TST (string dtype): resolve xfails in common IO tests (#60320)

Author: jorisvandenbossche

pandas/tests/io/test_clipboard.py

@@ -3,8 +3,6 @@
 import numpy as np
 import pytest
 
-from pandas._config import using_string_dtype
-
 from pandas.errors import (
     PyperclipException,
     PyperclipWindowsException,
@@ -26,10 +24,6 @@
     init_qt_clipboard,
 )
 
-pytestmark = pytest.mark.xfail(
-    using_string_dtype(), reason="TODO(infer_string)", strict=False
-)
-
 
 def build_kwargs(sep, excel):
     kwargs = {}
@@ -351,7 +345,7 @@ def test_raw_roundtrip(self, data):
 
     @pytest.mark.parametrize("engine", ["c", "python"])
     def test_read_clipboard_dtype_backend(
-        self, clipboard, string_storage, dtype_backend, engine
+        self, clipboard, string_storage, dtype_backend, engine, using_infer_string
     ):
         # GH#50502
         if dtype_backend == "pyarrow":
@@ -396,6 +390,11 @@ def test_read_clipboard_dtype_backend(
             )
             expected["g"] = ArrowExtensionArray(pa.array([None, None]))
 
+        if using_infer_string:
+            expected.columns = expected.columns.astype(
+                pd.StringDtype(string_storage, na_value=np.nan)
+            )
+
         tm.assert_frame_equal(result, expected)
 
     def test_invalid_dtype_backend(self):

pandas/tests/io/test_common.py

@@ -140,7 +140,6 @@ def test_bytesiowrapper_returns_correct_bytes(self):
             assert result == data.encode("utf-8")
 
     # Test that pyarrow can handle a file opened with get_handle
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
     def test_get_handle_pyarrow_compat(self):
         pa_csv = pytest.importorskip("pyarrow.csv")
 
@@ -155,6 +154,8 @@ def test_get_handle_pyarrow_compat(self):
         s = StringIO(data)
         with icom.get_handle(s, "rb", is_text=False) as handles:
             df = pa_csv.read_csv(handles.handle).to_pandas()
+            # TODO will have to update this when pyarrow' to_pandas() is fixed
+            expected = expected.astype("object")
             tm.assert_frame_equal(df, expected)
             assert not s.closed
 
@@ -338,7 +339,6 @@ def test_read_fspath_all(self, reader, module, path, datapath):
             ("to_stata", {"time_stamp": pd.to_datetime("2019-01-01 00:00")}, "os"),
         ],
     )
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
     def test_write_fspath_all(self, writer_name, writer_kwargs, module):
         if writer_name in ["to_latex"]:  # uses Styler implementation
             pytest.importorskip("jinja2")
@@ -365,7 +365,7 @@ def test_write_fspath_all(self, writer_name, writer_kwargs, module):
                     expected = f_path.read()
                     assert result == expected
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string) hdf support")
     def test_write_fspath_hdf5(self):
         # Same test as write_fspath_all, except HDF5 files aren't
         # necessarily byte-for-byte identical for a given dataframe, so we'll
@@ -438,14 +438,13 @@ def test_unknown_engine(self):
         with tm.ensure_clean() as path:
             df = pd.DataFrame(
                 1.1 * np.arange(120).reshape((30, 4)),
-                columns=pd.Index(list("ABCD"), dtype=object),
-                index=pd.Index([f"i-{i}" for i in range(30)], dtype=object),
+                columns=pd.Index(list("ABCD")),
+                index=pd.Index([f"i-{i}" for i in range(30)]),
             )
             df.to_csv(path)
             with pytest.raises(ValueError, match="Unknown engine"):
                 pd.read_csv(path, engine="pyt")
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
     def test_binary_mode(self):
         """
         'encoding' shouldn't be passed to 'open' in binary mode.
@@ -455,8 +454,8 @@ def test_binary_mode(self):
         with tm.ensure_clean() as path:
             df = pd.DataFrame(
                 1.1 * np.arange(120).reshape((30, 4)),
-                columns=pd.Index(list("ABCD"), dtype=object),
-                index=pd.Index([f"i-{i}" for i in range(30)], dtype=object),
+                columns=pd.Index(list("ABCD")),
+                index=pd.Index([f"i-{i}" for i in range(30)]),
             )
             df.to_csv(path, mode="w+b")
             tm.assert_frame_equal(df, pd.read_csv(path, index_col=0))
@@ -473,8 +472,8 @@ def test_warning_missing_utf_bom(self, encoding, compression_):
         """
         df = pd.DataFrame(
             1.1 * np.arange(120).reshape((30, 4)),
-            columns=pd.Index(list("ABCD"), dtype=object),
-            index=pd.Index([f"i-{i}" for i in range(30)], dtype=object),
+            columns=pd.Index(list("ABCD")),
+            index=pd.Index([f"i-{i}" for i in range(30)]),
         )
         with tm.ensure_clean() as path:
             with tm.assert_produces_warning(UnicodeWarning, match="byte order mark"):
@@ -504,15 +503,14 @@ def test_is_fsspec_url():
     assert icom.is_fsspec_url("RFC-3986+compliant.spec://something")
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
 @pytest.mark.parametrize("encoding", [None, "utf-8"])
 @pytest.mark.parametrize("format", ["csv", "json"])
 def test_codecs_encoding(encoding, format):
     # GH39247
     expected = pd.DataFrame(
         1.1 * np.arange(120).reshape((30, 4)),
-        columns=pd.Index(list("ABCD"), dtype=object),
-        index=pd.Index([f"i-{i}" for i in range(30)], dtype=object),
+        columns=pd.Index(list("ABCD")),
+        index=pd.Index([f"i-{i}" for i in range(30)]),
     )
     with tm.ensure_clean() as path:
         with codecs.open(path, mode="w", encoding=encoding) as handle:
@@ -525,13 +523,12 @@ def test_codecs_encoding(encoding, format):
     tm.assert_frame_equal(expected, df)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
 def test_codecs_get_writer_reader():
     # GH39247
     expected = pd.DataFrame(
         1.1 * np.arange(120).reshape((30, 4)),
-        columns=pd.Index(list("ABCD"), dtype=object),
-        index=pd.Index([f"i-{i}" for i in range(30)], dtype=object),
+        columns=pd.Index(list("ABCD")),
+        index=pd.Index([f"i-{i}" for i in range(30)]),
     )
     with tm.ensure_clean() as path:
         with open(path, "wb") as handle:
@@ -556,8 +553,8 @@ def test_explicit_encoding(io_class, mode, msg):
     # wrong mode is requested
     expected = pd.DataFrame(
         1.1 * np.arange(120).reshape((30, 4)),
-        columns=pd.Index(list("ABCD"), dtype=object),
-        index=pd.Index([f"i-{i}" for i in range(30)], dtype=object),
+        columns=pd.Index(list("ABCD")),
+        index=pd.Index([f"i-{i}" for i in range(30)]),
     )
     with io_class() as buffer:
         with pytest.raises(TypeError, match=msg):

