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DOC: remove _agg_template_seris
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pandas/core/groupby/groupby.py

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@@ -364,70 +364,6 @@ class providing the base-class of operations.
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--------
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%(example)s"""
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_agg_template_frame = """
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Aggregate using one or more operations.
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Parameters
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----------
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func : function, str, list, dict or None
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Function to use for aggregating the data. If a function, must either
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work when passed a {klass} or when passed to {klass}.apply.
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Accepted combinations are:
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- function
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- string function name
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- list of functions and/or function names, e.g. ``[np.sum, 'mean']``
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- dict of index labels -> functions, function names or list of such.
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- None, in which case ``**kwargs`` are used with Named Aggregation. Here the
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output has one column for each element in ``**kwargs``. The name of the
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column is keyword, whereas the value determines the aggregation used to compute
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the values in the column.
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Can also accept a Numba JIT function with
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``engine='numba'`` specified. Only passing a single function is supported
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with this engine.
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If the ``'numba'`` engine is chosen, the function must be
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a user defined function with ``values`` and ``index`` as the
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first and second arguments respectively in the function signature.
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Each group's index will be passed to the user defined function
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and optionally available for use.
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*args
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Positional arguments to pass to func.
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engine : str, default None
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* ``'cython'`` : Runs the function through C-extensions from cython.
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* ``'numba'`` : Runs the function through JIT compiled code from numba.
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* ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba``
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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 the function
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**kwargs
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* If ``func`` is None, ``**kwargs`` are used to define the output names and
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aggregations via Named Aggregation. See ``func`` entry.
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* Otherwise, keyword arguments to be passed into func.
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Returns
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-------
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{klass}
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See Also
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--------
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{klass}.groupby.apply : Apply function func group-wise
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and combine the results together.
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{klass}.groupby.transform : Transforms the Series on each group
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based on the given function.
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{klass}.aggregate : Aggregate using one or more operations.
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Notes
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-----
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When using ``engine='numba'``, there will be no "fall back" behavior internally.
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The group data and group index will be passed as numpy arrays to the JITed
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user defined function, and no alternative execution attempts will be tried.
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Functions that mutate the passed object can produce unexpected
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behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
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for more details.
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.. versionchanged:: 1.3.0
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The resulting dtype will reflect the return value of the passed ``func``,
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see the examples below.
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{examples}"""
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@final
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class GroupByPlot(PandasObject):

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