PERF: periodarr_to_dt64arr

asv_bench/benchmarks/tslibs/period.py

@@ -2,10 +2,15 @@
 Period benchmarks that rely only on tslibs.  See benchmarks.period for
 Period benchmarks that rely on other parts fo pandas.
 """
-from pandas import Period
+
+import numpy as np
+
+from pandas._libs.tslibs.period import Period, periodarr_to_dt64arr
 
 from pandas.tseries.frequencies import to_offset
 
+from .tslib import _sizes
+
 
 class PeriodProperties:
 
@@ -68,3 +73,34 @@ def setup(self, freq, is_offset):
 
     def time_period_constructor(self, freq, is_offset):
         Period("2012-06-01", freq=freq)
+
+
+class TimePeriodArrToDT64Arr:
+    params = [
+        _sizes,
+        [
+            1000,
+            1011,  # Annual - November End
+            2000,
+            2011,  # Quarterly - November End
+            3000,
+            4000,
+            4006,  # Weekly - Saturday End
+            5000,
+            6000,
+            7000,
+            8000,
+            9000,
+            10000,
+            11000,
+            12000,
+        ],
+    ]
+    param_names = ["size", "freq"]
+
+    def setup(self, size, freq):
+        arr = np.arange(10, dtype="i8").repeat(size // 10)
+        self.i8values = arr
+
+    def time_periodarray_to_dt64arr(self, size, freq):
+        periodarr_to_dt64arr(self.i8values, freq)

pandas/_libs/tslibs/period.pyx

@@ -56,6 +56,7 @@ from pandas._libs.tslibs.ccalendar cimport (
     get_days_in_month,
 )
 from pandas._libs.tslibs.ccalendar cimport c_MONTH_NUMBERS
+from pandas._libs.tslibs.conversion import ensure_datetime64ns
 
 from pandas._libs.tslibs.dtypes cimport (
     PeriodDtypeBase,
@@ -946,14 +947,34 @@ def periodarr_to_dt64arr(const int64_t[:] periodarr, int freq):
         int64_t[:] out
         Py_ssize_t i, l
 
-    l = len(periodarr)
+    if freq < 6000:  # i.e. FR_DAY, hard-code to avoid need to cast
+        l = len(periodarr)
+        out = np.empty(l, dtype="i8")
 
-    out = np.empty(l, dtype='i8')
+        # We get here with freqs that do not correspond to a datetime64 unit
+        for i in range(l):
+            out[i] = period_ordinal_to_dt64(periodarr[i], freq)
 
-    for i in range(l):
-        out[i] = period_ordinal_to_dt64(periodarr[i], freq)
+        return out.base  # .base to access underlying np.ndarray
 
-    return out.base  # .base to access underlying np.ndarray
+    else:
+        # Short-circuit for performance
+        if freq == FR_NS:
+            return periodarr.base
+
+        if freq == FR_US:
+            dta = periodarr.base.view("M8[us]")
+        elif freq == FR_MS:
+            dta = periodarr.base.view("M8[ms]")
+        elif freq == FR_SEC:
+            dta = periodarr.base.view("M8[s]")
+        elif freq == FR_MIN:
+            dta = periodarr.base.view("M8[m]")
+        elif freq == FR_HR:
+            dta = periodarr.base.view("M8[h]")
+        elif freq == FR_DAY:
+            dta = periodarr.base.view("M8[D]")
+        return ensure_datetime64ns(dta)
 
 
 cpdef int64_t period_asfreq(int64_t ordinal, int freq1, int freq2, bint end):