fix(utils): Suppress pandas date parsing warnings in normalize_dttm_col (#35042)

superset/utils/core.py

@@ -34,6 +34,7 @@ import tempfile
 import threading
 import traceback
 import uuid
+import warnings
 import zlib
 from collections.abc import Iterable, Iterator, Sequence
 from contextlib import closing, contextmanager
@@ -110,6 +111,7 @@ from superset.utils.backports import StrEnum
 from superset.utils.database import get_example_database
 from superset.utils.date_parser import parse_human_timedelta
 from superset.utils.hashing import md5_sha_from_dict, md5_sha_from_str
+from superset.utils.pandas import detect_datetime_format
 
 if TYPE_CHECKING:
     from superset.connectors.sqla.models import BaseDatasource, TableColumn
@@ -1858,6 +1860,62 @@ class DateColumn:
         )
 
 
+def _process_datetime_column(
+    df: pd.DataFrame,
+    col: DateColumn,
+) -> None:
+    """Process a single datetime column with format detection."""
+    if col.timestamp_format in ("epoch_s", "epoch_ms"):
+        dttm_series = df[col.col_label]
+        if is_numeric_dtype(dttm_series):
+            # Column is formatted as a numeric value
+            unit = col.timestamp_format.replace("epoch_", "")
+            df[col.col_label] = pd.to_datetime(
+                dttm_series,
+                utc=False,
+                unit=unit,
+                origin="unix",
+                errors="coerce",
+                exact=False,
+            )
+        else:
+            # Column has already been formatted as a timestamp.
+            try:
+                df[col.col_label] = dttm_series.apply(
+                    lambda x: pd.Timestamp(x) if pd.notna(x) else pd.NaT
+                )
+            except ValueError:
+                logger.warning(
+                    "Unable to convert column %s to datetime, ignoring",
+                    col.col_label,
+                )
+    else:
+        # Try to detect format if not specified
+        format_to_use = col.timestamp_format or detect_datetime_format(
+            df[col.col_label]
+        )
+
+        # Parse with or without format (suppress warning if no format)
+        if format_to_use:
+            df[col.col_label] = pd.to_datetime(
+                df[col.col_label],
+                utc=False,
+                format=format_to_use,
+                errors="coerce",
+                exact=False,
+            )
+        else:
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", message=".*Could not infer format.*")
+                df[col.col_label] = pd.to_datetime(
+                    df[col.col_label],
+                    utc=False,
+                    format=None,
+                    errors="coerce",
+                    exact=False,
+                )
+
+
 def normalize_dttm_col(
     df: pd.DataFrame,
     dttm_cols: tuple[DateColumn, ...] = tuple(),  # noqa: C408
@@ -1866,38 +1924,8 @@ def normalize_dttm_col(
         if _col.col_label not in df.columns:
             continue
 
-        if _col.timestamp_format in ("epoch_s", "epoch_ms"):
-            dttm_series = df[_col.col_label]
-            if is_numeric_dtype(dttm_series):
-                # Column is formatted as a numeric value
-                unit = _col.timestamp_format.replace("epoch_", "")
-                df[_col.col_label] = pd.to_datetime(
-                    dttm_series,
-                    utc=False,
-                    unit=unit,
-                    origin="unix",
-                    errors="coerce",
-                    exact=False,
-                )
-            else:
-                # Column has already been formatted as a timestamp.
-                try:
-                    df[_col.col_label] = dttm_series.apply(
-                        lambda x: pd.Timestamp(x) if pd.notna(x) else pd.NaT
-                    )
-                except ValueError:
-                    logger.warning(
-                        "Unable to convert column %s to datetime, ignoring",
-                        _col.col_label,
-                    )
-        else:
-            df[_col.col_label] = pd.to_datetime(
-                df[_col.col_label],
-                utc=False,
-                format=_col.timestamp_format,
-                errors="coerce",
-                exact=False,
-            )
+        _process_datetime_column(df, _col)
+
         if _col.offset:
             df[_col.col_label] += timedelta(hours=_col.offset)
         if _col.time_shift is not None: