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Merge branch 'main' into fix/type_coercion_for_unobserved_categories
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‎.circleci/config.yml

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no_output_timeout: 30m # Sometimes the tests won't generate any output, make sure the job doesn't get killed by that
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command: |
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pip3 install cibuildwheel==2.15.0
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# When this is a nightly wheel build, allow picking up NumPy 2.0 dev wheels:
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if [[ "$IS_SCHEDULE_DISPATCH" == "true" || "$IS_PUSH" != 'true' ]]; then
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export CIBW_ENVIRONMENT="PIP_EXTRA_INDEX_URL=https://pypi.anaconda.org/scientific-python-nightly-wheels/simple"
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fi
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cibuildwheel --prerelease-pythons --output-dir wheelhouse
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environment:

‎.github/workflows/code-checks.yml

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echo "PYTHONPATH=$PYTHONPATH" >> $GITHUB_ENV
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if: ${{ steps.build.outcome == 'success' && always() }}
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- name: Typing + pylint
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- name: Typing
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uses: pre-commit/[email protected]
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with:
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extra_args: --verbose --hook-stage manual --all-files

‎.github/workflows/wheels.yml

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shell: bash -el {0}
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run: echo "sdist_name=$(cd ./dist && ls -d */)" >> "$GITHUB_ENV"
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- name: Build normal wheels
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if: ${{ (env.IS_SCHEDULE_DISPATCH != 'true' || env.IS_PUSH == 'true') }}
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- name: Build wheels
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uses: pypa/[email protected]
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with:
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package-dir: ./dist/${{ startsWith(matrix.buildplat[1], 'macosx') && env.sdist_name || needs.build_sdist.outputs.sdist_file }}
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env:
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CIBW_PRERELEASE_PYTHONS: True
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CIBW_BUILD: ${{ matrix.python[0] }}-${{ matrix.buildplat[1] }}
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- name: Build nightly wheels (with NumPy pre-release)
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if: ${{ (env.IS_SCHEDULE_DISPATCH == 'true' && env.IS_PUSH != 'true') }}
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uses: pypa/[email protected]
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with:
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package-dir: ./dist/${{ startsWith(matrix.buildplat[1], 'macosx') && env.sdist_name || needs.build_sdist.outputs.sdist_file }}
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env:
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# The nightly wheels should be build witht he NumPy 2.0 pre-releases
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# which requires the additional URL.
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CIBW_ENVIRONMENT: PIP_EXTRA_INDEX_URL=https://pypi.anaconda.org/scientific-python-nightly-wheels/simple
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CIBW_PRERELEASE_PYTHONS: True
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CIBW_BUILD: ${{ matrix.python[0] }}-${{ matrix.buildplat[1] }}
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- name: Set up Python
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uses: mamba-org/setup-micromamba@v1
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with:

‎.pre-commit-config.yaml

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@@ -16,10 +16,10 @@ ci:
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autofix_prs: false
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autoupdate_schedule: monthly
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# manual stage hooks
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skip: [pylint, pyright, mypy]
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skip: [pyright, mypy]
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repos:
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.3.1
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rev: v0.3.4
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hooks:
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- id: ruff
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args: [--exit-non-zero-on-fix]
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files: ^pandas
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exclude: ^pandas/tests
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args: [--select, "ANN001,ANN2", --fix-only, --exit-non-zero-on-fix]
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- id: ruff
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name: ruff-use-pd_array-in-core
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alias: ruff-use-pd_array-in-core
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files: ^pandas/core/
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exclude: ^pandas/core/api\.py$
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args: [--select, "ICN001", --exit-non-zero-on-fix]
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- id: ruff-format
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exclude: ^scripts
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- repo: https://github.com/jendrikseipp/vulture
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rev: 'v2.10'
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rev: 'v2.11'
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hooks:
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- id: vulture
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entry: python scripts/run_vulture.py
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- id: fix-encoding-pragma
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args: [--remove]
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- id: trailing-whitespace
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- repo: https://github.com/pylint-dev/pylint
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rev: v3.0.1
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hooks:
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- id: pylint
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stages: [manual]
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args: [--load-plugins=pylint.extensions.redefined_loop_name, --fail-on=I0021]
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- id: pylint
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alias: redefined-outer-name
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name: Redefining name from outer scope
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files: ^pandas/
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exclude: |
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(?x)
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^pandas/tests # keep excluded
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|/_testing/ # keep excluded
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|^pandas/util/_test_decorators\.py # keep excluded
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|^pandas/_version\.py # keep excluded
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|^pandas/conftest\.py # keep excluded
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args: [--disable=all, --enable=redefined-outer-name]
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stages: [manual]
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- repo: https://github.com/PyCQA/isort
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rev: 5.12.0
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rev: 5.13.2
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hooks:
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- id: isort
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- repo: https://github.com/asottile/pyupgrade
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rev: v3.15.0
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rev: v3.15.2
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- id: pyupgrade
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args: [--py39-plus]
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hooks:
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- id: sphinx-lint
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- repo: https://github.com/pre-commit/mirrors-clang-format
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rev: v17.0.6
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rev: v18.1.2
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- id: clang-format
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files: ^pandas/_libs/src|^pandas/_libs/include

