如何通过对每组 N 个顺序元素执行操作来减少 pandas 系列

Question

假设我有一个 pandas 系列，我想取每组 8 行的平均值。我事先不知道系列的大小，索引可能不是基于 0 的。我目前有以下

N = 8

s = pd.Series(np.random.random(50 * N))

n_sets = s.shape[0] // N

split = ([m * N for m in range(n_sets)],
         [m * N for m in range(1, n_sets + 1)])

out_array = np.zeros(n_sets)

for i, (a, b) in enumerate(zip(*split)):

    out_array[i] = s.loc[s.index[a:b]].mean()

有更短的方法吗？

Answer 1

您可以尝试使用 groupby，通过对 N 中的索引进行切片（您可以查看 here 对切片的解释），然后使用 pd.Series.mean()：

newout_array=s.groupby(s.index//N).mean().to_list()

输出：

out_array  #original solution
[0.42147899 0.55668055 0.5222594  0.46066426 0.44378491 0.52719371
 0.42479113 0.46485387 0.2800083  0.57174865 0.59207811 0.58665479
 0.52414851 0.38158931 0.51884761 0.59007469 0.3449512  0.56385373
 0.34359674 0.44524997 0.44175351 0.42339394 0.5687501  0.3140091
 0.40985639 0.46649486 0.3101396  0.45664647 0.51829052 0.38875796
 0.45428001 0.52979064 0.62545921 0.64782618 0.65265239 0.56976799
 0.64277369 0.33528876 0.45973874 0.45341751 0.52690983 0.66427599
 0.59814577 0.35575622 0.62995929 0.61582329 0.38971679 0.4771326
 0.50889137 0.25105353]


newout_array  #new solution

[0.4214789945860148, 0.5566805507021909, 0.5222593998859411, 0.46066425607167216, 0.4437849132421554, 0.5271937114894408,
 0.424791134573943, 0.4648538659945887, 0.28000829556024387, 0.5717486453029332, 0.5920781058695997, 0.5866547941460012, 
 0.5241485100329547, 0.38158931177460725, 0.5188476113762392, 0.5900746905953183, 0.34495119855714756, 0.5638537286251522, 
 0.3435967359945349, 0.44524997190104454, 0.44175351484451975, 0.42339393886425913, 0.5687501027416468, 0.3140090963728155, 
 0.40985639015924036, 0.4664948621046134, 0.3101396034068746, 0.45664647332866076, 0.5182905157666298, 0.38875796468438406, 
 0.4542800111275337, 0.5297906368971982, 0.6254592119278896, 0.6478261817988752, 0.6526523935382951, 0.569767994485338, 
 0.642773691835847, 0.3352887578683835, 0.45973873832126594, 0.45341751320112617, 0.5269098312525405, 0.6642759923683706, 
 0.5981457683986061, 0.3557562229383897, 0.6299592930489117, 0.6158232897272005, 0.38971678834383916, 0.4771325988592886, 
 0.5088913710936904, 0.25105352820427246]

不同是因为每个格式的小数位数，如果你想像原来的out_array一样只有8位小数，你可以尝试map round的元素] 函数：

newout_array=s.groupby(s.index//N).mean().to_list()
newout_array=list(map(lambda x: round(x,8),newout_array))

如何通过对每组 N 个顺序元素执行操作来减少 pandas 系列

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