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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 About iPython Notebooks
iPython Notebooks are interactive coding environments embedded in a webpage. After writing your code, you can run the cell by either pressing "SHIFT"+"ENTER" or by clicking on "Run Cell" (denoted by a play symbol) in the upper bar of the notebook.
Step1:  NumPy
NumPy is the fundamental package for scientific computing with Python. It contains among other things
Step2:  shape
np.zeros
Step3:  np.ones
Step4:  np.empty
Step5:  np.arange
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Python Code:
test = "Hello World"
print ("test: " + test)
Explanation: About iPython Notebooks
iPython Notebooks are interactive coding environments embedded in a webpage. After writing your code, you can run the cell by either pressing "SHIFT"+"ENTER" or by clicking on "Run Cell" (denoted by a play symbol) in the upper bar of the notebook.
End of explanation
# 
Explanation: NumPy
NumPy is the fundamental package for scientific computing with Python. It contains among other things:
a powerful N-dimensional array object
sophisticated (broadcasting) functions
useful linear algebra, Fourier transform, and random number capabilities
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
Library documentation: <a>http://www.numpy.org/</a>
NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers. In NumPy dimensions are called axes. The number of axes is rank.
For example, the coordinates of a point in 3D space [1, 2, 1] is an array of rank 1, because it has one axis. That axis has a length of 3. In the example pictured below, the array has rank 2 (it is 2-dimensional). The first dimension (axis) has a length of 2, the second dimension has a length of 3.
NumPy’s array class is called ndarray. It is also known by the alias array. Note that numpy.array is not the same as the Standard Python Library class array.array, which only handles one-dimensional arrays and offers less functionality. The more important attributes of an ndarray object are:
np.array
list as argument to array
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#The function zeros creates an array full of zeros
Explanation: shape
np.zeros
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# function ones creates an array full of ones
Explanation: np.ones
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#function empty creates an array whose initial content is random and depends on the state of the memory
Explanation: np.empty
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# To create sequences of numbers, NumPy provides a function analogous to range that returns arrays instead of lists.
Explanation: np.arange
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