Repository: pandas-dev/pandas (python)

Issue:
docs: update `freq` description for `pd.Timedelta.ceil` to match `DatetimeIndex.ceil`'s `freq` description
### 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.

- [ ] 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
td = pd.Timedelta('1001ms')
td.ceil("s")
td.ceil("min")
td.ceil("m")
```


### Issue Description

Calling td.ceil("m") causes the error:

```
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "timedeltas.pyx", line 2011, in pandas._libs.tslibs.timedeltas.Timedelta.ceil
  File "timedeltas.pyx", line 1934, in pandas._libs.tslibs.timedeltas.Timedelta._round
  File "timedeltas.pyx", line 2309, in pandas._libs.tslibs.timedeltas.get_unit_for_round
  File "offsets.pyx", line 756, in pandas._libs.tslibs.offsets.BaseOffset.nanos.__get__
ValueError: <MonthEnd> is a non-fixed frequency
```

### Expected Behavior

td.ceil("m") should behave the same as td.ceil("min")

The documentation for [pandas.Timedelta.ceil](https://pandas.pydata.org/docs/reference/api/pandas.Timedelta.ceil.html#pandas.Timedelta.ceil) states that the freq parameter uses the same units as class constructor Timedelta.

The documentation for [pandas.Timedelta](https://pandas.pydata.org/docs/reference/api/pandas.Timedelta.html#pandas.Timedelta) lists (‘minutes’, ‘minute’, ‘min’, or ‘m’) as one of the possible value lines. This suggests to me that "min" and "m" are equivalent.

It seems that either the documentation could be clearer or there is a bug?

### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit                : 0691c5cf90477d3503834d983f69350f250a6ff7
python                : 3.12.4
python-bits           : 64
OS                    : Windows
OS-release            : 11
Version               : 10.0.22631
machine               : AMD64
processor             : Intel64 Family 6 Model 154 Stepping 3, GenuineIntel
byteorder             : little
LC_ALL                : None
LANG                  : en_US.UTF-8
LOCALE                : English_United Kingdom.1252

pandas                : 2.2.3
numpy                 : 2.0.1
pytz                  : 2024.2
dateutil              : 2.9.0.post0
pip                   : 24.1
Cython                : None
sphinx                : None
IPython               : None
adbc-driver-postgresql: None
adbc-driver-sqlite    : None
bs4                   : None
blosc                 : None
bottleneck            : None
dataframe-api-compat  : None
fastparquet           : None
fsspec                : None
html5lib              : None
hypothesis            : None
gcsfs                 : None
jinja2                : 3.1.4
lxml.etree            : None
matplotlib            : None
numba                 : None
numexpr               : None
odfpy                 : None
openpyxl              : None
pandas_gbq            : None
psycopg2              : None
pymysql               : None
pyarrow               : None
pyreadstat            : None
pytest                : None
python-calamine       : None
pyxlsb                : None
s3fs                  : None
scipy                 : 1.14.0
sqlalchemy            : None
tables                : None
tabulate              : None
xarray                : None
xlrd                  : None
xlsxwriter            : None
zstandard             : None
tzdata                : 2024.2
qtpy                  : None
pyqt5                 : None



</details>


Pull request description:
DOC: fix docstring of timedaltas.ceil
- [ ] closes #59902
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).

