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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 by_col(cls, df, events, populations, w=None, inplace=False, pvalue='sim', outvals=None, swapname='', **stat_kws):
 """ Function to compute a Moran_Rate statistic on a dataframe Arguments --------- df : pandas.DataFrame a pandas dataframe with a geometry column events : string or list of strings one or more names where events are stored populations : string or list of strings one or more names where the populations corresponding to the events are stored. If one population column is provided, it is used for all event columns. If more than one population column is provided but there is not a population for every event column, an exception will be raised. w : pysal weights object a weights object aligned with the dataframe. If not provided, this is searched for in the dataframe's metadata inplace : bool a boolean denoting whether to operate on the dataframe inplace or to return a series contaning the results of the computation. If operating inplace, the derived columns will be named 'column_moran_rate' pvalue : string a string denoting which pvalue should be returned. Refer to the the Moran_Rate statistic's documentation for available p-values outvals : list of strings list of arbitrary attributes to return as columns from the Moran_Rate statistic **stat_kws : keyword arguments options to pass to the underlying statistic. For this, see the documentation for the Moran_Rate statistic. Returns -------- If inplace, None, and operation is conducted on dataframe in memory. Otherwise, returns a copy of the dataframe with the relevant columns attached. See Also --------- For further documentation, refer to the Moran_Rate class in pysal.esda """

        if not inplace:
            new = df.copy()
            cls.by_col(new, events, populations, w=w, inplace=True,
                              pvalue=pvalue, outvals=outvals, swapname=swapname,
                              **stat_kws)
            return new
        if isinstance(events, str):
            events = [events]
        if isinstance(populations, str):
            populations = [populations]
        if len(populations) < len(events):
            populations = populations * len(events)
        if len(events) != len(populations):
            raise ValueError('There is not a one-to-one matching between events and '
                              'populations!\nEvents: {}\n\nPopulations:'
                              ' {}'.format(events, populations))
        adjusted = stat_kws.pop('adjusted', True)

        if isinstance(adjusted, bool):
            adjusted = [adjusted] * len(events)
        if swapname is '':
            swapname = cls.__name__.lower()

        rates = [assuncao_rate(df[e], df[pop]) if adj
                 else df[e].astype(float) / df[pop]
                 for e,pop,adj in zip(events, populations, adjusted)]
        names = ['-'.join((e,p)) for e,p in zip(events, populations)]
        out_df = df.copy()
        rate_df = out_df.from_items(list(zip(names, rates))) #trick to avoid importing pandas
        stat_df = _univariate_handler(rate_df, names, w=w, inplace=False,
                                      pvalue = pvalue, outvals = outvals,
                                      swapname=swapname,
                                      stat=Moran, #how would this get done w/super?
                                      **stat_kws)
        for col in stat_df.columns:
            df[col] = stat_df[col]