resolve conflict in whatsnew/v2.1.0.rst

Author: natmokval

.github/workflows/unit-tests.yml

@@ -337,7 +337,7 @@ jobs:
         run: |
           python --version
           python -m pip install --upgrade pip setuptools wheel
-          python -m pip install --pre --extra-index-url https://pypi.anaconda.org/scipy-wheels-nightly/simple numpy
+          python -m pip install --pre --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple numpy
           python -m pip install git+https://github.com/nedbat/coveragepy.git
           python -m pip install versioneer[toml]
           python -m pip install python-dateutil pytz cython hypothesis>=6.46.1 pytest>=7.0.0 pytest-xdist>=2.2.0 pytest-cov pytest-asyncio>=0.17

.github/workflows/wheels.yml

@@ -156,7 +156,7 @@ jobs:
           PANDAS_STAGING_UPLOAD_TOKEN: ${{ secrets.PANDAS_STAGING_UPLOAD_TOKEN }}
           PANDAS_NIGHTLY_UPLOAD_TOKEN: ${{ secrets.PANDAS_NIGHTLY_UPLOAD_TOKEN }}
         # trigger an upload to
-        # https://anaconda.org/scipy-wheels-nightly/pandas
+        # https://anaconda.org/scientific-python-nightly-wheels/pandas
         # for cron jobs or "Run workflow" (restricted to main branch).
         # Tags will upload to
         # https://anaconda.org/multibuild-wheels-staging/pandas

ci/code_checks.sh

@@ -263,50 +263,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.core.window.ewm.ExponentialMovingWindow.cov \
         pandas.api.indexers.BaseIndexer \
         pandas.api.indexers.VariableOffsetWindowIndexer \
-        pandas.core.groupby.DataFrameGroupBy.count \
-        pandas.core.groupby.DataFrameGroupBy.cummax \
-        pandas.core.groupby.DataFrameGroupBy.cummin \
-        pandas.core.groupby.DataFrameGroupBy.cumprod \
-        pandas.core.groupby.DataFrameGroupBy.cumsum \
-        pandas.core.groupby.DataFrameGroupBy.diff \
-        pandas.core.groupby.DataFrameGroupBy.ffill \
-        pandas.core.groupby.DataFrameGroupBy.max \
-        pandas.core.groupby.DataFrameGroupBy.median \
-        pandas.core.groupby.DataFrameGroupBy.min \
-        pandas.core.groupby.DataFrameGroupBy.ohlc \
-        pandas.core.groupby.DataFrameGroupBy.pct_change \
-        pandas.core.groupby.DataFrameGroupBy.prod \
-        pandas.core.groupby.DataFrameGroupBy.sem \
-        pandas.core.groupby.DataFrameGroupBy.shift \
-        pandas.core.groupby.DataFrameGroupBy.size \
-        pandas.core.groupby.DataFrameGroupBy.skew \
-        pandas.core.groupby.DataFrameGroupBy.std \
-        pandas.core.groupby.DataFrameGroupBy.sum \
-        pandas.core.groupby.DataFrameGroupBy.var \
-        pandas.core.groupby.SeriesGroupBy.count \
-        pandas.core.groupby.SeriesGroupBy.cummax \
-        pandas.core.groupby.SeriesGroupBy.cummin \
-        pandas.core.groupby.SeriesGroupBy.cumprod \
-        pandas.core.groupby.SeriesGroupBy.cumsum \
-        pandas.core.groupby.SeriesGroupBy.diff \
-        pandas.core.groupby.SeriesGroupBy.ffill \
-        pandas.core.groupby.SeriesGroupBy.max \
-        pandas.core.groupby.SeriesGroupBy.median \
-        pandas.core.groupby.SeriesGroupBy.min \
-        pandas.core.groupby.SeriesGroupBy.nunique \
-        pandas.core.groupby.SeriesGroupBy.ohlc \
-        pandas.core.groupby.SeriesGroupBy.pct_change \
-        pandas.core.groupby.SeriesGroupBy.prod \
-        pandas.core.groupby.SeriesGroupBy.sem \
-        pandas.core.groupby.SeriesGroupBy.shift \
-        pandas.core.groupby.SeriesGroupBy.size \
-        pandas.core.groupby.SeriesGroupBy.skew \
-        pandas.core.groupby.SeriesGroupBy.std \
-        pandas.core.groupby.SeriesGroupBy.sum \
-        pandas.core.groupby.SeriesGroupBy.var \
-        pandas.core.groupby.SeriesGroupBy.hist \
-        pandas.core.groupby.DataFrameGroupBy.plot \
-        pandas.core.groupby.SeriesGroupBy.plot \
+        pandas.core.groupby.SeriesGroupBy.fillna \
         pandas.io.formats.style.Styler \
         pandas.io.formats.style.Styler.from_custom_template \
         pandas.io.formats.style.Styler.set_caption \

