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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, observed=False, **kwargs):
 """ Group DataFrame or Series using a mapper or by a Series of columns. A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups. Parameters by : mapping, function, label, or list of labels Used to determine the groups for the groupby. If ``by`` is a function, it's called on each value of the object's index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series' values are first aligned; see ``.align()`` method). If an ndarray is passed, the values are used as-is determine the groups. A label or list of labels may be passed to group by the columns in ``self``. Notice that a tuple is interpreted a (single) key. axis : {0 or 'index', 1 or 'columns'}, default 0 Split along rows (0) or columns (1). level : int, level name, or sequence of such, default None If the axis is a MultiIndex (hierarchical), group by a particular level or levels. as_index : bool, default True For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively "SQL-style" grouped output. sort : bool, default True Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group. group_keys : bool, default True When calling apply, add group keys to index to identify pieces. squeeze : bool, default False Reduce the dimensionality of the return type if possible, otherwise return a consistent type. observed : bool, default False This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers. .. versionadded:: 0.23.0 **kwargs Optional, only accepts keyword argument 'mutated' and is passed to groupby. Returns ------- DataFrameGroupBy or SeriesGroupBy Depends on the calling object and returns groupby object that contains information about the groups. See Also -------- resample : Convenience method for frequency conversion and resampling of time series. Notes ----- See the `user guide <http://pandas.pydata.org/pandas-docs/stable/groupby.html>`_ for more. Examples -------- Animal Max Speed 0 Falcon 380.0 1 Falcon 370.0 2 Parrot 24.0 3 Parrot 26.0 Max Speed Animal Falcon 375.0 Parrot 25.0 **Hierarchical Indexes** We can groupby different levels of a hierarchical index using the `level` parameter: Max Speed Animal Type Falcon Captive 390.0 Wild 350.0 Parrot Captive 30.0 Wild 20.0 Max Speed Animal Falcon 370.0 Parrot 25.0 Max Speed Type Captive 210.0 Wild 185.0 """

        from pandas.core.groupby.groupby import groupby

        if level is None and by is None:
            raise TypeError("You have to supply one of 'by' and 'level'")
        axis = self._get_axis_number(axis)
        return groupby(self, by=by, axis=axis, level=level, as_index=as_index,
                       sort=sort, group_keys=group_keys, squeeze=squeeze,
                       observed=observed, **kwargs)