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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 min_cov_determinant(prices, frequency=252, random_state=None):
 """ Calculate the minimum covariance determinant, an estimator of the covariance matrix that is more robust to noise. :param prices: adjusted closing prices of the asset, each row is a date and each column is a ticker/id. :type prices: pd.DataFrame :param frequency: number of time periods in a year, defaults to 252 (the number of trading days in a year) :type frequency: int, optional :param random_state: random seed to make results reproducible, defaults to None :type random_state: int, optional :return: annualised estimate of covariance matrix :rtype: pd.DataFrame """

    if not isinstance(prices, pd.DataFrame):
        warnings.warn("prices are not in a dataframe", RuntimeWarning)
        prices = pd.DataFrame(prices)
    assets = prices.columns
    X = prices.pct_change().dropna(how="all")
    X = np.nan_to_num(X.values)
    raw_cov_array = covariance.fast_mcd(X, random_state=random_state)[1]
    return pd.DataFrame(raw_cov_array, index=assets, columns=assets) * frequency