perf(core): vectorize the date conversion in basemodel_to_df

basemodel_to_df ran three per-element Python passes over the date column of
every payload it converted. Series.apply(to_datetime) calls pandas.to_datetime
once per row, and because each call receives a lone scalar with no format hint,
pandas also re-runs its datetime format guesser on every row.

Parse the column in one call instead, and use vectorized comparisons for the
all-midnight test and the reduction to datetime.date.

The element-wise parse is kept as a fallback for one case that the vectorized
path cannot represent: a column mixing UTC offsets, such as a series crossing a
daylight-savings boundary. Parsing per element lets each value keep its own
offset and yields an object column of tz-aware Timestamps, whereas a single
to_datetime call over that column raises "Mixed timezones detected". That
behaviour is covered by test_to_df_daylight_savings and is preserved exactly.

Measured on a payload built through EquityHistoricalData, median of 9 under an
exclusive CPU lock:

  250 daily bars     8.92 ms -> 1.46 ms
  1,000 daily bars  32.93 ms -> 3.13 ms
  5,000 daily bars 162.25 ms -> 12.23 ms

Author: dexhunter

openbb_platform/core/openbb_core/app/utils.py

@@ -44,9 +44,22 @@ def basemodel_to_df(
 
     # If the date column contains dates only, convert them to a date to avoid encoding time data.
     if "date" in df.columns:
-        df["date"] = df["date"].apply(to_datetime)
-        if all(t.time() == time(0, 0) for t in df["date"]):
-            df["date"] = df["date"].apply(lambda x: x.date())
+        try:
+            # Parse the column in a single call instead of once per row.
+            parsed = to_datetime(df["date"])
+        except ValueError:
+            # A column that mixes UTC offsets - a series crossing a daylight
+            # savings boundary, for instance - has no single datetime64
+            # representation, so fall back to parsing each value on its own.
+            parsed = df["date"].apply(to_datetime)
+
+        df["date"] = parsed
+
+        if parsed.dtype == object:
+            if all(t.time() == time(0, 0) for t in parsed):
+                df["date"] = parsed.apply(lambda x: x.date())
+        elif (parsed == parsed.dt.normalize()).all():
+            df["date"] = parsed.dt.date
 
     if index and index in df.columns:
         if index == "date":