Use stats.quantiles to get quantile-value pairs, and then print them to fit.
    >>> import numpy as np
    >>> quantiles(np.random.normal(size=10000), qs=[0, 5, 50, 95, 100])
     0% < -3.670    5% < -1.675   50% < -0.002   95% < 1.6500   100% < 3.4697
    (Or something like that)

def quantiles(xs, qs=None, step=25, width=None, cellspacing=3):
    '''
    Use stats.quantiles to get quantile-value pairs, and then print them to fit.
    >>> import numpy as np
    >>> quantiles(np.random.normal(size=10000), qs=[0, 5, 50, 95, 100])
     0% < -3.670    5% < -1.675   50% < -0.002   95% < 1.6500   100% < 3.4697
    (Or something like that)
    '''

    if width is None:
        width = terminal.width()

    qs_values = stats.quantiles(xs, qs=qs, step=step)

    ncells = len(qs_values)
    cellspacing_total = (ncells - 1) * cellspacing
    cell_width = int((width - cellspacing_total) / ncells)

    cells = ['%2d%% < %s' % (q, format_float(value, cell_width - 6)) for q, value in qs_values]

    print (' '*cellspacing).join(cells)