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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 gausspars(data):
 """ calculates gaussian statistics for data """

    N, mean, d = len(data), 0., 0.
    if N < 1:
        return "", ""
    if N == 1:
        return data[0], 0
    for j in range(N):
        mean += old_div(data[j], float(N))
    for j in range(N):
        d += (data[j] - mean)**2
    stdev = np.sqrt(d * (1./(float(N - 1))))
    return mean, stdev