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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 _get_covariance(self, X):
 """Compute covariance matrix with transformed data. Args: X: `numpy.ndarray` or `pandas.DataFrame`. Returns: np.ndarray """

        result = pd.DataFrame(index=range(len(X)))
        column_names = self.get_column_names(X)
        for column_name in column_names:
            column = self.get_column(X, column_name)
            distrib = self.distribs[column_name]

            # get original distrib's cdf of the column
            cdf = distrib.cumulative_distribution(column)

            if distrib.constant_value is not None:
                # This is to avoid np.inf in the case the column is constant.
                cdf = np.ones(column.shape) - EPSILON

            # get inverse cdf using standard normal
            result = self.set_column(result, column_name, stats.norm.ppf(cdf))

        # remove any rows that have infinite values
        result = result[(result != np.inf).all(axis=1)]
        return pd.DataFrame(data=result).cov().values