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‎doc/source/reference/groupby.rst‎

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=======
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GroupBy
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=======
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.. currentmodule:: pandas.core.groupby
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.. currentmodule:: pandas.api.typing
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:class:`pandas.api.typing.DataFrameGroupBy` and :class:`pandas.api.typing.SeriesGroupBy`
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instances are returned by groupby calls :func:`pandas.DataFrame.groupby` and
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NamedAgg
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.. currentmodule:: pandas.core.groupby
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.. currentmodule:: pandas.api.typing
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Function application
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--------------------

‎doc/source/reference/resampling.rst‎

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==========
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Resampling
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==========
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.. currentmodule:: pandas.core.resample
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.. currentmodule:: pandas.api.typing
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:class:`pandas.api.typing.Resampler` instances are returned by
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resample calls: :func:`pandas.DataFrame.resample`, :func:`pandas.Series.resample`.

‎doc/source/reference/window.rst‎

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Rolling window functions
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------------------------
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.. currentmodule:: pandas.core.window.rolling
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.. currentmodule:: pandas.api.typing
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.. autosummary::
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:toctree: api/
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Weighted window functions
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-------------------------
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.. currentmodule:: pandas.core.window.rolling
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.. currentmodule:: pandas.api.typing
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.. autosummary::
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:toctree: api/
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Expanding window functions
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--------------------------
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.. currentmodule:: pandas.core.window.expanding
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.. currentmodule:: pandas.api.typing
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.. autosummary::
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:toctree: api/
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Exponentially-weighted window functions
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---------------------------------------
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.. currentmodule:: pandas.core.window.ewm
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.. currentmodule:: pandas.api.typing
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.. autosummary::
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:toctree: api/

‎doc/source/user_guide/enhancingperf.rst‎

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@@ -455,7 +455,7 @@ by evaluate arithmetic and boolean expression all at once for large :class:`~pan
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:func:`~pandas.eval` is many orders of magnitude slower for
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smaller expressions or objects than plain Python. A good rule of thumb is
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to only use :func:`~pandas.eval` when you have a
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:class:`~pandas.core.frame.DataFrame` with more than 10,000 rows.
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:class:`~pandas.DataFrame` with more than 10,000 rows.
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Supported syntax
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~~~~~~~~~~~~~~~~

‎pandas/_config/config.py‎

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@@ -412,6 +412,9 @@ def __init__(self, d: dict[str, Any], prefix: str = "") -> None:
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object.__setattr__(self, "prefix", prefix)
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def __setattr__(self, key: str, val: Any) -> None:
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if key == "__module__":
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super().__setattr__(key, val)
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return
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prefix = object.__getattribute__(self, "prefix")
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if prefix:
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prefix += "."
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options = DictWrapper(_global_config)
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options.__module__ = "pandas"
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#
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# Functions for use by pandas developers, in addition to User - api

‎pandas/_libs/lib.pyx‎

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# Note: no_default is exported to the public API in pandas.api.extensions
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no_default = _NoDefault.no_default # Sentinel indicating the default value.
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no_default.__module__ = "pandas.api.extensions"
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NoDefault = Literal[_NoDefault.no_default]
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NoDefault.__module__ = "pandas.api.typing"
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@cython.boundscheck(False)

‎pandas/_libs/missing.pyx‎

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C_NA = NAType() # C-visible
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NA = C_NA # Python-visible
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NA.__module__ = "pandas"

‎pandas/_libs/tslibs/nattype.pyx‎

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c_NaT = NaTType() # C-visible
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NaT = c_NaT # Python-visible
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NaT.__module__ = "pandas"
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# ----------------------------------------------------------------------

‎pandas/core/arrays/base.py‎

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.. code-block:: python
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def take(self, indices, allow_fill=False, fill_value=None):
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from pandas.core.algorithms import take
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from pandas.api.extensions import take
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# If the ExtensionArray is backed by an ndarray, then
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# just pass that here instead of coercing to object.

‎pandas/core/dtypes/common.py‎

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>>> is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
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True
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>>> from pandas.core.dtypes.dtypes import DatetimeTZDtype
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>>> from pandas.api.types import DatetimeTZDtype
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>>> dtype = DatetimeTZDtype("ns", tz="US/Eastern")
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>>> s = pd.Series([], dtype=dtype)
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>>> is_datetime64tz_dtype(dtype)
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Examples
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--------
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>>> from pandas.core.dtypes.common import is_timedelta64_dtype
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>>> from pandas.api.types import is_timedelta64_dtype
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>>> is_timedelta64_dtype(object)
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False
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>>> is_timedelta64_dtype(np.timedelta64)
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Examples
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>>> from pandas.core.dtypes.common import is_period_dtype
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>>> from pandas.api.types import is_period_dtype
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>>> is_period_dtype(object)
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False
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>>> is_period_dtype(pd.PeriodDtype(freq="D"))
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Examples
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>>> from pandas.core.dtypes.common import is_interval_dtype
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>>> from pandas.api.types import is_interval_dtype
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>>> is_interval_dtype(object)
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>>> is_interval_dtype(pd.IntervalDtype())
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True
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>>> is_dtype_equal(object, "category")
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False
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>>> from pandas.core.dtypes.dtypes import CategoricalDtype
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>>> from pandas.api.types import CategoricalDtype
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>>> is_dtype_equal(CategoricalDtype(), "category")
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True
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>>> from pandas.core.dtypes.dtypes import DatetimeTZDtype
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>>> from pandas.api.types import DatetimeTZDtype
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>>> is_dtype_equal(DatetimeTZDtype(tz="UTC"), "datetime64")
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"""
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>>> from pandas.core.dtypes.common import is_signed_integer_dtype
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>>> from pandas.api.types import is_signed_integer_dtype
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>>> is_signed_integer_dtype(str)
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>>> is_signed_integer_dtype(int)
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>>> from pandas.api.types import is_datetime64_any_dtype
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>>> from pandas.api.types import DatetimeTZDtype
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>>> is_datetime64_any_dtype(int)
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>>> from pandas.api.types import is_datetime64_ns_dtype
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>>> from pandas.core.dtypes.dtypes import DatetimeTZDtype
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>>> from pandas.api.types import DatetimeTZDtype
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>>> is_datetime64_ns_dtype(str)
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>>> is_datetime64_ns_dtype(int)
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>>> from pandas.core.dtypes.common import is_timedelta64_ns_dtype
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>>> from pandas.api.types import is_timedelta64_ns_dtype
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>>> is_timedelta64_ns_dtype(np.dtype("m8[ns]"))
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True
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>>> is_timedelta64_ns_dtype(np.dtype("m8[ps]")) # Wrong frequency

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