insert element in the start of the numpy array

You can do the insert by ommiting the axis param:

x = np.array([0,0,0,0])
x = np.insert(x, 0, -1)
x

That will give:

array([-1,  0,  0,  0,  0])

From the np.insert documentation:

Returns
out : ndarray
   A copy of `arr` with `values` inserted.  Note that `insert`
   does not occur in-place: a new array is returned. 

You can do the same with concatenate, joining the new value to the main value. hstack, append etc use concatenate; insert is more general, allowing insertion in the middle (for any axis), so it does its own indexing and new array creation.

In any case, the key point is that it does not operate in-place. You can't change the size of an array.

In [788]: x= np.array([0.30153836,  0.30376881,  0.29115761,  0.29074261,  0.28
     ...: 676876])
In [789]: y=np.insert(x,0,-1,axis=0)
In [790]: y
Out[790]: 
array([-1.        ,  0.30153836,  0.30376881,  0.29115761,  0.29074261,
        0.28676876])
In [791]: x
Out[791]: array([ 0.30153836,  0.30376881,  0.29115761,  0.29074261,  0.28676876])

Same action with concatenate; note that I had to add [], short for np.array([-1]), so both inputs are 1d arrays. Expanding the scalar to array is all that insert is doing special.

In [793]: np.concatenate([[-1],x])
Out[793]: 
array([-1.        ,  0.30153836,  0.30376881,  0.29115761,  0.29074261,
    0.28676876])

I'm not fully familiar with numpy, but it seems that the insert function does not affect the array you pass to it, but rather it returns a new array with the inserted value(s). You'll have to reassign to x if you really want x to change.

>>> x= [-1, 0.30153836,  0.30376881,  0.29115761,  0.29074261,  0.28676876]
>>> np.insert(x,0,-1,axis=0)
array([-1.        , -1.        ,  0.30153836,  0.30376881,  0.29115761,
        0.29074261,  0.28676876])
>>> x
[-1, 0.30153836, 0.30376881, 0.29115761, 0.29074261, 0.28676876]
>>> x = np.insert(x,0,-1,axis=0)
>>> x
array([-1.        , -1.        ,  0.30153836,  0.30376881,  0.29115761,
        0.29074261,  0.28676876])