BUG: merge_asof raising ValueError for read-only ndarrays

doc/source/whatsnew/v2.1.0.rst

@@ -446,6 +446,7 @@ Reshaping
 ^^^^^^^^^
 - Bug in :func:`crosstab` when ``dropna=False`` would not keep ``np.nan`` in the result (:issue:`10772`)
 - Bug in :func:`merge_asof` raising ``KeyError`` for extension dtypes (:issue:`52904`)
+- Bug in :func:`merge_asof` raising ``ValueError`` for data backed by read-only ndarrays (:issue:`53513`)
 - Bug in :meth:`DataFrame.agg` and :meth:`Series.agg` on non-unique columns would return incorrect type when dist-like argument passed in (:issue:`51099`)
 - Bug in :meth:`DataFrame.idxmin` and :meth:`DataFrame.idxmax`, where the axis dtype would be lost for empty frames (:issue:`53265`)
 - Bug in :meth:`DataFrame.merge` not merging correctly when having ``MultiIndex`` with single level (:issue:`52331`)

pandas/_libs/join.pyi

@@ -50,28 +50,28 @@ def outer_join_indexer(
     npt.NDArray[np.intp],
 ]: ...
 def asof_join_backward_on_X_by_Y(
-    left_values: np.ndarray,  # asof_t[:]
-    right_values: np.ndarray,  # asof_t[:]
-    left_by_values: np.ndarray,  # by_t[:]
-    right_by_values: np.ndarray,  # by_t[:]
+    left_values: np.ndarray,  # ndarray[numeric_t]
+    right_values: np.ndarray,  # ndarray[numeric_t]
+    left_by_values: np.ndarray,  # ndarray[by_t]
+    right_by_values: np.ndarray,  # ndarray[by_t]
     allow_exact_matches: bool = ...,
     tolerance: np.number | float | None = ...,
     use_hashtable: bool = ...,
 ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
 def asof_join_forward_on_X_by_Y(
-    left_values: np.ndarray,  # asof_t[:]
-    right_values: np.ndarray,  # asof_t[:]
-    left_by_values: np.ndarray,  # by_t[:]
-    right_by_values: np.ndarray,  # by_t[:]
+    left_values: np.ndarray,  # ndarray[numeric_t]
+    right_values: np.ndarray,  # ndarray[numeric_t]
+    left_by_values: np.ndarray,  # ndarray[by_t]
+    right_by_values: np.ndarray,  # ndarray[by_t]
     allow_exact_matches: bool = ...,
     tolerance: np.number | float | None = ...,
     use_hashtable: bool = ...,
 ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ...
 def asof_join_nearest_on_X_by_Y(
-    left_values: np.ndarray,  # asof_t[:]
-    right_values: np.ndarray,  # asof_t[:]
-    left_by_values: np.ndarray,  # by_t[:]
-    right_by_values: np.ndarray,  # by_t[:]
+    left_values: np.ndarray,  # ndarray[numeric_t]
+    right_values: np.ndarray,  # ndarray[numeric_t]
+    left_by_values: np.ndarray,  # ndarray[by_t]
+    right_by_values: np.ndarray,  # ndarray[by_t]
     allow_exact_matches: bool = ...,
     tolerance: np.number | float | None = ...,
     use_hashtable: bool = ...,

pandas/_libs/join.pyx

@@ -679,10 +679,10 @@ ctypedef fused by_t:
     uint64_t
 
 
-def asof_join_backward_on_X_by_Y(numeric_t[:] left_values,
-                                 numeric_t[:] right_values,
-                                 by_t[:] left_by_values,
-                                 by_t[:] right_by_values,
+def asof_join_backward_on_X_by_Y(ndarray[numeric_t] left_values,
+                                 ndarray[numeric_t] right_values,
+                                 ndarray[by_t] left_by_values,
+                                 ndarray[by_t] right_by_values,
                                  bint allow_exact_matches=True,
                                  tolerance=None,
                                  bint use_hashtable=True):
@@ -756,10 +756,10 @@ def asof_join_backward_on_X_by_Y(numeric_t[:] left_values,
     return left_indexer, right_indexer
 
 
-def asof_join_forward_on_X_by_Y(numeric_t[:] left_values,
-                                numeric_t[:] right_values,
-                                by_t[:] left_by_values,
-                                by_t[:] right_by_values,
+def asof_join_forward_on_X_by_Y(ndarray[numeric_t] left_values,
+                                ndarray[numeric_t] right_values,
+                                ndarray[by_t] left_by_values,
+                                ndarray[by_t] right_by_values,
                                 bint allow_exact_matches=1,
                                 tolerance=None,
                                 bint use_hashtable=True):

pandas/core/reshape/merge.py

@@ -2142,13 +2142,13 @@ def injection(obj):
             # we've verified above that no nulls exist
             left_values = left_values._data
         elif isinstance(left_values, ExtensionArray):
-            left_values = np.array(left_values)
+            left_values = left_values.to_numpy()
 
         if isinstance(right_values, BaseMaskedArray):
             # we've verified above that no nulls exist
             right_values = right_values._data
         elif isinstance(right_values, ExtensionArray):
-            right_values = np.array(right_values)
+            right_values = right_values.to_numpy()
 
         # a "by" parameter requires special handling
         if self.left_by is not None:

pandas/tests/reshape/merge/test_merge_asof.py

@@ -1627,3 +1627,15 @@ def test_merge_asof_extension_dtype(dtype):
     )
     expected = expected.astype({"join_col": dtype})
     tm.assert_frame_equal(result, expected)
+
+
+def test_merge_asof_read_only_ndarray():
+    # GH 53513
+    left = pd.Series([2], index=[2], name="left")
+    right = pd.Series([1], index=[1], name="right")
+    # set to read-only
+    left.index.values.flags.writeable = False
+    right.index.values.flags.writeable = False
+    result = merge_asof(left, right, left_index=True, right_index=True)
+    expected = pd.DataFrame({"left": [2], "right": [1]}, index=[2])
+    tm.assert_frame_equal(result, expected)