NumPy Pad

The NumPy pad() will be covered in this article. We will also take a look at its syntax and arguments for better overall knowledge. Then, using a few examples, we’ll show how all of the theory elements are put into practice. But first, let’s look at the function’s definition to acquire a better understanding of it.

NumPy Definition and Its Syntax

NumPy is a powerful Python mathematics package, as we all know. It includes a function called NumPy pad() that adds padding to the arrays. As we progress through this text, the definition we just discussed will become clearer. The syntax related to the function will be covered in the appended section.

# numpy.pad(array, pad_width, mode='')

Our function’s general syntax is shown above. There are various criteria that go along with it, which we’ll go through today. The ‘array’ option specifies the input array on which padding is to be applied. The number of values padded to the edge of each axis is represented by the ‘pad width’ argument.

‘mode’ is a parameter. It can represent one of the string values below or a user-supplied function.

  • constant: The padding is done with a constant value when this option is used.
  • edge: Padding is done with the array’s edge value in this situation.
  • maximum: When this option is selected, the padding is calculated by adding the largest value of all vector parts along the specified axis.
  • mean: The padding in this situation uses the mean value of all sections of the vector along the specified axis.
  • median: When this option is chosen, the padding uses the biggest value of all vector parts along the provided axis.
  • reflect: The vector is padded in this case by reflecting it between the initial and last values along each axis.

‘maximum,” mean,”median,’ and ‘minimum’ all use the “stat” length argument. The statistical value is calculated using the number of values at each axis’ edge.

In ‘constant,’ the parameter ‘constant values’ is used. The values are utilized to pad the values for each axis here.

Example 1:

We’ll look at how this method works and how it helps us reach our desired output in this part now that we have covered all of the theory behind the NumPy pad(). We’ll begin with a simple instance and proceed to more complicated ones. We’ll explore how the NumPy pad function works in our first example.

First, we imported the NumPy module into the sample program below. Following that, we have defined an input (shown as abc) on which the operation must be carried out. Then we utilized our syntax to get the result we wanted.

In this example, we have selected “Maximum” as our mode. As a result, the front and rear are padded to a maximum of 32 (max value). Below is the implementation as you can see.

import numpy as ppool

Here is the result where you can see the maximum value at the start and end of the array.

Example 2:

Let’s have a look at another example using a different mode. A second illustration that is alike to the first. However, we’ve utilized a different array in this case. In addition, we chose “reflect” as our observation mode. The change in the output may be seen.

import numpy as p

Here is the resultant screen of the above code.

Example 3:

The first argument, one (3,2) tuple, specifies that three components are added before the axis and two elements are added after the axis in this example.

The mode option determines the sort of value that will be used to pad the array. We utilize constant values 1 and 5 to pad the array in our code, but we may change this mode to median, mean, empty, wrap, and more. Each mode adds a new element to the array to pad it out.

import numpy as p
one = [2,4,6,8]
two = p.pad(one,(3, 2), mode =  'constant', constant_values = (1, 5))

Below is the output screen for your help.

Example 4:

In this instance, we have imported NumPy with the alias “pp” in the code above. With vector, pad width, axis, and kwargs, we developed the method pad. To obtain padding values from the provided get() function, we’ve declared the variable pad value.

The padding values have been provided to the vector’s portion.

We used the np.arange() function to build an array ‘one’ and the reshape() function to change its shape. The resulting value of the np.pad() function has been allocated to the variable ‘two’. The function has supplied the list ‘one’ and the pad width parameter. Finally, we attempted to print two’s value.’

The resultant screen shows the ndarray padded with the defined size and values in the output.


Pad() is a very important function for specific coding and has become a commonly used function in Python. The function allows for the alteration of the array to limit the number of memory system conflicts. The numpy.pad() function is extensively used to fully functionalize the AST. This function particularly allows the user to specify the new size or even let the system calculate the size for them automatically.

As a result, the memory resource optimization ability was customized to lower the system’s processing time. This post was about the same and we have discussed the function with multiple examples for your help.

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Kalsoom Bibi

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