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Merge branch 'main' into fix/group_by_agg_pyarrow_bool_numpy_same_type
2 parents e1ccef6 + 41f3c2e commit 0ce083d

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‎.pre-commit-config.yaml

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@@ -19,7 +19,7 @@ ci:
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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: v0.3.4
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rev: v0.4.3
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hooks:
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- id: ruff
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args: [--exit-non-zero-on-fix]
@@ -46,12 +46,12 @@ repos:
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types_or: [python, rst, markdown, cython, c]
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additional_dependencies: [tomli]
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- repo: https://github.com/MarcoGorelli/cython-lint
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rev: v0.16.0
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rev: v0.16.2
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hooks:
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- id: cython-lint
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- id: double-quote-cython-strings
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.5.0
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rev: v4.6.0
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hooks:
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- id: check-case-conflict
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- id: check-toml
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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: v18.1.2
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rev: v18.1.4
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hooks:
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- id: clang-format
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files: ^pandas/_libs/src|^pandas/_libs/include

‎ci/code_checks.sh

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@@ -75,24 +75,9 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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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.plot PR02,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.swaplevel SA01" \
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-i "pandas.DataFrame.to_markdown SA01" \
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-i "pandas.DataFrame.var PR01,RT03,SA01" \
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-i "pandas.Grouper PR02" \
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-i "pandas.Index PR07" \
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-i "pandas.Index.join PR07,RT03,SA01" \
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-i "pandas.Index.names GL08" \
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-i "pandas.Index.ravel PR01,RT03" \
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-i "pandas.Index.str PR01,SA01" \
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-i "pandas.Interval PR02" \
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-i "pandas.Interval.closed SA01" \
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-i "pandas.Interval.left SA01" \
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-i "pandas.Interval.mid SA01" \
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-i "pandas.Interval.right SA01" \
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-i "pandas.IntervalDtype PR01,SA01" \
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-i "pandas.IntervalDtype.subtype SA01" \
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-i "pandas.IntervalIndex.closed SA01" \
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-i "pandas.IntervalIndex.contains RT03" \
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-i "pandas.IntervalIndex.get_loc PR07,RT03,SA01" \
@@ -165,16 +150,12 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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-i "pandas.RangeIndex.start SA01" \
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-i "pandas.RangeIndex.step SA01" \
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-i "pandas.RangeIndex.stop SA01" \
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-i "pandas.Series SA01" \
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-i "pandas.Series.__iter__ RT03,SA01" \
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-i "pandas.Series.add PR07" \
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-i "pandas.Series.backfill PR01,SA01" \
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-i "pandas.Series.case_when RT03" \
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-i "pandas.Series.cat PR07,SA01" \
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-i "pandas.Series.cat PR07" \
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-i "pandas.Series.cat.add_categories PR01,PR02" \
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-i "pandas.Series.cat.as_ordered PR01" \
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-i "pandas.Series.cat.as_unordered PR01" \
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-i "pandas.Series.cat.codes SA01" \
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-i "pandas.Series.cat.remove_categories PR01,PR02" \
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-i "pandas.Series.cat.remove_unused_categories PR01" \
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-i "pandas.Series.cat.rename_categories PR01,PR02" \
@@ -185,7 +166,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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-i "pandas.Series.dt.ceil PR01,PR02" \
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-i "pandas.Series.dt.components SA01" \
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-i "pandas.Series.dt.day_name PR01,PR02" \
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-i "pandas.Series.dt.days SA01" \
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-i "pandas.Series.dt.days_in_month SA01" \
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-i "pandas.Series.dt.daysinmonth SA01" \
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-i "pandas.Series.dt.floor PR01,PR02" \
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-i "pandas.Series.dt.tz_convert PR01,PR02" \
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-i "pandas.Series.dt.tz_localize PR01,PR02" \
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-i "pandas.Series.dt.unit GL08" \
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-i "pandas.Series.dtype SA01" \
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-i "pandas.Series.eq PR07,SA01" \
