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 strings, but strings of the same lengths are acceptable. All of the elements of a Numpy array must be the same type as each other.