# NumPy Tutorial FAQ#

Here is a compilation of questions and issues that arose during the Numpy session of the Python Tutorial Seminar Series.

The live video recording of this content can be found here.

**Q.** Is it possible to provide a “step” instead of a number of elements when using `numpy.linspace`

to create an array?

**A.** The step size is derived from the input arguments `start`

, `stop`

, and `number`

with `numpy.linspace`

. If you instead want to supply the step size and derive the number of steps, you would instead use the function `numpy.arange`

that takes the input arguments `start`

, `stop`

, and `number`

. `numpy.arange()`

differs from Python’s built-in `range()`

function which only supports integers and produces a `list`

rather than an `array`

.

**Q.** Are there Python methods that mutate the object, or do they always generate a new object? If so, are there any good heuristics for knowing which methods change their objects and which generate changed copies?

**A.** Yes there are Python functions that mutate the object. Checking the documentation is the safest way to confirm how the functions from library or package behaves.

**Q.** Is it faster to work with a Python `list`

or a Numpy `array`

?

**A.** Arrays are faster if you want to modify the object. You can test which is faster in a Jupyter notebook by typing `%%time`

at the top of your cells, or `%%timeit`

which will run the cell multiple times and produce the average time of computation.

**Q.** Can numpy Arrays hold non-numeric data?

**A.** Yes, a Numpy `array`

can hold any data type of equivalent size as well as any Numpy native data type. It cannot hold varying like `string`

s, but `string`

s of the same lengths are acceptable. All of the elements of a Numpy `array`

must be the same type as each other.