Repository: pandas-dev/pandas (python)

Issue:
BUG: `to_latex()` does not handle braces in new headers gracefully
### Pandas version checks

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.


### Reproducible Example

```python
import pandas as pd

df = pd.DataFrame({"a": [1, 2]})
result = df.to_latex(header=[r"$\bar{y}$"])
```


### Issue Description

This gives a `KeyError`. I looked into the reason, and it happens because at some point `r"$\bar{y}$".format(x)` is called, and the braces are interpreted as string interpolation.

### Expected Behavior

I expect one of the following:
1. An error message saying that single braces should be escaped using `{{`
2. Documentation stating the same thing
3. That the single brace is not interpreted as string interpolation

### Installed Versions

<details>

>>> pd.show_versions()

INSTALLED VERSIONS
------------------
commit                : 2a10e04a099d5f1633abcdfbb2dd9fdf09142f8d
python                : 3.10.15
python-bits           : 64
OS                    : Linux
OS-release            : 6.8.0-45-generic
Version               : #45-Ubuntu SMP PREEMPT_DYNAMIC Fri Aug 30 12:02:04 UTC 2024
machine               : x86_64
processor             : x86_64
byteorder             : little
LC_ALL                : None
LANG                  : en_US.UTF-8
LOCALE                : en_US.UTF-8

pandas                : 3.0.0.dev0+1579.g2a10e04a09
numpy                 : 1.26.4
dateutil              : 2.9.0
pip                   : 24.2
Cython                : 3.0.11
sphinx                : 8.1.3
IPython               : 8.28.0
adbc-driver-postgresql: None
adbc-driver-sqlite    : None
bs4                   : 4.12.3
blosc                 : None
bottleneck            : 1.4.1
fastparquet           : 2024.5.0
fsspec                : 2024.9.0
html5lib              : 1.1
hypothesis            : 6.115.2
gcsfs                 : 2024.9.0post1
jinja2                : 3.1.4
lxml.etree            : 5.3.0
matplotlib            : 3.9.2
numba                 : 0.60.0
numexpr               : 2.10.1
odfpy                 : None
openpyxl              : 3.1.5
psycopg2              : 2.9.9
pymysql               : 1.4.6
pyarrow               : 17.0.0
pyreadstat            : 1.2.7
pytest                : 8.3.3
python-calamine       : None
pytz                  : 2024.2
pyxlsb                : 1.0.10
s3fs                  : 2024.9.0
scipy                 : 1.14.1
sqlalchemy            : 2.0.36
tables                : 3.10.1
tabulate              : 0.9.0
xarray                : 2024.9.0
xlrd                  : 2.0.1
xlsxwriter            : 3.2.0
zstandard             : 0.23.0
tzdata                : 2024.2
qtpy                  : None
pyqt5                 : None


</details>


Pull request description:
DOC: to_latex braces in headers must be escaped (#60063)
- [x] closes #60063
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.

Any braces used in the headers must be escaped with `{{` or `}}`, and it is not trivial to figure out what goes wrong. I have added that the braces must be escaped, as agreed in the linked issue.

Patch:
diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 30f0ec226d..768e05b16c 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -85,8 +85,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Timestamp.resolution PR02" \
         -i "pandas.Timestamp.tzinfo GL08" \
         -i "pandas.Timestamp.year GL08" \
-        -i "pandas.api.types.is_integer PR01,SA01" \
-        -i "pandas.api.types.is_iterator PR07,SA01" \
         -i "pandas.api.types.is_re_compilable PR07,SA01" \
         -i "pandas.api.types.pandas_dtype PR07,RT03,SA01" \
         -i "pandas.arrays.ArrowExtensionArray PR07,SA01" \
@@ -123,11 +121,9 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.resample.Resampler.quantile PR01,PR07" \
         -i "pandas.core.resample.Resampler.sem SA01" \
         -i "pandas.core.resample.Resampler.std SA01" \
-        -i "pandas.core.resample.Resampler.sum SA01" \
         -i "pandas.core.resample.Resampler.transform PR01,RT03,SA01" \
         -i "pandas.core.resample.Resampler.var SA01" \
         -i "pandas.errors.AttributeConflictWarning SA01" \
-        -i "pandas.errors.CSSWarning SA01" \
         -i "pandas.errors.ChainedAssignmentError SA01" \
         -i "pandas.errors.DataError SA01" \
         -i "pandas.errors.DuplicateLabelError SA01" \
@@ -136,21 +132,17 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.errors.NullFrequencyError SA01" \
         -i "pandas.errors.NumExprClobberingError SA01" \
         -i "pandas.errors.NumbaUtilError SA01" \
-        -i "pandas.errors.OptionError SA01" \
         -i "pandas.errors.OutOfBoundsTimedelta SA01" \
         -i "pandas.errors.PerformanceWarning SA01" \
         -i "pandas.errors.PossibleDataLossError SA01" \
         -i "pandas.errors.UndefinedVariableError PR01,SA01" \
         -i "pandas.errors.UnsortedIndexError SA01" \
-        -i "pandas.errors.UnsupportedFunctionCall SA01" \
         -i "pandas.errors.ValueLabelTypeMismatch SA01" \
         -i "pandas.infer_freq SA01" \
         -i "pandas.io.json.build_table_schema PR07,RT03,SA01" \
         -i "pandas.io.stata.StataWriter.write_file SA01" \
-        -i "pandas.json_normalize RT03,SA01" \
         -i "pandas.plotting.andrews_curves RT03,SA01" \
         -i "pandas.plotting.scatter_matrix PR07,SA01" \
-        -i "pandas.set_eng_float_format RT03,SA01" \
         -i "pandas.tseries.offsets.BDay PR02,SA01" \
         -i "pandas.tseries.offsets.BQuarterBegin.is_on_offset GL08" \
         -i "pandas.tseries.offsets.BQuarterBegin.n GL08" \
@@ -296,7 +288,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.tseries.offsets.Second.is_on_offset GL08" \
         -i "pandas.tseries.offsets.Second.n GL08" \
         -i "pandas.tseries.offsets.Second.normalize GL08" \
-        -i "pandas.tseries.offsets.SemiMonthBegin SA01" \
         -i "pandas.tseries.offsets.SemiMonthBegin.day_of_month GL08" \
         -i "pandas.tseries.offsets.SemiMonthBegin.is_on_offset GL08" \
         -i "pandas.tseries.offsets.SemiMonthBegin.n GL08" \
diff --git a/doc/source/development/contributing_codebase.rst b/doc/source/development/contributing_codebase.rst
index 670ffe6996..c1cfb0d7a6 100644
--- a/doc/source/development/contributing_codebase.rst
+++ b/doc/source/development/contributing_codebase.rst
@@ -298,6 +298,12 @@ So, before actually writing any code, you should write your tests.  Often the te
 taken from the original GitHub issue.  However, it is always worth considering additional
 use cases and writing corresponding tests.
 
