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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 efficient_frontier( self, returns, cov_matrix, min_return, max_return, count ):
 """ Returns a DataFrame of efficient portfolio allocations for `count` risk indices. """


        columns = [coin for coin in self.SUPPORTED_COINS]
        # columns.append('Return')
        # columns.append('Risk')
        values = pd.DataFrame(columns=columns)
        weights = [1/len(self.SUPPORTED_COINS)] * len(self.SUPPORTED_COINS)

        def func(weights):
            """The objective function that minimizes variance."""
            return np.matmul(np.matmul(weights.transpose(), cov_matrix), weights)

        def func_deriv(weights):
            """The derivative of the objective function."""
            return (
                np.matmul(weights.transpose(), cov_matrix.transpose()) +
                np.matmul(weights.transpose(), cov_matrix)
            )

        for point in np.linspace(min_return, max_return, count):
            constraints = (
                {'type': 'eq', 'fun': lambda weights: (weights.sum() - 1)},
                {'type': 'ineq', 'fun': lambda weights, i=point: (
                    np.dot(weights, returns.values) - i
                )}
            )

            solution = self.solve_minimize(func, weights, constraints, func_deriv=func_deriv)

            columns = {}
            for index, coin in enumerate(self.SUPPORTED_COINS):
                columns[coin] = math.floor(solution.x[index] * 100 * 100) / 100

            # NOTE: These lines could be helpful, but are commented out right now.
            # columns['Return'] = round(np.dot(solution.x, returns), 6)
            # columns['Risk'] = round(solution.fun, 6)

            values = values.append(columns, ignore_index=True)

        return values