Example of uniform assignment of array elements in numpy
The editor below will share with you an example of unified assignment of array elements in numpy. It has a good reference value and I hope it will be helpful to everyone. Let’s follow the editor and take a look.
The overall array processing and assignment operation in Numpy has always made me a little confused, and many times I don’t understand it deeply. Today I will list the relevant knowledge points separately and summarize them.
Let’s look at two small examples of code snippets:
Example 1:
In [2]: arr =np.empty((8,4))
In [3]: arr
Out[3]:
array([[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]])
In [4]: arr[1] = 1
In [5]: arr
Out[5]:
array([[ 0., 0., 0., 0.],
[ 1., 1., 1., 1.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]])Example 2:
In [6]: arr1 =np.empty(2) In [8]: arr1 Out[8]:array([ 7.74860419e-304, 7.74860419e-304]) In [9]: arr1 = 0 In [10]: arr1 Out[10]: 0
These two paragraphs seem to have inconsistent behavior. In fact, it can still be understood using the general object-oriented label understanding model.
In Example 1, the label after adding the index actually refers to the specific storage area, while in Example 2, a label is used directly. So how to implement the overall assignment of a one-dimensional array? In fact, you only need to index all elements.
The specific method is as follows:
In [11]: arr1 =np.empty(2) In [12]: arr1 Out[12]: array([0., 0.]) In [13]: arr1[:] Out[13]: array([0., 0.]) In [14]: arr1[:] =0 In [15]: arr1 Out[15]: array([0., 0.])
It seems like It's quite simple, but without some in-depth analysis, it is indeed a little difficult to understand.
Related recommendations:
A brief discussion on several sorting methods of numpy arrays_python
Simple example of numpy array splicing_ python
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