Python Numpy.pad Function
In Python, we have NumPy
the array module to create and use arrays. Arrays can have different sizes and dimensions. Padding is a useful method that can be used to compensate for the size of an array. We can mutate an array and add some padding values to change its shape and size.
There are other methods we can use to reshape the array. Nevertheless, this function is beneficial because it automatically resizes the array memory after use.
numpy.pad()
The function is used to achieve this. The following code shows an example of this function.
import numpy as np
a = [1, 2, 3, 4]
b = np.pad(a, (3, 2), mode="constant", constant_values=(0, 5))
print(b)
Output:
[0 0 0 1 2 3 4 5 5]
In the example above, the first argument (3,2)
tuple specifies to add 3 elements before the axis and 2 elements at the end of the axis.
mode
The parameter specifies what type of values will be used when filling the array. In our code, we fill the array with constants 0 and 5, but we can change this pattern to different types such as median
, mean
, empty
, wrap
etc. Each pattern provides different elements to fill the array with.
We can also use this function with multidimensional arrays. For example,
import numpy as np
a = np.array(
[[1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0]]
)
b = np.pad(a, [(0, 1), (0, 1)], mode="constant")
print(b)
Output:
[[1. 1. 1. 1. 1. 0.]
[1. 1. 1. 1. 1. 0.]
[1. 1. 1. 1. 1. 0.]
[0. 0. 0. 0. 0. 0.]]
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