Merge branch 'main' into bug-DatetimeIndex-is_year_start-breaks-on-freq-BusinessMonthStart

Author: natmokval

.github/workflows/wheels.yml

@@ -140,7 +140,7 @@ jobs:
         run: echo "sdist_name=$(cd ./dist && ls -d */)" >> "$GITHUB_ENV"
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.17.0
+        uses: pypa/cibuildwheel@v2.18.0
         with:
          package-dir: ./dist/${{ startsWith(matrix.buildplat[1], 'macosx') && env.sdist_name || needs.build_sdist.outputs.sdist_file }}
         env:

ci/code_checks.sh

@@ -76,7 +76,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.DataFrame.min RT03" \
         -i "pandas.DataFrame.plot PR02,SA01" \
         -i "pandas.Grouper PR02" \
-        -i "pandas.IntervalIndex.left GL08" \
         -i "pandas.MultiIndex PR01" \
         -i "pandas.MultiIndex.append PR07,SA01" \
         -i "pandas.MultiIndex.copy PR07,RT03,SA01" \
@@ -139,7 +138,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.RangeIndex.start SA01" \
         -i "pandas.RangeIndex.step SA01" \
         -i "pandas.RangeIndex.stop SA01" \
-        -i "pandas.Series.case_when RT03" \
         -i "pandas.Series.cat.add_categories PR01,PR02" \
         -i "pandas.Series.cat.as_ordered PR01" \
         -i "pandas.Series.cat.as_unordered PR01" \
@@ -230,7 +228,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.str.strip RT03" \
         -i "pandas.Series.str.swapcase RT03" \
         -i "pandas.Series.str.title RT03" \
-        -i "pandas.Series.str.translate RT03,SA01" \
         -i "pandas.Series.str.upper RT03" \
         -i "pandas.Series.str.wrap RT03,SA01" \
         -i "pandas.Series.str.zfill RT03" \
@@ -245,7 +242,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.truediv PR07" \
         -i "pandas.Series.update PR07,SA01" \
         -i "pandas.Series.var PR01,RT03,SA01" \
-        -i "pandas.SparseDtype SA01" \
         -i "pandas.Timedelta PR07,SA01" \
         -i "pandas.Timedelta.as_unit SA01" \
         -i "pandas.Timedelta.asm8 SA01" \
@@ -326,7 +322,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.api.extensions.ExtensionArray._reduce RT03,SA01" \
         -i "pandas.api.extensions.ExtensionArray._values_for_factorize SA01" \
         -i "pandas.api.extensions.ExtensionArray.astype SA01" \
-        -i "pandas.api.extensions.ExtensionArray.copy RT03,SA01" \
         -i "pandas.api.extensions.ExtensionArray.dropna RT03,SA01" \
         -i "pandas.api.extensions.ExtensionArray.dtype SA01" \
         -i "pandas.api.extensions.ExtensionArray.duplicated RT03,SA01" \
@@ -339,7 +334,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.api.extensions.ExtensionArray.nbytes SA01" \
         -i "pandas.api.extensions.ExtensionArray.ndim SA01" \
         -i "pandas.api.extensions.ExtensionArray.ravel RT03,SA01" \
-        -i "pandas.api.extensions.ExtensionArray.shape SA01" \
         -i "pandas.api.extensions.ExtensionArray.shift SA01" \
         -i "pandas.api.extensions.ExtensionArray.take RT03" \
         -i "pandas.api.extensions.ExtensionArray.tolist RT03,SA01" \
@@ -571,7 +565,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.tseries.offsets.BQuarterEnd.normalize GL08" \
         -i "pandas.tseries.offsets.BQuarterEnd.rule_code GL08" \
         -i "pandas.tseries.offsets.BQuarterEnd.startingMonth GL08" \
-        -i "pandas.tseries.offsets.BYearBegin PR02" \
         -i "pandas.tseries.offsets.BYearBegin.freqstr SA01" \
         -i "pandas.tseries.offsets.BYearBegin.is_on_offset GL08" \
         -i "pandas.tseries.offsets.BYearBegin.month GL08" \
@@ -838,7 +831,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.tseries.offsets.WeekOfMonth.rule_code GL08" \
         -i "pandas.tseries.offsets.WeekOfMonth.week GL08" \
         -i "pandas.tseries.offsets.WeekOfMonth.weekday GL08" \
-        -i "pandas.tseries.offsets.YearBegin PR02" \
         -i "pandas.tseries.offsets.YearBegin.freqstr SA01" \
         -i "pandas.tseries.offsets.YearBegin.is_on_offset GL08" \
         -i "pandas.tseries.offsets.YearBegin.month GL08" \

