NumPy 使用索引數組進行索引

>>> a = np.arange(12)**2  # the first 12 square numbers
>>> i = np.array([1, 1, 3, 8, 5])  # an array of indices
>>> a[i]  # the elements of `a` at the positions `i`
array([ 1,  1,  9, 64, 25])
>>>
>>> j = np.array([[3, 4], [9, 7]])  # a bidimensional array of indices
>>> a[j]  # the same shape as `j`
array([[ 9, 16],
       [81, 49]])

當索引數組a是多維時，單個索引數組指的是a的第一維。以下示例通過使用調色板將標簽圖像轉換為彩色圖像來展示此行為。

>>> palette = np.array([[0, 0, 0],         # black
...                     [255, 0, 0],       # red
...                     [0, 255, 0],       # green
...                     [0, 0, 255],       # blue
...                     [255, 255, 255]])  # white
>>> image = np.array([[0, 1, 2, 0],  # each value corresponds to a color in the palette
...                   [0, 3, 4, 0]])
>>> palette[image]  # the (2, 4, 3) color image
array([[[  0,   0,   0],
        [255,   0,   0],
        [  0, 255,   0],
        [  0,   0,   0]],


       [[  0,   0,   0],
        [  0,   0, 255],
        [255, 255, 255],
        [  0,   0,   0]]])

我們還可以為多個維度提供索引。每個維度的索引數組必須具有相同的形狀。

>>> a = np.arange(12).reshape(3, 4)
>>> a
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> i = np.array([[0, 1],  # indices for the first dim of `a`
...               [1, 2]])
>>> j = np.array([[2, 1],  # indices for the second dim
...               [3, 3]])
>>>
>>> a[i, j]  # i and j must have equal shape
array([[ 2,  5],
       [ 7, 11]])
>>>
>>> a[i, 2]
array([[ 2,  6],
       [ 6, 10]])
>>>
>>> a[:, j]
array([[[ 2,  1],
        [ 3,  3]],


       [[ 6,  5],
        [ 7,  7]],


       [[10,  9],
        [11, 11]]])

在 Python 中，arr[i,?j]與arr[(i,?j)]完全相同，因此我們可以將i和j放入tuple 中，然后使用它進行索引。

>>> l = (i, j)
>>> # equivalent to a[i, j]
>>> a[l]
array([[ 2,  5],
       [ 7, 11]])

但是，我們不能通過將i和j放入數組來做到這一點，因為該數組將被解釋為索引a的第一維。

>>> s = np.array([i, j])
>>> # not what we want
>>> a[s]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
IndexError: index 3 is out of bounds for axis 0 with size 3
>>> # same as `a[i, j]`
>>> a[tuple(s)]
array([[ 2,  5],
       [ 7, 11]])

數組索引的另一個常見用途是搜索時間相關序列的最大值：

>>> time = np.linspace(20, 145, 5)  # time scale
>>> data = np.sin(np.arange(20)).reshape(5, 4)  # 4 time-dependent series
>>> time
array([ 20.  ,  51.25,  82.5 , 113.75, 145.  ])
>>> data
array([[ 0.        ,  0.84147098,  0.90929743,  0.14112001],
       [-0.7568025 , -0.95892427, -0.2794155 ,  0.6569866 ],
       [ 0.98935825,  0.41211849, -0.54402111, -0.99999021],
       [-0.53657292,  0.42016704,  0.99060736,  0.65028784],
       [-0.28790332, -0.96139749, -0.75098725,  0.14987721]])
>>> # index of the maxima for each series
>>> ind = data.argmax(axis=0)
>>> ind
array([2, 0, 3, 1])
>>> # times corresponding to the maxima
>>> time_max = time[ind]
>>>
>>> data_max = data[ind, range(data.shape[1])]  # => data[ind[0], 0], data[ind[1], 1]...
>>> time_max
array([ 82.5 ,  20.  , 113.75,  51.25])
>>> data_max
array([0.98935825, 0.84147098, 0.99060736, 0.6569866 ])
>>> np.all(data_max == data.max(axis=0))
True

還可以將索引與數組一起用作分配給的目標：

>>> a = np.arange(5)
>>> a
array([0, 1, 2, 3, 4])
>>> a[[1, 3, 4]] = 0
>>> a
array([0, 0, 2, 0, 0])

然而，當索引列表包含重復時，賦值會進行多次，留下最后一個值：

>>> a = np.arange(5)
>>> a[[0, 0, 2]] = [1, 2, 3]
>>> a
array([2, 1, 3, 3, 4])

這是合理的，但是如果你想使用 Python 的?+=構造，請注意，因為它可能不會達到您的預期：

>>> a = np.arange(5)
>>> a[[0, 0, 2]] += 1
>>> a
array([1, 1, 3, 3, 4])

即使 0 在索引列表中出現兩次，第 0 個元素也只增加一次。這是因為 Python 需要a?+=?1等效于a?=?a?+?1.