Merge pull request statsmodels#8797 from bashtage/improve-warning-check

MAINT: Improve specificity of warning check

Author: bashtage

statsmodels/compat/pandas.py

@@ -31,12 +31,14 @@
     "get_cached_doc",
     "call_cached_func",
     "PD_LT_1_4",
+    "PD_LT_2",
 ]
 
 version = parse(pd.__version__)
 
 PD_LT_1_0_0 = version < Version("1.0.0")
 PD_LT_1_4 = version < Version("1.3.99")
+PD_LT_2 = version < Version("1.9.99")
 
 try:
     from pandas.api.types import is_numeric_dtype

statsmodels/imputation/tests/test_mice.py

@@ -121,7 +121,9 @@ def test_settingwithcopywarning(self):
             with warnings.catch_warnings(record=True) as ws:
                 warnings.simplefilter('always')
                 miceData.update_all()
-
+                # Only include pandas warnings. There are many from patsy
+                # and sometimes warnings from other packages here
+                ws = [w for w in ws if "\\pandas\\" in w.filename]
                 assert len(ws) == 0
 
     def test_next_sample(self):

statsmodels/stats/descriptivestats.py

@@ -1,11 +1,17 @@
 from statsmodels.compat.pandas import Appender, is_numeric_dtype
 from statsmodels.compat.scipy import SP_LT_19
-
+from statsmodels.compat.pandas import PD_LT_2
 from typing import Sequence, Union
 
 import numpy as np
 import pandas as pd
-from pandas.core.dtypes.common import is_categorical_dtype
+if PD_LT_2:
+    from pandas.core.dtypes.common import is_categorical_dtype
+else:
+    # After pandas 2 is the minium, use the isinstance check
+    def is_categorical_dtype(dtype):
+        return isinstance(dtype, pd.CategoricalDtype)
+
 from scipy import stats
 
 from statsmodels.iolib.table import SimpleTable
@@ -392,10 +398,10 @@ def numeric(self) -> pd.DataFrame:
             q = stats.norm.ppf(1.0 - self._alpha / 2)
 
         def _mode(ser):
-            if SP_LT_19:
-                mode_res = stats.mode(ser.dropna())
-            else:
-                mode_res = stats.mode(ser.dropna(), keepdims=True)
+            dtype = ser.dtype if isinstance(ser.dtype, np.dtype) else ser.dtype.numpy_dtype
+            ser_no_missing = ser.dropna().to_numpy(dtype=dtype)
+            kwargs = {} if SP_LT_19 else {"keepdims": True}
+            mode_res = stats.mode(ser_no_missing, **kwargs)
             # Changes in SciPy 1.10
             if np.isscalar(mode_res[0]):
                 return float(mode_res[0]), mode_res[1]