‎asv_bench/asv.conf.json

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// pip (with all the conda available packages installed first,
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// followed by the pip installed packages).
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"matrix": {
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"pip+build": [],
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"Cython": ["3.0"],
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"matplotlib": [],
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"sqlalchemy": [],

‎asv_bench/benchmarks/categoricals.py

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self.codes = np.tile(range(len(self.categories)), N)
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self.datetimes = pd.Series(
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pd.date_range("1995-01-01 00:00:00", periods=N / 10, freq="s")
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pd.date_range("1995-01-01 00:00:00", periods=N // 10, freq="s")
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)
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self.datetimes_with_nat = self.datetimes.copy()
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self.datetimes_with_nat.iloc[-1] = pd.NaT

‎asv_bench/benchmarks/frame_methods.py

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class Update:
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def setup(self):
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rng = np.random.default_rng()
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self.df = DataFrame(rng.uniform(size=(1_000_000, 10)))
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idx = rng.choice(range(1_000_000), size=1_000_000, replace=False)
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self.df_random = DataFrame(self.df, index=idx)
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idx = rng.choice(range(1_000_000), size=100_000, replace=False)
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cols = rng.choice(range(10), size=2, replace=False)
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self.df_sample = DataFrame(
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rng.uniform(size=(100_000, 2)), index=idx, columns=cols
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)
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def time_to_update_big_frame_small_arg(self):
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self.df.update(self.df_sample)
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def time_to_update_random_indices(self):
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self.df_random.update(self.df_sample)
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def time_to_update_small_frame_big_arg(self):
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self.df_sample.update(self.df)
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from .pandas_vb_common import setup # noqa: F401 isort:skip

‎asv_bench/benchmarks/timeseries.py

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"dst": date_range(
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start="10/29/2000 1:00:00", end="10/29/2000 1:59:59", freq="s"
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),
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"repeated": date_range(start="2000", periods=N / 10, freq="s").repeat(10),
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"repeated": date_range(start="2000", periods=N // 10, freq="s").repeat(10),
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"tz_aware": date_range(start="2000", periods=N, freq="s", tz="US/Eastern"),
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"tz_local": date_range(
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start="2000", periods=N, freq="s", tz=dateutil.tz.tzlocal()

