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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 bootstrap_stat(arr, stat=np.mean, n_iters=1000, alpha=0.05):
 """ Produce a boot-strap distribution of the mean of an array on axis 0 Parameters --------- arr : ndarray The array with data to be bootstrapped stat : callable The statistical function to call. will be called as `stat(arr, 0)`, so needs to accept that call signature. n_iters : int The number of bootstrap iterations to sample alpha : float The confidence interval size will be 1-alpha """

    stat_orig = stat(arr, 0)

    boot_arr = np.empty((arr.shape[-1] , n_iters))
    for ii in xrange(n_iters):
        this_arr=arr[np.random.random_integers(0, arr.shape[0]-1, arr.shape[0])]
        boot_arr[:, ii] = stat(this_arr, 0)

    eb = np.array([stats.scoreatpercentile(boot_arr[xx], 1-(alpha/2)) -
                   stats.scoreatpercentile(boot_arr[xx], alpha/2)
                   for xx in range(boot_arr.shape[0])])

    return stat_orig, eb