Patch:
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 1cb7b288ab..8721230972 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -2,9 +2,9 @@ minimum_pre_commit_version: 2.15.0
 exclude: ^LICENSES/|\.(html|csv|svg)$
 # reserve "manual" for relatively slow hooks which we still want to run in CI
 default_stages: [
-    commit,
-    merge-commit,
-    push,
+    pre-commit,
+    pre-merge-commit,
+    pre-push,
     prepare-commit-msg,
     commit-msg,
     post-checkout,
diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 1974c98a1d..b727d93879 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -85,7 +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" \
@@ -97,43 +96,35 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.arrays.NumpyExtensionArray SA01" \
         -i "pandas.arrays.SparseArray PR07,SA01" \
         -i "pandas.arrays.TimedeltaArray PR07,SA01" \
-        -i "pandas.core.groupby.DataFrameGroupBy.__iter__ RT03,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.boxplot PR07,RT03,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.get_group RT03,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.groups SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.indices SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.nth PR02" \
         -i "pandas.core.groupby.DataFrameGroupBy.nunique SA01" \
-        -i "pandas.core.groupby.DataFrameGroupBy.ohlc SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.plot PR02" \
         -i "pandas.core.groupby.DataFrameGroupBy.sem SA01" \
-        -i "pandas.core.groupby.SeriesGroupBy.__iter__ RT03,SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.get_group RT03,SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.groups SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.indices SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.nth PR02" \
-        -i "pandas.core.groupby.SeriesGroupBy.ohlc SA01" \
         -i "pandas.core.groupby.SeriesGroupBy.plot PR02" \
         -i "pandas.core.groupby.SeriesGroupBy.sem SA01" \
-        -i "pandas.core.resample.Resampler.__iter__ RT03,SA01" \
         -i "pandas.core.resample.Resampler.get_group RT03,SA01" \
         -i "pandas.core.resample.Resampler.groups SA01" \
         -i "pandas.core.resample.Resampler.indices SA01" \
         -i "pandas.core.resample.Resampler.max PR01,RT03,SA01" \
         -i "pandas.core.resample.Resampler.mean SA01" \
         -i "pandas.core.resample.Resampler.min PR01,RT03,SA01" \
-        -i "pandas.core.resample.Resampler.ohlc SA01" \
         -i "pandas.core.resample.Resampler.prod SA01" \
         -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" \
@@ -142,19 +133,15 @@ 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.PossiblePrecisionLoss 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" \
@@ -303,7 +290,6 @@ 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/ci/deps/actions-310.yaml b/ci/deps/actions-310.yaml
index c33c0344e7..b1c7fda910 100644
--- a/ci/deps/actions-310.yaml
+++ b/ci/deps/actions-310.yaml
@@ -52,7 +52,7 @@ dependencies:
   - scipy>=1.10.0
   - sqlalchemy>=2.0.0
   - tabulate>=0.9.0
-  - xarray>=2022.12.0
+  - xarray>=2022.12.0, <=2024.9.0
   - xlrd>=2.0.1
   - xlsxwriter>=3.0.5
   - zstandard>=0.19.0
diff --git a/ci/deps/actions-311-downstream_compat.yaml b/ci/deps/actions-311-downstream_compat.yaml
index 8692b6e35a..f7fc4c38ad 100644
--- a/ci/deps/actions-311-downstream_compat.yaml
+++ b/ci/deps/actions-311-downstream_compat.yaml
@@ -53,7 +53,7 @@ dependencies:
   - scipy>=1.10.0
   - sqlalchemy>=2.0.0
   - tabulate>=0.9.0
-  - xarray>=2022.12.0
+  - xarray>=2022.12.0, <=2024.9.0
   - xlrd>=2.0.1
   - xlsxwriter>=3.0.5
   - zstandard>=0.19.0
diff --git a/ci/deps/actions-311.yaml b/ci/deps/actions-311.yaml
index 8e7d9aba78..f1ab3c37c4 100644
--- a/ci/deps/actions-311.yaml
+++ b/ci/deps/actions-311.yaml
@@ -52,7 +52,7 @@ dependencies:
   - scipy>=1.10.0
   - sqlalchemy>=2.0.0
   - tabulate>=0.9.0
-  - xarray>=2022.12.0
+  - xarray>=2022.12.0, <=2024.9.0
   - xlrd>=2.0.1
   - xlsxwriter>=3.0.5
   - zstandard>=0.19.0
diff --git a/ci/deps/actions-312.yaml b/ci/deps/actions-312.yaml
index 6c97960a62..d39d572eda 100644
--- a/ci/deps/actions-312.yaml
+++ b/ci/deps/actions-312.yaml
@@ -52,7 +52,7 @@ dependencies:
   - scipy>=1.10.0
