Performance of maybe_box_datetimelike #30520

asv_bench/benchmarks/inference.py

@@ -1,6 +1,9 @@
+import datetime
+
 import numpy as np
 
-from pandas import DataFrame, Series, to_numeric
+from pandas import DataFrame, Series, Timestamp, to_numeric
+from pandas.core.common import maybe_box_datetimelike
 import pandas.util.testing as tm
 
 from .pandas_vb_common import lib, numeric_dtypes
@@ -52,7 +55,6 @@ def time_add_timedeltas(self, df):
 
 
 class ToNumeric:
-
     params = ["ignore", "coerce"]
     param_names = ["errors"]
 
@@ -73,7 +75,6 @@ def time_from_str(self, errors):
 
 
 class ToNumericDowncast:
-
     param_names = ["dtype", "downcast"]
     params = [
         [
@@ -121,4 +122,13 @@ def time_convert(self, data):
         lib.maybe_convert_numeric(data, set(), coerce_numeric=False)
 
 
-from .pandas_vb_common import setup  # noqa: F401 isort:skip
+class MaybeBoxDatetimelike:
+    def setup(self):
+        self.pd_timestamp = Timestamp.now()
+        self.py_timestamp = datetime.datetime.now()
+
+    def pd_datetime_box(self):
+        maybe_box_datetimelike(self.pd_timestamp)
+
+    def py_datetime_box(self):
+        maybe_box_datetimelike(self.py_timestamp)

pandas/core/common.py

@@ -72,7 +72,6 @@ def consensus_name_attr(objs):
 
 
 def maybe_box(indexer, values, obj, key):
-
     # if we have multiples coming back, box em
     if isinstance(values, np.ndarray):
         return obj[indexer.get_loc(key)]
@@ -84,6 +83,11 @@ def maybe_box(indexer, values, obj, key):
 def maybe_box_datetimelike(value):
     # turn a datetime like into a Timestamp/timedelta as needed
 
+    if isinstance(value, (tslibs.Timedelta, tslibs.Timestamp)) or (
+            value is tslibs.NaT
+    ):
+        return value
+
     if isinstance(value, (np.datetime64, datetime)):
         value = tslibs.Timestamp(value)
     elif isinstance(value, (np.timedelta64, timedelta)):
@@ -119,7 +123,7 @@ def is_bool_indexer(key: Any) -> bool:
     """
     na_msg = "cannot index with vector containing NA / NaN values"
     if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)) or (
-        is_array_like(key) and is_extension_array_dtype(key.dtype)
+            is_array_like(key) and is_extension_array_dtype(key.dtype)
     ):
         if key.dtype == np.object_:
             key = np.asarray(values_from_object(key))
@@ -215,7 +219,6 @@ def try_sort(iterable):
 
 
 def asarray_tuplesafe(values, dtype=None):
-
     if not (isinstance(values, (list, tuple)) or hasattr(values, "__array__")):
         values = list(values)
     elif isinstance(values, ABCIndexClass):
@@ -284,10 +287,10 @@ def is_null_slice(obj):
     We have a null slice.
     """
     return (
-        isinstance(obj, slice)
-        and obj.start is None
-        and obj.stop is None
-        and obj.step is None
+            isinstance(obj, slice)
+            and obj.start is None
+            and obj.stop is None
+            and obj.step is None
     )
 
 
@@ -304,7 +307,7 @@ def is_full_slice(obj, l):
     We have a full length slice.
     """
     return (
-        isinstance(obj, slice) and obj.start == 0 and obj.stop == l and obj.step is None
+            isinstance(obj, slice) and obj.start == 0 and obj.stop == l and obj.step is None
     )