8000 Merge branch 'main' into add-warning-timestamp-blank-string · pandas-dev/pandas@3e26a62 · GitHub
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Merge branch 'main' into add-warning-timestamp-blank-string
2 parents 6d81c60 + 5736b96 commit 3e26a62

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‎.github/CODEOWNERS

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@@ -10,10 +10,8 @@ doc/source/development @noatamir
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# pandas
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pandas/_libs/ @WillAyd
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pandas/_libs/tslibs/* @MarcoGorelli
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pandas/_typing.py @Dr-Irv
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pandas/core/groupby/* @rhshadrach
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pandas/core/tools/datetimes.py @MarcoGorelli
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pandas/io/excel/* @rhshadrach
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pandas/io/formats/style.py @attack68
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pandas/io/formats/style_render.py @attack68

‎.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/cibuildwheel@v2.23.1
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uses: pypa/cibuildwheel@v2.23.2
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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:

‎asv_bench/benchmarks/indexing_engines.py

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@@ -67,6 +67,14 @@ class NumericEngineIndexing:
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def setup(self, engine_and_dtype, index_type, unique, N):
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engine, dtype = engine_and_dtype
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if (
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index_type == "non_monotonic"
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and dtype in [np.int16, np.int8, np.uint8]
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and unique
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):
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# Values overflow
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raise NotImplementedError
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if index_type == "monotonic_incr":
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if unique:
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arr = np.arange(N * 3, dtype=dtype)
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engine, dtype = engine_and_dtype
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dtype = dtype.lower()
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if (
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index_type == "non_monotonic"
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and dtype in ["int16", "int8", "uint8"]
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and unique
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):
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# Values overflow
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raise NotImplementedError
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if index_type == "monotonic_incr":
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if unique:
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arr = np.arange(N * 3, dtype=dtype)

‎ci/meta.yaml

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- datapythonista
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- phofl
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- lithomas1
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- marcogorelli

‎doc/source/development/debugging_extensions.rst

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@@ -23,7 +23,7 @@ By default building pandas from source will generate a release build. To generat
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.. note::
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conda environments update CFLAGS/CPPFLAGS with flags that are geared towards generating releases. If using conda, you may need to set ``CFLAGS="$CFLAGS -O0"`` and ``CPPFLAGS="$CPPFLAGS -O0"`` to ensure optimizations are turned off for debugging
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conda environments update CFLAGS/CPPFLAGS with flags that are geared towards generating releases, and may work counter towards usage in a development environment. If using conda, you should unset these environment variables via ``export CFLAGS=`` and ``export CPPFLAGS=``
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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.
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‎doc/source/getting_started/comparison/comparison_with_r.rst

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@@ -383,7 +383,7 @@ In Python, since ``a`` is a list, you can simply use list comprehension.
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.. ipython:: python
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a = np.array(list(range(1, 24)) + [np.NAN]).reshape(2, 3, 4)
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a = np.array(list(range(1, 24)) + [np.nan]).reshape(2, 3, 4)
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pd.DataFrame([tuple(list(x) + [val]) for x, val in np.ndenumerate(a)])
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meltlist
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.. ipython:: python
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a = list(enumerate(list(range(1, 5)) + [np.NAN]))
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a = list(enumerate(list(range(1, 5)) + [np.nan]))
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pd.DataFrame(a)
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For more details and examples see :ref:`the Intro to Data Structures

‎doc/source/user_guide/basics.rst

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Attributes and underlying data
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------------------------------
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pandas objects have a number of attributes enabling you to access the metadata
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pandas objects have a number of attributes enabling you to access the metadata.
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* **shape**: gives the axis dimensions of the object, consistent with ndarray
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* Axis labels
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(see :ref:`basics.dtypes`).
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To get the actual data inside a :class:`Index` or :class:`Series`, use
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the ``.array`` property
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the ``.array`` property.
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.. ipython:: python
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are two possibly useful representations:
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1. An object-dtype :class:`numpy.ndarray` with :class:`Timestamp` objects, each
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with the correct ``tz``
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with the correct ``tz``.
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2. A ``datetime64[ns]`` -dtype :class:`numpy.ndarray`, where the values have
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been converted to UTC and the timezone discarded
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been converted to UTC and the timezone discarded.
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Timezones may be preserved with ``dtype=object``
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Timezones may be preserved with ``dtype=object``:
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.. ipython:: python
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ser = pd.Series(pd.date_range("2000", periods=2, tz="CET"))
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ser.to_numpy(dtype=object)
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Or thrown away with ``dtype='datetime64[ns]'``
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Or thrown away with ``dtype='datetime64[ns]'``:
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.. ipython:: python
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.. ipython:: python
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df1 = pd.DataFrame(np.random.randn(8, 1), columns=["A"], dtype="float32")
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df1 = pd.DataFrame(np.random.randn(8, 1), columns=["A"], dtype="float64")
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df1
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df1.dtypes
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df2 = pd.DataFrame(
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{
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"A": pd.Series(np.random.randn(8), dtype="float16"),
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"A": pd.Series(np.random.randn(8), dtype="float32"),
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"B": pd.Series(np.random.randn(8)),
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"C": pd.Series(np.random.randint(0, 255, size=8), dtype="uint8"), # [0,255] (range of uint8)
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}

‎doc/source/user_guide/enhancingperf.rst

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In [4]: %%cython
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...: cimport numpy as np
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...: import numpy as np
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...: np.import_array()
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...: cdef double f_typed(double x) except? -2:
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...: cpdef double integrate_f_typed(double a, double b, int N):
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...: cimport cython
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...: import numpy as np
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...: np.import_array()
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...: cdef np.float64_t f_typed(np.float64_t x) except? -2:
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...: return x * (x - 1)
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...: cpdef np.float64_t integrate_f_typed(np.float64_t a, np.float64_t b, np.int64_t N):

‎doc/source/whatsnew/v0.11.0.rst

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df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32')
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df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float64')
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df1
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df1.dtypes
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df2 = pd.DataFrame({'A': pd.Series(np.random.randn(8), dtype='float16'),
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df2 = pd.DataFrame({'A': pd.Series(np.random.randn(8), dtype='float32'),
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'B': pd.Series(np.random.randn(8)),
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'C': pd.Series(range(8), dtype='uint8')})
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df2

‎doc/source/whatsnew/v3.0.0.rst

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Numeric
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^^^^^^^
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- Bug in :meth:`DataFrame.corr` where numerical precision errors resulted in correlations above ``1.0`` (:issue:`61120`)
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- Bug in :meth:`DataFrame.quantile` where the column type was not preserved when ``numeric_only=True`` with a list-like ``q`` produced an empty result (:issue:`59035`)
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- Bug in ``np.matmul`` with :class:`Index` inputs raising a ``TypeError`` (:issue:`57079`)
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- Bug in :meth:`.DataFrameGroupBy.quantile` when ``interpolation="nearest"`` is inconsistent with :meth:`DataFrame.quantile` (:issue:`47942`)
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- Bug in :meth:`.Resampler.interpolate` on a :class:`DataFrame` with non-uniform sampling and/or indices not aligning with the resulting resampled index would result in wrong interpolation (:issue:`21351`)
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- Bug in :meth:`DataFrame.ewm` and :meth:`Series.ewm` when passed ``times`` and aggregation functions other than mean (:issue:`51695`)
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- Bug in :meth:`DataFrame.resample` changing index type to :class:`MultiIndex` when the dataframe is empty and using an upsample method (:issue:`55572`)
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- Bug in :meth:`DataFrameGroupBy.agg` that raises ``AttributeError`` when there is dictionary input and duplicated columns, instead of returning a DataFrame with the aggregation of all duplicate columns. (:issue:`55041`)
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- Bug in :meth:`DataFrameGroupBy.apply` and :meth:`SeriesGroupBy.apply` for empty data frame with ``group_keys=False`` still creating output index using group keys. (:issue:`60471`)
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- Bug in :meth:`DataFrameGroupBy.apply` that was returning a completely empty DataFrame when all return values of ``func`` were ``None`` instead of returning an empty DataFrame with the original columns and dtypes. (:issue:`57775`)
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- Bug in :class:`DataFrame` when passing a ``dict`` with a NA scalar and ``columns`` that would always return ``np.nan`` (:issue:`57205`)
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- Bug in :class:`Series` ignoring errors when trying to convert :class:`Series` input data to the given ``dtype`` (:issue:`60728`)
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- Bug in :func:`eval` on :class:`ExtensionArray` on including division ``/`` failed with a ``TypeError``. (:issue:`58748`)
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- Bug in :func:`eval` where method calls on binary operations like ``(x + y).dropna()`` would raise ``AttributeError: 'BinOp' object has no attribute 'value'`` (:issue:`61175`)
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- Bug in :func:`eval` where the names of the :class:`Series` were not preserved when using ``engine="numexpr"``. (:issue:`10239`)
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- Bug in :func:`eval` with ``engine="numexpr"`` returning unexpected result for float division. (:issue:`59736`)
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- Bug in :func:`to_numeric` raising ``TypeError`` when ``arg`` is a :class:`Timedelta` or :class:`Timestamp` scalar. (:issue:`59944`)
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- Bug in :meth:`DataFrame 8771 .eval` and :meth:`DataFrame.query` which did not allow to use ``tan`` function. (:issue:`55091`)
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- Bug in :meth:`DataFrame.query` where using duplicate column names led to a ``TypeError``. (:issue:`59950`)
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- Bug in :meth:`DataFrame.query` which raised an exception or produced incorrect results when expressions contained backtick-quoted column names containing the hash character ``#``, backticks, or characters that fall outside the ASCII range (U+0001..U+007F). (:issue:`59285`) (:issue:`49633`)
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- Bug in :meth:`DataFrame.query` which raised an exception when querying integer column names using backticks. (:issue:`60494`)
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- Bug in :meth:`DataFrame.shift` where passing a ``freq`` on a DataFrame with no columns did not shift the index correctly. (:issue:`60102`)
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- Bug in :meth:`DataFrame.sort_index` when passing ``axis="columns"`` and ``ignore_index=True`` and ``ascending=False`` not returning a :class:`RangeIndex` columns (:issue:`57293`)
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- Bug in :meth:`DataFrame.transform` that was returning the wrong order unless the index was monotonically increasing. (:issue:`57069`)
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- Bug in :meth:`DataFrame.where` where using a non-bool type array in the function would return a ``ValueError`` instead of a ``TypeError`` (:issue:`56330`)
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- Bug in :meth:`Index.sort_values` when passing a key function that turns values into tuples, e.g. ``key=natsort.natsort_key``, would raise ``TypeError`` (:issue:`56081`)
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- Bug in :meth:`MultiIndex.fillna` error message was referring to ``isna`` instead of ``fillna`` (:issue:`60974`)
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- Bug in :meth:`Series.describe` where median percentile was always included when the ``percentiles`` argument was passed (:issue:`60550`).
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- Bug in :meth:`Series.diff` allowing non-integer values for the ``periods`` argument. (:issue:`56607`)
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- Bug in :meth:`Series.dt` methods in :class:`ArrowDtype` that were returning incorrect values. (:issue:`57355`)
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- Bug in :meth:`Series.isin` raising ``TypeError`` when series is large (>10**6) and ``values`` contains NA (:issue:`60678`)

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