doc/source/development/policies.rst

@@ -46,6 +46,8 @@ deprecation removed in the next major release (2.0.0).
 These policies do not apply to features marked as **experimental** in the documentation.
 pandas may change the behavior of experimental features at any time.
 
+.. _policies.python_support:
+
 Python support
 ~~~~~~~~~~~~~~
 

doc/source/getting_started/install.rst

@@ -21,7 +21,7 @@ Instructions for installing :ref:`from source <install.source>`,
 Python version support
 ----------------------
 
-Officially Python 3.9, 3.10, 3.11 and 3.12.
+See :ref:`Python support policy <policies.python_support>`.
 
 Installing pandas
 -----------------

doc/source/getting_started/intro_tutorials/01_table_oriented.rst

@@ -192,8 +192,8 @@ Check more options on ``describe`` in the user guide section about :ref:`aggrega
 .. note::
     This is just a starting point. Similar to spreadsheet
     software, pandas represents data as a table with columns and rows. Apart
-    from the representation, also the data manipulations and calculations
-    you would do in spreadsheet software are supported by pandas. Continue
+    from the representation, the data manipulations and calculations
+    you would do in spreadsheet software are also supported by pandas. Continue
     reading the next tutorials to get started!
 
 .. raw:: html
@@ -204,7 +204,7 @@ Check more options on ``describe`` in the user guide section about :ref:`aggrega
 -  Import the package, aka ``import pandas as pd``
 -  A table of data is stored as a pandas ``DataFrame``
 -  Each column in a ``DataFrame`` is a ``Series``
--  You can do things by applying a method to a ``DataFrame`` or ``Series``
+-  You can do things by applying a method on a ``DataFrame`` or ``Series``
 
 .. raw:: html
 
@@ -215,7 +215,7 @@ Check more options on ``describe`` in the user guide section about :ref:`aggrega
     <div class="d-flex flex-row gs-torefguide">
         <span class="badge badge-info">To user guide</span>
 
-A more extended explanation to ``DataFrame`` and ``Series`` is provided in the :ref:`introduction to data structures <dsintro>`.
+A more extended explanation of ``DataFrame`` and ``Series`` is provided in the :ref:`introduction to data structures <dsintro>` page.
 
 .. raw:: html
 

doc/source/getting_started/intro_tutorials/02_read_write.rst

@@ -172,11 +172,11 @@ The method :meth:`~DataFrame.info` provides technical information about a
 -  The table has 12 columns. Most columns have a value for each of the
    rows (all 891 values are ``non-null``). Some columns do have missing
    values and less than 891 ``non-null`` values.
--  The columns ``Name``, ``Sex``, ``Cabin`` and ``Embarked`` consists of
+-  The columns ``Name``, ``Sex``, ``Cabin`` and ``Embarked`` consist of
    textual data (strings, aka ``object``). The other columns are
-   numerical data with some of them whole numbers (aka ``integer``) and
-   others are real numbers (aka ``float``).
--  The kind of data (characters, integers,…) in the different columns
+   numerical data, some of them are whole numbers (``integer``) and
+   others are real numbers (``float``).
+-  The kind of data (characters, integers, …) in the different columns
    are summarized by listing the ``dtypes``.
 -  The approximate amount of RAM used to hold the DataFrame is provided
    as well.
@@ -194,7 +194,7 @@ The method :meth:`~DataFrame.info` provides technical information about a
 -  Getting data in to pandas from many different file formats or data
    sources is supported by ``read_*`` functions.
 -  Exporting data out of pandas is provided by different
-   ``to_*``\ methods.
+   ``to_*`` methods.
 -  The ``head``/``tail``/``info`` methods and the ``dtypes`` attribute
    are convenient for a first check.
 

doc/source/getting_started/intro_tutorials/03_subset_data.rst

