Add asvs, modify tests for caches

asv_bench/benchmarks/timeseries.py

@@ -346,27 +346,21 @@ class ToDatetime(object):
 
     def setup(self):
         self.rng = date_range(start='1/1/2000', periods=10000, freq='D')
-        self.stringsD = Series((((self.rng.year * 10000) + (self.rng.month * 100)) + self.rng.day), dtype=np.int64).apply(str)
+        self.stringsD = Series(self.rng.strftime('%Y%m%d'))
 
         self.rng = date_range(start='1/1/2000', periods=20000, freq='H')
-        self.strings = [x.strftime('%Y-%m-%d %H:%M:%S') for x in self.rng]
-        self.strings_nosep = [x.strftime('%Y%m%d %H:%M:%S') for x in self.rng]
+        self.strings = self.rng.strftime('%Y-%m-%d %H:%M:%S').tolist()
+        self.strings_nosep = self.rng.strftime('%Y%m%d %H:%M:%S').tolist()
         self.strings_tz_space = [x.strftime('%Y-%m-%d %H:%M:%S') + ' -0800'
                                  for x in self.rng]
 
         self.s = Series((['19MAY11', '19MAY11:00:00:00'] * 100000))
         self.s2 = self.s.str.replace(':\\S+$', '')
-        self.dup_numeric_data_10_5 = Series([1000] * 100000)
-        self.dup_string_data_10_5 = ['2013-01-01 01:00:00'] * 100000
-        self.dup_datetime_data_10_5 = [dt.datetime(2010, 1, 1)] * 100000
 
-        self.dup_numeric_data_10_3 = Series([1000] * 100)
-        self.dup_string_data_10_3 = ['2013-01-01 01:00:00'] * 100
-        self.dup_datetime_data_10_3 = [dt.datetime(2010, 1, 1)] * 100
-
-        self.dup_numeric_data_10_7 = Series([1000] * 10**7)
-        self.dup_string_data_10_7 = ['2013-01-01 01:00:00'] * 10**7
-        self.dup_datetime_data_10_7 = [dt.datetime(2010, 1, 1)] * 10**7
+        self.unique_numeric_seconds = range(10000)
+        self.dup_numeric_seconds = [1000] * 10000
+        self.dup_string_dates = ['2000-02-11'] * 10000
+        self.dup_string_with_tz = ['2000-02-11 15:00:00-0800'] * 10000
 
     def time_format_YYYYMMDD(self):
         to_datetime(self.stringsD, format='%Y%m%d')
@@ -392,32 +386,23 @@ def time_format_exact(self):
     def time_format_no_exact(self):
         to_datetime(self.s, format='%d%b%y', exact=False)
 
-    def time_cache_dup_numeric_data_10_3(self):
-        to_datetime(self.dup_numeric_data_10_3, unit='s')
-
-    def time_cache_dup_datetime_data_10_3(self):
-        to_datetime(self.dup_datetime_data_10_3)
-
-    def time_cache_dup_string_data_10_3(self):
-        to_datetime(self.dup_string_data_10_3)
-
-    def time_cache_dup_numeric_data_10_5(self):
-        to_datetime(self.dup_numeric_data_10_5, unit='s')
+    def time_cache_with_unique_seconds_and unit(self):
+    	to_datetime(self.unique_numeric_seconds, unit='s')
 
-    def time_cache_dup_datetime_data_10_5(self):
-        to_datetime(self.dup_datetime_data_10_5)
+    def time_cache_with_dup_seconds_and_unit(self):
+    	to_datetime(self.dup_numeric_seconds, unit='s')
 
-    def time_cache_dup_string_data_10_5(self):
-        to_datetime(self.dup_string_data_10_5)
+    def time_cache_with_dup_string_dates(self):
+    	to_datetime(self.dup_string_dates)
 
-    def time_cache_dup_numeric_data_10_7(self):
-        to_datetime(self.dup_numeric_data_10_7, unit='s')
+    def time_cache_with_dup_string_dates_and_format(self):
+    	to_datetime(self.dup_string_dates, format='%Y-%m-%d')
 
-    def time_cache_dup_datetime_data_10_7(self):
-        to_datetime(self.dup_datetime_data_10_7)
+    def time_cache_with_dup_string_tzoffset_dates(self):
+    	to_datetime(self.dup_string_with_tz)
 
-    def time_cache_dup_string_data_10_7(self):
-        to_datetime(self.dup_string_data_10_7)
+    def time_cache_with_dup_string_tzoffset_dates_and_format(self):
+    	to_datetim(self.dup_string_with_tz, format='%Y-%m-%d %H:%M:%S%z')
 
 
 class Offsets(object):

pandas/core/tools/datetimes.py

@@ -261,7 +261,7 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
           origin.
 
         .. versionadded: 0.20.0
-    cache_datetime : boolean, default False
+    cache : boolean, default False
         If True, use a cache of unique, converted dates to apply the datetime
         conversion. Produces signficant speed-ups when parsing duplicate date.
 
@@ -355,7 +355,6 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
 
     def _convert_listlike(arg, box, format, name=None, tz=tz):
 
-        import pdb; pdb.set_trace()
         if isinstance(arg, (list, tuple)):
             arg = np.array(arg, dtype='O')
 
@@ -523,18 +522,12 @@ def _convert_listlike(arg, box, format, name=None, tz=tz):
 
     convert_cache = None
     if cache and is_list_like(arg):
-        # Create a cache only if there are more than 10k values and the user
-        # passes in datestrings
-        #min_cache_threshold = 10**5
-        #if len(arg) >= min_cache_threshold and is_string_dtype(arg):
-        # unique currently cannot determine dates that are out of bounds
-        # recurison errors with datetime
-        unique_dates = algorithms.unique(arg)
-        # Essentially they need to all be the same value
-        if len(unique_dates) != len(arg):
-            from pandas import Series
-            cache_data = _convert_listlike(unique_dates, False, format)
-            convert_cache = Series(cache_data, index=unique_dates)
+        if len(arg) >= 1000:
+            unique_dates = algorithms.unique(arg)
+            if len(unique_dates) != len(arg):
+                from pandas import Series
+                cache_dates = _convert_listlike(unique_dates, False, format)
+                convert_cache = Series(cache_dates, index=unique_dates)
 
     if isinstance(arg, tslib.Timestamp):
         result = arg