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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 mean_std(fractions):
 """ Given an N x M matrix, returns mean and std computed on the rows, i.e. two M-dimensional vectors. """

    n = fractions.shape[0]
    if n == 1:  # avoid warnings when computing the stddev
        return fractions[0], numpy.ones_like(fractions[0]) * numpy.nan
    return numpy.mean(fractions, axis=0), numpy.std(fractions, axis=0, ddof=1)