import numpy as np
a = np.zeros((3, 4), dtype=int)
aarray([[0, 0, 0, 0],[0, 0, 0, 0],[0, 0, 0, 0]])# pad(array, pad_width, mode, **kwargs)
# array: array_like
# pad_width: ((1, 2), (3, 4))
#    1: width of top
#    2: width of bottom
#    3: width of left
#    4: width of right
# mode: str or function
#    'constant': Pads with a constant value
# constant_values: Used in 'constant'. The values to set the padded values for each axis.
#    ((1, 2), (3, 4))
#    1: value of top
#    2: value of bottom
#    3: vlaue of left
#    4: vlaue of right
np.pad(a, ((1, 2), (3, 4)), 'constant', constant_values=((1, 2), (3, 4)))array([[3, 3, 3, 1, 1, 1, 1, 4, 4, 4, 4],[3, 3, 3, 0, 0, 0, 0, 4, 4, 4, 4],[3, 3, 3, 0, 0, 0, 0, 4, 4, 4, 4],[3, 3, 3, 0, 0, 0, 0, 4, 4, 4, 4],[3, 3, 3, 2, 2, 2, 2, 4, 4, 4, 4],[3, 3, 3, 2, 2, 2, 2, 4, 4, 4, 4]])# (1, 2)
# top:bottom=1:2
# left:right=1:2
np.pad(a, (1, 2), 'constant', constant_values=(1, 2))array([[1, 1, 1, 1, 1, 2, 2],[1, 0, 0, 0, 0, 2, 2],[1, 0, 0, 0, 0, 2, 2],[1, 0, 0, 0, 0, 2, 2],[1, 2, 2, 2, 2, 2, 2],[1, 2, 2, 2, 2, 2, 2]])a = [1, 2, 3, 4, 5]
np.pad(a, (2, 3), 'constant', constant_values=(4, 6))array([4, 4, 1, 2, 3, 4, 5, 6, 6, 6])# mode: 'edge': Pads with the edge values of array
np.pad(a, (2, 3), 'edge')array([1, 1, 1, 2, 3, 4, 5, 5, 5, 5])np.pad(a, (2, 3), 'linear_ramp', end_values=(5, 1))array([5, 3, 1, 2, 3, 4, 5, 4, 2, 1])np.pad(a, (2, 3), 'maximum')array([5, 5, 1, 2, 3, 4, 5, 5, 5, 5])np.pad(a, (2, 3), 'mean')array([3, 3, 1, 2, 3, 4, 5, 3, 3, 3])np.pad(a, (2, 3), 'median')array([3, 3, 1, 2, 3, 4, 5, 3, 3, 3])a = [[1, 2], [3, 4]]
np.pad(a, (2, 3), 'minimum')array([[1, 1, 1, 2, 1, 1, 1],[1, 1, 1, 2, 1, 1, 1],[1, 1, 1, 2, 1, 1, 1],[3, 3, 3, 4, 3, 3, 3],[1, 1, 1, 2, 1, 1, 1],[1, 1, 1, 2, 1, 1, 1],[1, 1, 1, 2, 1, 1, 1]])

>>> help(np.pad)
Help on function pad in module numpy.lib.arraypad:pad(array, pad_width, mode, **kwargs)Pads an array.Parameters----------array : array_like of rank NInput arraypad_width : {sequence, array_like, int}Number of values padded to the edges of each axis.((before_1, after_1), ... (before_N, after_N)) unique pad widthsfor each axis.((before, after),) yields same before and after pad for each axis.(pad,) or int is a shortcut for before = after = pad width for allaxes.mode : str or functionOne of the following string values or a user supplied function.'constant'Pads with a constant value.'edge'Pads with the edge values of array.'linear_ramp'Pads with the linear ramp between end_value and thearray edge value.'maximum'Pads with the maximum value of all or part of thevector along each axis.'mean'Pads with the mean value of all or part of thevector along each axis.'median'Pads with the median value of all or part of thevector along each axis.'minimum'Pads with the minimum value of all or part of thevector along each axis.'reflect'Pads with the reflection of the vector mirrored onthe first and last values of the vector along eachaxis.'symmetric'Pads with the reflection of the vector mirroredalong the edge of the array.'wrap'Pads with the wrap of the vector along the axis.The first values are used to pad the end and theend values are used to pad the beginning.<function>Padding function, see Notes.stat_length : sequence or int, optionalUsed in 'maximum', 'mean', 'median', and 'minimum'.  Number ofvalues at edge of each axis used to calculate the statistic value.((before_1, after_1), ... (before_N, after_N)) unique statisticlengths for each axis.((before, after),) yields same before and after statistic lengthsfor each axis.(stat_length,) or int is a shortcut for before = after = statisticlength for all axes.Default is ``None``, to use the entire axis.constant_values : sequence or int, optionalUsed in 'constant'.  The values to set the padded values for eachaxis.((before_1, after_1), ... (before_N, after_N)) unique pad constantsfor each axis.