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resolve conflicts, remove dict c_REMOVED_ABBREVS, add msg if raise KeyError in get_reso_from_freqstr
2 parents 4085d5c + 236d89b commit 44740c0

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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: v0.4.7
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rev: v0.5.0
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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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hooks:
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- id: isort
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- repo: https://github.com/asottile/pyupgrade
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rev: v3.15.2
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rev: v3.16.0
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hooks:
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- id: pyupgrade
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args: [--py310-plus]
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- id: sphinx-lint
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args: ["--enable", "all", "--disable", "line-too-long"]
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- repo: https://github.com/pre-commit/mirrors-clang-format
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rev: v18.1.5
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rev: v18.1.8
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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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@@ -244,13 +244,11 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
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-i "pandas.Timestamp.month_name SA01" \
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-i "pandas.Timestamp.nanosecond GL08" \
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-i "pandas.Timestamp.normalize SA01" \
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-i "pandas.Timestamp.now SA01" \
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-i "pandas.Timestamp.quarter SA01" \
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-i "pandas.Timestamp.replace PR07,SA01" \
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-i "pandas.Timestamp.resolution PR02" \
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-i "pandas.Timestamp.second GL08" \
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-i "pandas.Timestamp.strptime PR01,SA01" \
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-i "pandas.Timestamp.time SA01" \
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-i "pandas.Timestamp.timestamp SA01" \
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-i "pandas.Timestamp.timetuple SA01" \
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-i "pandas.Timestamp.timetz SA01" \

‎doc/source/user_guide/io.rst

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@@ -2161,7 +2161,7 @@ a JSON string with two fields, ``schema`` and ``data``.
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{
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"A": [1, 2, 3],
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"B": ["a", "b", "c"],
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"C": pd.date_range("2016-01-01", freq="d", periods=3),
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"C": pd.date_range("2016-01-01", freq="D", periods=3),
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},
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index=pd.Index(range(3), name="idx"),
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)
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{
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"foo": [1, 2, 3, 4],
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"bar": ["a", "b", "c", "d"],
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"baz": pd.date_range("2018-01-01", freq="d", periods=4),
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"baz": pd.date_range("2018-01-01", freq="D", periods=4),
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"qux": pd.Categorical(["a", "b", "c", "c"]),
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},
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index=pd.Index(range(4), name="idx"),

‎doc/source/whatsnew/v0.18.0.rst

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@@ -322,15 +322,28 @@ Tz-aware are rounded, floored and ceiled in local times
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Timedeltas
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.. ipython:: python
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.. code-block:: ipython
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In [37]: t = pd.timedelta_range('1 days 2 hr 13 min 45 us', periods=3, freq='d')
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t = pd.timedelta_range('1 days 2 hr 13 min 45 us', periods=3, freq='d')
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t
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t.round('10min')
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In [38]: t
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Out[38]:
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TimedeltaIndex(['1 days 02:13:00.000045', '2 days 02:13:00.000045',
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'3 days 02:13:00.000045'],
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dtype='timedelta64[ns]', freq='D')
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In [39]: t.round('10min')
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Out[39]:
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TimedeltaIndex(['1 days 02:10:00', '2 days 02:10:00',
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'3 days 02:10:00'],
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dtype='timedelta64[ns]', freq=None)
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# Timedelta scalar
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t[0]
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t[0].round('2h')
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In [40]: t[0]
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Out[40]: Timedelta('1 days 02:13:00.000045')
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In [41]: t[0].round('2h')
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Out[41]: Timedelta('1 days 02:00:00')
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In addition, ``.round()``, ``.floor()`` and ``.ceil()`` will be available through the ``.dt`` accessor of ``Series``.

