Introduce also numpy.full as well as zeros_like etc.

docs/mooc/numerical-computing/creating-and-accessing.md

@@ -99,31 +99,45 @@ print(b)
 # output: [-4.5  -2.25  0.    2.25  4.5]
 ~~~
 
-One can also create an array of a given shape and initialise it to zeros or
-ones using the handy functions **zeros** or **ones**.
+One can also create an array of a given shape and initialise it to zeros, 
+ones , or arbitrary value using the handy functions **zeros**,  **ones**, or 
+**full**.
 
 ~~~python
 c = numpy.zeros((4, 6), float)
 d = numpy.ones((2, 4))
+e = numpy.full((3, 2), 4.2)
 
 print(c.shape)
 print(d)
+print(e)
 # output:
 #   (4, 6)
 #   [[ 1.  1.  1.  1.]
 #    [ 1.  1.  1.  1.]]
+#   [[4.2 4.2]
+#    [4.2 4.2]
+#    [4.2 4.2]]
 ~~~
 
-To initialise to another value than 1, one can simply multiply the array
-created by `ones()` with the desired value.
-
 One can also create a truly empty array by using the function **empty**. This
 will create an array and allocate memory for it, but not assign any values to
 it. In practice, this means that the values of an empty array are unspecified
 (whatever happened to be in the computer memory allocated to the array). Thus,
 if one uses `empty`, it is crucial to always assign values to all the
 elements before trying to use them.
 
+Sometimes one would like to create an array with the same shape as an existing
+array. In this case one can utilize the functions **zeros_like**, 
+**ones_like**, **full_like** and **empty_like**:
+
+~~~python
+a = numpy.zeros((4, 6), float)
+b = numpy.empty_like(a)
+c = numpy.ones_like(a)
+d = numpy.full_like(a, 9.1)
+~~~
+
 In addition to numbers, NumPy supports also storing non-numerical data, e.g.
 strings. For strings, the largest element determines the item size, so in
 practice arbitrary strings are not that suitable, but character arrays can be