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If I run the following:
import numpy as np a = np.arange(9) a = a.reshape((3,3)) I will get this:
a = [[0 1 2] [3 4 5] [6 7 8]] If I create a larger array like this:
b = np.zeros((5,5)) b = [[ 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.]] How do I efficiently copy a into b to get an array like this?
# border of 0 surrounding a to be filled in with other data later b = [[ 0. 0. 0. 0. 0.] [ 0. 0. 1. 2. 0.] [ 0. 3. 4. 5. 0.] [ 0. 6. 7. 8. 0.] [ 0. 0. 0. 0. 0.]] I am looking for a function built into numpy if it exists.
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Conclusion: to copy data from a numpy array to another use one of the built-in numpy functions numpy. array(src) or numpy. copyto(dst, src) wherever possible.
The Array. Copy() method in C# is used to copy section of one array to another array. Array. Copy(src, dest, length);
Slicing an array does not make a copy, it just creates a new view on the existing array's data.
The library function copy. copy() is supposed to create a shallow copy of its argument, but when applied to a NumPy array it creates a shallow copy in sense B, i.e. the new array gets its own copy of the data buffer, so changes to one array do not affect the other. x_copy = x.
You can specify b[1:4, 1:4] to denote the part:
>>> import numpy as np >>> a = np.arange(9) >>> a = a.reshape((3, 3)) >>> b = np.zeros((5, 5)) >>> b[1:4, 1:4] = a >>> b array([[ 0., 0., 0., 0., 0.], [ 0., 0., 1., 2., 0.], [ 0., 3., 4., 5., 0.], [ 0., 6., 7., 8., 0.], [ 0., 0., 0., 0., 0.]]) >>> b[1:4,1:4] = a + 1 # If you really meant `[1, 2, ..., 9]` >>> b array([[ 0., 0., 0., 0., 0.], [ 0., 1., 2., 3., 0.], [ 0., 4., 5., 6., 0.], [ 0., 7., 8., 9., 0.], [ 0., 0., 0., 0., 0.]]) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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