+ Ŝi`Rt^RIHt^RIHt^RIHt]!R/]BtRRltRt ]!R4PRR ^4t ]!R 4PRR ^4t ]!R 4PRR ^4t ]!R 4PRR ^4t]!R 4PRR ^4tRtRtRRRlltR#)z>> h = lambda x, arg2, arg3: x + 1 - arg2 * arg3 >>> g = lambda x, arg1: x * 5 / arg1 >>> f = lambda x: x ** 4 >>> df = pd.DataFrame({'A': [1, 2, 3, 4]}, index=pd.date_range('2012-08-02', periods=4)) >>> h(g(f(df.rolling('2D')), arg1=1), arg2=2, arg3=3) # doctest: +SKIP You can write >>> (df.rolling('2D') ... .pipe(f) ... .pipe(g, arg1=1) ... .pipe(h, arg2=2, arg3=3)) # doctest: +SKIP which is much more readable. Parameters ---------- func : callable or tuple of (callable, str) Function to apply to this %(klass)s object or, alternatively, a `(callable, data_keyword)` tuple where `data_keyword` is a string indicating the keyword of `callable` that expects the %(klass)s object. *args : iterable, optional Positional arguments passed into `func`. **kwargs : dict, optional A dictionary of keyword arguments passed into `func`. Returns ------- %(klass)s The original object with the function `func` applied. See Also -------- Series.pipe : Apply a function with arguments to a series. DataFrame.pipe: Apply a function with arguments to a dataframe. apply : Apply function to each group instead of to the full %(klass)s object. Notes ----- See more `here `_ Examples -------- %(examples)s zSee :ref:`window.numba_engine` and :ref:`enhancingperf.numba` for extended documentation and performance considerations for the Numba engine. c V^8dQhRRRR/#)rversionrr r )r s"r r r srcZ\R4PRR^4PRV4#)a engine : str, default None * ``'cython'`` : Runs the operation through C-extensions from cython. * ``'numba'`` : Runs the operation through JIT compiled code from numba. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` .. versionadded:: {version}.0 engine_kwargs : dict, default None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` .. versionadded:: {version}.0 rrz {version})rreplace)rs&r window_agg_numba_parametersrs0  & r1  g &+rNr )z1.3)__doc__ __future__rtextwraprpandas.core.shared_docsrdictrtemplate_headerrtemplate_returnstemplate_see_alsokwargs_numeric_only kwargs_scipywindow_apply_parameters template_pipe numba_notesrr rr r'sB"0#l# . S  '$A  '$A '$A  '$A  !!#F '$AGJ7 tV r