Merge branch 'main' into string_dtype_tests

Author: phofl

.github/actions/build_pandas/action.yml

@@ -25,8 +25,8 @@ runs:
     - name: Build Pandas
       run: |
         if [[ ${{ inputs.editable }} == "true" ]]; then
-          pip install -e . --no-build-isolation -v
+          pip install -e . --no-build-isolation -v --no-deps
         else
-          pip install . --no-build-isolation -v
+          pip install . --no-build-isolation -v --no-deps
         fi
       shell: bash -el {0}

.github/workflows/code-checks.yml

@@ -124,7 +124,7 @@ jobs:
       run: |
         cd asv_bench
         asv machine --yes
-        asv run --quick --dry-run --durations=30 --python=same
+        asv run --quick --dry-run --durations=30 --python=same --show-stderr
 
   build_docker_dev_environment:
     name: Build Docker Dev Environment

.github/workflows/docbuild-and-upload.yml

@@ -67,7 +67,7 @@ jobs:
       run: cp doc/cheatsheet/Pandas_Cheat_Sheet* web/build/
 
     - name: Upload web
-      run: rsync -az --delete --exclude='pandas-docs' --exclude='docs' web/build/ web@${{ secrets.server_ip }}:/var/www/html
+      run: rsync -az --delete --exclude='pandas-docs' --exclude='docs' --exclude='benchmarks' web/build/ web@${{ secrets.server_ip }}:/var/www/html
       if: github.event_name == 'push' && github.ref == 'refs/heads/main'
 
     - name: Upload dev docs

.github/workflows/unit-tests.yml

@@ -236,7 +236,7 @@ jobs:
           . ~/virtualenvs/pandas-dev/bin/activate
           python -m pip install --no-cache-dir -U pip wheel setuptools meson[ninja]==1.2.1 meson-python==0.13.1
           python -m pip install numpy --config-settings=setup-args="-Dallow-noblas=true"
-          python -m pip install --no-cache-dir versioneer[toml] cython python-dateutil pytz pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-asyncio>=0.17 hypothesis>=6.46.1
+          python -m pip install --no-cache-dir versioneer[toml] "cython<3.0.3" python-dateutil pytz pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-asyncio>=0.17 hypothesis>=6.46.1
           python -m pip install --no-cache-dir --no-build-isolation -e .
           python -m pip list --no-cache-dir
           export PANDAS_CI=1
@@ -274,7 +274,7 @@ jobs:
           /opt/python/cp311-cp311/bin/python -m venv ~/virtualenvs/pandas-dev
           . ~/virtualenvs/pandas-dev/bin/activate
           python -m pip install --no-cache-dir -U pip wheel setuptools meson-python==0.13.1 meson[ninja]==1.2.1
-          python -m pip install --no-cache-dir versioneer[toml] cython numpy python-dateutil pytz pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-asyncio>=0.17 hypothesis>=6.46.1
+          python -m pip install --no-cache-dir versioneer[toml] "cython<3.0.3" numpy python-dateutil pytz pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-asyncio>=0.17 hypothesis>=6.46.1
           python -m pip install --no-cache-dir --no-build-isolation -e .
           python -m pip list --no-cache-dir
 
@@ -347,8 +347,8 @@ jobs:
           python -m pip install --upgrade pip setuptools wheel meson[ninja]==1.2.1 meson-python==0.13.1
           python -m pip install --pre --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple numpy
           python -m pip install versioneer[toml]
-          python -m pip install python-dateutil pytz tzdata cython hypothesis>=6.46.1 pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-cov pytest-asyncio>=0.17
-          python -m pip install -ve . --no-build-isolation --no-index
+          python -m pip install python-dateutil pytz tzdata "cython<3.0.3" hypothesis>=6.46.1 pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-cov pytest-asyncio>=0.17
+          python -m pip install -ve . --no-build-isolation --no-index --no-deps
           python -m pip list
 
       - name: Run Tests

.github/workflows/wheels.yml

@@ -138,7 +138,7 @@ jobs:
         run: echo "sdist_name=$(cd ./dist && ls -d */)" >> "$GITHUB_ENV"
 
       - name: Build wheels
-        uses: pypa/[email protected].1
+        uses: pypa/[email protected].2
         with:
          package-dir: ./dist/${{ matrix.buildplat[1] == 'macosx_*' && env.sdist_name || needs.build_sdist.outputs.sdist_file }}
         env:

