Numpy.pad: To pad an array, use the NumPy pad() function. This method has an optional parameter mode that can be used to specify string values (predefined padding style) or a user-supplied padding function.

Syntax:

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

Parameters

array: This is required. It is an array (rank N) to pad.

pad_width: This is required. It may be a sequence, array_like, int. It defines a number of values padded to each axis edges. ((before_1, after_1), … (before_N, after_N)) unique pad widths for each axis. ((before, after),) gives the same before and after pad for each axis. (pad,) or int is a shortcut for before = after = pad width for all axes.

mode: This is optional. Choose one of the string values or a user-supplied function from the list below:

• ‘constant’: It is default value. Pads having a fixed/constant value.
• ‘edge’: Pads the array’s edge values.
• ‘linear_ramp’: It Pads between end value and array edge value with a linear ramp.
• maximum’: It Pads(Fills the gap) with the maximum/greatest value of all or part of the vector along each axis.
• ‘mean’: This function pads with the mean value of all or part of the vector along each axis.
• ‘median’: The median function pads with the median value of all or part of the vector along each axis.
• ‘minimum’: The minimum function pads with the minimum/smallest value of all or part of the vector along each axis.
• ‘reflect’: The reflect function pads with the vector’s reflection mirrored on the first and last values of each axis.
• ‘symmetric’: The symmetric function pads with the vector’s reflection mirrored along the array’s edge.
• ‘wrap’: The wrap function pads with the vector’s wrap along the axis. The first values are used to pad the end, whereas the last values are used to pad the start.
• ’empty’: The empty function pads with undefined values.
• <function>: It is a padding function.

Return value:

Returns a padded array with rank equal to array and shape increased by pad_width.

Example1

Approach:

• Import numpy module using the import keyword.
• Give the list as static input and store it in a variable.
• Pass the given list, pad_width, mode for the pad() function of the numpy module(here we passed mode as ‘constant’ by passing some random constant values) and print the result.
• Pass the given list, pad_width, mode for the pad() function of the numpy module (here we passed mode as ‘edge’ to pad with edge value) and print the result.
• Pass the given list, pad_width, mode for the pad() function of the numpy module (here we passed mode as ‘maximum’ to pad with maximum value) and print the result.
• Pass the given list, pad_width, mode for the pad() function of the numpy module (here we passed mode as ‘minimum’ to pad with minimum value) and print the result.
• Pass the given list, pad_width, mode for the pad() function of the numpy module (here we passed mode as ‘mean’ to pad with mean value) and print the result.
• Pass the given list, pad_width, mode, end_values for the pad() function of the numpy module (here we passed mode as ‘linear_ramp’ to pad with linear ramp values) and print the result.
• The Exit of the Program.

Below is the implementation:

# Import numpy module using the import keyword
import numpy as np
# Give the list as static input and store it in a variable.
gvn_lst = [25, 35]

# Pass the given list, pad_width, mode for the pad() function of the numpy module
# (here we passed mode as 'constant' by passing some random constant values)
# and print the result.
print("Pad the given list with the given constant values:")

# Pass the given list, pad_width, mode for the pad() function of the numpy module
# (here we passed mode as 'edge' to pad with edge value)
# and print the result.
print("Pad the given list with the edge value:")

# Pass the given list, pad_width, mode for the pad() function of the numpy module
# (here we passed mode as 'maximum' to pad with maximum value)
# and print the result.
print("Pad the given list with the maximum value:")

# Pass the given list, pad_width, mode for the pad() function of the numpy module
# (here we passed mode as 'minimum' to pad with minimum value)
# and print the result.
print("Pad the given list with the minimum value:")

# Pass the given list, pad_width, mode for the pad() function of the numpy module
# (here we passed mode as 'mean' to pad with mean value)
# and print the result.
print("Pad the given list with the mean value:")

# Pass the given list, pad_width, mode for the pad() function of the numpy module
# (here we passed mode as 'linear_ramp' to pad with linear ramp values)
# and print the result.
print("Pad the given list with the linear map value:")
print(np.pad(gvn_lst, (4,5), 'linear_ramp', end_values=(0, 0)))

Output:

Pad the given list with the given constant values:
[ 6 6 6 6 25 35 12 12 12 12 12]
Pad the given list with the edge value:
[25 25 25 25 25 35 35 35 35 35 35]
Pad the given list with the maximum value:
[35 35 35 35 25 35 35 35 35 35 35]
Pad the given list with the minimum value:
[25 25 25 25 25 35 25 25 25 25 25]
Pad the given list with the mean value:
[30 30 30 30 25 35 30 30 30 30 30]
Pad the given list with the linear map value:
[ 0 6 12 18 25 35 28 21 14 7 0]

Example2

Approach:

• Import numpy module using the import keyword.
• Give the list(multi-dimensional) as static input and store it in a variable.
• Pass the given list, multi-dimensional padwidth, mode as constant, and some random constant values as the argument to the pad() function of the numpy module and print the result.
• The Exit of the Program.

Below is the implementation:

# Import numpy module using the import keyword
import numpy as np
# Give the list(multi-dimensional)as static input and store it in a variable.
gvn_lst = [[50, 60],
[70, 80]]

# Pass the given list, multi-dimensional padwidth, mode as constant
# and some random constant values as the argument to the pad()
# function of the numpy module and print the result.
print("Pad with the given constant values:")
constant_values=((1, 2),(3, 4))))

Output:

Pad with the given constant values:
[[ 3 3 1 1 4 4 4]
[ 3 3 1 1 4 4 4]
[ 3 3 1 1 4 4 4]
[ 3 3 1 1 4 4 4]
[ 3 3 50 60 4 4 4]
[ 3 3 70 80 4 4 4]
[ 3 3 2 2 4 4 4]
[ 3 3 2 2 4 4 4]
[ 3 3 2 2 4 4 4]
[ 3 3 2 2 4 4 4]
[ 3 3 2 2 4 4 4]]