import numpy as np

a = np.zeros((3, 4), dtype=int)

a

array([[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 N

        Input array

    pad_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 widths

        for 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 all

        axes.

    mode : str or function

        One 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 the

            array edge value.

        'maximum'

            Pads with the maximum value of all or part of the

            vector along each axis.

        'mean'

            Pads with the mean value of all or part of the

            vector along each axis.

        'median'

            Pads with the median value of all or part of the

            vector along each axis.

        'minimum'

            Pads with the minimum value of all or part of the

            vector along each axis.

        'reflect'

            Pads with the reflection of the vector mirrored on

            the first and last values of the vector along each

            axis.

        'symmetric'

            Pads with the reflection of the vector mirrored

            along 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 the

            end values are used to pad the beginning.

        <function>

            Padding function, see Notes.

    stat_length : sequence or int, optional

        Used in 'maximum', 'mean', 'median', and 'minimum'.  Number of

        values at edge of each axis used to calculate the statistic value.

        ((before_1, after_1), ... (before_N, after_N)) unique statistic

        lengths for each axis.

        ((before, after),) yields same before and after statistic lengths

        for each axis.

        (stat_length,) or int is a shortcut for before = after = statistic

        length for all axes.

        Default is ``None``, to use the entire axis.

    constant_values : sequence or int, optional

        Used in 'constant'.  The values to set the padded values for each

        axis.

        ((before_1, after_1), ... (before_N, after_N)) unique pad constants

        for each axis.

        ((before, after),) yields same before and after constants for each

        axis.

        (constant,) or int is a shortcut for before = after = constant for

        all axes.

        Default is 0.

    end_values : sequence or int, optional

        Used in 'linear_ramp'.  The values used for the ending value of the

        linear_ramp and that will form the edge of the padded array.

        ((before_1, after_1), ... (before_N, after_N)) unique end values

        for each axis.

        ((before, after),) yields same before and after end values for each

        axis.

        (constant,) or int is a shortcut for before = after = end value for

        all axes.

        Default is 0.

    reflect_type : {'even', 'odd'}, optional

        Used in 'reflect', and 'symmetric'.  The 'even' style is the

        default with an unaltered reflection around the edge value.  For

        the 'odd' style, the extended part of the array is created by

        subtracting the reflected values from two times the edge value.

    Returns

    -------

    pad : ndarray

        Padded array of rank equal to `array` with shape increased

        according to `pad_width`.

    Notes

    -----

    .. versionadded:: 1.7.0

    For an array with rank greater than 1, some of the padding of later

    axes is calculated from padding of previous axes.  This is easiest to

    think about with a rank 2 array where the corners of the padded array

    are calculated by using padded values from the first axis.

    The padding function, if used, should return a rank 1 array equal in

    length to the vector argument with padded values replaced. It has the

    following signature::

        padding_func(vector, iaxis_pad_width, iaxis, kwargs)

    where

        vector : ndarray

            A rank 1 array already padded with zeros.  Padded values are

            vector[:pad_tuple[0]] and vector[-pad_tuple[1]:].

        iaxis_pad_width : tuple

            A 2-tuple of ints, iaxis_pad_width[0] represents the number of

            values padded at the beginning of vector where

            iaxis_pad_width[1] represents the number of values padded at

            the end of vector.

        iaxis : int

            The axis currently being calculated.

        kwargs : dict

            Any 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]])