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-i "pandas.Series.floordiv PR07" \
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-i "pandas.Series.ge PR07,SA01" \
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-i "pandas.Series.gt PR07,SA01" \
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-i "pandas.Series.hasnans SA01" \
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-i "pandas.Series.is_monotonic_decreasing SA01" \
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-i "pandas.Series.is_monotonic_increasing SA01" \
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-i "pandas.Series.is_unique SA01" \
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-i "pandas.Series.kurt RT03,SA01" \
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-i "pandas.Series.kurtosis RT03,SA01" \
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-i "pandas.Series.le PR07,SA01" \
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-i "pandas.Series.list.__getitem__ 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.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.min RT03" \
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-i "pandas.Series.mod PR07" \
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-i "pandas.Series.mode SA01" \
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-i "pandas.Series.mul PR07" \
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-i "pandas.Series.ne PR07,SA01" \
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-i "pandas.Series.pad PR01,SA01" \
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-i "pandas.Series.plot PR02,SA01" \
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-i "pandas.Series.rsub PR07" \
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-i "pandas.Series.rtruediv PR07" \
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-i "pandas.Series.sem PR01,RT03,SA01" \
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-i "pandas.Series.shape SA01" \
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-i "pandas.Series.skew RT03,SA01" \
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-i "pandas.Series.sparse PR01,SA01" \
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-i "pandas.Series.sparse.density SA01" \
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-i "pandas.Series.sparse.sp_values SA01" \
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-i "pandas.Series.sparse.to_coo PR07,RT03,SA01" \
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-i "pandas.Series.std PR01,RT03,SA01" \
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-i "pandas.Series.str PR01,SA01" \
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-i "pandas.Series.str.capitalize RT03" \
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-i "pandas.Series.str.casefold RT03" \
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-i "pandas.Series.str.center RT03,SA01" \
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-i "pandas.Timedelta.view SA01" \
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-i "pandas.TimedeltaIndex.as_unit RT03,SA01" \
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-i "pandas.TimedeltaIndex.components SA01" \
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-i "pandas.TimedeltaIndex.days SA01" \
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-i "pandas.TimedeltaIndex.microseconds SA01" \
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-i "pandas.TimedeltaIndex.nanoseconds SA01" \
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-i "pandas.TimedeltaIndex.seconds SA01" \
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-i "pandas.TimedeltaIndex.to_pytimedelta RT03,SA01" \
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-i "pandas.Timestamp PR07,SA01" \
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-i "pandas.Timestamp.as_unit SA01" \
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-i "pandas.Timestamp.asm8 SA01" \
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-i "pandas.Timestamp.astimezone SA01" \
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-i "pandas.Timestamp.ctime SA01" \
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-i "pandas.Timestamp.date SA01" \
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-i "pandas.Timestamp.day GL08" \
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-i "pandas.Timestamp.day_name SA01" \
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-i "pandas.Timestamp.day_of_week SA01" \
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-i "pandas.Timestamp.day_of_year SA01" \
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-i "pandas.Timestamp.dayofyear SA01" \
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-i "pandas.Timestamp.days_in_month SA01" \
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-i "pandas.Timestamp.daysinmonth SA01" \
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-i "pandas.Timestamp.floor SA01" \
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-i "pandas.Timestamp.fold GL08" \
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-i "pandas.Timestamp.isocalendar SA01" \
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-i "pandas.Timestamp.isoformat SA01" \
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-i "pandas.Timestamp.isoweekday SA01" \
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-i "pandas.Timestamp.max PR02" \
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-i "pandas.Timestamp.min PR02,PR07,SA01" \
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-i "pandas.Timestamp.min PR02" \
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-i "pandas.Timestamp.minute GL08" \
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-i "pandas.Timestamp.month GL08" \
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-i "pandas.Timestamp.weekofyear SA01" \
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-i "pandas.Timestamp.year GL08" \
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-i "pandas.api.extensions.ExtensionArray SA01" \
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-i "pandas.api.extensions.ExtensionArray._accumulate RT03,SA01" \
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-i "pandas.api.extensions.ExtensionArray._concat_same_type PR07,SA01" \
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-i "pandas.api.extensions.ExtensionArray._formatter SA01" \
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-i "pandas.api.extensions.ExtensionArray._from_sequence SA01" \
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-i "pandas.api.extensions.ExtensionArray._from_sequence_of_strings SA01" \
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-i "pandas.api.extensions.ExtensionArray._hash_pandas_object RT03,SA01" \
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-i "pandas.api.extensions.ExtensionArray._pad_or_backfill PR01,RT03,SA01" \

