Revert "fix(waterdata): Force float64 dtype on physical-measurement columns"

This reverts commit 0f3b6ec.

Author: thodson-usgs

dataretrieval/waterdata/utils.py

@@ -896,15 +896,7 @@ def _type_cols(df: pd.DataFrame) -> pd.DataFrame:
         df[col] = pd.to_datetime(df[col], errors="coerce")
 
     for col in cols.intersection(numerical_cols):
-        # ``pd.to_numeric`` infers ``int64`` when every value happens to
-        # be integer-shaped (e.g. all-whole-CFS discharge values at a
-        # small stream). These are physical measurements — discharge,
-        # altitude, drainage area — and are continuous quantities by
-        # nature, so force ``float64`` to avoid surprise arithmetic
-        # surfaces (e.g. integer division on a chained subsequent
-        # query) and to keep dtype stable across sites with mixed
-        # whole vs fractional observations.
-        df[col] = pd.to_numeric(df[col], errors="coerce").astype("float64")
+        df[col] = pd.to_numeric(df[col], errors="coerce")
 
     return df
 

tests/waterdata_utils_test.py

@@ -12,7 +12,6 @@
     _format_api_dates,
     _get_args,
     _handle_stats_nesting,
-    _type_cols,
     _walk_pages,
 )
 
@@ -293,27 +292,6 @@ def test_get_stats_data_warning_includes_next_token(caplog, monkeypatch):
     assert any("tok2" in m for m in warnings_), warnings_
 
 
-def test_type_cols_value_is_always_float_even_for_whole_numbers():
-    """Regression: ``pd.to_numeric`` infers ``int64`` when every value
-    is integer-shaped. The USGS API returns whole-CFS discharge as
-    string-typed integers at small streams (e.g. ``"12"`` for 12 cfs),
-    so ``_type_cols`` would coerce ``value`` to ``int64`` and a
-    downstream chained query mixing this site with another that has
-    fractional readings would surface a confusing dtype upcast. Force
-    ``float64`` for every physical-measurement column."""
-    df = pd.DataFrame(
-        {
-            "value": ["12", "15", "18", "14", "11"],
-            "altitude": ["100", "200"] + [None] * 3,
-            "drainage_area": ["5", "10", "15", "20", "25"],
-        }
-    )
-    out = _type_cols(df)
-    assert out["value"].dtype == "float64"
-    assert out["altitude"].dtype == "float64"
-    assert out["drainage_area"].dtype == "float64"
-
-
 def test_handle_stats_nesting_tolerates_missing_drop_columns():
     """If the upstream stats response shape ever changes such that one of
     the columns we try to drop ("type", "properties.data") is absent, the