ci/upload_wheels.sh

@@ -10,7 +10,7 @@ set_upload_vars() {
         export ANACONDA_UPLOAD="true"
     elif [[ "$IS_SCHEDULE_DISPATCH" == "true" ]]; then
         echo scheduled or dispatched event
-        export ANACONDA_ORG="scipy-wheels-nightly"
+        export ANACONDA_ORG="scientific-python-nightly-wheels"
         export TOKEN="$PANDAS_NIGHTLY_UPLOAD_TOKEN"
         export ANACONDA_UPLOAD="true"
     else
@@ -28,12 +28,12 @@ upload_wheels() {
             if compgen -G "./dist/*.gz"; then
                 echo "Found sdist"
                 anaconda -q -t ${TOKEN} upload --skip -u ${ANACONDA_ORG} ./dist/*.gz
-            elif compgen -G "./wheelhouse/*.whl"; then
+                echo "Uploaded sdist"
+            fi
+            if compgen -G "./wheelhouse/*.whl"; then
                 echo "Found wheel"
                 anaconda -q -t ${TOKEN} upload --skip -u ${ANACONDA_ORG} ./wheelhouse/*.whl
-            else
-                echo "Files do not exist"
-                return 1
+                echo "Uploaded wheel"
             fi
             echo "PyPI-style index: https://pypi.anaconda.org/$ANACONDA_ORG/simple"
         fi

doc/source/getting_started/comparison/includes/missing.rst

@@ -19,7 +19,7 @@ Forward fill from previous rows
 
 .. ipython:: python
 
-   outer_join.fillna(method="ffill")
+   outer_join.ffill()
 
 Replace missing values with a specified value
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

doc/source/getting_started/install.rst

@@ -201,9 +201,9 @@ Installing a nightly build is the quickest way to:
 * Try a new feature that will be shipped in the next release (that is, a feature from a pull-request that was recently merged to the main branch).
 * Check whether a bug you encountered has been fixed since the last release.
 
-You can install the nightly build of pandas using the scipy-wheels-nightly index from the PyPI registry of anaconda.org with the following command::
+You can install the nightly build of pandas using the scientific-python-nightly-wheels index from the PyPI registry of anaconda.org with the following command::
 
-    pip install --pre --extra-index https://pypi.anaconda.org/scipy-wheels-nightly/simple pandas
+    pip install --pre --extra-index https://pypi.anaconda.org/scientific-python-nightly-wheels/simple pandas
 
 Note that first uninstalling pandas might be required to be able to install nightly builds::
 

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

@@ -99,9 +99,9 @@ strings (``object``).
 .. note::
     When asking for the ``dtypes``, no brackets are used!
     ``dtypes`` is an attribute of a ``DataFrame`` and ``Series``. Attributes
-    of ``DataFrame`` or ``Series`` do not need brackets. Attributes
-    represent a characteristic of a ``DataFrame``/``Series``, whereas a
-    method (which requires brackets) *do* something with the
+    of a ``DataFrame`` or ``Series`` do not need brackets. Attributes
+    represent a characteristic of a ``DataFrame``/``Series``, whereas
+    methods (which require brackets) *do* something with the
     ``DataFrame``/``Series`` as introduced in the :ref:`first tutorial <10min_tut_01_tableoriented>`.
 