‎doc/source/user_guide/basics.rst

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@@ -160,11 +160,10 @@ Here is a sample (using 100 column x 100,000 row ``DataFrames``):
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.. csv-table::
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:header: "Operation", "0.11.0 (ms)", "Prior Version (ms)", "Ratio to Prior"
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163-
:delim: ;
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``df1 > df2``; 13.32; 125.35; 0.1063
166-
``df1 * df2``; 21.71; 36.63; 0.5928
167-
``df1 + df2``; 22.04; 36.50; 0.6039
164+
``df1 > df2``, 13.32, 125.35, 0.1063
165+
``df1 * df2``, 21.71, 36.63, 0.5928
166+
``df1 + df2``, 22.04, 36.50, 0.6039
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You are highly encouraged to install both libraries. See the section
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:ref:`Recommended Dependencies <install.recommended_dependencies>` for more installation info.

‎doc/source/user_guide/gotchas.rst

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Many people have suggested that NumPy should simply emulate the ``NA`` support
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present in the more domain-specific statistical programming language `R
318-
<https://www.r-project.org/>`__. Part of the reason is the NumPy type hierarchy:
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.. csv-table::
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:header: "Typeclass","Dtypes"
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:widths: 30,70
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:delim: |
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``numpy.floating`` | ``float16, float32, float64, float128``
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``numpy.integer`` | ``int8, int16, int32, int64``
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``numpy.unsignedinteger`` | ``uint8, uint16, uint32, uint64``
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``numpy.object_`` | ``object_``
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``numpy.bool_`` | ``bool_``
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``numpy.character`` | ``bytes_, str_``
318+
<https://www.r-project.org/>`__. Part of the reason is the
319+
`NumPy type hierarchy <https://numpy.org/doc/stable/user/basics.types.html>`__.
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332321
The R language, by contrast, only has a handful of built-in data types:
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``integer``, ``numeric`` (floating-point), ``character``, and