+We use `code coverage <https://en.wikipedia.org/wiki/Code_coverage>`_ to help understand
+the amount of code which is covered by a test. We recommend striving to ensure code
+you add or change within Pandas is covered by a test. Please see our
+`code coverage dashboard through Codecov <https://app.codecov.io/github/pandas-dev/pandas>`_
+for more information.
+
 Adding tests is one of the most common requests after code is pushed to pandas.  Therefore,
 it is worth getting in the habit of writing tests ahead of time so this is never an issue.
 
diff --git a/pandas/_config/config.py b/pandas/_config/config.py
index 25760df6bd..1d57aa806e 100644
--- a/pandas/_config/config.py
+++ b/pandas/_config/config.py
@@ -105,6 +105,10 @@ class OptionError(AttributeError, KeyError):
 
     Backwards compatible with KeyError checks.
 
+    See Also
+    --------
+    options : Access and modify global pandas settings.
+
     Examples
     --------
     >>> pd.options.context
diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index 23e0f38746..de603beff7 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -259,15 +259,23 @@ def is_iterator(obj: object) -> bool:
     Check if the object is an iterator.
 
     This is intended for generators, not list-like objects.
+    This method checks whether the passed object is an iterator. It
+    returns `True` if the object is an iterator, and `False` otherwise.
 
     Parameters
     ----------
     obj : The object to check
+        The object to check for iterator type.
 
     Returns
     -------
     is_iter : bool
         Whether `obj` is an iterator.
+        `True` if the object is of iterator type, otherwise `False`.
+
+    See Also
+    --------
+    api.types.is_list_like : Check if the input is list-like.
 
     Examples
     --------
@@ -1122,9 +1130,23 @@ def is_integer(obj: object) -> bool:
     """
     Return True if given object is integer.
 
+    This method checks whether the passed object is an integer type. It
+    returns `True` if the object is an integer, and `False` otherwise.
+
+    Parameters
+    ----------
+    obj : object
+        The object to check for integer type.
+
     Returns
     -------
     bool
+        `True` if the object is of integer type, otherwise `False`.
+
+    See Also
+    --------
+    api.types.is_float : Check if an object is of float type.
+    api.types.is_numeric_dtype : Check if an object is of numeric type.
 
     Examples
     --------
diff --git a/pandas/_libs/tslibs/offsets.pyx b/pandas/_libs/tslibs/offsets.pyx
index 4db96fbaa3..7569f8e886 100644
--- a/pandas/_libs/tslibs/offsets.pyx
+++ b/pandas/_libs/tslibs/offsets.pyx
@@ -3371,6 +3371,10 @@ cdef class SemiMonthBegin(SemiMonthOffset):
     """
     Two DateOffset's per month repeating on the first day of the month & day_of_month.
 
+    This offset moves dates to the first day of the month and an additional specified
+    day (typically the 15th by default), useful in scenarios where bi-monthly processing
+    occurs on set days.
+
     Attributes
     ----------
     n : int, default 1
@@ -3380,6 +3384,13 @@ cdef class SemiMonthBegin(SemiMonthOffset):
     day_of_month : int, {1, 3,...,27}, default 15
         A specific integer for the day of the month.
 
+    See Also
+    --------
+    tseries.offsets.SemiMonthEnd : Two DateOffset's per month repeating on the last day
+        of the month & day_of_month.
+    tseries.offsets.MonthEnd : Offset to the last calendar day of the month.
+    tseries.offsets.MonthBegin : Offset to the first calendar day of the month.
+
     Examples
     --------
     >>> ts = pd.Timestamp(2022, 1, 1)
diff --git a/pandas/_libs/tslibs/timedeltas.pyx b/pandas/_libs/tslibs/timedeltas.pyx
index bbefea7c47..15b629624b 100644
--- a/pandas/_libs/tslibs/timedeltas.pyx
+++ b/pandas/_libs/tslibs/timedeltas.pyx
@@ -1864,10 +1864,12 @@ class Timedelta(_Timedelta):
 
     Parameters
     ----------
-    value : Timedelta, timedelta, np.timedelta64, str, or int
+    value : Timedelta, timedelta, np.timedelta64, str, int or float
         Input value.
     unit : str, default 'ns'
-        Denote the unit of the input, if input is an integer.
+        If input is an integer, denote the unit of the input.
+        If input is a float, denote the unit of the integer parts.
+        The decimal parts with resolution lower than 1 nanosecond are ignored.
 
         Possible values:
 
@@ -2176,8 +2178,10 @@ class Timedelta(_Timedelta):
         Parameters
         ----------
         freq : str
-            Frequency string indicating the ceiling resolution.
-            It uses the same units as class constructor :class:`~pandas.Timedelta`.
+            Frequency string indicating the ceiling resolution. Must be a fixed
+            frequency like 's' (second) not 'ME' (month end). See
+            :ref:`frequency aliases <timeseries.offset_aliases>` for
+            a list of possible `freq` values.
 
