Python:numpy.insert NaN值 [英] Python: numpy.insert NaN value

问题描述

我正在尝试将NaN值插入到numpy数组的特定索引中.我不断收到此错误:

I'm trying to insert NaN values to specific indices of a numpy array. I keep getting this error:

TypeError:无法根据规则安全"将数组数据从dtype('float64')强制转换为dtype('int64')

TypeError: Cannot cast array data from dtype('float64') to dtype('int64') according to the rule 'safe'

尝试使用以下代码进行操作.

When trying to do so with the following code.

x = np.array(range(1,11))
x = np.insert(x, 5, np.nan, axis=0)

但是，我可以毫无问题地将NaN值附加到数组的末尾.

However, I can append NaN values to the end of the array with no problem.

x = np.array(range(1,11))
x = np.append(x, np.nan)

这是为什么?如何在数组中插入NaN值?

Why is this and how can I insert NaN values in my array?

推荐答案

对于x=np.array(range(1,11))，默认情况下dtype是int64，这将阻止您插入浮点数.

With x=np.array(range(1,11)), the dtype by default is int64, which prevents you to insert a float.

最简单的方法是强制dtype直接浮动:

The easiest is to force the dtype to float directly:

x = np.array(range(1, 11), dtype=float)

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使用np.insert时，您只能使用初始数组的dtype(在引擎盖下创建的临时数组使用输入的dtype).

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With np.insert, you're limited to the dtype of the initial array (the temporary arrays created below the hood use the dtype of the input).

但是，使用np.append时，实际上是使用np.concatenate，它会创建一个数组，其输入的最大为dtype:在您的示例中，x随后被强制转换为float.

With np.append, however, you're actually using np.concatenate, which creates an array with the "largest" dtype of its inputs: in your example, x is then cast to float.

请注意，您可以简单地使用np.arange函数:

Note that you could simply use the np.arange function:

x = np.arange(1, 11, dtype=float)

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