我有一个数组,我需要找到序列,然后将其拆分为数组数组。
如果我发现异常值并据此进行排序,我会怎么想。我尝试了以下
data = [ 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 2987,
2988, 2989, 2990, 2991, 2992, 2993, 2994, 2995, 2996, 4992, 4993,
4994, 4995, 5007, 5008, 5009, 5010, 5011, 5012, 5013, 5014, 5015,
5016, 5987, 5988, 5989, 5990, 5991, 5992, 5993, 5994, 5995, 5996,
6036, 6037, 6038, 6039, 6040, 6041, 6042, 6043, 6044, 6045, 6046,
6047]
def reject_outliers(data, m = 2.):
d = np.abs(data - np.median(data))
mdev = np.median(d)
s = d/mdev if mdev else 0.
return data[s<m], data[s>m]
def sort_sequences(data):
d = tuple()
sorted_data = tuple()
seq = reject_outliers(data)
sorted_data = + (seq[1])
print(sorted_data)
main_part = seq[0]
another_part = seq[1]
if len(another_part) != 0:
sort_sequences(main_part)
return sorted_data
现在,如果我应用此
`data_sorted = sort_sequences(actual)`
我得到:
[347 348 349 350 351 352 353 354 355 356]
这不是我想要的
答案 0 :(得分:2)
那
>>> ddata = data[1:] - data[:-1] # faster than np.diff(data)
>>> ddata
array([ 1, 1, 1, 1, 1, 1, 1, 1, 1, 2631, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1996, 1, 1,
1, 12, 1, 1, 1, 1, 1, 1, 1, 1, 1,
971, 1, 1, 1, 1, 1, 1, 1, 1, 1, 40,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
最后
>>> sequences = np.split(data, np.argwhere(ddata>1).flatten() + 1)
>>> sequences[0]
array([347, 348, 349, 350, 351, 352, 353, 354, 355, 356])
>>> sequences[2]
array([4992, 4993, 4994, 4995])
>>> sequences[-1]
array([6036, 6037, 6038, 6039, 6040, 6041, 6042, 6043, 6044, 6045, 6046,
6047])
data[1:] - data[:-1]
VS np.diff(data)
-tested via repl.it
setup_="""
import numpy as np
MULT = 100
data = np.array(MULT*[ 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 2987,
2988, 2989, 2990, 2991, 2992, 2993, 2994, 2995, 2996, 4992, 4993,
4994, 4995, 5007, 5008, 5009, 5010, 5011, 5012, 5013, 5014, 5015,
5016, 5987, 5988, 5989, 5990, 5991, 5992, 5993, 5994, 5995, 5996,
6036, 6037, 6038, 6039, 6040, 6041, 6042, 6043, 6044, 6045, 6046,
6047])
"""
import timeit
然后
Python 3.6.1 (default, Dec 2015, 13:05:11)
[GCC 4.8.2] on linux
>>> timeit.Timer('data[1:] - data[:-1]', setup=setup_).timeit()
35.17186516011134
>>> timeit.Timer('np.diff(data)', setup=setup_).timeit()
63.88404295803048
答案 1 :(得分:1)
编写更少的代码:
diffs = np.diff(data)
sequences = np.split(data, np.argwhere(diffs>1).flatten() + 1)