Why use numpy.array() on numpy.arange()? Isn't it redundant?

[Sivanagamahesh Gorijala]

In my Numpy/Pandas lesson I am educated to make information with the accompanying code: 

sample_numpy_data = np.array(np.arange(24)).reshape((6,4)) 

Is it accurate to say that it isn't repetitive to utilize np.array() on the np.arange() as np.arange() as of now delivers a cluster? 

Why is this essential? illustration? 

The case found in Lynda course "Pandas for Data Science" Pandas Overview - Operations.