         Returns
         -------
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 24a164aa15..c4defdb243 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -718,7 +718,7 @@ class DataFrame(NDFrame, OpsMixin):
                     "is deprecated and will raise in a future version. "
                     "Use public APIs instead.",
                     DeprecationWarning,
-                    stacklevel=1,  # bump to 2 once pyarrow 15.0 is released with fix
+                    stacklevel=2,
                 )
 
             data = data.copy(deep=False)
@@ -10823,7 +10823,7 @@ class DataFrame(NDFrame, OpsMixin):
         self, decimals: int | dict[IndexLabel, int] | Series = 0, *args, **kwargs
     ) -> DataFrame:
         """
-        Round a DataFrame to a variable number of decimal places.
+        Round numeric columns in a DataFrame to a variable number of decimal places.
 
         Parameters
         ----------
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 42516f0a85..1759e1ef91 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -3339,7 +3339,7 @@ class NDFrame(PandasObject, indexing.IndexingMixin):
             The subset of columns to write. Writes all columns by default.
         header : bool or list of str, default True
             Write out the column names. If a list of strings is given,
-            it is assumed to be aliases for the column names.
+            it is assumed to be aliases for the column names. Braces must be escaped.
         index : bool, default True
             Write row names (index).
         na_rep : str, default 'NaN'
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 42fed83398..ca4d3fc768 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -1021,6 +1021,10 @@ class Resampler(BaseGroupBy, PandasObject):
         """
         Compute sum of group values.
 
+        This method provides a simple way to compute the sum of values within each
+        resampled group, particularly useful for aggregating time-based data into
+        daily, monthly, or yearly sums.
+
         Parameters
         ----------
         numeric_only : bool, default False
@@ -1039,6 +1043,14 @@ class Resampler(BaseGroupBy, PandasObject):
         Series or DataFrame
             Computed sum of values within each group.
 
+        See Also
+        --------
+        core.resample.Resampler.mean : Compute mean of groups, excluding missing values.
+        core.resample.Resampler.count : Compute count of group, excluding missing
+            values.
+        DataFrame.resample : Resample time-series data.
+        Series.sum : Return the sum of the values over the requested axis.
+
         Examples
         --------
         >>> ser = pd.Series(
diff --git a/pandas/core/strings/accessor.py b/pandas/core/strings/accessor.py
index 3cb0e75cfb..05e1a36877 100644
--- a/pandas/core/strings/accessor.py
+++ b/pandas/core/strings/accessor.py
@@ -255,7 +255,9 @@ class StringMethods(NoNewAttributesMixin):
         inferred_dtype = lib.infer_dtype(values, skipna=True)
 
         if inferred_dtype not in allowed_types:
-            raise AttributeError("Can only use .str accessor with string values!")
+            raise AttributeError(
+                f"Can only use .str accessor with string values, not {inferred_dtype}"
+            )
         return inferred_dtype
 
     def __getitem__(self, key):
diff --git a/pandas/errors/__init__.py b/pandas/errors/__init__.py
index b6df34e33e..0aaee1ec17 100644
--- a/pandas/errors/__init__.py
+++ b/pandas/errors/__init__.py
@@ -76,6 +76,12 @@ class UnsupportedFunctionCall(ValueError):
 
     For example, ``np.cumsum(groupby_object)``.
 
+    See Also
+    --------
+    DataFrame.groupby : Group DataFrame using a mapper or by a Series of columns.
+    Series.groupby : Group Series using a mapper or by a Series of columns.
+    core.groupby.GroupBy.cumsum : Compute cumulative sum for each group.
+
     Examples
     --------
     >>> df = pd.DataFrame(
@@ -591,6 +597,14 @@ class CSSWarning(UserWarning):
     This can be due to the styling not having an equivalent value or because the
     styling isn't properly formatted.
 
+    See Also
+    --------
+    DataFrame.style : Returns a Styler object for applying CSS-like styles.
+    io.formats.style.Styler : Helps style a DataFrame or Series according to the
+        data with HTML and CSS.
+    io.formats.style.Styler.to_excel : Export styled DataFrame to Excel.
+    io.formats.style.Styler.to_html : Export styled DataFrame to HTML.
+
     Examples
     --------
     >>> df = pd.DataFrame({"A": [1, 1, 1]})
diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py
index 5aecc6af71..861f5885f8 100644
--- a/pandas/io/formats/format.py
+++ b/pandas/io/formats/format.py
@@ -1926,6 +1926,9 @@ def set_eng_float_format(accuracy: int = 3, use_eng_prefix: bool = False) -> Non
     """
     Format float representation in DataFrame with SI notation.
 
+    Sets the floating-point display format for ``DataFrame`` objects using engineering
+    notation (SI units), allowing easier readability of values across wide ranges.
+
     Parameters
     ----------
     accuracy : int, default 3
@@ -1936,6 +1939,13 @@ def set_eng_float_format(accuracy: int = 3, use_eng_prefix: bool = False) -> Non
     Returns
     -------
     None
+        This method does not return a value. it updates the global display format
+        for floats in DataFrames.
+
+    See Also
+    --------
+    set_option : Set the value of the specified option or options.
+    reset_option : Reset one or more options to their default value.
 
     Examples
     --------
diff --git a/pandas/io/json/_normalize.py b/pandas/io/json/_normalize.py
index 7d3eefae39..45c8876dbe 100644
--- a/pandas/io/json/_normalize.py
+++ b/pandas/io/json/_normalize.py
@@ -279,6 +279,10 @@ def json_normalize(
     """
     Normalize semi-structured JSON data into a flat table.
 
+    This method is designed to transform semi-structured JSON data, such as nested
+    dictionaries or lists, into a flat table. This is particularly useful when
+    handling JSON-like data structures that contain deeply nested fields.
+
     Parameters
     ----------
     data : dict, list of dicts, or Series of dicts
@@ -310,8 +314,13 @@ def json_normalize(
 
     Returns
     -------
-    frame : DataFrame
-    Normalize semi-structured JSON data into a flat table.
+    DataFrame
+        The normalized data, represented as a pandas DataFrame.
+
+    See Also
+    --------
+    DataFrame : Two-dimensional, size-mutable, potentially heterogeneous tabular data.
+    Series : One-dimensional ndarray with axis labels (including time series).
 
