索引 numpy 数组时出错

Question

我正在运行以下代码对矩阵的行进行排序，然后从另一个向量中选取相应的元素：

import pandas as pd
import numpy as np
# compute ids
coeff = np.dot(matrix1, np.transpose(matrix2))  
print coeff.shape, ids.shape
indices = coeff.argsort()[:, ::-1]
print indices.shape
coeff_idx = ids[indices]

但是当程序到达最后一行时，我收到错误：

(11396, 45582) (11396,)
(11396, 45582)
...
File "pandas/hashtable.pyx", line 359, in pandas.hashtable.Int64HashTable.lookup (pandas/hashtable.c:7427)
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)

Answer 1

NumPy 数组允许整数数组索引，但 Pandas 系列不允许：

In [168]: arr = np.arange(5)
In [169]: ser = pd.Series(arr)
In [170]: indices = np.array([[0,1],[4,3],[2,2]])
In [171]: arr[indices]
Out[171]: 
array([[0, 1],
       [4, 3],
       [2, 2]])
In [172]: ser[indices]
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)

---

因此，在尝试使用 indices 索引之前，请将ids从 Pandas 系列更改为 NumPy 数组：

ids = ids.values
coeff_idx = ids[indices]