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136 lines
2.0 KiB
Markdown
136 lines
2.0 KiB
Markdown
# choose 函数实现条件筛选
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对于数组，我们有时候需要进行类似 `switch` 和 `case` 进行条件选择，此时使用 choose 函数十分方便：
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In [1]:
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```py
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import numpy as np
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```
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In [2]:
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```py
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control = np.array([[1,0,1],
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[2,1,0],
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[1,2,2]])
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np.choose(control, [10, 11, 12])
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```
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Out[2]:
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```py
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array([[11, 10, 11],
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[12, 11, 10],
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[11, 12, 12]])
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```
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在上面的例子中，`choose` 将 `0,1,2` 对应的值映射为了 `10, 11, 12`，这里的 `0,1,2` 表示对应的下标。
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事实上， `choose` 不仅仅能接受下标参数，还可以接受下标所在的位置：
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In [3]:
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```py
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i0 = np.array([[0,1,2],
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[3,4,5],
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[6,7,8]])
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i2 = np.array([[20,21,22],
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[23,24,25],
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[26,27,28]])
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control = np.array([[1,0,1],
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[2,1,0],
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[1,2,2]])
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np.choose(control, [i0, 10, i2])
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```
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Out[3]:
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```py
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array([[10, 1, 10],
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[23, 10, 5],
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[10, 27, 28]])
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```
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这里，`control` 传入第一个 `1` 对应的是 10，传入的第一个 `0` 对应于 `i0` 相应位置的值即 `1`，剩下的以此类推。
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下面的例子将数组中所有小于 `10` 的值变成了 `10`。
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In [4]:
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```py
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a = np.array([[ 0, 1, 2],
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[10,11,12],
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[20,21,22]])
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a < 10
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```
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Out[4]:
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```py
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array([[ True, True, True],
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[False, False, False],
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[False, False, False]], dtype=bool)
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```
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In [5]:
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```py
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np.choose(a < 10, (a, 10))
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```
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Out[5]:
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```py
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array([[10, 10, 10],
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[10, 11, 12],
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[20, 21, 22]])
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```
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下面的例子将数组中所有小于 10 的值变成了 10，大于 15 的值变成了 15。
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In [6]:
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```py
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a = np.array([[ 0, 1, 2],
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[10,11,12],
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[20,21,22]])
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lt = a < 10
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gt = a > 15
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choice = lt + 2 * gt
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choice
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```
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Out[6]:
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```py
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array([[1, 1, 1],
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[0, 0, 0],
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[2, 2, 2]])
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```
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In [7]:
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```py
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np.choose(choice, (a, 10, 15))
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```
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Out[7]:
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```py
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array([[10, 10, 10],
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[10, 11, 12],
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[15, 15, 15]])
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``` |