ENH: Improve error message in corr/cov for Rolling/Expanding/EWM when…

… other isn't a DataFrame/Series (#41741)

doc/source/whatsnew/v1.3.0.rst

@@ -233,6 +233,7 @@ Other enhancements
 - Add keyword ``sort`` to :func:`pivot_table` to allow non-sorting of the result (:issue:`39143`)
 - Add keyword ``dropna`` to :meth:`DataFrame.value_counts` to allow counting rows that include ``NA`` values (:issue:`41325`)
 - :meth:`Series.replace` will now cast results to ``PeriodDtype`` where possible instead of ``object`` dtype (:issue:`41526`)
+- Improved error message in ``corr` and ``cov`` methods on :class:`.Rolling`, :class:`.Expanding`, and :class:`.ExponentialMovingWindow` when ``other`` is not a :class:`DataFrame` or :class:`Series` (:issue:`41741`)
 
 .. ---------------------------------------------------------------------------
 

pandas/core/window/common.py

@@ -1,7 +1,6 @@
 """Common utility functions for rolling operations"""
 from collections import defaultdict
 from typing import cast
-import warnings
 
 import numpy as np
 
@@ -15,17 +14,7 @@
 
 def flex_binary_moment(arg1, arg2, f, pairwise=False):
 
-    if not (
-        isinstance(arg1, (np.ndarray, ABCSeries, ABCDataFrame))
-        and isinstance(arg2, (np.ndarray, ABCSeries, ABCDataFrame))
-    ):
-        raise TypeError(
-            "arguments to moment function must be of type np.ndarray/Series/DataFrame"
-        )
-
-    if isinstance(arg1, (np.ndarray, ABCSeries)) and isinstance(
-        arg2, (np.ndarray, ABCSeries)
-    ):
+    if isinstance(arg1, ABCSeries) and isinstance(arg2, ABCSeries):
         X, Y = prep_binary(arg1, arg2)
         return f(X, Y)
 
@@ -43,31 +32,25 @@ def dataframe_from_int_dict(data, frame_template):
             if pairwise is False:
                 if arg1 is arg2:
                     # special case in order to handle duplicate column names
-                    for i, col in enumerate(arg1.columns):
+                    for i in range(len(arg1.columns)):
                         results[i] = f(arg1.iloc[:, i], arg2.iloc[:, i])
                     return dataframe_from_int_dict(results, arg1)
                 else:
                     if not arg1.columns.is_unique:
                         raise ValueError("'arg1' columns are not unique")
                     if not arg2.columns.is_unique:
                         raise ValueError("'arg2' columns are not unique")
-                    with warnings.catch_warnings(record=True):
-                        warnings.simplefilter("ignore", RuntimeWarning)
-                        X, Y = arg1.align(arg2, join="outer")
-                    X = X + 0 * Y
-                    Y = Y + 0 * X
-
-                    with warnings.catch_warnings(record=True):
-                        warnings.simplefilter("ignore", RuntimeWarning)
-                        res_columns = arg1.columns.union(arg2.columns)
+                    X, Y = arg1.align(arg2, join="outer")
+                    X, Y = prep_binary(X, Y)
+                    res_columns = arg1.columns.union(arg2.columns)
                     for col in res_columns:
                         if col in X and col in Y:
                             results[col] = f(X[col], Y[col])
                     return DataFrame(results, index=X.index, columns=res_columns)
             elif pairwise is True:
                 results = defaultdict(dict)
-                for i, k1 in enumerate(arg1.columns):
-                    for j, k2 in enumerate(arg2.columns):
+                for i in range(len(arg1.columns)):
+                    for j in range(len(arg2.columns)):
                         if j < i and arg2 is arg1:
                             # Symmetric case
                             results[i][j] = results[j][i]
@@ -85,10 +68,10 @@ def dataframe_from_int_dict(data, frame_template):
                     result = concat(
                         [
                             concat(
-                                [results[i][j] for j, c in enumerate(arg2.columns)],
+                                [results[i][j] for j in range(len(arg2.columns))],
                                 ignore_index=True,
                             )
-                            for i, c in enumerate(arg1.columns)
+                            for i in range(len(arg1.columns))
                         ],
                         ignore_index=True,
                         axis=1,
@@ -135,13 +118,10 @@ def dataframe_from_int_dict(data, frame_template):
                 )
 
                 return result
-
-            else:
-                raise ValueError("'pairwise' is not True/False")
         else:
             results = {
                 i: f(*prep_binary(arg1.iloc[:, i], arg2))
-                for i, col in enumerate(arg1.columns)
+                for i in range(len(arg1.columns))
             }
             return dataframe_from_int_dict(results, arg1)
 
@@ -165,11 +145,7 @@ def zsqrt(x):
 
 
 def prep_binary(arg1, arg2):
-    if not isinstance(arg2, type(arg1)):
-        raise Exception("Input arrays must be of the same type!")
-
     # mask out values, this also makes a common index...
     X = arg1 + 0 * arg2
     Y = arg2 + 0 * arg1
-
     return X, Y

pandas/core/window/rolling.py

@@ -472,6 +472,8 @@ def _apply_pairwise(
             other = target
             # only default unset
             pairwise = True if pairwise is None else pairwise
+        elif not isinstance(other, (ABCDataFrame, ABCSeries)):
+            raise ValueError("other must be a DataFrame or Series")
 
         return flex_binary_moment(target, other, func, pairwise=bool(pairwise))
 

pandas/tests/window/moments/test_moments_consistency_ewm.py

@@ -64,9 +64,9 @@ def test_different_input_array_raise_exception(name):
     A = Series(np.random.randn(50), index=np.arange(50))
     A[:10] = np.NaN
 
-    msg = "Input arrays must be of the same type!"
+    msg = "other must be a DataFrame or Series"
     # exception raised is Exception
-    with pytest.raises(Exception, match=msg):
+    with pytest.raises(ValueError, match=msg):
         getattr(A.ewm(com=20, min_periods=5), name)(np.random.randn(50))
 
 

pandas/tests/window/moments/test_moments_consistency_rolling.py

@@ -13,7 +13,6 @@
     Series,
 )
 import pandas._testing as tm
-from pandas.core.window.common import flex_binary_moment
 
 
 def _rolling_consistency_cases():
@@ -133,14 +132,6 @@ def test_rolling_corr_with_zero_variance(window):
     assert s.rolling(window=window).corr(other=other).isna().all()
 
 
-def test_flex_binary_moment():
-    # GH3155
-    # don't blow the stack
-    msg = "arguments to moment function must be of type np.ndarray/Series/DataFrame"
-    with pytest.raises(TypeError, match=msg):
-        flex_binary_moment(5, 6, None)
-
-
 def test_corr_sanity():
     # GH 3155
     df = DataFrame(