     Examples
     --------
diff --git a/pandas/io/stata.py b/pandas/io/stata.py
index 04bd1e3260..722e2c79c4 100644
--- a/pandas/io/stata.py
+++ b/pandas/io/stata.py
@@ -569,7 +569,11 @@ def _cast_to_stata_types(data: DataFrame) -> DataFrame:
             if getattr(data[col].dtype, "numpy_dtype", None) is not None:
                 data[col] = data[col].astype(data[col].dtype.numpy_dtype)
             elif is_string_dtype(data[col].dtype):
+                # TODO could avoid converting string dtype to object here,
+                # but handle string dtype in _encode_strings
                 data[col] = data[col].astype("object")
+                # generate_table checks for None values
+                data.loc[data[col].isna(), col] = None
 
         dtype = data[col].dtype
         empty_df = data.shape[0] == 0
@@ -2725,6 +2729,7 @@ class StataWriter(StataParser):
                 continue
             column = self.data[col]
             dtype = column.dtype
+            # TODO could also handle string dtype here specifically
             if dtype.type is np.object_:
                 inferred_dtype = infer_dtype(column, skipna=True)
                 if not ((inferred_dtype == "string") or len(column) == 0):
diff --git a/pyproject.toml b/pyproject.toml
index d6a963e94f..6dfee8f491 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -2,8 +2,8 @@
 # Minimum requirements for the build system to execute.
 # See https://github.com/scipy/scipy/pull/12940 for the AIX issue.
 requires = [
-    "meson-python==0.13.1",
-    "meson==1.2.1",
+    "meson-python>=0.13.1",
+    "meson>=1.2.1,<2",
     "wheel",
     "Cython~=3.0.5",  # Note: sync with setup.py, environment.yml and asv.conf.json
     # Force numpy higher than 2.0rc1, so that built wheels are compatible
diff --git a/web/pandas/about/team.md b/web/pandas/about/team.md
index 49b8a26ab5..b66e134fa5 100644
--- a/web/pandas/about/team.md
+++ b/web/pandas/about/team.md
@@ -43,7 +43,7 @@ If you want to support pandas development, you can find information in the [dona
 
 Wes McKinney is the Benevolent Dictator for Life (BDFL).
 
-The project governance is available in the [project governance page]({{ base_url }}governance.html).
+The project governance is available in the [project governance page]({{ base_url }}about/governance.html).
 
 ## Workgroups
 
diff --git a/web/pandas/community/ecosystem.md b/web/pandas/community/ecosystem.md
index 2ea10954fc..6c69ff7602 100644
--- a/web/pandas/community/ecosystem.md
+++ b/web/pandas/community/ecosystem.md
@@ -239,6 +239,17 @@ Console](https://docs.spyder-ide.org/current/panes/ipythonconsole.html), and Spy
 render Numpydoc documentation on pandas objects in rich text with Sphinx
 both automatically and on-demand.
 
+### [marimo](https://marimo.io)
+
+marimo is a reactive notebook for Python and SQL that enhances productivity when working with dataframes. It provides several features to make data manipulation and visualization more interactive and fun:
+
+1. Rich, interactive displays: marimo can display pandas dataframes in interactive tables or charts with filtering and sorting capabilities.
+2. Data selection: Users can select data in tables or pandas-backed plots, and the selections are automatically sent to Python as pandas dataframes.
+3. No-code transformations: Users can interactively transform pandas dataframes using a GUI, without writing code. The generated code can be copied and pasted into the notebook.
+4. Custom filters: marimo allows the creation of pandas-backed filters using UI elements like sliders and dropdowns.
+5. Dataset explorer: marimo automatically discovers and displays all dataframes in the notebook, allowing users to explore and visualize data interactively.
+6. SQL integration: marimo allows users to write SQL queries against any pandas dataframes existing in memory.
+
 ## API
 
 ### [pandas-datareader](https://github.com/pydata/pandas-datareader)


Test patch:
diff --git a/pandas/conftest.py b/pandas/conftest.py
index e2db9260ac..7ad322d050 100644
--- a/pandas/conftest.py
+++ b/pandas/conftest.py
@@ -667,7 +667,8 @@ def _create_mi_with_dt64tz_level():
 
 
 indices_dict = {
-    "string": Index([f"pandas_{i}" for i in range(10)]),
+    "object": Index([f"pandas_{i}" for i in range(10)], dtype=object),
+    "string": Index([f"pandas_{i}" for i in range(10)], dtype="str"),
     "datetime": date_range("2020-01-01", periods=10),
     "datetime-tz": date_range("2020-01-01", periods=10, tz="US/Pacific"),
     "period": period_range("2020-01-01", periods=10, freq="D"),
diff --git a/pandas/tests/indexes/object/test_astype.py b/pandas/tests/indexes/object/test_astype.py
index ce05b5e9f2..7e0de138aa 100644
--- a/pandas/tests/indexes/object/test_astype.py
+++ b/pandas/tests/indexes/object/test_astype.py
@@ -3,25 +3,7 @@ import pytest
 from pandas import (
     Index,
     NaT,
-    Series,
 )
-import pandas._testing as tm
-
-
-def test_astype_str_from_bytes():
-    # https://github.com/pandas-dev/pandas/issues/38607
-    # GH#49658 pre-2.0 Index called .values.astype(str) here, which effectively
-    #  did a .decode() on the bytes object.  In 2.0 we go through
-    #  ensure_string_array which does f"{val}"
-    idx = Index(["あ", b"a"], dtype="object")
-    result = idx.astype(str)
-    expected = Index(["あ", "a"], dtype="str")
-    tm.assert_index_equal(result, expected)
-
-    # while we're here, check that Series.astype behaves the same
-    result = Series(idx).astype(str)
-    expected = Series(expected, dtype="str")
-    tm.assert_series_equal(result, expected)
 
 
 def test_astype_invalid_nas_to_tdt64_raises():
diff --git a/pandas/tests/indexes/object/test_indexing.py b/pandas/tests/indexes/object/test_indexing.py
index ea3d068a67..89648bc316 100644
--- a/pandas/tests/indexes/object/test_indexing.py
+++ b/pandas/tests/indexes/object/test_indexing.py
@@ -3,12 +3,8 @@ from decimal import Decimal
 import numpy as np
 import pytest
 