 .. raw:: html

doc/source/user_guide/basics.rst

@@ -1377,12 +1377,12 @@ These methods require that the indexes are **ordered** increasing or
 decreasing.
 
 Note that the same result could have been achieved using
-:ref:`fillna <missing_data.fillna>` (except for ``method='nearest'``) or
+:ref:`ffill <missing_data.fillna>` (except for ``method='nearest'``) or
 :ref:`interpolate <missing_data.interpolate>`:
 
 .. ipython:: python
 
-   ts2.reindex(ts.index).fillna(method="ffill")
+   ts2.reindex(ts.index).ffill()
 
 :meth:`~Series.reindex` will raise a ValueError if the index is not monotonically
 increasing or decreasing. :meth:`~Series.fillna` and :meth:`~Series.interpolate`

doc/source/user_guide/missing_data.rst

@@ -145,7 +145,7 @@ objects.
    :suppress:
 
    df = df2.loc[:, ["one", "two", "three"]]
-   a = df2.loc[df2.index[:5], ["one", "two"]].fillna(method="pad")
+   a = df2.loc[df2.index[:5], ["one", "two"]].ffill()
    b = df2.loc[df2.index[:5], ["one", "two", "three"]]
 
 .. ipython:: python
@@ -237,7 +237,7 @@ can propagate non-NA values forward or backward:
 .. ipython:: python
 
    df
-   df.fillna(method="pad")
+   df.ffill()
 
 .. _missing_data.fillna.limit:
 
@@ -254,7 +254,7 @@ we can use the ``limit`` keyword:
 .. ipython:: python
 
    df
-   df.fillna(method="pad", limit=1)
+   df.ffill(limit=1)
 
 To remind you, these are the available filling methods:
 

doc/source/user_guide/style.ipynb

@@ -59,9 +59,9 @@
     "\n",
     "### Formatting Values\n",
     "\n",
-    "The [Styler][styler] distinguishes the *display* value from the *actual* value, in both data values and index or columns headers. To control the display value, the text is printed in each cell as string, and we can use the [.format()][formatfunc] and [.format_index()][formatfuncindex] methods to manipulate this according to a [format spec string][format] or a callable that takes a single value and returns a string. It is possible to define this for the whole table, or index, or for individual columns, or MultiIndex levels. We can also overwrite index names\n",
+    "The [Styler][styler] distinguishes the *display* value from the *actual* value, in both data values and index or columns headers. To control the display value, the text is printed in each cell as a string, and we can use the [.format()][formatfunc] and [.format_index()][formatfuncindex] methods to manipulate this according to a [format spec string][format] or a callable that takes a single value and returns a string. It is possible to define this for the whole table, or index, or for individual columns, or MultiIndex levels. We can also overwrite index names.\n",
     "\n",
-    "Additionally, the format function has a **precision** argument to specifically help formatting floats, as well as **decimal** and **thousands** separators to support other locales, an **na_rep** argument to display missing data, and an **escape** and **hyperlinks** arguments to help displaying safe-HTML or safe-LaTeX. The default formatter is configured to adopt pandas' global options such as `styler.format.precision` option, controllable using `with pd.option_context('format.precision', 2):` \n",
+    "Additionally, the format function has a **precision** argument to specifically help format floats, as well as **decimal** and **thousands** separators to support other locales, an **na_rep** argument to display missing data, and an **escape** and **hyperlinks** arguments to help displaying safe-HTML or safe-LaTeX. The default formatter is configured to adopt pandas' global options such as `styler.format.precision` option, controllable using `with pd.option_context('format.precision', 2):`\n",
     "\n",
     "[styler]: ../reference/api/pandas.io.formats.style.Styler.rst\n",
     "[format]: https://docs.python.org/3/library/string.html#format-specification-mini-language\n",

doc/source/user_guide/timeseries.rst

@@ -1299,6 +1299,31 @@ frequencies. We will refer to these aliases as *offset aliases*.
    given frequency it will roll to the next value for ``start_date``
    (respectively previous for the ``end_date``)
 
