Python 对于小分数（参见比较）。非常简洁，但我认为比较圆形版ed版会更公平；如果接近perc%的元素比“物理”分布更重要，你应该从这里开始。@jornsharpe。事实上，您是对的，将其更改为四舍五入版本。@jornsharpe对于round版本，最大偏差从100

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对于小分数（参见比较）。非常简洁，但我认为比较

圆形版

ed版会更公平；如果接近

perc

%的元素比“物理”分布更重要，你应该从这里开始。@jornsharpe。事实上，您是对的，将其更改为四舍五入版本。@jornsharpe对于

round

版本，最大偏差从100%降至45%左右。

def select_elements(seq, perc):
    """Select a defined percentage of the elements of seq."""
    return seq[::int(100.0/perc)]

>>> select_elements(range(10), 50)
[0, 2, 4, 6, 8]
>>> select_elements(range(10), 33)
[0, 3, 6, 9]
>>> select_elements(range(10), 25)
[0, 4, 8]

>>> int(3.6)
3
>>> int(round(3.6))
4

from bisect import bisect_left
def equal_dist_els(my_list, fraction):
    """
    Chose a fraction of equally distributed elements.
    :param my_list: The list to draw from
    :param fraction: The ideal fraction of elements
    :return: Elements of the list with the best match
    """
    length = len(my_list)
    list_indexes = range(length)
    nbr_bins = int(round(length * fraction))
    step = length / float(nbr_bins)  # the size of a single bin
    bins = [step * i for i in xrange(nbr_bins)]  # list of bin ends
    # distribute indexes into the bins
    splits = [bisect_left(list_indexes, wall) for wall in bins]
    splits.append(length)  # add the end for the last bin
    # get a list of (start, stop) indexes for each bin
    bin_limits = [(splits[i], splits[i + 1]) for i in xrange(len(splits) - 1)]
    out = []
    for bin_lim in bin_limits:
        f, t = bin_lim
        in_bin = my_list[f:t]  # choose the elements in my_list belonging in this bin
        out.append(in_bin[int(0.5 * len(in_bin))])  # choose the most central element
    return out

from matplotlib import pyplot as plt
# def of equal_dist_els see above
def select_els(seq, perc):
    """Select a defined percentage of the elements of seq."""
    return seq[::int(round(1./perc if perc != 0 else 0))]
list_length = 50
my_list = range(list_length)
percentages = range(1, 101)
fracts = map(lambda x: x * 0.01, percentages)

equal_dist = map(lambda x: abs(len(equal_dist_els(my_list, x)) / float(len(my_list)) - x), fracts)
slicing = map(lambda x: abs(len(select_els(my_list, x)) / float(len(my_list)) - x), fracts)

plt.plot(fracts, equal_dist, color='blue', alpha=0.8, linewidth=2, label=r'equal_dist_elements')
plt.plot(fracts, slicing, color='red', alpha=0.8, linewidth=2, label=r'select_elements by @jonrsharpe')
plt.title('Choosing equally dist. fraction of els from a list of length %s' % str(list_length))
plt.xlabel('requested fraction')
plt.ylabel('absolute deviation')
plt.legend(loc='upper left')
plt.show()