Merge remote-tracking branch 'upstream/main' into dtype_backend_validation

# Conflicts:
#	doc/source/user_guide/io.rst
#	pandas/core/generic.py
#	pandas/core/tools/numeric.py
#	pandas/io/clipboards.py
#	pandas/io/excel/_base.py
#	pandas/io/feather_format.py
#	pandas/io/html.py
#	pandas/io/json/_json.py
#	pandas/io/orc.py
#	pandas/io/parquet.py
#	pandas/io/parsers/readers.py
#	pandas/io/spss.py
#	pandas/io/sql.py
#	pandas/io/xml.py
#	pandas/tests/io/parser/test_read_fwf.py
#	pandas/tests/io/test_clipboard.py
#	pandas/tests/io/test_sql.py
#	pandas/tests/io/xml/test_xml.py

Author: phofl

.circleci/setup_env.sh

@@ -55,8 +55,7 @@ if pip list | grep -q ^pandas; then
 fi
 
 echo "Build extensions"
-# GH 47305: Parallel build can causes flaky ImportError from pandas/_libs/tslibs
-python setup.py build_ext -q -j1
+python setup.py build_ext -q -j4
 
 echo "Install pandas"
 python -m pip install --no-build-isolation --no-use-pep517 -e .

.github/actions/build_pandas/action.yml

@@ -16,7 +16,5 @@ runs:
         python -m pip install -e . --no-build-isolation --no-use-pep517 --no-index
       shell: bash -el {0}
       env:
-        # Cannot use parallel compilation on Windows, see https://github.com/pandas-dev/pandas/issues/30873
-        # GH 47305: Parallel build causes flaky ImportError: /home/runner/work/pandas/pandas/pandas/_libs/tslibs/timestamps.cpython-38-x86_64-linux-gnu.so: undefined symbol: pandas_datetime_to_datetimestruct
-        N_JOBS: 1
-        #N_JOBS: ${{ runner.os == 'Windows' && 1 || 2 }}
+        # https://docs.github.com/en/actions/using-github-hosted-runners/about-github-hosted-runners#supported-runners-and-hardware-resources
+        N_JOBS: ${{ runner.os == 'macOS' && 3 || 2 }}

.github/actions/setup-conda/action.yml

@@ -30,7 +30,7 @@ runs:
         environment-name: ${{ inputs.environment-name }}
         extra-specs: ${{ inputs.extra-specs }}
         channels: conda-forge
-        channel-priority: ${{ runner.os == 'macOS' && 'flexible' || 'strict' }}
+        channel-priority: 'strict'
         condarc-file: ci/condarc.yml
         cache-env: true
         cache-downloads: true

.github/workflows/32-bit-linux.yml

@@ -42,7 +42,7 @@ jobs:
           python -m pip install --no-deps -U pip wheel 'setuptools<60.0.0' && \
           python -m pip install versioneer[toml] && \
           python -m pip install cython numpy python-dateutil pytz pytest>=7.0.0 pytest-xdist>=2.2.0 pytest-asyncio>=0.17 hypothesis>=6.34.2 && \
-          python setup.py build_ext -q -j1 && \
+          python setup.py build_ext -q -j$(nproc) && \
           python -m pip install --no-build-isolation --no-use-pep517 -e . && \
           python -m pip list && \
           export PANDAS_CI=1 && \

.github/workflows/python-dev.yml

@@ -82,10 +82,9 @@ jobs:
         python -m pip install python-dateutil pytz cython hypothesis>=6.34.2 pytest>=7.0.0 pytest-xdist>=2.2.0 pytest-cov pytest-asyncio>=0.17
         python -m pip list
 
-    # GH 47305: Parallel build can cause flaky ImportError from pandas/_libs/tslibs
     - name: Build Pandas
       run: |
-        python setup.py build_ext -q -j1
+        python setup.py build_ext -q -j4
         python -m pip install -e . --no-build-isolation --no-use-pep517 --no-index
 
     - name: Build Version

.github/workflows/ubuntu.yml

@@ -142,7 +142,7 @@ jobs:
           - 5432:5432
 
       moto:
-        image: motoserver/moto
+        image: motoserver/moto:4.1.4
         env:
           AWS_ACCESS_KEY_ID: foobar_key
           AWS_SECRET_ACCESS_KEY: foobar_secret

.pre-commit-config.yaml

@@ -31,6 +31,7 @@ repos:
     rev: v0.0.253
     hooks:
     -   id: ruff
+        args: [--exit-non-zero-on-fix]
 -   repo: https://github.com/jendrikseipp/vulture
     rev: 'v2.7'
     hooks:

MANIFEST.in

@@ -58,3 +58,5 @@ prune pandas/tests/io/parser/data
 # Selectively re-add *.cxx files that were excluded above
 graft pandas/_libs/src
 graft pandas/_libs/tslibs/src
+include pandas/_libs/pd_parser.h
+include pandas/_libs/pd_parser.c

asv_bench/benchmarks/algorithms.py

@@ -23,6 +23,7 @@ class Factorize:
             "uint",
             "float",
             "object",
+            "object_str",
             "datetime64[ns]",
             "datetime64[ns, tz]",
             "Int64",
@@ -46,7 +47,8 @@ def setup(self, unique, sort, dtype):
             "int": pd.Index(np.arange(N), dtype="int64"),
             "uint": pd.Index(np.arange(N), dtype="uint64"),
             "float": pd.Index(np.random.randn(N), dtype="float64"),
-            "object": string_index,
+            "object_str": string_index,
+            "object": pd.Index(np.arange(N), dtype="object"),
             "datetime64[ns]": pd.date_range("2011-01-01", freq="H", periods=N),
             "datetime64[ns, tz]": pd.date_range(
                 "2011-01-01", freq="H", periods=N, tz="Asia/Tokyo"
@@ -62,6 +64,9 @@ def setup(self, unique, sort, dtype):
     def time_factorize(self, unique, sort, dtype):
         pd.factorize(self.data, sort=sort)
 
+    def peakmem_factorize(self, unique, sort, dtype):
+        pd.factorize(self.data, sort=sort)
+
 
 class Duplicated:
     params = [

doc/source/_static/css/getting_started.css

@@ -236,11 +236,11 @@ ul.task-bullet > li > p:first-child {
 
 .tutorial-card .card-header {
   cursor: pointer;
-  background-color: white;
+  background-color: transparent;
 }
 
 .tutorial-card .card-body {
-  background-color: #F0F0F0;
+  background-color: transparent;
 }
 
 .tutorial-card .badge {

doc/source/_static/legacy_0.10.h5

@@ -21,7 +21,7 @@ locally before pushing your changes. It's recommended to also install the :ref:`
 Step 1: install a C compiler
 ----------------------------
 
-How to do this will depend on your platform. If you choose to user ``Docker``
+How to do this will depend on your platform. If you choose to use ``Docker``
 in the next step, then you can skip this step.
 
 **Windows**

doc/source/_static/stub

@@ -377,8 +377,8 @@ aggregations for step functions defined over real numbers, datetime and timedelt
 
 xarray brings the labeled data power of pandas to the physical sciences by
 providing N-dimensional variants of the core pandas data structures. It aims to
-provide a pandas-like and pandas-compatible toolkit for analytics on multi-
-dimensional arrays, rather than the tabular data for which pandas excels.
+provide a pandas-like and pandas-compatible toolkit for analytics on
+multi-dimensional arrays, rather than the tabular data for which pandas excels.
 
 
 .. _ecosystem.io: