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asv_bench/benchmarks/reshape.py

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@@ -268,7 +268,9 @@ def setup(self, bins):
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self.datetime_series = pd.Series(
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np.random.randint(N, size=N), dtype="datetime64[ns]"
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)
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self.interval_bins = pd.IntervalIndex.from_breaks(np.linspace(0, N, bins))
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self.interval_bins = pd.IntervalIndex.from_breaks(
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np.linspace(0, N, bins), "right"
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)
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def time_cut_int(self, bins):
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pd.cut(self.int_series, bins)

ci/deps/actions-310.yaml

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- python=3.10
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# test dependencies
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- cython=0.29.24
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- cython=0.29.30
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- pytest>=6.0
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- pytest-cov
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- pytest-xdist>=1.31

ci/deps/actions-38-downstream_compat.yaml

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- python=3.8
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# test dependencies
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- cython=0.29.24
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- cython=0.29.30
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- pytest>=6.0
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- pytest-cov
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- pytest-xdist>=1.31

ci/deps/actions-38-minimum_versions.yaml

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- python=3.8.0
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# test dependencies
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- cython=0.29.24
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- cython=0.29.30
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- pytest>=6.0
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- pytest-cov
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- pytest-xdist>=1.31

ci/deps/actions-38.yaml

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- python=3.8
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# test dependencies
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- cython=0.29.24
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- cython=0.29.30
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- pytest>=6.0
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- pytest-cov
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- pytest-xdist>=1.31

ci/deps/actions-39.yaml

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- python=3.9
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# test dependencies
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- cython=0.29.24
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- cython=0.29.30
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- pytest>=6.0
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- pytest-cov
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- pytest-xdist>=1.31

ci/deps/actions-pypy-38.yaml

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- python=3.8[build=*_pypy] # TODO: use this once pypy3.8 is available
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# tools
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- cython>=0.29.24
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- cython>=0.29.30
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- pytest>=6.0
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- pytest-cov
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- pytest-asyncio

ci/deps/circle-38-arm64.yaml

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- python=3.8
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# test dependencies
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- cython=0.29.24
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- cython=0.29.30
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- pytest>=6.0
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- pytest-cov
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- pytest-xdist>=1.31

doc/data/fx_prices

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doc/data/mindex_ex.csv

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doc/data/test.xls

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doc/redirects.csv

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@@ -741,11 +741,11 @@ generated/pandas.Index.values,../reference/api/pandas.Index.values
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generated/pandas.Index.view,../reference/api/pandas.Index.view
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generated/pandas.Index.where,../reference/api/pandas.Index.where
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generated/pandas.infer_freq,../reference/api/pandas.infer_freq
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generated/pandas.Interval.closed,../reference/api/pandas.Interval.closed
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generated/pandas.Interval.inclusive,../reference/api/pandas.Interval.inclusive
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generated/pandas.Interval.closed_left,../reference/api/pandas.Interval.closed_left
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generated/pandas.Interval.closed_right,../reference/api/pandas.Interval.closed_right
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generated/pandas.Interval,../reference/api/pandas.Interval
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generated/pandas.IntervalIndex.closed,../reference/api/pandas.IntervalIndex.closed
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generated/pandas.IntervalIndex.inclusive,../reference/api/pandas.IntervalIndex.inclusive
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generated/pandas.IntervalIndex.contains,../reference/api/pandas.IntervalIndex.contains
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generated/pandas.IntervalIndex.from_arrays,../reference/api/pandas.IntervalIndex.from_arrays
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generated/pandas.IntervalIndex.from_breaks,../reference/api/pandas.IntervalIndex.from_breaks

doc/source/reference/arrays.rst

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@@ -303,7 +303,7 @@ Properties
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.. autosummary::
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:toctree: api/
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Interval.closed
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Interval.inclusive
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Interval.closed_left
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Interval.closed_right
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Interval.is_empty
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arrays.IntervalArray.left
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arrays.IntervalArray.right
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arrays.IntervalArray.closed
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arrays.IntervalArray.inclusive
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arrays.IntervalArray.mid
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arrays.IntervalArray.length
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arrays.IntervalArray.is_empty

doc/source/reference/indexing.rst

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IntervalIndex.left
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IntervalIndex.right
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IntervalIndex.mid
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IntervalIndex.closed
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IntervalIndex.inclusive
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IntervalIndex.length
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IntervalIndex.values
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IntervalIndex.is_empty

doc/source/reference/testing.rst

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errors.ParserError
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errors.ParserWarning
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errors.PerformanceWarning
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errors.SettingWithCopyError
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errors.SpecificationError
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errors.UnsortedIndexError
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errors.UnsupportedFunctionCall
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doc/source/user_guide/advanced.rst

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In [7]: df.loc[pd.Interval(0.5, 2.5)]
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---------------------------------------------------------------------------
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KeyError: Interval(0.5, 2.5, closed='right')
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KeyError: Interval(0.5, 2.5, inclusive='right')
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Selecting all ``Intervals`` that overlap a given ``Interval`` can be performed using the
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:meth:`~IntervalIndex.overlaps` method to create a boolean indexer.

doc/source/user_guide/indexing.rst

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``mode.chained_assignment`` to one of these values:
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* ``'warn'``, the default, means a ``SettingWithCopyWarning`` is printed.
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* ``'raise'`` means pandas will raise a ``SettingWithCopyException``
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* ``'raise'`` means pandas will raise a ``SettingWithCopyError``
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you have to deal with.
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* ``None`` will suppress the warnings entirely.
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>>> dfd.loc[0]['a'] = 1111
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Traceback (most recent call last)
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...
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SettingWithCopyException:
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SettingWithCopyError:
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A value is trying to be set on a copy of a slice from a DataFrame.
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Try using .loc[row_index,col_indexer] = value instead
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