如何考虑到nan数，有条件地更改numpy数组中的值? [英] How I can i conditionally change the values in a numpy array taking into account nan numbers?

问题描述

我的数组是一个2D矩阵，除了负值和正值外，它还具有numpy.nan值:

My array is a 2D matrix and it has numpy.nan values besides negative and positive values:

>>> array
array([[        nan,         nan,         nan, ..., -0.04891211,
            nan,         nan],
   [        nan,         nan,         nan, ...,         nan,
            nan,         nan],
   [        nan,         nan,         nan, ...,         nan,
            nan,         nan],
   ..., 
   [-0.02510989, -0.02520096, -0.02669156, ...,         nan,
            nan,         nan],
   [-0.02725595, -0.02715945, -0.0286231 , ...,         nan,
            nan,         nan],
   [        nan,         nan,         nan, ...,         nan,
            nan,         nan]], dtype=float32)

我想用一个数字替换所有正数，用另一个数字替换所有负数.

And I want to replace all the positive numbers with a number and all the negative numbers with another number.

如何使用python/numpy执行该操作?

How can I perform that using python/numpy?

(根据记录，矩阵是地理图像的结果，我要对其进行分类)

(For the record, the matrix is a result of geoimage, which I want to perform a classification)

推荐答案

数组中有np.nan的事实无关紧要.只需使用花式索引:

The fact that you have np.nan in your array should not matter. Just use fancy indexing:

x[x>0] = new_value_for_pos
x[x<0] = new_value_for_neg

如果要替换您的np.nans:

x[np.isnan(x)] = something_not_nan

有关花式索引的更多信息教程和

More info on fancy indexing a tutorial and the NumPy documentation.

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