Backport PR #46325: Regression in read csv causing segfault for invalid file input (#46333)

Co-authored-by: Patrick Hoefler <[email protected]>

Author: meeseeksmachine

doc/source/whatsnew/v1.4.2.rst

@@ -15,6 +15,7 @@ including other versions of pandas.
 Fixed regressions
 ~~~~~~~~~~~~~~~~~
 - Fixed regression in :meth:`DataFrame.drop` and :meth:`Series.drop` when :class:`Index` had extension dtype and duplicates (:issue:`45860`)
+- Fixed regression in :func:`read_csv` killing python process when invalid file input was given for ``engine="c"`` (:issue:`45957`)
 - Fixed memory performance regression in :meth:`Series.fillna` when called on a :class:`DataFrame` column with ``inplace=True`` (:issue:`46149`)
 - Provided an alternative solution for passing custom Excel formats in :meth:`.Styler.to_excel`, which was a regression based on stricter CSS validation. Examples available in the documentation for :meth:`.Styler.format` (:issue:`46152`)
 - Fixed regression in :meth:`DataFrame.loc.__setitem__` losing :class:`MultiIndex` names if :class:`DataFrame`  was empty before (:issue:`46317`)

pandas/io/parsers/readers.py

@@ -1227,6 +1227,10 @@ def _make_engine(
             assert self.handles is not None
             f = self.handles.handle
 
+        elif engine != "python":
+            msg = f"Invalid file path or buffer object type: {type(f)}"
+            raise ValueError(msg)
+
         try:
             return mapping[engine](f, **self.options)
         except Exception:

pandas/tests/io/parser/test_unsupported.py

@@ -189,3 +189,13 @@ def test_close_file_handle_on_invalide_usecols(all_parsers):
                 parser.read_csv(fname, usecols=["col1", "col2", "col3"])
         # unlink fails on windows if file handles still point to it
         os.unlink(fname)
+
+
+def test_invalid_file_inputs(all_parsers):
+    # GH#45957
+    parser = all_parsers
+    if parser.engine == "python":
+        pytest.skip("Python engine supports lists.")
+
+    with pytest.raises(ValueError, match="Invalid"):
+        parser.read_csv([])