ERR: lib.is_xyz_array errors, some ignored

[jbrockmendel]

See #18054

In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: from pandas._libs import lib
In [4]: values = np.array([2, 1, 1, 2], dtype=np.int64)    # The relevant arg in TestMergeCategorical.test_other_columns
In [5]: lib.is_bool_array(values)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
ValueError: Buffer dtype mismatch, expected 'Python object' but got 'long'
Exception ValueError: "Buffer dtype mismatch, expected 'Python object' but got 'long'" in 'pandas._libs.lib.is_bool_array' ignored
Out[5]: False

In [7]: lib.is_bool_array(values.astype(bool))
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
ValueError: Does not understand character buffer dtype format string ('?')
Exception ValueError: "Does not understand character buffer dtype format string ('?')" in 'pandas._libs.lib.is_bool_array' ignored
Out[7]: False

In [8]: lib.is_datetime_array(values)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-8-a5b7fc4b6701> in <module>()
----> 1 lib.is_datetime_array(values)

pandas/_libs/src/inference.pyx in pandas._libs.lib.is_datetime_array()

ValueError: Buffer dtype mismatch, expected 'Python object' but got 'long'

In [9]: lib.is_datetime_array(values.astype('datetime64[ns]'))
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-9-0c109014e134> in <module>()
----> 1 lib.is_datetime_array(values.astype('datetime64[ns]'))

pandas/_libs/src/inference.pyx in pandas._libs.lib.is_datetime_array()

ValueError: cannot include dtype 'M' in a buffer

In [11]: lib.is_integer_array(values)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
ValueError: Buffer dtype mismatch, expected 'Python object' but got 'long'
Exception ValueError: "Buffer dtype mismatch, expected 'Python object' but got 'long'" in 'pandas._libs.lib.is_integer_array' ignored
Out[11]: False

In [14]: lib.is_bytes_array(values)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
ValueError: Buffer dtype mismatch, expected 'Python object' but got 'long'
Exception ValueError: "Buffer dtype mismatch, expected 'Python object' but got 'long'" in 'pandas._libs.lib.is_bytes_array' ignored
Out[14]: False

In [15]: lib.is_bytes_array(values.astype(bytes))
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
ValueError: Buffer dtype mismatch, expected 'Python object' but got a string
Exception ValueError: "Buffer dtype mismatch, expected 'Python object' but got a string" in 'pandas._libs.lib.is_bytes_array' ignored
Out[15]: False

The closest thing to good news is that most of these functions are not used very much (at least not outside of inference.pyx, where maybe they behave better because of [mumble]). Four of them are explicitly tested in tests.dtypes.test_inference (is_datetime_array, is_datetime64_array, is_timdelta_array, is_timedelta64_array). Other than that, its just two uses of is_bool_array (core.common, core.internals) and one of is_datetime_array (in core.indexes.base)

[jreback]

this fixes

(pandas) bash-3.2$ git diff
diff --git a/pandas/_libs/src/inference.pyx b/pandas/_libs/src/inference.pyx
index 5d550148b..7dcb94b14 100644
--- a/pandas/_libs/src/inference.pyx
+++ b/pandas/_libs/src/inference.pyx
@@ -612,7 +612,7 @@ cdef class Validator:
         self.dtype = dtype
         self.skipna = skipna
 
-    cdef bint validate(self, object[:] values) except -1:
+    cdef bint validate(self, ndarray values) except -1:
         if not self.n:
             return False
 
@@ -628,7 +628,7 @@ cdef class Validator:
 
     @cython.wraparound(False)
     @cython.boundscheck(False)
-    cdef bint _validate(self, object[:] values) except -1:
+    cdef bint _validate(self, ndarray values) except -1:
         cdef:
             Py_ssize_t i
             Py_ssize_t n = self.n
@@ -641,7 +641,7 @@ cdef class Validator:
 
     @cython.wraparound(False)
     @cython.boundscheck(False)
-    cdef bint _validate_skipna(self, object[:] values) except -1:
+    cdef bint _validate_skipna(self, ndarray values) except -1:
         cdef:
             Py_ssize_t i
             Py_ssize_t n = self.n