.gitignore

@@ -39,7 +39,7 @@
 .mesonpy-native-file.ini
 MANIFEST
 compile_commands.json
-debug
+.debug
 
 # Python files #
 ################

asv_bench/benchmarks/series_methods.py

@@ -28,9 +28,6 @@ def time_constructor_dict(self):
     def time_constructor_no_data(self):
         Series(data=None, index=self.idx)
 
-    def time_constructor_fastpath(self):
-        Series(self.array, index=self.idx2, name="name", fastpath=True)
-
 
 class ToFrame:
     params = [["int64", "datetime64[ns]", "category", "Int64"], [None, "foo"]]

asv_bench/benchmarks/tslibs/period.py

@@ -72,7 +72,7 @@ def time_now(self, freq):
         self.per.now(freq)
 
     def time_asfreq(self, freq):
-        self.per.asfreq("A")
+        self.per.asfreq("Y")
 
     def time_str(self, freq):
         str(self.per)

ci/deps/actions-310.yaml

@@ -6,7 +6,7 @@ dependencies:
 
   # build dependencies
   - versioneer[toml]
-  - cython>=0.29.33
+  - cython>=0.29.33, <3.0.3
   - meson[ninja]=1.2.1
   - meson-python=0.13.1
 
@@ -20,7 +20,7 @@ dependencies:
 
   # required dependencies
   - python-dateutil
-  - numpy
+  - numpy<2
   - pytz
 
   # optional dependencies

ci/deps/actions-311-downstream_compat.yaml

@@ -7,7 +7,7 @@ dependencies:
 
   # build dependencies
   - versioneer[toml]
-  - cython>=0.29.33
+  - cython>=0.29.33, <3.0.3
   - meson[ninja]=1.2.1
   - meson-python=0.13.1
 
@@ -21,7 +21,7 @@ dependencies:
 
   # required dependencies
   - python-dateutil
-  - numpy
+  - numpy<2
   - pytz
 
   # optional dependencies

ci/deps/actions-311-numpydev.yaml

@@ -8,7 +8,7 @@ dependencies:
   - versioneer[toml]
   - meson[ninja]=1.2.1
   - meson-python=0.13.1
-  - cython>=0.29.33
+  - cython>=0.29.33, <3.0.3
 
   # test dependencies
   - pytest>=7.3.2

ci/deps/actions-311-pyarrownightly.yaml

@@ -7,7 +7,7 @@ dependencies:
   # build dependencies
   - versioneer[toml]
   - meson[ninja]=1.2.1
-  - cython>=0.29.33
+  - cython>=0.29.33, <3.0.3
   - meson-python=0.13.1
 
   # test dependencies
@@ -19,7 +19,7 @@ dependencies:
 
   # required dependencies
   - python-dateutil
-  - numpy
+  - numpy<2
   - pytz
   - pip
 

ci/deps/actions-311.yaml

@@ -6,7 +6,7 @@ dependencies:
 
   # build dependencies
   - versioneer[toml]
-  - cython>=0.29.33
+  - cython>=0.29.33, <3.0.3
   - meson[ninja]=1.2.1
   - meson-python=0.13.1
 
@@ -20,7 +20,7 @@ dependencies:
 
   # required dependencies
   - python-dateutil
-  - numpy
+  - numpy<2
   - pytz
 
   # optional dependencies

ci/deps/actions-39-minimum_versions.yaml

@@ -8,7 +8,7 @@ dependencies:
 
   # build dependencies
   - versioneer[toml]
-  - cython>=0.29.33
+  - cython>=0.29.33, <3.0.3
   - meson[ninja]=1.2.1
   - meson-python=0.13.1
 
@@ -22,7 +22,7 @@ dependencies:
 
   # required dependencies
   - python-dateutil=2.8.2
-  - numpy=1.22.4
+  - numpy=1.22.4, <2
   - pytz=2020.1
 
   # optional dependencies

ci/deps/actions-39.yaml

@@ -6,7 +6,7 @@ dependencies:
 
   # build dependencies
   - versioneer[toml]
-  - cython>=0.29.33
+  - cython>=0.29.33, <3.0.3
   - meson[ninja]=1.2.1
   - meson-python=0.13.1
 
