numpy数组的元素明智测试是数值 [英] element wise test of numpy array is numeric

问题描述

我有一个如下数组:

In [1]: x = array(['1.2', '2.3', '1.2.3'])

我想测试数组中的每个元素是否可以转换为数值.也就是说，函数:is_numeric(x)将返回True/False数组，如下所示:

I want to test if each element in the array can be converted into numerical value. That is, a function: is_numeric(x) will return a True/False array as following:

In [2]: is_numeric(x)
Out[2]: array([True, True, False])

该怎么做?

推荐答案

import numpy as np

def is_float(val):
        try:
            float(val)
        except ValueError:
            return False
        else:
            return True

a = np.array(['1.2', '2.3', '1.2.3'])

is_numeric_1 = lambda x: map(is_float, x)              # return python list
is_numeric_2 = lambda x: np.array(map(is_float, x))    # return numpy array
is_numeric_3 = np.vectorize(is_float, otypes = [bool]) # return numpy array

根据 a 数组的大小和返回值的类型，这些函数的速度不同.

Depend on the size of a array and the type of the returned values, these functions have different speed.

In [26]: %timeit is_numeric_1(a)
100000 loops, best of 3: 2.34 µs per loop

In [27]: %timeit is_numeric_2(a)
100000 loops, best of 3: 3.13 µs per loop

In [28]: %timeit is_numeric_3(a)
100000 loops, best of 3: 6.7 µs per loop

In [29]: a = np.array(['1.2', '2.3', '1.2.3']*1000)

In [30]: %timeit is_numeric_1(a)
1000 loops, best of 3: 1.53 ms per loop

In [31]: %timeit is_numeric_2(a)
1000 loops, best of 3: 1.6 ms per loop

In [32]: %timeit is_numeric_3(a)
1000 loops, best of 3: 1.58 ms per loop

如果list没问题，请使用is_numeric_1.

如果需要numpy array，并且 a 的大小较小，请使用is_numeric_2.

If you want a numpy array, and size of a is small, use is_numeric_2.

否则，请使用is_numeric_3

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