‎doc/source/user_guide/groupby.rst

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.. csv-table::
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:header: "Method", "Description"
508508
:widths: 20, 80
509-
:delim: ;
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511-
:meth:`~.DataFrameGroupBy.any`;Compute whether any of the values in the groups are truthy
512-
:meth:`~.DataFrameGroupBy.all`;Compute whether all of the values in the groups are truthy
513-
:meth:`~.DataFrameGroupBy.count`;Compute the number of non-NA values in the groups
514-
:meth:`~.DataFrameGroupBy.cov` * ;Compute the covariance of the groups
515-
:meth:`~.DataFrameGroupBy.first`;Compute the first occurring value in each group
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:meth:`~.DataFrameGroupBy.idxmax`;Compute the index of the maximum value in each group
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:meth:`~.DataFrameGroupBy.idxmin`;Compute the index of the minimum value in each group
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:meth:`~.DataFrameGroupBy.last`;Compute the last occurring value in each group
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:meth:`~.DataFrameGroupBy.max`;Compute the maximum value in each group
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:meth:`~.DataFrameGroupBy.mean`;Compute the mean of each group
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:meth:`~.DataFrameGroupBy.median`;Compute the median of each group
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:meth:`~.DataFrameGroupBy.min`;Compute the minimum value in each group
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:meth:`~.DataFrameGroupBy.nunique`;Compute the number of unique values in each group
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:meth:`~.DataFrameGroupBy.prod`;Compute the product of the values in each group
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:meth:`~.DataFrameGroupBy.quantile`;Compute a given quantile of the values in each group
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:meth:`~.DataFrameGroupBy.sem`;Compute the standard error of the mean of the values in each group
527-
:meth:`~.DataFrameGroupBy.size`;Compute the number of values in each group
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:meth:`~.DataFrameGroupBy.skew` *;Compute the skew of the values in each group
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:meth:`~.DataFrameGroupBy.std`;Compute the standard deviation of the values in each group
530-
:meth:`~.DataFrameGroupBy.sum`;Compute the sum of the values in each group
531-
:meth:`~.DataFrameGroupBy.var`;Compute the variance of the values in each group
509+
510+
:meth:`~.DataFrameGroupBy.any`,Compute whether any of the values in the groups are truthy
511+
:meth:`~.DataFrameGroupBy.all`,Compute whether all of the values in the groups are truthy
512+
:meth:`~.DataFrameGroupBy.count`,Compute the number of non-NA values in the groups
513+
:meth:`~.DataFrameGroupBy.cov` * ,Compute the covariance of the groups
514+
:meth:`~.DataFrameGroupBy.first`,Compute the first occurring value in each group
515+
:meth:`~.DataFrameGroupBy.idxmax`,Compute the index of the maximum value in each group
516+
:meth:`~.DataFrameGroupBy.idxmin`,Compute the index of the minimum value in each group
517+
:meth:`~.DataFrameGroupBy.last`,Compute the last occurring value in each group
518+
:meth:`~.DataFrameGroupBy.max`,Compute the maximum value in each group
519+
:meth:`~.DataFrameGroupBy.mean`,Compute the mean of each group
520+
:meth:`~.DataFrameGroupBy.median`,Compute the median of each group
521+
:meth:`~.DataFrameGroupBy.min`,Compute the minimum value in each group
522+
:meth:`~.DataFrameGroupBy.nunique`,Compute the number of unique values in each group
523+
:meth:`~.DataFrameGroupBy.prod`,Compute the product of the values in each group
524+
:meth:`~.DataFrameGroupBy.quantile`,Compute a given quantile of the values in each group
525+
:meth:`~.DataFrameGroupBy.sem`,Compute the standard error of the mean of the values in each group
526+
:meth:`~.DataFrameGroupBy.size`,Compute the number of values in each group
527+
:meth:`~.DataFrameGroupBy.skew` * ,Compute the skew of the values in each group
528+
:meth:`~.DataFrameGroupBy.std`,Compute the standard deviation of the values in each group
529+
:meth:`~.DataFrameGroupBy.sum`,Compute the sum of the values in each group
530+
:meth:`~.DataFrameGroupBy.var`,Compute the variance of the values in each group
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533532
Some examples:
534533

