以不同顺序对多列上的结构化numpy数组进行排序 [英] Sort Structured Numpy Array On Multiple Columns In Different Order

问题描述

我有一个结构化的numpy数组:

I have a structured numpy array:

dtype = [('price', float), ('counter', int)]
values = [(35, 1), (36, 2),
          (36, 3)]
a = np.array(values, dtype=dtype)

如果价格相等，我想对价格进行排序，然后对计数器进行排序:

I want to sort for price and then for counter if price is equal:

a_sorted = np.sort(a, order=['price', 'counter'])[::-1]

我需要按降序排列价格，当价格相等时，请考虑按升序排列.在上面的示例中，价格和计数器均按降序排列.

I need the price in a descending order and when prices are equal consider counter in ASCENDING order. In the example above both the price and the counter are in descending order.

我得到的是:

a_sorted: [(36., 3), (36., 2), (35., 1)]

我需要的是:

 a_sorted: [(36., 2), (36., 3), (35., 1)]

推荐答案

您可以使用 np.lexsort :

You can use np.lexsort:

a_sorted = a[np.lexsort((a['counter'], -a['price']))]

结果:

array([(36.0, 2), (36.0, 3), (35.0, 1)], 
      dtype=[('price', '<f8'), ('counter', '<i4')])

请记住，顺序是相反的，即首先由-a['price']执行排序.否定会照顾下降"方面.

Just remember the order is reversed, i.e. sorting is performed first by -a['price']. Negation takes care of the "descending" aspect.

这篇关于以不同顺序对多列上的结构化numpy数组进行排序的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！