Question

我想从不符合特定条件的numpy数组theoretical_price_for_bonds中删除元素。我知道我可以使用下面的代码行完成此操作。但是，我还想跟踪已删除元素的索引，我想知道如何做到这一点。

theoretical_price_for_bonds = theoretical_price_for_bonds[(theoretical_price_for_bonds>75)]

我尝试使用循环从numpy数组中动态删除元素。价格还可以，但dropped_indices只是一个填充None的列表：

#To insert values into a list dynamically
class GrowingList(list):
    def __setitem__(self, index, value):
        if index >= len(self):
            self.extend([None]*(index + 1 - len(self)))
        list.__setitem__(self, index, value)

count = 0
dropped_indices = GrowingList()
for x,value in np.ndenumerate(theoretical_price_for_bonds):
    count = count + 1         
    if count < theoretical_price_for_bonds.shape[0]:
        if theoretical_price_for_bonds[count] < 75:
            theoretical_price_for_bonds = np.delete(theoretical_price_for_bonds, (count), axis=0)
            dropped_indices[count] = count

谢谢

Answer 1

如果您想跟踪已删除元素的索引，只需按住用于索引数组的布尔掩码并使用np.where：

>>> x = np.array([2,8,3,4,7,6,1])
>>> lix = x > 4
>>> x = x[lix] # this "drops" everything 4 or less
>>> x
array([8, 7, 6])
>>> [dropped] = np.where(~lix) # find the indices that weren't dropped
>>> dropped
array([0, 2, 3, 6])

Answer 2

您还可以考虑使用pandas.Series，其.index属性可用于跟踪已删除的值：

import numpy as np
import pandas as pd

s = pd.Series(np.array([2,8,3,4,7,6,1]))
print(s.values, s.index)
# (array([2, 8, 3, 4, 7, 6, 1]), Int64Index([0, 1, 2, 3, 4, 5, 6], dtype='int64'))

s2 = s[s > 4]
print(s2.values, s2.index)
# (array([8, 7, 6]), Int64Index([1, 4, 5], dtype='int64'))

从numpy数组中删除元素时跟踪丢弃的索引

2 个答案: