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",