126 lines
4.2 KiB
Python
126 lines
4.2 KiB
Python
"""Any shareable docstring components for rolling/expanding/ewm"""
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from textwrap import dedent
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from pandas.core.shared_docs import _shared_docs
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_shared_docs = dict(**_shared_docs)
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def create_section_header(header: str) -> str:
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"""Create numpydoc section header"""
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return "\n".join((header, "-" * len(header))) + "\n"
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template_header = "\nCalculate the {window_method} {aggregation_description}.\n\n"
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template_returns = dedent(
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"""
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Series or DataFrame
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Return type is the same as the original object with ``np.float64`` dtype.\n
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"""
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).replace("\n", "", 1)
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template_see_also = dedent(
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"""
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pandas.Series.{window_method} : Calling {window_method} with Series data.
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pandas.DataFrame.{window_method} : Calling {window_method} with DataFrames.
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pandas.Series.{agg_method} : Aggregating {agg_method} for Series.
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pandas.DataFrame.{agg_method} : Aggregating {agg_method} for DataFrame.\n
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"""
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).replace("\n", "", 1)
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args_compat = dedent(
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"""
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*args
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For NumPy compatibility and will not have an effect on the result.\n
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"""
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).replace("\n", "", 1)
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kwargs_compat = dedent(
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"""
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**kwargs
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For NumPy compatibility and will not have an effect on the result.\n
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"""
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).replace("\n", "", 1)
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kwargs_scipy = dedent(
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"""
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**kwargs
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Keyword arguments to configure the ``SciPy`` weighted window type.\n
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"""
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).replace("\n", "", 1)
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window_apply_parameters = dedent(
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"""
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func : function
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Must produce a single value from an ndarray input if ``raw=True``
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or a single value from a Series if ``raw=False``. Can also accept a
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Numba JIT function with ``engine='numba'`` specified.
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.. versionchanged:: 1.0.0
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raw : bool, default False
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* ``False`` : passes each row or column as a Series to the
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function.
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* ``True`` : the passed function will receive ndarray
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objects instead.
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If you are just applying a NumPy reduction function this will
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achieve much better performance.
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engine : str, default None
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* ``'cython'`` : Runs rolling apply through C-extensions from cython.
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* ``'numba'`` : Runs rolling apply through JIT compiled code from numba.
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Only available when ``raw`` is set to ``True``.
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* ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba``
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.. versionadded:: 1.0.0
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engine_kwargs : dict, default None
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* For ``'cython'`` engine, there are no accepted ``engine_kwargs``
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* For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
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and ``parallel`` dictionary keys. The values must either be ``True`` or
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``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
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``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be
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applied to both the ``func`` and the ``apply`` rolling aggregation.
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.. versionadded:: 1.0.0
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args : tuple, default None
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Positional arguments to be passed into func.
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kwargs : dict, default None
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Keyword arguments to be passed into func.\n
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"""
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).replace("\n", "", 1)
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numba_notes = (
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"See :ref:`window.numba_engine` and :ref:`enhancingperf.numba` for "
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"extended documentation and performance considerations for the Numba engine.\n\n"
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)
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def window_agg_numba_parameters(version: str = "1.3") -> str:
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return (
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dedent(
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"""
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engine : str, default None
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* ``'cython'`` : Runs the operation through C-extensions from cython.
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* ``'numba'`` : Runs the operation through JIT compiled code from numba.
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* ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba``
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.. versionadded:: {version}.0
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engine_kwargs : dict, default None
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* For ``'cython'`` engine, there are no accepted ``engine_kwargs``
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* For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil``
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and ``parallel`` dictionary keys. The values must either be ``True`` or
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``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is
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``{{'nopython': True, 'nogil': False, 'parallel': False}}``
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.. versionadded:: {version}.0\n
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"""
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)
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.replace("\n", "", 1)
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.replace("{version}", version)
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)
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