pandas/tests/io/test_compression.py

@@ -12,8 +12,6 @@
 import numpy as np
 import pytest
 
-from pandas._config import using_string_dtype
-
 from pandas.compat import is_platform_windows
 
 import pandas as pd
@@ -139,7 +137,6 @@ def test_compression_warning(compression_only):
                 df.to_csv(handles.handle, compression=compression_only)
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
 def test_compression_binary(compression_only):
     """
     Binary file handles support compression.
@@ -148,8 +145,8 @@ def test_compression_binary(compression_only):
     """
     df = pd.DataFrame(
         1.1 * np.arange(120).reshape((30, 4)),
-        columns=pd.Index(list("ABCD"), dtype=object),
-        index=pd.Index([f"i-{i}" for i in range(30)], dtype=object),
+        columns=pd.Index(list("ABCD")),
+        index=pd.Index([f"i-{i}" for i in range(30)]),
     )
 
     # with a file
@@ -180,8 +177,8 @@ def test_gzip_reproducibility_file_name():
     """
     df = pd.DataFrame(
         1.1 * np.arange(120).reshape((30, 4)),
-        columns=pd.Index(list("ABCD"), dtype=object),
-        index=pd.Index([f"i-{i}" for i in range(30)], dtype=object),
+        columns=pd.Index(list("ABCD")),
+        index=pd.Index([f"i-{i}" for i in range(30)]),
     )
     compression_options = {"method": "gzip", "mtime": 1}
 
@@ -203,8 +200,8 @@ def test_gzip_reproducibility_file_object():
     """
     df = pd.DataFrame(
         1.1 * np.arange(120).reshape((30, 4)),
-        columns=pd.Index(list("ABCD"), dtype=object),
-        index=pd.Index([f"i-{i}" for i in range(30)], dtype=object),
+        columns=pd.Index(list("ABCD")),
+        index=pd.Index([f"i-{i}" for i in range(30)]),
     )
     compression_options = {"method": "gzip", "mtime": 1}
 

pandas/tests/io/test_gcs.py

@@ -158,7 +158,6 @@ def assert_equal_zip_safe(result: bytes, expected: bytes, compression: str):
         assert result == expected
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
 @pytest.mark.parametrize("encoding", ["utf-8", "cp1251"])
 def test_to_csv_compression_encoding_gcs(
     gcs_buffer, compression_only, encoding, compression_to_extension
@@ -171,8 +170,8 @@ def test_to_csv_compression_encoding_gcs(
     """
     df = DataFrame(
         1.1 * np.arange(120).reshape((30, 4)),
-        columns=Index(list("ABCD"), dtype=object),
-        index=Index([f"i-{i}" for i in range(30)], dtype=object),
+        columns=Index(list("ABCD")),
+        index=Index([f"i-{i}" for i in range(30)]),
     )
 
     # reference of compressed and encoded file