   - sqlalchemy>=2.0.0
   - tabulate>=0.9.0
-  - xarray>=2022.12.0
+  - xarray>=2022.12.0, <=2024.9.0
   - xlrd>=2.0.1
   - xlsxwriter>=3.0.5
   - zstandard>=0.19.0
diff --git a/ci/deps/circle-311-arm64.yaml b/ci/deps/circle-311-arm64.yaml
index c86534871b..def7faeb8b 100644
--- a/ci/deps/circle-311-arm64.yaml
+++ b/ci/deps/circle-311-arm64.yaml
@@ -52,7 +52,7 @@ dependencies:
   - scipy>=1.10.0
   - sqlalchemy>=2.0.0
   - tabulate>=0.9.0
-  - xarray>=2022.12.0
+  - xarray>=2022.12.0, <2024.10.0
   - xlrd>=2.0.1
   - xlsxwriter>=3.0.5
   - zstandard>=0.19.0
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/environment.yml b/environment.yml
index ab83473544..c05f8dbebd 100644
--- a/environment.yml
+++ b/environment.yml
@@ -55,7 +55,7 @@ dependencies:
   - scipy>=1.10.0
   - sqlalchemy>=2.0.0
   - tabulate>=0.9.0
-  - xarray>=2022.12.0
+  - xarray>=2022.12.0, <=2024.9.0
   - xlrd>=2.0.1
   - xlsxwriter>=3.0.5
   - zstandard>=0.19.0
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..8b6d73cda3 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -1122,9 +1122,23 @@ 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/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 8dfef9e70d..a0bd25525c 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -767,10 +767,24 @@ class BaseGroupBy(PandasObject, SelectionMixin[NDFrameT], GroupByIndexingMixin):
         """
         Groupby iterator.
 
+        This method provides an iterator over the groups created by the ``resample``
+        or ``groupby`` operation on the object. The method yields tuples where
+        the first element is the label (group key) corresponding to each group or
+        resampled bin, and the second element is the subset of the data that falls
+        within that group or bin.
+
         Returns
         -------
-        Generator yielding sequence of (name, subsetted object)
-        for each group
+        Iterator
+            Generator yielding a sequence of (name, subsetted object)
+            for each group.
+
+        See Also
+        --------
+        Series.groupby : Group data by a specific key or column.
+        DataFrame.groupby : Group DataFrame using mapper or by columns.
+        DataFrame.resample : Resample a DataFrame.
+        Series.resample : Resample a Series.
 
         Examples
         --------
@@ -3224,6 +3238,12 @@ class GroupBy(BaseGroupBy[NDFrameT]):
         DataFrame
             Open, high, low and close values within each group.
 
+        See Also
+        --------
+        DataFrame.agg : Aggregate using one or more operations over the specified axis.
+        DataFrame.resample : Resample time-series data.
+        DataFrame.groupby : Group DataFrame using a mapper or by a Series of columns.
+
         Examples
         --------
 
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 efc032b0b5..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]})
@@ -691,6 +705,10 @@ class PossiblePrecisionLoss(Warning):
     When the column value is outside or equal to the int64 value the column is
     converted to a float64 dtype.
 
+    See Also
+    --------
+    DataFrame.to_stata : Export DataFrame object to Stata dta format.
+
     Examples
     --------
     >>> df = pd.DataFrame({"s": pd.Series([1, 2**53], dtype=np.int64)})
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/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/requirements-dev.txt b/requirements-dev.txt
index 1bf42af6bf..00e320e637 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -44,7 +44,7 @@ s3fs>=2022.11.0
 scipy>=1.10.0
 SQLAlchemy>=2.0.0
 tabulate>=0.9.0
-xarray>=2022.12.0
+xarray>=2022.12.0, <=2024.9.0
 xlrd>=2.0.1
 xlsxwriter>=3.0.5
 zstandard>=0.19.0
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_spss.py b/pandas/tests/io/test_spss.py
index 1aa9f6dca0..950f74a686 100644
--- a/pandas/tests/io/test_spss.py
+++ b/pandas/tests/io/test_spss.py
@@ -177,4 +177,6 @@ def test_spss_metadata(datapath):
                 "modification_time": datetime.datetime(2015, 2, 6, 14, 33, 36),
             }
         )
-    assert df.attrs == metadata
+    if Version(pyreadstat.__version__) >= Version("1.2.8"):
+        metadata["mr_sets"] = {}
+    tm.assert_dict_equal(df.attrs, metadata)
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)