fix: nullable integer streaming stats on pandas < 2 (#192)

test-matrix (3.9) has failed on main since #187: with pandas 1.5, the
streaming state converted numeric columns via
pd.to_numeric(s).to_numpy(dtype="float64"), which raises "cannot convert to
'float64'-dtype NumPy array with missing values" for nullable (masked)
integer columns with NA. fd.clean_timeseries and StreamingCleaner hit it on
any Int*/UInt* column with gaps.

Pass na_value=np.nan there and in the time-series interpolation helper,
which has the same conversion. Also pin the alert step #188 added to
fetch-fixtures.yml, which #190's SHA sweep predated.

Author: kevincostner17

.github/workflows/fetch-fixtures.yml

@@ -33,7 +33,7 @@ jobs:
         run: pytest tests/test_online_datasets.py -m "not online" -q --no-cov
       - name: Open/refresh alert issue on failure
         if: failure() && github.event_name == 'schedule'
-        uses: actions/github-script@v7
+        uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3 # v9.0.0
         with:
           script: |
             const title = "weekly online fixture refresh failing";

src/freshdata/streaming/_state.py

@@ -107,7 +107,9 @@ def update(self, s: pd.Series) -> None:
             self.nunique_stat.update(np.array([float(s.nunique(dropna=True))]))
 
         if is_numeric_dtype(s) and not is_bool_dtype(s):
-            values = pd.to_numeric(s, errors="coerce").to_numpy(dtype="float64")
+            # na_value: nullable (masked) columns with missing cells refuse a
+            # plain float64 conversion on pandas < 2.
+            values = pd.to_numeric(s, errors="coerce").to_numpy(dtype="float64", na_value=np.nan)
             values = values[~np.isnan(values)]
             if values.size:
                 self._welford.update(values)

src/freshdata/streaming/_timeseries.py

@@ -428,18 +428,19 @@ def _interp_series(s: pd.Series, ts: pd.Series, method: str) -> pd.Series:
             return s.ffill()
         if method == "bfill":
             return s.bfill()
+        # na_value: nullable (masked) columns with missing cells refuse a plain
+        # float64 conversion on pandas < 2.
+        values = s.to_numpy(dtype="float64", na_value=np.nan)
         if method == "time":
-            tmp = pd.Series(s.to_numpy(dtype="float64"),
-                            index=pd.DatetimeIndex(ts))
+            tmp = pd.Series(values, index=pd.DatetimeIndex(ts))
             try:
                 out = tmp.interpolate(method="time", limit_area="inside")
             except ValueError:  # non-monotonic / non-datetime index → linear fallback
-                out = pd.Series(s.to_numpy(dtype="float64")).interpolate(
-                    method="linear", limit_area="inside")
+                out = pd.Series(values).interpolate(method="linear", limit_area="inside")
             return pd.Series(out.to_numpy(), index=s.index)
         # linear
-        return pd.Series(s.to_numpy(dtype="float64"),
-                         index=s.index).interpolate(method="linear", limit_area="inside")
+        return pd.Series(values, index=s.index).interpolate(
+            method="linear", limit_area="inside")
 
     # -- step 3: seasonal imputation -------------------------------------------