Repository: pandas-dev/pandas (python)

Issue:
DOC: Update contributing documentation with code coverage details
### Pandas version checks

- [X] I have checked that the issue still exists on the latest versions of the docs on `main` [here](https://pandas.pydata.org/docs/dev/)


### Location of the documentation

https://pandas.pydata.org/docs/development/contributing_codebase.html

### Documentation problem

Thanks for the great work on Pandas! I was interested in finding out how Pandas measures code coverage and found that there doesn't seem to be much content yet on how to do this for the project. There's a dead link at https://pandas-coverage-12d2130077bc.herokuapp.com/ for the link mentioned in the content `(tip: you can use the [pandas-coverage app](https://pandas-coverage-12d2130077bc.herokuapp.com/))`. 

I also noticed there's a `codecov.yml` file in the project so I looked this up manually by viewing: https://app.codecov.io/github/pandas-dev/pandas . This looks like a nice way to measure code coverage for the project which has already been configured, but isn't already mentioned within documentation (perhaps in addition to the already mentioned tool, once the link is fixed).

### Suggested fix for documentation

I suggest adding the Codecov link somewhere in the contributing documentation and fixing the broken link for the `pandas-coverage app` (multiple measurements / perspectives could be helpful). 

Pull request:
Add information about code coverage to docs
Hello! This change adds code coverage documentation and also includes a link to the Codecov dashboard for Pandas within the contributing docs. Some of this information is otherwise implicit and I thought could be helpful for developers to understand or have an easy link handy.

Thank you for any thoughts or feedback you may have! And cheers on the great work with Pandas more broadly.

- [x] closes #60010 
- [ ] [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
- [x] 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.



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/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/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


Test patch:
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):