ValueError("cannot create timedelta string converter for [{0}]".format(r))
 
 def _possibly_cast_to_timedelta(value, coerce=True):
     """ try to cast to timedelta64, if already a timedeltalike, then make

vb_suite/suite.py

@@ -26,6 +26,7 @@
            'reshape',
            'stat_ops',
            'timeseries',
+           'timedelta',
            'eval']
 
 by_module = {}

vb_suite/timedelta.py

@@ -0,0 +1,32 @@
+from vbench.api import Benchmark
+from datetime import datetime
+
+common_setup = """from pandas_vb_common import *
+from pandas import to_timedelta
+"""
+
+#----------------------------------------------------------------------
+# conversion
+
+setup = common_setup + """
+arr = np.random.randint(0,1000,size=10000)
+"""
+
+stmt = "to_timedelta(arr,unit='s')"
+timedelta_convert_int = Benchmark(stmt, setup, start_date=datetime(2014, 1, 1))
+
+setup = common_setup + """
+arr = np.random.randint(0,1000,size=10000)
+arr = [ '{0} days'.format(i) for i in arr ]
+"""
+
+stmt = "to_timedelta(arr)"
+timedelta_convert_string = Benchmark(stmt, setup, start_date=datetime(2014, 1, 1))
+
+setup = common_setup + """
+arr = np.random.randint(0,60,size=10000)
+arr = [ '00:00:{0:02d}'.format(i) for i in arr ]
+"""
+
+stmt = "to_timedelta(arr)"
+timedelta_convert_string_seconds = Benchmark(stmt, setup, start_date=datetime(2014, 1, 1))

vb_suite/timeseries.py

@@ -278,7 +278,7 @@ def date_range(start=None, end=None, periods=None, freq=None):
 """
 
 datetimeindex_converter = \
-    Benchmark('DatetimeConverter.convert(rng, None, None)', 
+    Benchmark('DatetimeConverter.convert(rng, None, None)',
               setup, start_date=datetime(2013, 1, 1))
 
 # Adding custom business day