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

Question

In my Numpy / Pandas lesson I am taught to make data with the following code:

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

Isn't it redundant to use np.array() on the np.arange() as np.arange() already produces an array?

Why is this necessary? example?

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

Yup. That is redundant.

James
– James

2017-04-10 00:11:56 +00:00
Commented Apr 10, 2017 at 0:11 — James
– James, Commented Apr 10, 2017 at 0:11

MSeifert · Accepted Answer · 2017-04-10 00:30:51Z

7

It is not only redundant, it also introduces unnecessary overhead because it makes a copy of the array by default.

If you're not sure if something is an array (maybe because it's an argument for a function) you can use np.asarray on it. That only copies if it's not an array.

answered Apr 10, 2017 at 0:30

MSeifert

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