@@ -20,7 +20,7 @@ dependencies:
 
   # required dependencies
   - python-dateutil
-  - numpy
+  - numpy<2
   - pytz
 
   # optional dependencies

ci/deps/actions-pypy-39.yaml

@@ -9,7 +9,7 @@ dependencies:
 
   # build dependencies
   - versioneer[toml]
-  - cython>=0.29.33
+  - cython>=0.29.33, <3.0.3
   - meson[ninja]=1.2.1
   - meson-python=0.13.1
 
@@ -21,7 +21,7 @@ dependencies:
   - hypothesis>=6.46.1
 
   # required
-  - numpy
+  - numpy<2
   - python-dateutil
   - pytz
   - pip:

ci/deps/circle-310-arm64.yaml

@@ -6,7 +6,7 @@ dependencies:
 
   # build dependencies
   - versioneer[toml]
-  - cython>=0.29.33
+  - cython>=0.29.33, <3.0.3
   - meson[ninja]=1.2.1
   - meson-python=0.13.1
 
@@ -20,7 +20,7 @@ dependencies:
 
   # required dependencies
   - python-dateutil
-  - numpy
+  - numpy<2
   - pytz
 
   # optional dependencies

doc/redirects.csv

@@ -127,7 +127,6 @@ generated/pandas.api.types.is_number,../reference/api/pandas.api.types.is_number
 generated/pandas.api.types.is_numeric_dtype,../reference/api/pandas.api.types.is_numeric_dtype
 generated/pandas.api.types.is_object_dtype,../reference/api/pandas.api.types.is_object_dtype
 generated/pandas.api.types.is_period_dtype,../reference/api/pandas.api.types.is_period_dtype
-generated/pandas.api.types.is_period,../reference/api/pandas.api.types.is_period
 generated/pandas.api.types.is_re_compilable,../reference/api/pandas.api.types.is_re_compilable
 generated/pandas.api.types.is_re,../reference/api/pandas.api.types.is_re
 generated/pandas.api.types.is_scalar,../reference/api/pandas.api.types.is_scalar

doc/source/development/contributing_codebase.rst

@@ -528,7 +528,7 @@ If a test is known to fail but the manner in which it fails
 is not meant to be captured, use ``pytest.mark.xfail`` It is common to use this method for a test that
 exhibits buggy behavior or a non-implemented feature. If
 the failing test has flaky behavior, use the argument ``strict=False``. This
-will make it so pytest does not fail if the test happens to pass.
+will make it so pytest does not fail if the test happens to pass. Using ``strict=False`` is highly undesirable, please use it only as a last resort.
 
 Prefer the decorator ``@pytest.mark.xfail`` and the argument ``pytest.param``
 over usage within a test so that the test is appropriately marked during the

doc/source/development/debugging_extensions.rst

@@ -14,8 +14,8 @@ For Python developers with limited or no C/C++ experience this can seem a daunti
   2. `Fundamental Python Debugging Part 2 - Python Extensions <https://willayd.com/fundamental-python-debugging-part-2-python-extensions.html>`_
   3. `Fundamental Python Debugging Part 3 - Cython Extensions <https://willayd.com/fundamental-python-debugging-part-3-cython-extensions.html>`_
 
-Generating debug builds
------------------------
+Debugging locally
+-----------------
 
 By default building pandas from source will generate a release build. To generate a development build you can type::
 
@@ -27,6 +27,32 @@ By default building pandas from source will generate a release build. To generat
 
 By specifying ``builddir="debug"`` all of the targets will be built and placed in the debug directory relative to the project root. This helps to keep your debug and release artifacts separate; you are of course able to choose a different directory name or omit altogether if you do not care to separate build types.
 
