If you have used Python's standard list indexing before, then you won't find many issues with indexing in NumPy. In a 1D array, the i^th value (counting from zero) can be accessed by specifying the desired index in square brackets, just as with Python lists:

In [13]: int_arr
Out[13]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
In [14]: int_arr[0]
Out[14]: 0
In [15]: int_arr[3]
Out[15]: 3

To index from the end of the array, you can use negative indices:

In [16]: int_arr[-1]
Out[16]: 9
In [17]: int_arr[-2]
Out[17]: 8

There are a few other cool tricks for slicing arrays, as follows:

In [18]: int_arr[2:5]  # from index 2 up to index 5 - 1
Out[18]: array([2, 3, 4])
In [19]: int_arr[:5]    # from the beginning up to index 5 - 1
Out[19]: array([0, 1, 2, 3, 4])
In [20]: int_arr[5:]    # from index 5 up to the end of the array
Out[20]: array([5, 6, 7, 8, 9])
In [21]: int_arr[::2]   # every other element
Out[21]: array([0, 2, 4, 6, 8])
In [22]: int_arr[::-1]  # the entire array in reverse order
Out[22]: array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])

I encourage you to play around with these arrays yourself!