-from pandas._libs.missing import (
-    NA,
-    is_matching_na,
-)
+from pandas._libs.missing import is_matching_na
 
-import pandas as pd
 from pandas import Index
 import pandas._testing as tm
 
@@ -23,13 +19,13 @@ class TestGetIndexer:
     )
     def test_get_indexer_strings(self, method, expected):
         expected = np.array(expected, dtype=np.intp)
-        index = Index(["b", "c"])
+        index = Index(["b", "c"], dtype=object)
         actual = index.get_indexer(["a", "b", "c", "d"], method=method)
 
         tm.assert_numpy_array_equal(actual, expected)
 
-    def test_get_indexer_strings_raises(self, using_infer_string):
-        index = Index(["b", "c"])
+    def test_get_indexer_strings_raises(self):
+        index = Index(["b", "c"], dtype=object)
 
         msg = "|".join(
             [
@@ -68,13 +64,9 @@ class TestGetIndexer:
 
 
 class TestGetIndexerNonUnique:
-    def test_get_indexer_non_unique_nas(
-        self, nulls_fixture, request, using_infer_string
-    ):
+    def test_get_indexer_non_unique_nas(self, nulls_fixture):
         # even though this isn't non-unique, this should still work
-        if using_infer_string and (nulls_fixture is None or nulls_fixture is NA):
-            request.applymarker(pytest.mark.xfail(reason="NAs are cast to NaN"))
-        index = Index(["a", "b", nulls_fixture])
+        index = Index(["a", "b", nulls_fixture], dtype=object)
         indexer, missing = index.get_indexer_non_unique([nulls_fixture])
 
         expected_indexer = np.array([2], dtype=np.intp)
@@ -83,7 +75,7 @@ class TestGetIndexerNonUnique:
         tm.assert_numpy_array_equal(missing, expected_missing)
 
         # actually non-unique
-        index = Index(["a", nulls_fixture, "b", nulls_fixture])
+        index = Index(["a", nulls_fixture, "b", nulls_fixture], dtype=object)
         indexer, missing = index.get_indexer_non_unique([nulls_fixture])
 
         expected_indexer = np.array([1, 3], dtype=np.intp)
@@ -92,10 +84,10 @@ class TestGetIndexerNonUnique:
 
