resize an array with a specific value

Making the assumption that you want your array to have shape (MAXLINE,5) and that your array is a 2-dimensional array and not a list of tuples (as the format in your question seems to suggest), this would work:

import numpy as np
MAXLINE = 4
a=np.array([ [3.0535400914168154, 0.371345899229, 0.312953794281, -0.0125231427371, 0.0],
 [3.056684825749555, 0.373971853521, 0.313682391117, -0.0264543909236, 0.0],
 [3.0598295600822953, 0.376861295611, 0.314535588286, -0.041169303628, 0.0]])
np.append(a,np.ones((MAXLINE-a.shape[0],a.shape[1]))*np.NAN,axis=0)

produces:

array([[ 3.05354009,  0.3713459 ,  0.31295379, -0.01252314,  0.        ],
       [ 3.05668483,  0.37397185,  0.31368239, -0.02645439,  0.        ],
       [ 3.05982956,  0.3768613 ,  0.31453559, -0.0411693 ,  0.        ],
       [        nan,         nan,         nan,         nan,         nan]])

Explanation:

np.ones() takes a shape parameter, so I'm adding enough rows to make the final shape (MAXLINE,5), where 5 is the number of columns of a (ie: a.shape[1]).

In np.append(), the axis=0 parameter tells numpy to add rows. If you don't have this, it flattens the arrays.

Of course, you can replace np.NAN with any value you prefer.

Because Python arrays don't might resize arrays you can use numpy or write own functions, similar below:

def resize(array, new_size, new_value=0):
    """Resize to biggest or lesser size."""
    element_size = len(array[0]) #Quantity of new elements equals to quantity of first element
    if new_size > len(array):
        new_size = new_size - 1
        while len(array)<=new_size:
            n = tuple(new_value for i in range(element_size))
            array.append(n)
    else:
        array = array[:new_size]
    return array
#Test it
a =  [ (3.0535400914168154, 0.371345899229, 0.312953794281, -0.0125231427371, 0.0),
(3.056684825749555, 0.373971853521, 0.313682391117, -0.0264543909236, 0.0),
(3.0598295600822953, 0.376861295611, 0.314535588286, -0.041169303628, 0.0)]
a = resize(a, 5)
print a
a = resize(a, 2)
print a
a = resize(a, 3, 28)
print a 

#Output:
#New size 5, default value 0
#[(3.0535400914168154, 0.371345899229, 0.312953794281, -0.0125231427371, 0.0), (3.056684825749555, 0.373971853521, 0.313682391117, -0.0264543909236, 0.0), (3.0598295600822953, 0.376861295611, 0.314535588286, -0.041169303628, 0.0), (0, 0, 0, 0, 0), (0, 0, 0, 0, 0)]
#new size 2
#[(3.0535400914168154, 0.371345899229, 0.312953794281, -0.0125231427371, 0.0), (3.056684825749555, 0.373971853521, 0.313682391117, -0.0264543909236, 0.0)]
#New size 4, default value 28
#[(3.0535400914168154, 0.371345899229, 0.312953794281, -0.0125231427371, 0.0), (3.056684825749555, 0.373971853521, 0.313682391117, -0.0264543909236, 0.0), (28, 28, 28, 28, 28)]

If you are not attached to np.resize() you can do it this way:

import numpy as np


old_array =  [ (3.0535400914168154, 0.371345899229, 0.312953794281, -0.0125231427371, 0.0),
(3.056684825749555, 0.373971853521, 0.313682391117, -0.0264543909236, 0.0),
(3.0598295600822953, 0.376861295611, 0.314535588286, -0.041169303628, 0.0)]

maxline = 20


# If you want fullfil extra dimension with NaN 
arr = [(np.NAN,)*len(old_array[0])]*(maxline - len(old_array))

# If you want fullfil extra dimension with anything else
# arr = np.array([(ANYTHING_YOU_WANT,)*len(old_array[0])]*(maxline - old_array.size))

new_ = old_array + arr

print numpy.array(new_)

>> 

[(3.0535400914168154, 0.371345899229, 0.312953794281, -0.0125231427371, 0.0), (3
.056684825749555, 0.373971853521, 0.313682391117, -0.0264543909236, 0.0), (3.059
8295600822953, 0.376861295611, 0.314535588286, -0.041169303628, 0.0), (nan, nan,
 nan, nan, nan), (nan, nan, nan, nan, nan), (nan, nan, nan, nan, nan), (nan, nan
, nan, nan, nan), (nan, nan, nan, nan, nan), (nan, nan, nan, nan, nan), (nan, na
n, nan, nan, nan), (nan, nan, nan, nan, nan), (nan, nan, nan, nan, nan), (nan, n
an, nan, nan, nan), (nan, nan, nan, nan, nan), (nan, nan, nan, nan, nan), (nan,
nan, nan, nan, nan), (nan, nan, nan, nan, nan), (nan, nan, nan, nan, nan), (nan,
 nan, nan, nan, nan), (nan, nan, nan, nan, nan)]