+.. _timeseries.period_aliases:
+
+Period aliases
+~~~~~~~~~~~~~~
+
+A number of string aliases are given to useful common time series
+frequencies. We will refer to these aliases as *period aliases*.
+
+.. csv-table::
+    :header: "Alias", "Description"
+    :widths: 15, 100
+
+    "B", "business day frequency"
+    "D", "calendar day frequency"
+    "W", "weekly frequency"
+    "M", "monthly frequency"
+    "Q", "quarterly frequency"
+    "A, Y", "yearly frequency"
+    "H", "hourly frequency"
+    "T, min", "minutely frequency"
+    "S", "secondly frequency"
+    "L, ms", "milliseconds"
+    "U, us", "microseconds"
+    "N", "nanoseconds"
+
 
 Combining aliases
 ~~~~~~~~~~~~~~~~~
@@ -2083,7 +2108,7 @@ Period dtypes
 dtype similar to the :ref:`timezone aware dtype <timeseries.timezone_series>` (``datetime64[ns, tz]``).
 
 The ``period`` dtype holds the ``freq`` attribute and is represented with
-``period[freq]`` like ``period[D]`` or ``period[M]``, using :ref:`frequency strings <timeseries.offset_aliases>`.
+``period[freq]`` like ``period[D]`` or ``period[M]``, using :ref:`frequency strings <timeseries.period_aliases>`.
 
 .. ipython:: python
 

doc/source/whatsnew/v0.10.0.rst

@@ -226,6 +226,7 @@ labeled the aggregated group with the end of the interval: the next day).
   valid code. You must either specify a fill value or an interpolation method:
 
 .. ipython:: python
+   :okwarning:
 
    s = pd.Series([np.nan, 1.0, 2.0, np.nan, 4])
    s

doc/source/whatsnew/v2.1.0.rst

@@ -14,10 +14,12 @@ including other versions of pandas.
 Enhancements
 ~~~~~~~~~~~~
 
-.. _whatsnew_210.enhancements.enhancement1:
+.. _whatsnew_210.enhancements.cow:
 
-enhancement1
-^^^^^^^^^^^^
+Copy-on-Write improvements
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- Setting a :class:`Series` into a :class:`DataFrame` now creates a lazy instead of a deep copy (:issue:`53142`)
 
 .. _whatsnew_210.enhancements.enhancement2:
 
