Fix/na_values_GH59303

doc/source/whatsnew/v3.0.0.rst

@@ -627,6 +627,7 @@ I/O
 - Bug in :meth:`read_csv` causing segmentation fault when ``encoding_errors`` is not a string. (:issue:`59059`)
 - Bug in :meth:`read_csv` raising ``TypeError`` when ``index_col`` is specified and ``na_values`` is a dict containing the key ``None``. (:issue:`57547`)
 - Bug in :meth:`read_csv` raising ``TypeError`` when ``nrows`` and ``iterator`` are specified without specifying a ``chunksize``. (:issue:`59079`)
+- Bug in :meth:`read_csv` where the order of the ``na_values`` makes an inconsistency when ``na_values`` is a list non-string values. (:issue:`59303`)
 - Bug in :meth:`read_excel` raising ``ValueError`` when passing array of boolean values when ``dtype="boolean"``. (:issue:`58159`)
 - Bug in :meth:`read_json` not validating the ``typ`` argument to not be exactly ``"frame"`` or ``"series"`` (:issue:`59124`)
 - Bug in :meth:`read_stata` raising ``KeyError`` when input file is stored in big-endian format and contains strL data. (:issue:`58638`)

pandas/io/parsers/readers.py

@@ -1648,7 +1648,7 @@ def _clean_na_values(na_values, keep_default_na: bool = True, floatify: bool = T
             if keep_default_na:
                 v = set(v) | STR_NA_VALUES
 
-            na_values[k] = v
+            na_values[k] = _stringify_na_values(v, floatify)
         na_fvalues = {k: _floatify_na_values(v) for k, v in na_values.items()}
     else:
         if not is_list_like(na_values):

pandas/tests/io/parser/test_na_values.py

@@ -812,3 +812,21 @@ def test_bool_and_nan_to_float(all_parsers):
     result = parser.read_csv(StringIO(data), dtype="float")
     expected = DataFrame.from_dict({"0": [np.nan, 1.0, 0.0]})
     tm.assert_frame_equal(result, expected)
+
+
+@xfail_pyarrow
+@pytest.mark.parametrize(
+    "na_values",
+    [[-99.0, -99], [-99, -99.0]],
+)
+def test_na_values_dict_without_dtype(all_parsers, na_values):
+    parser = all_parsers
+    data = """A
+-99
+-99
+-99.0
+-99.0"""
+
+    result = parser.read_csv(StringIO(data), na_values=na_values)
+    expected = DataFrame({"A": [np.nan, np.nan, np.nan, np.nan]})
+    tm.assert_frame_equal(result, expected)