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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 confidential_interval(x, alpha=0.98):
 """ Return a numpy array of column confidential interval Parameters x : ndarray A numpy array instance alpha : float Alpha value of confidential interval Returns ------- ndarray A 1 x n numpy array which indicate the each difference from sample average point to confidential interval point """

    from scipy.stats import t
    if x.ndim == 1:
        df = len(x) - 1
        # calculate positive critical value of student's T distribution
        cv = t.interval(alpha, df)
        # calculate sample standard distribution
        std = np.std(x)
    else:
        # calculate degree of freedom
        df = len(x[0]) - 1
        # calculate positive critical value of student's T distribution
        cv = t.interval(alpha, df)[1]
        # calculate sample standard distribution
        std = np.std(x, axis=1)
    # calculate positive difference from
    # sample average to confidential interval
    return std * cv / np.sqrt(df)