((before, after),) yields same before and after constants for eachaxis.(constant,) or int is a shortcut for before = after = constant forall axes.Default is 0.end_values : sequence or int, optionalUsed in 'linear_ramp'.  The values used for the ending value of thelinear_ramp and that will form the edge of the padded array.((before_1, after_1), ... (before_N, after_N)) unique end valuesfor each axis.((before, after),) yields same before and after end values for eachaxis.(constant,) or int is a shortcut for before = after = end value forall axes.Default is 0.reflect_type : {'even', 'odd'}, optionalUsed in 'reflect', and 'symmetric'.  The 'even' style is thedefault with an unaltered reflection around the edge value.  Forthe 'odd' style, the extended part of the array is created bysubtracting the reflected values from two times the edge value.Returns-------pad : ndarrayPadded array of rank equal to `array` with shape increasedaccording to `pad_width`.Notes-----.. versionadded:: 1.7.0For an array with rank greater than 1, some of the padding of lateraxes is calculated from padding of previous axes.  This is easiest tothink about with a rank 2 array where the corners of the padded arrayare calculated by using padded values from the first axis.The padding function, if used, should return a rank 1 array equal inlength to the vector argument with padded values replaced. It has thefollowing signature::padding_func(vector, iaxis_pad_width, iaxis, kwargs)wherevector : ndarrayA rank 1 array already padded with zeros.  Padded values arevector[:pad_tuple[0]] and vector[-pad_tuple[1]:].iaxis_pad_width : tupleA 2-tuple of ints, iaxis_pad_width[0] represents the number ofvalues padded at the beginning of vector whereiaxis_pad_width[1] represents the number of values padded atthe end of vector.iaxis : intThe axis currently being calculated.kwargs : dictAny keyword arguments the function requires.Examples-------->>> a = [1, 2, 3, 4, 5]>>> np.pad(a, (2,3), 'constant', constant_values=(4, 6))array([4, 4, 1, 2, 3, 4, 5, 6, 6, 6])>>> np.pad(a, (2, 3), 'edge')array([1, 1, 1, 2, 3, 4, 5, 5, 5, 5])>>> np.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4))array([ 5,  3,  1,  2,  3,  4,  5,  2, -1, -4])>>> np.pad(a, (2,), 'maximum')array([5, 5, 1, 2, 3, 4, 5, 5, 5])>>> np.pad(a, (2,), 'mean')array([3, 3, 1, 2, 3, 4, 5, 3, 3])>>> np.pad(a, (2,), 'median')array([3, 3, 1, 2, 3, 4, 5, 3, 3])>>> a = [[1, 2], [3, 4]]>>> np.pad(a, ((3, 2), (2, 3)), 'minimum')array([[1, 1, 1, 2, 1, 1, 1],[1, 1, 1, 2, 1, 1, 1],[1, 1, 1, 2, 1, 1, 1],[1, 1, 1, 2, 1, 1, 1],[3, 3, 3, 4, 3, 3, 3],[1, 1, 1, 2, 1, 1, 1],[1, 1, 1, 2, 1, 1, 1]])>>> a = [1, 2, 3, 4, 5]>>> np.pad(a, (2, 3), 'reflect')array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])>>> np.pad(a, (2, 3), 'reflect', reflect_type='odd')array([-1,  0,  1,  2,  3,  4,  5,  6,  7,  8])>>> np.pad(a, (2, 3), 'symmetric')array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])>>> np.pad(a, (2, 3), 'symmetric', reflect_type='odd')array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])>>> np.pad(a, (2, 3), 'wrap')array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])>>> def pad_with(vector, pad_width, iaxis, kwargs):...     pad_value = kwargs.get('padder', 10)...     vector[:pad_width[0]] = pad_value...     vector[-pad_width[1]:] = pad_value...     return vector>>> a = np.arange(6)>>> a = a.reshape((2, 3))>>> np.pad(a, 2, pad_with)array([[10, 10, 10, 10, 10, 10, 10],[10, 10, 10, 10, 10, 10, 10],[10, 10,  0,  1,  2, 10, 10],[10, 10,  3,  4,  5, 10, 10],[10, 10, 10, 10, 10, 10, 10],[10, 10, 10, 10, 10, 10, 10]])>>> np.pad(a, 2, pad_with, padder=100)array([[100, 100, 100, 100, 100, 100, 100],[100, 100, 100, 100, 100, 100, 100],[100, 100,   0,   1,   2, 100, 100],[100, 100,   3,   4,   5, 100, 100],[100, 100, 100, 100, 100, 100, 100],[100, 100, 100, 100, 100, 100, 100]])