BENCH: implement asvs for ints_to_pydatetime

asv_bench/benchmarks/tslibs/tslib.py

@@ -0,0 +1,55 @@
+"""
+ipython analogue:
+
+tr = TimeIntsToPydatetime()
+mi = pd.MultiIndex.from_product(
+    tr.params[:-1] + ([str(x) for x in tr.params[-1]],)
+)
+df = pd.DataFrame(np.nan, index=mi, columns=["mean", "stdev"])
+for box in tr.params[0]:
+    for size in tr.params[1]:
+        for tz in tr.params[2]:
+            tr.setup(box, size, tz)
+            key = (box, size, str(tz))
+            print(key)
+            val = %timeit -o tr.time_ints_to_pydatetime(box, size, tz)
+            df.loc[key] = (val.average, val.stdev)
+"""
+from datetime import timedelta, timezone
+
+from dateutil.tz import gettz, tzlocal
+import numpy as np
+import pytz
+
+from pandas._libs.tslib import ints_to_pydatetime
+
+_tzs = [
+    None,
+    timezone.utc,
+    timezone(timedelta(minutes=60)),
+    pytz.timezone("US/Pacific"),
+    gettz("Asia/Tokyo"),
+    tzlocal(),
+]
+_sizes = [0, 1, 100, 10 ** 4, 10 ** 6]
+
+
+class TimeIntsToPydatetime:
+    params = (
+        ["time", "date", "datetime", "timestamp"],
+        _sizes,
+        _tzs,
+    )
+    param_names = ["box", "size", "tz"]
+    # TODO: fold? freq?
+
+    def setup(self, box, size, tz):
+        arr = np.random.randint(0, 10, size=size, dtype="i8")
+        self.i8data = arr
+
+    def time_ints_to_pydatetime(self, box, size, tz):
+        if box == "date":
+            # ints_to_pydatetime does not allow non-None tz with date;
+            #  this will mean doing some duplicate benchmarks
+            tz = None
+        ints_to_pydatetime(self.i8data, tz, box=box)