‎ci/code_checks.sh

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-i "pandas.DataFrame.__iter__ SA01" \
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-i "pandas.DataFrame.assign SA01" \
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-i "pandas.DataFrame.at_time PR01" \
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-i "pandas.DataFrame.axes SA01" \
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-i "pandas.DataFrame.backfill PR01,SA01" \
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-i "pandas.DataFrame.bfill SA01" \
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-i "pandas.DataFrame.columns SA01" \
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-i "pandas.DataFrame.copy SA01" \
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-i "pandas.DataFrame.kurt RT03,SA01" \
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-i "pandas.DataFrame.kurtosis RT03,SA01" \
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-i "pandas.DataFrame.last_valid_index SA01" \
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-i "pandas.DataFrame.mask RT03" \
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-i "pandas.DataFrame.max RT03" \
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-i "pandas.DataFrame.mean RT03,SA01" \
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-i "pandas.DataFrame.median RT03,SA01" \
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-i "pandas.DataFrame.min RT03" \
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-i "pandas.DataFrame.pad PR01,SA01" \
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-i "pandas.DataFrame.plot PR02,SA01" \
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-i "pandas.DataFrame.pop SA01" \
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-i "pandas.DataFrame.prod RT03" \
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-i "pandas.DataFrame.sparse.to_dense SA01" \
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-i "pandas.DataFrame.std PR01,RT03,SA01" \
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-i "pandas.DataFrame.sum RT03" \
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-i "pandas.DataFrame.swapaxes PR01,SA01" \
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-i "pandas.DataFrame.swaplevel SA01" \
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-i "pandas.DataFrame.to_feather SA01" \
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-i "pandas.DataFrame.to_markdown SA01" \
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-i "pandas.DataFrame.to_parquet RT03" \
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-i "pandas.DataFrame.to_period SA01" \
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-i "pandas.DataFrame.to_timestamp SA01" \
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-i "pandas.DataFrame.tz_convert SA01" \
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-i "pandas.DataFrame.tz_localize SA01" \
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-i "pandas.DataFrame.unstack RT03" \
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-i "pandas.DataFrame.value_counts RT03" \
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-i "pandas.DataFrame.var PR01,RT03,SA01" \
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-i "pandas.DataFrame.where RT03" \
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-i "pandas.DatetimeIndex.ceil SA01" \
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-i "pandas.DatetimeIndex.date SA01" \
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-i "pandas.DatetimeIndex.day SA01" \
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-i "pandas.DatetimeTZDtype SA01" \
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-i "pandas.DatetimeTZDtype.tz SA01" \
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-i "pandas.DatetimeTZDtype.unit SA01" \
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-i "pandas.ExcelFile PR01,SA01" \
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-i "pandas.ExcelFile.parse PR01,SA01" \
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-i "pandas.ExcelWriter SA01" \
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-i "pandas.Float32Dtype SA01" \
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-i "pandas.Float64Dtype SA01" \
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-i "pandas.Grouper PR02,SA01" \
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-i "pandas.HDFStore.append PR01,SA01" \
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-i "pandas.HDFStore.get SA01" \
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-i "pandas.Index.to_list RT03" \
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-i "pandas.Index.union PR07,RT03,SA01" \
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-i "pandas.Index.unique RT03" \
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-i "pandas.Index.value_counts RT03" \
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-i "pandas.Index.view GL08" \
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-i "pandas.Int16Dtype SA01" \
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-i "pandas.Int32Dtype SA01" \
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-i "pandas.Series.list.flatten SA01" \
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-i "pandas.Series.list.len SA01" \
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-i "pandas.Series.lt PR07,SA01" \
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-i "pandas.Series.mask RT03" \
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-i "pandas.Series.max RT03" \
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-i "pandas.Series.mean RT03,SA01" \
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-i "pandas.Series.median RT03,SA01" \
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-i "pandas.Series.to_frame SA01" \
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-i "pandas.Series.to_list RT03" \
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-i "pandas.Series.to_markdown SA01" \
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-i "pandas.Series.to_period SA01" \
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-i "pandas.Series.to_string SA01" \
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-i "pandas.Series.to_timestamp RT03,SA01" \
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-i "pandas.Series.truediv PR07" \
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-i "pandas.Series.tz_convert SA01" \
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-i "pandas.Series.tz_localize SA01" \
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-i "pandas.Series.unstack SA01" \
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-i "pandas.Series.update PR07,SA01" \
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-i "pandas.Series.value_counts RT03" \
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-i "pandas.Series.var PR01,RT03,SA01" \
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-i "pandas.Series.where RT03" \
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-i "pandas.SparseDtype SA01" \
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-i "pandas.Timedelta PR07,SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.agg RT03" \
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-i "pandas.core.groupby.DataFrameGroupBy.aggregate RT03" \
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-i "pandas.core.groupby.DataFrameGroupBy.apply RT03" \
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-i "pandas.core.groupby.DataFrameGroupBy.boxplot PR07,RT03,SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.cummax RT03" \
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-i "pandas.core.groupby.DataFrameGroupBy.cummin RT03" \
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-i "pandas.core.groupby.DataFrameGroupBy.filter SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.get_group RT03,SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.groups SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.hist RT03" \
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‎doc/redirects.csv

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reference/api/pandas.Index.transpose,pandas.Index.T
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reference/api/pandas.Index.notnull,pandas.Index.notna
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reference/api/pandas.Index.tolist,pandas.Index.to_list
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reference/api/pandas.arrays.PandasArray,pandas.arrays.NumpyExtensionArray
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reference/api/pandas.core.resample.Resampler.backfill,pandas.core.resample.Resampler.bfill

‎doc/source/getting_started/install.rst

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xarray 2022.12.0 computation pandas-like API for N-dimensional data
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========================= ================== =============== =============================================================
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.. _install.excel_dependencies:
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Excel files
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^^^^^^^^^^^
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‎doc/source/getting_started/intro_tutorials/02_read_write.rst

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My colleague requested the Titanic data as a spreadsheet.
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.. note::
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If you want to use :func:`~pandas.to_excel` and :func:`~pandas.read_excel`,
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you need to install an Excel reader as outlined in the
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:ref:`Excel files <install.excel_dependencies>` section of the
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installation documentation.
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.. ipython:: python
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titanic.to_excel("titanic.xlsx", sheet_name="passengers", index=False)

‎doc/source/user_guide/basics.rst

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@@ -476,15 +476,15 @@ For example:
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.. ipython:: python
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df
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df.mean(0)
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df.mean(1)
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df.mean(axis=0)
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df.mean(axis=1)
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All such methods have a ``skipna`` option signaling whether to exclude missing
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data (``True`` by default):
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.. ipython:: python
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df.sum(0, skipna=False)
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df.sum(axis=0, skipna=False)
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df.sum(axis=1, skipna=True)
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Combined with the broadcasting / arithmetic behavior, one can describe various
@@ -495,8 +495,8 @@ standard deviation of 1), very concisely:
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ts_stand = (df - df.mean()) / df.std()
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ts_stand.std()
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xs_stand = df.sub(df.mean(1), axis=0).div(df.std(1), axis=0)
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xs_stand.std(1)
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xs_stand = df.sub(df.mean(axis=1), axis=0).div(df.std(axis=1), axis=0)
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xs_stand.std(axis=1)
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Note that methods like :meth:`~DataFrame.cumsum` and :meth:`~DataFrame.cumprod`
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preserve the location of ``NaN`` values. This is somewhat different from

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