‎doc/source/whatsnew/v0.20.0.rst

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@@ -308,15 +308,26 @@ The new orient ``'table'`` for :meth:`DataFrame.to_json`
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will generate a `Table Schema`_ compatible string representation of
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the data.
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.. ipython:: python
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.. code-block:: ipython
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df = pd.DataFrame(
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{'A': [1, 2, 3],
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'B': ['a', 'b', 'c'],
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'C': pd.date_range('2016-01-01', freq='d', periods=3)},
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index=pd.Index(range(3), name='idx'))
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df
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df.to_json(orient='table')
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In [38]: df = pd.DataFrame(
314+
....: {'A': [1, 2, 3],
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....: 'B': ['a', 'b', 'c'],
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....: 'C': pd.date_range('2016-01-01', freq='d', periods=3)},
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....: index=pd.Index(range(3), name='idx'))
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In [39]: df
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Out[39]:
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A B C
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idx
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0 1 a 2016-01-01
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1 2 b 2016-01-02
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2 3 c 2016-01-03
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[3 rows x 3 columns]
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In [40]: df.to_json(orient='table')
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Out[40]:
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'{"schema":{"fields":[{"name":"idx","type":"integer"},{"name":"A","type":"integer"},{"name":"B","type":"string"},{"name":"C","type":"datetime"}],"primaryKey":["idx"],"pandas_version":"1.4.0"},"data":[{"idx":0,"A":1,"B":"a","C":"2016-01-01T00:00:00.000"},{"idx":1,"A":2,"B":"b","C":"2016-01-02T00:00:00.000"},{"idx":2,"A":3,"B":"c","C":"2016-01-03T00:00:00.000"}]}'
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See :ref:`IO: Table Schema for more information <io.table_schema>`.

‎doc/source/whatsnew/v0.22.0.rst

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*pandas 0.22.0*
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.. ipython:: python
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.. code-block:: ipython
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In [11]: s = pd.Series([1, 1, np.nan, np.nan],
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....: index=pd.date_range("2017", periods=4))
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s = pd.Series([1, 1, np.nan, np.nan], index=pd.date_range("2017", periods=4))
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s.resample("2d").sum()
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In [12]: s.resample("2d").sum()
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Out[12]:
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2017-01-01 2.0
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2017-01-03 0.0
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Freq: 2D, Length: 2, dtype: float64
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To restore the 0.21 behavior of returning ``NaN``, use ``min_count>=1``.
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.. ipython:: python
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.. code-block:: ipython
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In [13]: s.resample("2d").sum(min_count=1)
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Out[13]:
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2017-01-01 2.0
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2017-01-03 NaN
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Freq: 2D, Length: 2, dtype: float64
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s.resample("2d").sum(min_count=1)
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In particular, upsampling and taking the sum or product is affected, as
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upsampling introduces missing values even if the original series was

‎doc/source/whatsnew/v0.23.0.rst

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A ``DataFrame`` can now be written to and subsequently read back via JSON while preserving metadata through usage of the ``orient='table'`` argument (see :issue:`18912` and :issue:`9146`). Previously, none of the available ``orient`` values guaranteed the preservation of dtypes and index names, amongst other metadata.
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53-
.. ipython:: python
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.. code-block:: ipython
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df = pd.DataFrame({'foo': [1, 2, 3, 4],
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'bar': ['a', 'b', 'c', 'd'],
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'baz': pd.date_range('2018-01-01', freq='d', periods=4),
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'qux': pd.Categorical(['a', 'b', 'c', 'c'])},
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index=pd.Index(range(4), name='idx'))
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df
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df.dtypes
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df.to_json('test.json', orient='table')
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new_df = pd.read_json('test.json', orient='table')
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new_df
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new_df.dtypes
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In [1]: df = pd.DataFrame({'foo': [1, 2, 3, 4],
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...: 'bar': ['a', 'b', 'c', 'd'],
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...: 'baz': pd.date_range('2018-01-01', freq='d', periods=4),
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...: 'qux': pd.Categorical(['a', 'b', 'c', 'c'])},
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...: index=pd.Index(range(4), name='idx'))
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In [2]: df
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Out[2]:
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foo bar baz qux
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idx
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0 1 a 2018-01-01 a
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1 2 b 2018-01-02 b
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2 3 c 2018-01-03 c
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3 4 d 2018-01-04 c
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[4 rows x 4 columns]
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In [3]: df.dtypes
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Out[3]:
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foo int64
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bar object
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baz datetime64[ns]
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qux category
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Length: 4, dtype: object
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In [4]: df.to_json('test.json', orient='table')
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In [5]: new_df = pd.read_json('test.json', orient='table')
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In [6]: new_df
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Out[6]:
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foo bar baz qux
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idx
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0 1 a 2018-01-01 a
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1 2 b 2018-01-02 b
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2 3 c 2018-01-03 c
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3 4 d 2018-01-04 c
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[4 rows x 4 columns]
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In [7]: new_df.dtypes
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Out[7]:
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foo int64
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bar object
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baz datetime64[ns]
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qux category
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Length: 4, dtype: object
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Please note that the string ``index`` is not supported with the round trip format, as it is used by default in ``write_json`` to indicate a missing index name.
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‎doc/source/whatsnew/v3.0.0.rst

