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‎.github/workflows/wheels.yml

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run: echo "sdist_name=$(cd ./dist && ls -d */)" >> "$GITHUB_ENV"
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- name: Build wheels
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uses: pypa/[email protected].0
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uses: pypa/[email protected].3
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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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‎.pre-commit-config.yaml

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exclude: ^LICENSES/|\.(html|csv|svg)$
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# reserve "manual" for relatively slow hooks which we still want to run in CI
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default_stages: [
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commit,
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merge-commit,
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push,
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pre-commit,
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pre-merge-commit,
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pre-push,
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prepare-commit-msg,
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commit-msg,
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post-checkout,

‎ci/code_checks.sh

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-i "pandas.Timestamp.resolution PR02" \
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-i "pandas.Timestamp.tzinfo GL08" \
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-i "pandas.Timestamp.year GL08" \
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-i "pandas.api.types.is_integer PR01,SA01" \
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-i "pandas.api.types.is_iterator PR07,SA01" \
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-i "pandas.api.types.is_re_compilable PR07,SA01" \
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-i "pandas.api.types.pandas_dtype PR07,RT03,SA01" \
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-i "pandas.arrays.ArrowExtensionArray PR07,SA01" \
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-i "pandas.arrays.NumpyExtensionArray SA01" \
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-i "pandas.arrays.SparseArray PR07,SA01" \
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-i "pandas.arrays.TimedeltaArray PR07,SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.__iter__ RT03,SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.boxplot PR07,RT03,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.nunique SA01" \
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-i "pandas.core.groupby.DataFrameGroupBy.plot PR02" \
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-i "pandas.core.groupby.DataFrameGroupBy.sem SA01" \
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-i "pandas.core.groupby.SeriesGroupBy.__iter__ RT03,SA01" \
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-i "pandas.core.groupby.SeriesGroupBy.get_group RT03,SA01" \
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-i "pandas.core.groupby.SeriesGroupBy.groups SA01" \
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-i "pandas.core.groupby.SeriesGroupBy.indices SA01" \
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-i "pandas.core.groupby.SeriesGroupBy.nth PR02" \
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-i "pandas.core.groupby.SeriesGroupBy.plot PR02" \
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-i "pandas.core.groupby.SeriesGroupBy.sem SA01" \
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-i "pandas.core.resample.Resampler.__iter__ RT03,SA01" \
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-i "pandas.core.resample.Resampler.get_group RT03,SA01" \
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-i "pandas.core.resample.Resampler.groups SA01" \
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-i "pandas.core.resample.Resampler.indices SA01" \
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-i "pandas.core.resample.Resampler.quantile PR01,PR07" \
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-i "pandas.core.resample.Resampler.sem SA01" \
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-i "pandas.core.resample.Resampler.std SA01" \
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-i "pandas.core.resample.Resampler.sum SA01" \
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-i "pandas.core.resample.Resampler.transform PR01,RT03,SA01" \
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-i "pandas.core.resample.Resampler.var SA01" \
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-i "pandas.errors.AttributeConflictWarning SA01" \
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-i "pandas.errors.CSSWarning SA01" \
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-i "pandas.errors.ChainedAssignmentError SA01" \
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-i "pandas.errors.DataError SA01" \
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-i "pandas.errors.DuplicateLabelError SA01" \
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-i "pandas.errors.NullFrequencyError SA01" \
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-i "pandas.errors.NumExprClobberingError SA01" \
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-i "pandas.errors.NumbaUtilError SA01" \
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-i "pandas.errors.OptionError SA01" \
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-i "pandas.errors.OutOfBoundsTimedelta SA01" \
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-i "pandas.errors.PerformanceWarning SA01" \
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-i "pandas.errors.PossibleDataLossError SA01" \
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-i "pandas.errors.PossiblePrecisionLoss SA01" \
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-i "pandas.errors.UndefinedVariableError PR01,SA01" \
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-i "pandas.errors.UnsortedIndexError SA01" \
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-i "pandas.errors.UnsupportedFunctionCall SA01" \
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-i "pandas.errors.ValueLabelTypeMismatch SA01" \
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-i "pandas.infer_freq SA01" \
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-i "pandas.io.json.build_table_schema PR07,RT03,SA01" \
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-i "pandas.io.stata.StataWriter.write_file SA01" \
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-i "pandas.json_normalize RT03,SA01" \
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-i "pandas.plotting.andrews_curves RT03,SA01" \
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-i "pandas.plotting.scatter_matrix PR07,SA01" \
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-i "pandas.set_eng_float_format RT03,SA01" \
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-i "pandas.tseries.offsets.BDay PR02,SA01" \
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-i "pandas.tseries.offsets.BQuarterBegin.is_on_offset GL08" \
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-i "pandas.tseries.offsets.Second.is_on_offset GL08" \
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-i "pandas.tseries.offsets.Second.n GL08" \
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-i "pandas.tseries.offsets.Second.normalize GL08" \
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-i "pandas.tseries.offsets.SemiMonthBegin SA01" \
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-i "pandas.tseries.offsets.SemiMonthBegin.day_of_month GL08" \
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-i "pandas.tseries.offsets.SemiMonthBegin.is_on_offset GL08" \
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-i "pandas.tseries.offsets.SemiMonthBegin.n GL08" \

‎ci/deps/actions-310.yaml

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- scipy>=1.10.0
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- sqlalchemy>=2.0.0
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- tabulate>=0.9.0
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- xarray>=2022.12.0
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- xarray>=2022.12.0, <=2024.9.0
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- xlrd>=2.0.1
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- xlsxwriter>=3.0.5
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- zstandard>=0.19.0

‎ci/deps/actions-311-downstream_compat.yaml

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- scipy>=1.10.0
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- sqlalchemy>=2.0.0
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- xarray>=2022.12.0
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- xarray>=2022.12.0, <=2024.9.0
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- xlrd>=2.0.1
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‎ci/deps/actions-311.yaml

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- xarray>=2022.12.0
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- xarray>=2022.12.0, <=2024.9.0
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- xlrd>=2.0.1
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- xlsxwriter>=3.0.5
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- zstandard>=0.19.0

‎ci/deps/actions-312.yaml

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- tabulate>=0.9.0
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- xarray>=2022.12.0
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- xarray>=2022.12.0, <=2024.9.0
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- xlrd>=2.0.1
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- xlsxwriter>=3.0.5
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- zstandard>=0.19.0

‎ci/deps/circle-311-arm64.yaml

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- sqlalchemy>=2.0.0
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- tabulate>=0.9.0
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- xarray>=2022.12.0
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- xarray>=2022.12.0, <2024.10.0
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- xlrd>=2.0.1
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- xlsxwriter>=3.0.5
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- zstandard>=0.19.0

‎doc/source/development/contributing_codebase.rst

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taken from the original GitHub issue. However, it is always worth considering additional
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use cases and writing corresponding tests.
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We use `code coverage <https://en.wikipedia.org/wiki/Code_coverage>`_ to help understand
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the amount of code which is covered by a test. We recommend striving to ensure code
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you add or change within Pandas is covered by a test. Please see our
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`code coverage dashboard through Codecov <https://app.codecov.io/github/pandas-dev/pandas>`_
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for more information.
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Adding tests is one of the most common requests after code is pushed to pandas. Therefore,
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it is worth getting in the habit of writing tests ahead of time so this is never an issue.
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‎doc/source/user_guide/cookbook.rst

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df
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If-then...
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**********
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Selection
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Constant series
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Constant Series
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To assess if a series has a constant value, we can check if ``series.nunique() <= 1``.

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