@@ -300,7 +300,7 @@ want to select.
         </li>
     </ul>
 
-When using the column names, row labels or a condition expression, use
+When using column names, row labels or a condition expression, use
 the ``loc`` operator in front of the selection brackets ``[]``. For both
 the part before and after the comma, you can use a single label, a list
 of labels, a slice of labels, a conditional expression or a colon. Using
@@ -342,7 +342,7 @@ the name ``anonymous`` to the first 3 elements of the fourth column:
     <div class="d-flex flex-row gs-torefguide">
         <span class="badge badge-info">To user guide</span>
 
-See the user guide section on :ref:`different choices for indexing <indexing.choice>` to get more insight in the usage of ``loc`` and ``iloc``.
+See the user guide section on :ref:`different choices for indexing <indexing.choice>` to get more insight into the usage of ``loc`` and ``iloc``.
 
 .. raw:: html
 
@@ -357,10 +357,8 @@ See the user guide section on :ref:`different choices for indexing <indexing.cho
 -  Inside these square brackets, you can use a single column/row label, a list
    of column/row labels, a slice of labels, a conditional expression or
    a colon.
--  Select specific rows and/or columns using ``loc`` when using the row
-   and column names.
--  Select specific rows and/or columns using ``iloc`` when using the
-   positions in the table.
+-  Use ``loc`` for label-based selection (using row/column names).
+-  Use ``iloc`` for position-based selection (using table positions).
 -  You can assign new values to a selection based on ``loc``/``iloc``.
 
 .. raw:: html

doc/source/getting_started/intro_tutorials/04_plotting.rst

@@ -85,7 +85,7 @@ I want to plot only the columns of the data table with the data from Paris.
     air_quality["station_paris"].plot()
     plt.show()
 
-To plot a specific column, use the selection method of the
+To plot a specific column, use a selection method from the
 :ref:`subset data tutorial <10min_tut_03_subset>` in combination with the :meth:`~DataFrame.plot`
 method. Hence, the :meth:`~DataFrame.plot` method works on both ``Series`` and
 ``DataFrame``.
@@ -127,7 +127,7 @@ standard Python to get an overview of the available plot methods:
     ]
 
 .. note::
-    In many development environments as well as IPython and
+    In many development environments such as IPython and
     Jupyter Notebook, use the TAB button to get an overview of the available
     methods, for example ``air_quality.plot.`` + TAB.
 
@@ -238,7 +238,7 @@ This strategy is applied in the previous example:
 
 -  The ``.plot.*`` methods are applicable on both Series and DataFrames.
 -  By default, each of the columns is plotted as a different element
-   (line, boxplot,…).
+   (line, boxplot, …).
 -  Any plot created by pandas is a Matplotlib object.
 
 .. raw:: html

doc/source/getting_started/intro_tutorials/05_add_columns.rst

@@ -89,8 +89,8 @@ values in each row*.
         </li>
     </ul>
 
-Also other mathematical operators (``+``, ``-``, ``*``, ``/``,…) or
-logical operators (``<``, ``>``, ``==``,…) work element-wise. The latter was already
+Other mathematical operators (``+``, ``-``, ``*``, ``/``, …) and logical
+operators (``<``, ``>``, ``==``, …) also work element-wise. The latter was already
 used in the :ref:`subset data tutorial <10min_tut_03_subset>` to filter
 rows of a table using a conditional expression.
 

doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst

@@ -235,7 +235,7 @@ category in a column.
         </li>
     </ul>
 
-The function is a shortcut, as it is actually a groupby operation in combination with counting of the number of records
+The function is a shortcut, it is actually a groupby operation in combination with counting the number of records
 within each group:
 
 .. ipython:: python