         # matching-but-not-identical nans
         if is_matching_na(nulls_fixture, float("NaN")):
-            index = Index(["a", float("NaN"), "b", float("NaN")])
+            index = Index(["a", float("NaN"), "b", float("NaN")], dtype=object)
             match_but_not_identical = True
         elif is_matching_na(nulls_fixture, Decimal("NaN")):
-            index = Index(["a", Decimal("NaN"), "b", Decimal("NaN")])
+            index = Index(["a", Decimal("NaN"), "b", Decimal("NaN")], dtype=object)
             match_but_not_identical = True
         else:
             match_but_not_identical = False
@@ -156,59 +148,3 @@ class TestGetIndexerNonUnique:
             expected_indexer = np.array([1, 3], dtype=np.intp)
             tm.assert_numpy_array_equal(indexer, expected_indexer)
             tm.assert_numpy_array_equal(missing, expected_missing)
-
-
-class TestSliceLocs:
-    @pytest.mark.parametrize(
-        "in_slice,expected",
-        [
-            # error: Slice index must be an integer or None
-            (pd.IndexSlice[::-1], "yxdcb"),
-            (pd.IndexSlice["b":"y":-1], ""),  # type: ignore[misc]
-            (pd.IndexSlice["b"::-1], "b"),  # type: ignore[misc]
-            (pd.IndexSlice[:"b":-1], "yxdcb"),  # type: ignore[misc]
-            (pd.IndexSlice[:"y":-1], "y"),  # type: ignore[misc]
-            (pd.IndexSlice["y"::-1], "yxdcb"),  # type: ignore[misc]
-            (pd.IndexSlice["y"::-4], "yb"),  # type: ignore[misc]
-            # absent labels
-            (pd.IndexSlice[:"a":-1], "yxdcb"),  # type: ignore[misc]
-            (pd.IndexSlice[:"a":-2], "ydb"),  # type: ignore[misc]
-            (pd.IndexSlice["z"::-1], "yxdcb"),  # type: ignore[misc]
-            (pd.IndexSlice["z"::-3], "yc"),  # type: ignore[misc]
-            (pd.IndexSlice["m"::-1], "dcb"),  # type: ignore[misc]
-            (pd.IndexSlice[:"m":-1], "yx"),  # type: ignore[misc]
-            (pd.IndexSlice["a":"a":-1], ""),  # type: ignore[misc]
-            (pd.IndexSlice["z":"z":-1], ""),  # type: ignore[misc]
-            (pd.IndexSlice["m":"m":-1], ""),  # type: ignore[misc]
-        ],
-    )
-    def test_slice_locs_negative_step(self, in_slice, expected, any_string_dtype):
-        index = Index(list("bcdxy"), dtype=any_string_dtype)
-
-        s_start, s_stop = index.slice_locs(in_slice.start, in_slice.stop, in_slice.step)
-        result = index[s_start : s_stop : in_slice.step]
-        expected = Index(list(expected), dtype=any_string_dtype)
-        tm.assert_index_equal(result, expected)
-
-    def test_slice_locs_negative_step_oob(self, any_string_dtype):
-        index = Index(list("bcdxy"), dtype=any_string_dtype)
-
-        result = index[-10:5:1]
-        tm.assert_index_equal(result, index)
-
-        result = index[4:-10:-1]
-        expected = Index(list("yxdcb"), dtype=any_string_dtype)
-        tm.assert_index_equal(result, expected)
-
-    def test_slice_locs_dup(self):
-        index = Index(["a", "a", "b", "c", "d", "d"])
-        assert index.slice_locs("a", "d") == (0, 6)
-        assert index.slice_locs(end="d") == (0, 6)
-        assert index.slice_locs("a", "c") == (0, 4)
-        assert index.slice_locs("b", "d") == (2, 6)
-
-        index2 = index[::-1]
-        assert index2.slice_locs("d", "a") == (0, 6)
-        assert index2.slice_locs(end="a") == (0, 6)
-        assert index2.slice_locs("d", "b") == (0, 4)
-        assert index2.slice_locs("c", "a") == (2, 6)
diff --git a/pandas/tests/indexes/string/__init__.py b/pandas/tests/indexes/string/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/pandas/tests/indexes/string/test_astype.py b/pandas/tests/indexes/string/test_astype.py
new file mode 100644
index 0000000000..0349d85f23
--- /dev/null
+++ b/pandas/tests/indexes/string/test_astype.py
@@ -0,0 +1,21 @@
+from pandas import (
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+def test_astype_str_from_bytes():
+    # https://github.com/pandas-dev/pandas/issues/38607
+    # GH#49658 pre-2.0 Index called .values.astype(str) here, which effectively
+    #  did a .decode() on the bytes object.  In 2.0 we go through
+    #  ensure_string_array which does f"{val}"
+    idx = Index(["あ", b"a"], dtype="object")
+    result = idx.astype(str)
+    expected = Index(["あ", "a"], dtype="str")
+    tm.assert_index_equal(result, expected)
+
+    # while we're here, check that Series.astype behaves the same
+    result = Series(idx).astype(str)
+    expected = Series(expected, dtype="str")
+    tm.assert_series_equal(result, expected)
diff --git a/pandas/tests/indexes/string/test_indexing.py b/pandas/tests/indexes/string/test_indexing.py
new file mode 100644
index 0000000000..755b7109a5
--- /dev/null
+++ b/pandas/tests/indexes/string/test_indexing.py
@@ -0,0 +1,118 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Index
+import pandas._testing as tm
+
+
+class TestGetIndexer:
+    @pytest.mark.parametrize(
+        "method,expected",
+        [
+            ("pad", [-1, 0, 1, 1]),
+            ("backfill", [0, 0, 1, -1]),
+        ],
+    )
+    def test_get_indexer_strings(self, any_string_dtype, method, expected):
+        expected = np.array(expected, dtype=np.intp)
+        index = Index(["b", "c"], dtype=any_string_dtype)
+        actual = index.get_indexer(["a", "b", "c", "d"], method=method)
+
+        tm.assert_numpy_array_equal(actual, expected)
+
+    def test_get_indexer_strings_raises(self, any_string_dtype):
+        index = Index(["b", "c"], dtype=any_string_dtype)
+
+        msg = "|".join(
+            [
+                "operation 'sub' not supported for dtype 'str",
+                r"unsupported operand type\(s\) for -: 'str' and 'str'",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            index.get_indexer(["a", "b", "c", "d"], method="nearest")
+
+        with pytest.raises(TypeError, match=msg):
+            index.get_indexer(["a", "b", "c", "d"], method="pad", tolerance=2)
+
+        with pytest.raises(TypeError, match=msg):
+            index.get_indexer(
+                ["a", "b", "c", "d"], method="pad", tolerance=[2, 2, 2, 2]
+            )
+
+
+class TestGetIndexerNonUnique:
+    @pytest.mark.xfail(reason="TODO(infer_string)", strict=False)
+    def test_get_indexer_non_unique_nas(self, any_string_dtype, nulls_fixture):
+        index = Index(["a", "b", None], dtype=any_string_dtype)
+        indexer, missing = index.get_indexer_non_unique([nulls_fixture])
+
+        expected_indexer = np.array([2], dtype=np.intp)
+        expected_missing = np.array([], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected_indexer)
+        tm.assert_numpy_array_equal(missing, expected_missing)
+
+        # actually non-unique
+        index = Index(["a", None, "b", None], dtype=any_string_dtype)
+        indexer, missing = index.get_indexer_non_unique([nulls_fixture])
+
+        expected_indexer = np.array([1, 3], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected_indexer)
+        tm.assert_numpy_array_equal(missing, expected_missing)
+
+
+class TestSliceLocs:
+    @pytest.mark.parametrize(
+        "in_slice,expected",
+        [
+            # error: Slice index must be an integer or None
+            (pd.IndexSlice[::-1], "yxdcb"),
+            (pd.IndexSlice["b":"y":-1], ""),  # type: ignore[misc]
+            (pd.IndexSlice["b"::-1], "b"),  # type: ignore[misc]
+            (pd.IndexSlice[:"b":-1], "yxdcb"),  # type: ignore[misc]
+            (pd.IndexSlice[:"y":-1], "y"),  # type: ignore[misc]
+            (pd.IndexSlice["y"::-1], "yxdcb"),  # type: ignore[misc]
+            (pd.IndexSlice["y"::-4], "yb"),  # type: ignore[misc]
+            # absent labels
+            (pd.IndexSlice[:"a":-1], "yxdcb"),  # type: ignore[misc]
+            (pd.IndexSlice[:"a":-2], "ydb"),  # type: ignore[misc]
+            (pd.IndexSlice["z"::-1], "yxdcb"),  # type: ignore[misc]
+            (pd.IndexSlice["z"::-3], "yc"),  # type: ignore[misc]
+            (pd.IndexSlice["m"::-1], "dcb"),  # type: ignore[misc]
+            (pd.IndexSlice[:"m":-1], "yx"),  # type: ignore[misc]
+            (pd.IndexSlice["a":"a":-1], ""),  # type: ignore[misc]
+            (pd.IndexSlice["z":"z":-1], ""),  # type: ignore[misc]
+            (pd.IndexSlice["m":"m":-1], ""),  # type: ignore[misc]
+        ],
+    )
+    def test_slice_locs_negative_step(self, in_slice, expected, any_string_dtype):
+        index = Index(list("bcdxy"), dtype=any_string_dtype)
+
+        s_start, s_stop = index.slice_locs(in_slice.start, in_slice.stop, in_slice.step)
+        result = index[s_start : s_stop : in_slice.step]
+        expected = Index(list(expected), dtype=any_string_dtype)
+        tm.assert_index_equal(result, expected)
+
+    def test_slice_locs_negative_step_oob(self, any_string_dtype):
+        index = Index(list("bcdxy"), dtype=any_string_dtype)
+
+        result = index[-10:5:1]
+        tm.assert_index_equal(result, index)
+
+        result = index[4:-10:-1]
+        expected = Index(list("yxdcb"), dtype=any_string_dtype)
+        tm.assert_index_equal(result, expected)
+
+    def test_slice_locs_dup(self, any_string_dtype):
+        index = Index(["a", "a", "b", "c", "d", "d"], dtype=any_string_dtype)
+        assert index.slice_locs("a", "d") == (0, 6)
+        assert index.slice_locs(end="d") == (0, 6)
+        assert index.slice_locs("a", "c") == (0, 4)
+        assert index.slice_locs("b", "d") == (2, 6)
+
+        index2 = index[::-1]
+        assert index2.slice_locs("d", "a") == (0, 6)
+        assert index2.slice_locs(end="a") == (0, 6)
+        assert index2.slice_locs("d", "b") == (0, 4)
+        assert index2.slice_locs("c", "a") == (2, 6)
diff --git a/pandas/tests/indexes/test_any_index.py b/pandas/tests/indexes/test_any_index.py
index e1ed96195e..a4c18732ef 100644
--- a/pandas/tests/indexes/test_any_index.py
+++ b/pandas/tests/indexes/test_any_index.py
@@ -40,7 +40,7 @@ def test_map_identity_mapping(index, request):
     # GH#12766
 