@@ -99,6 +101,7 @@ Other enhancements
 - :meth:`Categorical.from_codes` has gotten a ``validate`` parameter (:issue:`50975`)
 - :meth:`DataFrame.stack` gained the ``sort`` keyword to dictate whether the resulting :class:`MultiIndex` levels are sorted (:issue:`15105`)
 - :meth:`DataFrame.unstack` gained the ``sort`` keyword to dictate whether the resulting :class:`MultiIndex` levels are sorted (:issue:`15105`)
+- :meth:`DataFrameGroupby.agg` and :meth:`DataFrameGroupby.transform` now support grouping by multiple keys when the index is not a :class:`MultiIndex` for ``engine="numba"`` (:issue:`53486`)
 - :meth:`SeriesGroupby.agg` and :meth:`DataFrameGroupby.agg` now support passing in multiple functions for ``engine="numba"`` (:issue:`53486`)
 - Added ``engine_kwargs`` parameter to :meth:`DataFrame.to_excel` (:issue:`53220`)
 - Added a new parameter ``by_row`` to :meth:`Series.apply`. When set to ``False`` the supplied callables will always operate on the whole Series (:issue:`53400`).
@@ -224,6 +227,7 @@ Other API changes
 Deprecations
 ~~~~~~~~~~~~
 - Deprecated 'broadcast_axis' keyword in :meth:`Series.align` and :meth:`DataFrame.align`, upcast before calling ``align`` with ``left = DataFrame({col: left for col in right.columns}, index=right.index)`` (:issue:`51856`)
+- Deprecated 'fill_method' and 'limit' keywords in :meth:`DataFrame.pct_change`, :meth:`Series.pct_change`, :meth:`DataFrameGroupBy.pct_change`, and :meth:`SeriesGroupBy.pct_change`, explicitly call ``ffill`` or ``bfill`` before calling ``pct_change`` instead (:issue:`53491`)
 - Deprecated 'method', 'limit', and 'fill_axis' keywords in :meth:`DataFrame.align` and :meth:`Series.align`, explicitly call ``fillna`` on the alignment results instead (:issue:`51856`)
 - Deprecated 'quantile' keyword in :meth:`Rolling.quantile` and :meth:`Expanding.quantile`, renamed as 'q' instead (:issue:`52550`)
 - Deprecated :meth:`.DataFrameGroupBy.apply` and methods on the objects returned by :meth:`.DataFrameGroupBy.resample` operating on the grouping column(s); select the columns to operate on after groupby to either explicitly include or exclude the groupings and avoid the ``FutureWarning`` (:issue:`7155`)
@@ -280,6 +284,7 @@ Deprecations
 - Deprecated option "mode.use_inf_as_na", convert inf entries to ``NaN`` before instead (:issue:`51684`)
 - Deprecated positional indexing on :class:`Series` with :meth:`Series.__getitem__` and :meth:`Series.__setitem__`, in a future version ``ser[item]`` will *always* interpret ``item`` as a label, not a position (:issue:`50617`)
 - Deprecated strings ``T``, ``t``, ``L`` and ``l`` denoting units in :func:`to_timedelta` (:issue:`52536`)
+- Deprecated the "method" and "limit" keywords on :meth:`Series.fillna`, :meth:`DataFrame.fillna`, :meth:`SeriesGroupBy.fillna`, :meth:`DataFrameGroupBy.fillna`, and :meth:`Resampler.fillna`, use ``obj.bfill()`` or ``obj.ffill()`` instead (:issue:`53394`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_210.performance:

pandas/_testing/__init__.py

@@ -97,7 +97,6 @@
 from pandas._testing.contexts import (
     decompress_file,
     ensure_clean,
-    ensure_safe_environment_variables,
     raises_chained_assignment_error,
     set_timezone,
     use_numexpr,
@@ -1104,7 +1103,6 @@ def shares_memory(left, right) -> bool:
     "EMPTY_STRING_PATTERN",
     "ENDIAN",
     "ensure_clean",
-    "ensure_safe_environment_variables",
     "equalContents",
     "external_error_raised",
     "FLOAT_EA_DTYPES",

pandas/_testing/contexts.py

@@ -138,22 +138,6 @@ def ensure_clean(
             path.unlink()
 
 
-@contextmanager
-def ensure_safe_environment_variables() -> Generator[None, None, None]:
-    """
-    Get a context manager to safely set environment variables
-
-    All changes will be undone on close, hence environment variables set
-    within this contextmanager will neither persist nor change global state.
-    """
-    saved_environ = dict(os.environ)
-    try:
-        yield
-    finally:
-        os.environ.clear()
-        os.environ.update(saved_environ)
-
-
 @contextmanager
 def with_csv_dialect(name: str, **kwargs) -> Generator[None, None, None]:
     """

pandas/conftest.py

@@ -150,6 +150,18 @@ def pytest_collection_modifyitems(items, config) -> None:
             "first is deprecated and will be removed in a future version. "
             "Please create a mask and filter using `.loc` instead",
         ),
+        (
+            "Resampler.fillna",
+            "DatetimeIndexResampler.fillna is deprecated",
+        ),
+        (
+            "DataFrameGroupBy.fillna",
+            "DataFrameGroupBy.fillna with 'method' is deprecated",
+        ),
+        (
+            "DataFrameGroupBy.fillna",
+            "DataFrame.fillna with 'method' is deprecated",
+        ),
     ]
 
     for item in items:

pandas/core/arrays/datetimes.py

@@ -1145,13 +1145,13 @@ def to_period(self, freq=None) -> PeriodArray:
 
         Parameters
         ----------
-        freq : str or Offset, optional
-            One of pandas' :ref:`offset strings <timeseries.offset_aliases>`
-            or an Offset object. Will be inferred by default.
+        freq : str or Period, optional
+            One of pandas' :ref:`period aliases <timeseries.period_aliases>`
+            or an Period object. Will be inferred by default.
 
         Returns
         -------
-        PeriodArray/Index
+        PeriodArray/PeriodIndex
 
         Raises
         ------