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- Deprecated allowing non-keyword arguments in :meth:`Series.to_markdown` except ``buf``. (:issue:`57280`)
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- Deprecated allowing non-keyword arguments in :meth:`Series.to_string` except ``buf``. (:issue:`57280`)
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- Deprecated behavior of :meth:`Series.dt.to_pytimedelta`, in a future version this will return a :class:`Series` containing python ``datetime.timedelta`` objects instead of an ``ndarray`` of timedelta; this matches the behavior of other :meth:`Series.dt` properties. (:issue:`57463`)
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- Deprecated lowercase strings ``d``, ``b`` and ``c`` denoting frequencies in :class:`Day`, :class:`BusinessDay` and :class:`CustomBusinessDay` in favour of ``D``, ``B`` and ``C`` (:issue:`58998`)
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- Deprecated lowercase strings ``w``, ``w-mon``, ``w-tue``, etc. denoting frequencies in :class:`Week` in favour of ``W``, ``W-MON``, ``W-TUE``, etc. (:issue:`58998`)
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- Deprecated parameter ``method`` in :meth:`DataFrame.reindex_like` / :meth:`Series.reindex_like` (:issue:`58667`)
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- Deprecated strings ``w``, ``d``, ``MIN``, ``MS``, ``US`` and ``NS`` denoting units in :class:`Timedelta` in favour of ``W``, ``D``, ``min``, ``ms``, ``us`` and ``ns`` (:issue:`59051`)
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- Deprecated using ``epoch`` date format in :meth:`DataFrame.to_json` and :meth:`Series.to_json`, use ``iso`` instead. (:issue:`57063`)
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- Bug in :meth:`read_csv` raising ``TypeError`` when ``index_col`` is specified and ``na_values`` is a dict containing the key ``None``. (:issue:`57547`)
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- Bug in :meth:`read_csv` raising ``TypeError`` when ``nrows`` and ``iterator`` are specified without specifying a ``chunksize``. (:issue:`59079`)
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- Bug in :meth:`read_excel` raising ``ValueError`` when passing array of boolean values when ``dtype="boolean"``. (:issue:`58159`)
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- Bug in :meth:`read_json` not validating the ``typ`` argument to not be exactly ``"frame"`` or ``"series"`` (:issue:`59124`)
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- Bug in :meth:`read_stata` raising ``KeyError`` when input file is stored in big-endian format and contains strL data. (:issue:`58638`)
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-
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571573
Period
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^^^^^^
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598600
^^^^^^^^^
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- Bug in :meth:`DataFrame.join` inconsistently setting result index name (:issue:`55815`)
600602
- Bug in :meth:`DataFrame.unstack` producing incorrect results when ``sort=False`` (:issue:`54987`, :issue:`55516`)
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- Bug in :meth:`DataFrame.unstack` producing incorrect results when manipulating empty :class:`DataFrame` with an :class:`ExtentionDtype` (:issue:`59123`)
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Sparse
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^^^^^^
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617620
^^^^^
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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`)
619622
- Bug in :func:`eval` on :class:`ExtensionArray` on including division ``/`` failed with a ``TypeError``. (:issue:`58748`)
623+
- Bug in :func:`eval` on :class:`complex` including division ``/`` discards imaginary part. (:issue:`21374`)
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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:`unique` on :class:`Index` not always returning :class:`Index` (:issue:`57043`)
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- Bug in :meth:`DataFrame.apply` where passing ``engine="numba"`` ignored ``args`` passed to the applied function (:issue:`58712`)

‎pandas/_libs/src/datetime/pd_datetime.c

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}
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static PyModuleDef_Slot pandas_datetime_slots[] = {
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{Py_mod_exec, pandas_datetime_exec}, {0, NULL}};
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{Py_mod_exec, pandas_datetime_exec},
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#if PY_VERSION_HEX >= 0x030D0000
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{Py_mod_gil, Py_MOD_GIL_NOT_USED},
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#endif
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{0, NULL},
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};
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static struct PyModuleDef pandas_datetimemodule = {
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PyModuleDef_HEAD_INIT,

‎pandas/_libs/src/parser/pd_parser.c

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static PyModuleDef_Slot pandas_parser_slots[] = {
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{Py_mod_exec, pandas_parser_exec},
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#if PY_VERSION_HEX >= 0x030D0000
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{Py_mod_gil, Py_MOD_GIL_NOT_USED},
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#endif
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{0, NULL},
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};
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static struct PyModuleDef pandas_parsermodule = {
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PyModuleDef_HEAD_INIT,

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