     result = index.map(lambda x: x)
-    if index.dtype == object and result.dtype == bool:
+    if index.dtype == object and (result.dtype == bool or result.dtype == "string"):
         assert (index == result).all()
         # TODO: could work that into the 'exact="equiv"'?
         return  # FIXME: doesn't belong in this file anymore!
diff --git a/pandas/tests/indexes/test_old_base.py b/pandas/tests/indexes/test_old_base.py
index 0199e21bfc..65feb07e05 100644
--- a/pandas/tests/indexes/test_old_base.py
+++ b/pandas/tests/indexes/test_old_base.py
@@ -256,7 +256,7 @@ class TestBase:
                 "RangeIndex cannot be initialized from data, "
                 "MultiIndex and CategoricalIndex are tested separately"
             )
-        elif index.dtype == object and index.inferred_type == "boolean":
+        elif index.dtype == object and index.inferred_type in ["boolean", "string"]:
             init_kwargs["dtype"] = index.dtype
 
         index_type = type(index)
diff --git a/pandas/tests/indexes/test_setops.py b/pandas/tests/indexes/test_setops.py
index e5dc47be20..5f934ca3e6 100644
--- a/pandas/tests/indexes/test_setops.py
+++ b/pandas/tests/indexes/test_setops.py
@@ -299,7 +299,13 @@ class TestSetOps:
                 first.difference([1, 2, 3], sort)
 
     @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
-    def test_symmetric_difference(self, index):
+    def test_symmetric_difference(self, index, using_infer_string, request):
+        if (
+            using_infer_string
+            and index.dtype == "object"
+            and index.inferred_type == "string"
+        ):
+            request.applymarker(pytest.mark.xfail(reason="TODO: infer_string"))
         if isinstance(index, CategoricalIndex):
             pytest.skip(f"Not relevant for {type(index).__name__}")
         if len(index) < 2:
diff --git a/pandas/tests/io/test_stata.py b/pandas/tests/io/test_stata.py
index 9f5085ff2a..4b5369d61b 100644
--- a/pandas/tests/io/test_stata.py
+++ b/pandas/tests/io/test_stata.py
@@ -11,8 +11,6 @@ import zipfile
 import numpy as np
 import pytest
 
-from pandas._config import using_string_dtype
-
 import pandas.util._test_decorators as td
 
 import pandas as pd
@@ -435,9 +433,8 @@ class TestStata:
             check_index_type=False,
         )
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
     @pytest.mark.parametrize("version", [114, 117, 118, 119, None])
-    def test_read_write_dta10(self, version, temp_file):
+    def test_read_write_dta10(self, version, temp_file, using_infer_string):
         original = DataFrame(
             data=[["string", "object", 1, 1.1, np.datetime64("2003-12-25")]],
             columns=["string", "object", "integer", "floating", "datetime"],
@@ -451,9 +448,11 @@ class TestStata:
         original.to_stata(path, convert_dates={"datetime": "tc"}, version=version)
         written_and_read_again = self.read_dta(path)
 
-        expected = original[:]
+        expected = original.copy()
         # "tc" convert_dates means we store in ms
         expected["datetime"] = expected["datetime"].astype("M8[ms]")
+        if using_infer_string:
+            expected["object"] = expected["object"].astype("str")
 
         tm.assert_frame_equal(
             written_and_read_again.set_index("index"),
@@ -1276,7 +1275,6 @@ class TestStata:
             assert parsed[col].cat.ordered
             assert not parsed_unordered[col].cat.ordered
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
     @pytest.mark.filterwarnings("ignore::UserWarning")
     @pytest.mark.parametrize(
         "file",
@@ -1340,6 +1338,10 @@ class TestStata:
                 if cat.categories.dtype == object:
                     categories = pd.Index._with_infer(cat.categories._values)
                     cat = cat.set_categories(categories)
+                elif cat.categories.dtype == "string" and len(cat.categories) == 0:
+                    # if the read categories are empty, it comes back as object dtype
+                    categories = cat.categories.astype(object)
+                    cat = cat.set_categories(categories)
                 from_frame[col] = cat
         return from_frame
 
