[Autoloop: perf-comparison] Iteration 416: benchmark QuarterEnd/QuarterBegin/BMonthEnd/BMonthBegin/BYearEnd/BYearBegin offsets

Run: https://github.com/githubnext/tsb/actions/runs/29923579451

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Author: github-actions[bot]

benchmarks/pandas/bench_business_offsets.py

@@ -0,0 +1,58 @@
+"""
+Benchmark: Business and Quarter date offsets — QuarterEnd, QuarterBegin,
+BMonthEnd, BMonthBegin, BYearEnd, BYearBegin.
+Mirrors tsb bench_business_offsets.ts.
+Dataset: 5,000 dates; 50 measured iterations.
+Outputs JSON: {"function": "business_offsets", "mean_ms": ..., "iterations": ..., "total_ms": ...}
+"""
+import json
+import time
+from datetime import datetime, timedelta, timezone
+
+import pandas as pd
+from pandas.tseries.offsets import (
+    BMonthBegin,
+    BMonthEnd,
+    BYearBegin,
+    BYearEnd,
+    QuarterBegin,
+    QuarterEnd,
+)
+
+SIZE = 5_000
+WARMUP = 5
+ITERATIONS = 50
+
+q_end = QuarterEnd(1)
+q_begin = QuarterBegin(1)
+bm_end = BMonthEnd(1)
+bm_begin = BMonthBegin(1)
+by_end = BYearEnd(1)
+by_begin = BYearBegin(1)
+
+base = datetime(2020, 1, 15, tzinfo=timezone.utc)
+dates = [base + timedelta(days=i) for i in range(SIZE)]
+ts_dates = [pd.Timestamp(d) for d in dates]
+
+for _ in range(WARMUP):
+    for d in ts_dates[:100]:
+        d + q_end
+        d + q_begin
+        d + bm_end
+        d + bm_begin
+        d + by_end
+        d + by_begin
+
+t0 = time.perf_counter()
+for _ in range(ITERATIONS):
+    for d in ts_dates:
+        d + q_end
+        d + q_begin
+        d + bm_end
+        d + bm_begin
+        d + by_end
+        d + by_begin
+total_ms = (time.perf_counter() - t0) * 1000
+mean_ms = total_ms / ITERATIONS
+
+print(json.dumps({"function": "business_offsets", "mean_ms": mean_ms, "iterations": ITERATIONS, "total_ms": total_ms}))

benchmarks/tsb/bench_business_offsets.ts

@@ -0,0 +1,56 @@
+/**
+ * Benchmark: Business and Quarter date offsets — QuarterEnd, QuarterBegin,
+ * BMonthEnd, BMonthBegin, BYearEnd, BYearBegin.
+ * Mirrors pandas.tseries.offsets quarter/business-month/business-year classes.
+ * Dataset: 5,000 dates; 50 measured iterations.
+ * Outputs JSON: {"function": "business_offsets", "mean_ms": ..., "iterations": ..., "total_ms": ...}
+ */
+import {
+  QuarterEnd,
+  QuarterBegin,
+  BMonthEnd,
+  BMonthBegin,
+  BYearEnd,
+  BYearBegin,
+} from "../../src/index.ts";
+
+const SIZE = 5_000;
+const WARMUP = 5;
+const ITERATIONS = 50;
+
+const qEnd = new QuarterEnd(1);
+const qBegin = new QuarterBegin(1);
+const bmEnd = new BMonthEnd(1);
+const bmBegin = new BMonthBegin(1);
+const byEnd = new BYearEnd(1);
+const byBegin = new BYearBegin(1);
+
+const base = new Date(Date.UTC(2020, 0, 15));
+const dates = Array.from({ length: SIZE }, (_, i) => new Date(base.getTime() + i * 86_400_000));
+
+for (let i = 0; i < WARMUP; i++) {
+  for (const d of dates.slice(0, 100)) {
+    qEnd.apply(d);
+    qBegin.apply(d);
+    bmEnd.apply(d);
+    bmBegin.apply(d);
+    byEnd.apply(d);
+    byBegin.apply(d);
+  }
+}
+
+const t0 = performance.now();
+for (let i = 0; i < ITERATIONS; i++) {
+  for (const d of dates) {
+    qEnd.apply(d);
+    qBegin.apply(d);
+    bmEnd.apply(d);
+    bmBegin.apply(d);
+    byEnd.apply(d);
+    byBegin.apply(d);
+  }
+}
+const total_ms = performance.now() - t0;
+const mean_ms = total_ms / ITERATIONS;
+
+console.log(JSON.stringify({ function: "business_offsets", mean_ms, iterations: ITERATIONS, total_ms }));