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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 compute_panel(cls, data, scales, **params):
 """ Calculate the stats of all the groups and return the results in a single dataframe. This is a default function that can be overriden by individual stats Parameters data : dataframe data for the computing scales : types.SimpleNamespace x (``scales.x``) and y (``scales.y``) scale objects. The most likely reason to use scale information is to find out the physical size of a scale. e.g:: range_x = scales.x.dimension() params : dict The parameters for the stat. It includes default values if user did not set a particular parameter. """

        if not len(data):
            return type(data)()

        stats = []
        for _, old in data.groupby('group'):
            new = cls.compute_group(old, scales, **params)
            unique = uniquecols(old)
            missing = unique.columns.difference(new.columns)
            u = unique.loc[[0]*len(new), missing].reset_index(drop=True)
            # concat can have problems with empty dataframes that
            # have an index
            if u.empty and len(u):
                u = type(data)()

            df = pd.concat([new, u], axis=1)
            stats.append(df)

        stats = pd.concat(stats, axis=0, ignore_index=True)

        # Note: If the data coming in has columns with non-unique
        # values with-in group(s), this implementation loses the
        # columns. Individual stats may want to do some preparation
        # before then fall back on this implementation or override
        # it completely.
        return stats