@@ -1369,7 +1371,6 @@ class TestStata:
             from_chunks = pd.concat(itr)
         tm.assert_frame_equal(parsed, from_chunks)
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
     @pytest.mark.filterwarnings("ignore::UserWarning")
     @pytest.mark.parametrize(
         "file",
@@ -1674,12 +1675,11 @@ The repeated labels are:\n-+\nwolof
             path = temp_file
             df.to_stata(path)
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
     def test_path_pathlib(self):
         df = 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.index.name = "index"
         reader = lambda x: read_stata(x).set_index("index")
@@ -1699,13 +1699,12 @@ The repeated labels are:\n-+\nwolof
             value_labels = dta_iter.value_labels()
         assert value_labels == {"A": {0: "A", 1: "B", 2: "C", 3: "E"}}
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
     def test_set_index(self, temp_file):
         # GH 17328
         df = 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.index.name = "index"
         path = temp_file
@@ -1733,9 +1732,9 @@ The repeated labels are:\n-+\nwolof
         formatted = df.loc[0, column + "_fmt"]
         assert unformatted == formatted
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+    # @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
     @pytest.mark.parametrize("byteorder", ["little", "big"])
-    def test_writer_117(self, byteorder, temp_file):
+    def test_writer_117(self, byteorder, temp_file, using_infer_string):
         original = DataFrame(
             data=[
                 [
@@ -1802,6 +1801,9 @@ The repeated labels are:\n-+\nwolof
         expected = original[:]
         # "tc" for convert_dates means we store with "ms" resolution
         expected["datetime"] = expected["datetime"].astype("M8[ms]")
+        if using_infer_string:
+            # object dtype (with only strings/None) comes back as string dtype
+            expected["object"] = expected["object"].astype("str")
 
         tm.assert_frame_equal(
             written_and_read_again.set_index("index"),
@@ -1845,15 +1847,14 @@ The repeated labels are:\n-+\nwolof
         with pytest.raises(ValueError, match=msg):
             original.to_stata(path, convert_dates={"wrong_name": "tc"})
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
     @pytest.mark.parametrize("version", [114, 117, 118, 119, None])
     def test_nonfile_writing(self, version, temp_file):
         # GH 21041
         bio = io.BytesIO()
         df = 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.index.name = "index"
         path = temp_file
@@ -1864,13 +1865,12 @@ The repeated labels are:\n-+\nwolof
         reread = read_stata(path, index_col="index")
         tm.assert_frame_equal(df, reread)
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
     def test_gzip_writing(self, temp_file):
         # writing version 117 requires seek and cannot be used with gzip
         df = 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.index.name = "index"
         path = temp_file
@@ -1907,8 +1907,7 @@ The repeated labels are:\n-+\nwolof
 
         tm.assert_frame_equal(unicode_df, expected)
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
-    def test_mixed_string_strl(self, temp_file):
+    def test_mixed_string_strl(self, temp_file, using_infer_string):
         # GH 23633
         output = [{"mixed": "string" * 500, "number": 0}, {"mixed": None, "number": 1}]
         output = DataFrame(output)
@@ -1925,6 +1924,8 @@ The repeated labels are:\n-+\nwolof
         output.to_stata(path, write_index=False, convert_strl=["mixed"], version=117)
         reread = read_stata(path)
         expected = output.fillna("")
+        if using_infer_string:
+            expected["mixed"] = expected["mixed"].astype("str")
         tm.assert_frame_equal(reread, expected)
 
     @pytest.mark.parametrize("version", [114, 117, 118, 119, None])
@@ -2000,7 +2001,6 @@ the string values returned are correct."""
                 reader._ensure_open()
                 assert reader._nvar == 32999
 
-    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
     @pytest.mark.parametrize("version", [118, 119, None])
     @pytest.mark.parametrize("byteorder", ["little", "big"])
     def test_utf8_writer(self, version, byteorder, temp_file):
@@ -2348,13 +2348,12 @@ def test_iterator_errors(datapath, chunksize):
             pass
 
 
-@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
 def test_iterator_value_labels(temp_file):
     # GH 31544
     values = ["c_label", "b_label"] + ["a_label"] * 500
     df = DataFrame({f"col{k}": pd.Categorical(values, ordered=True) for k in range(2)})
     df.to_stata(temp_file, write_index=False)
-    expected = pd.Index(["a_label", "b_label", "c_label"], dtype="object")
+    expected = pd.Index(["a_label", "b_label", "c_label"])
     with read_stata(temp_file, chunksize=100) as reader:
         for j, chunk in enumerate(reader):
             for i in range(2):
diff --git a/pandas/tests/series/accessors/test_str_accessor.py b/pandas/tests/series/accessors/test_str_accessor.py
index 09d965ef1f..ff530459b7 100644
--- a/pandas/tests/series/accessors/test_str_accessor.py
+++ b/pandas/tests/series/accessors/test_str_accessor.py
@@ -15,7 +15,8 @@ class TestStrAccessor:
 
         # str accessor only valid with string values
         ser = Series(range(5))
-        with pytest.raises(AttributeError, match="only use .str accessor"):
+        msg = "Can only use .str accessor with string values, not integer"
+        with pytest.raises(AttributeError, match=msg):
             ser.str.repeat(2)
 
     def test_str_accessor_updates_on_inplace(self):
diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py
index dac74a0e32..81e7d3774b 100644
--- a/pandas/tests/test_algos.py
+++ b/pandas/tests/test_algos.py
@@ -65,6 +65,7 @@ class TestFactorize:
         expected_uniques = np.array([(1 + 0j), (2 + 0j), (2 + 1j)], dtype=complex)
         tm.assert_numpy_array_equal(uniques, expected_uniques)
 
+    @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False)
     def test_factorize(self, index_or_series_obj, sort):
         obj = index_or_series_obj
         result_codes, result_uniques = obj.factorize(sort=sort)