+Using Docker
+------------
+
+To simplify the debugging process, pandas has created a Docker image with a debug build of Python and the gdb/Cython debuggers pre-installed. You may either ``docker pull pandas/pandas-debug`` to get access to this image or build it from the ``tooling/debug`` folder locallly.
+
+You can then mount your pandas repository into this image via:
+
+.. code-block:: sh
+
+   docker run --rm -it -w /data -v ${PWD}:/data pandas/pandas-debug
+
+Inside the image, you can use meson to build/install pandas and place the build artifacts into a ``debug`` folder using a command as follows:
+
+.. code-block:: sh
+
+    python -m pip install -ve . --no-build-isolation --config-settings=builddir="debug" --config-settings=setup-args="-Dbuildtype=debug"
+
+If planning to use cygdb, the files required by that application are placed within the build folder. So you have to first ``cd`` to the build folder, then start that application.
+
+.. code-block:: sh
+
+   cd debug
+   cygdb
+
+Within the debugger you can use `cygdb commands <https://docs.cython.org/en/latest/src/userguide/debugging.html#using-the-debugger>`_ to navigate cython extensions.
+
 Editor support
 --------------
 

doc/source/development/maintaining.rst

@@ -44,6 +44,9 @@ reading.
 Issue triage
 ------------
 
+Triage is an important first step in addressing issues reported by the community, and even
+partial contributions are a great way to help maintain pandas. Only remove the "Needs Triage"
+tag once all of the steps below have been completed.
 
 Here's a typical workflow for triaging a newly opened issue.
 
@@ -67,9 +70,9 @@ Here's a typical workflow for triaging a newly opened issue.
 3. **Is this a duplicate issue?**
 
    We have many open issues. If a new issue is clearly a duplicate, label the
-   new issue as "Duplicate" assign the milestone "No Action", and close the issue
-   with a link to the original issue. Make sure to still thank the reporter, and
-   encourage them to chime in on the original issue, and perhaps try to fix it.
+   new issue as "Duplicate" and close the issue with a link to the original issue.
+   Make sure to still thank the reporter, and encourage them to chime in on the
+   original issue, and perhaps try to fix it.
 
    If the new issue provides relevant information, such as a better or slightly
    different example, add it to the original issue as a comment or an edit to
@@ -90,15 +93,20 @@ Here's a typical workflow for triaging a newly opened issue.
    If a reproducible example is provided, but you see a simplification,
    edit the original post with your simpler reproducible example.
 
+   Ensure the issue exists on the main branch and that it has the "Needs Triage" tag
+   until all steps have been completed. Add a comment to the issue once you have
+   verified it exists on the main branch, so others know it has been confirmed.
+
 5. **Is this a clearly defined feature request?**
 
    Generally, pandas prefers to discuss and design new features in issues, before
    a pull request is made. Encourage the submitter to include a proposed API
    for the new feature. Having them write a full docstring is a good way to
    pin down specifics.
 
-   We'll need a discussion from several pandas maintainers before deciding whether
-   the proposal is in scope for pandas.
+   Tag new feature requests with "Needs Discussion", as we'll need a discussion
+   from several pandas maintainers before deciding whether the proposal is in
+   scope for pandas.
 
 6. **Is this a usage question?**
 
@@ -117,10 +125,6 @@ Here's a typical workflow for triaging a newly opened issue.
    If the issue is clearly defined and the fix seems relatively straightforward,
    label the issue as "Good first issue".
 
-   Typically, new issues will be assigned the "Contributions welcome" milestone,
-   unless it's know that this issue should be addressed in a specific release (say
-   because it's a large regression).
-
    Once you have completed the above, make sure to remove the "needs triage" label.
 
 .. _maintaining.regressions:

doc/source/user_guide/advanced.rst

@@ -976,7 +976,7 @@ of :ref:`frequency aliases <timeseries.offset_aliases>` with datetime-like inter
 
    pd.interval_range(start=pd.Timestamp("2017-01-01"), periods=4, freq="W")
 
-   pd.interval_range(start=pd.Timedelta("0 days"), periods=3, freq="9H")
+   pd.interval_range(start=pd.Timedelta("0 days"), periods=3, freq="9h")
 
 Additionally, the ``closed`` parameter can be used to specify which side(s) the intervals
 are closed on.  Intervals are closed on the right side by default.

doc/source/user_guide/basics.rst

@@ -408,20 +408,6 @@ raise a ValueError:
 
     pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo'])
 
-Note that this is different from the NumPy behavior where a comparison can
-be broadcast:
-
-.. ipython:: python
-
-    np.array([1, 2, 3]) == np.array([2])
-
-or it can return False if broadcasting can not be done:
-
-.. ipython:: python
-   :okwarning:
-
-    np.array([1, 2, 3]) == np.array([1, 2])
-
 Combining overlapping data sets
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~