@@ -832,19 +831,18 @@ The following methods on GroupBy act as transformations.
832831
.. csv-table::
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835-
:delim: ;
836-
837-
:meth:`~.DataFrameGroupBy.bfill`;Back fill NA values within each group
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:meth:`~.DataFrameGroupBy.cumcount`;Compute the cumulative count within each group
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:meth:`~.DataFrameGroupBy.cummax`;Compute the cumulative max within each group
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:meth:`~.DataFrameGroupBy.cummin`;Compute the cumulative min within each group
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:meth:`~.DataFrameGroupBy.cumprod`;Compute the cumulative product within each group
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:meth:`~.DataFrameGroupBy.cumsum`;Compute the cumulative sum within each group
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:meth:`~.DataFrameGroupBy.diff`;Compute the difference between adjacent values within each group
844-
:meth:`~.DataFrameGroupBy.ffill`;Forward fill NA values within each group
845-
:meth:`~.DataFrameGroupBy.pct_change`;Compute the percent change between adjacent values within each group
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:meth:`~.DataFrameGroupBy.rank`;Compute the rank of each value within each group
847-
:meth:`~.DataFrameGroupBy.shift`;Shift values up or down within each group
834+
835+
:meth:`~.DataFrameGroupBy.bfill`,Back fill NA values within each group
836+
:meth:`~.DataFrameGroupBy.cumcount`,Compute the cumulative count within each group
837+
:meth:`~.DataFrameGroupBy.cummax`,Compute the cumulative max within each group
838+
:meth:`~.DataFrameGroupBy.cummin`,Compute the cumulative min within each group
839+
:meth:`~.DataFrameGroupBy.cumprod`,Compute the cumulative product within each group
840+
:meth:`~.DataFrameGroupBy.cumsum`,Compute the cumulative sum within each group
841+
:meth:`~.DataFrameGroupBy.diff`,Compute the difference between adjacent values within each group
842+
:meth:`~.DataFrameGroupBy.ffill`,Forward fill NA values within each group
843+
:meth:`~.DataFrameGroupBy.pct_change`,Compute the percent change between adjacent values within each group
844+
:meth:`~.DataFrameGroupBy.rank`,Compute the rank of each value within each group
845+
:meth:`~.DataFrameGroupBy.shift`,Shift values up or down within each group
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849847
In addition, passing any built-in aggregation method as a string to
850848
:meth:`~.DataFrameGroupBy.transform` (see the next section) will broadcast the result
@@ -1092,11 +1090,10 @@ efficient, GroupBy-specific, implementation.
10921090
.. csv-table::
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10941092
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1095-
:delim: ;
10961093

1097-
:meth:`~.DataFrameGroupBy.head`;Select the top row(s) of each group
1098-
:meth:`~.DataFrameGroupBy.nth`;Select the nth row(s) of each group
1099-
:meth:`~.DataFrameGroupBy.tail`;Select the bottom row(s) of each group
1094+
:meth:`~.DataFrameGroupBy.head`,Select the top row(s) of each group
1095+
:meth:`~.DataFrameGroupBy.nth`,Select the nth row(s) of each group
1096+
:meth:`~.DataFrameGroupBy.tail`,Select the bottom row(s) of each group
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11011098
Users can also use transformations along with Boolean indexing to construct complex
11021099
filtrations within groups. For example, suppose we are given groups of products and

‎doc/source/user_guide/indexing.rst

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@@ -94,13 +94,14 @@ well). Any of the axes accessors may be the null slice ``:``. Axes left out of
9494
the specification are assumed to be ``:``, e.g. ``p.loc['a']`` is equivalent to
9595
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.. csv-table::
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:header: "Object Type", "Indexers"
99-
:widths: 30, 50
100-
:delim: ;
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102-
Series; ``s.loc[indexer]``
103-
DataFrame; ``df.loc[row_indexer,column_indexer]``
98+
.. ipython:: python
99+
100+
ser = pd.Series(range(5), index=list("abcde"))
101+
ser.loc[["a", "c", "e"]]
102+
103+
df = pd.DataFrame(np.arange(25).reshape(5, 5), index=list("abcde"), columns=list("abcde"))
104+
df.loc[["a", "c", "e"], ["b", "d"]]
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.. _indexing.basics:
106107

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.. csv-table::
117118
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118119
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119-
:delim: ;
120120

121-
Series; ``series[label]``; scalar value
122-
DataFrame; ``frame[colname]``; ``Series`` corresponding to colname
121+
Series, ``series[label]``, scalar value
122+
DataFrame, ``frame[colname]``, ``Series`` corresponding to colname
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124124
Here we construct a simple time series data set to use for illustrating the
125125
indexing functionality:

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