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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 train(self, data, **kwargs):
 """ Calculate the standard deviations and means in the training data """

        self.data = data
        for i in xrange(0,data.shape[1]):
            column_mean = np.mean(data.icol(i))
            column_stdev = np.std(data.icol(i))

            #Have to do += or "list" type will fail (ie with append)
            self.column_means += [column_mean]
            self.column_stdevs += [column_stdev]

        self.data = self.predict(data)