how to populate a numpy 2D array with function returning a numpy 1D array

Question

I have function predicton like

def predictions(degree):
  some magic,
  return an np.ndarray([0..100])

I want to call this function for a few values of degree and use it to populate a larger np.ndarray (n=2), filling each row with the outcome of the function predictions. It seems like a simple task but somehow I cant get it working. I tried with

for deg in [1,2,4,8,10]:
   np.append(result, predictions(deg),axis=1)

with result being an np.empty(100). But that failed with Singleton array array(1) cannot be considered a valid collection.

I could not get fromfunction it only works on a coordinate tuple, and the irregular list of degrees is not covered in the docs.

The singleton array(1) error message was originating from function predictions.` result = np.array([predictions(1), predictions(2), predictions(4),predictions(8)])` is working but not very nice.. — dr jerry
– dr jerry, Commented May 14, 2018 at 19:18

hpaulj · Accepted Answer · 2018-05-14 20:14:05Z

2

Don't use np.ndarray until you are older and wiser! I couldn't even use it without rereading the docs.

arr1d = np.array([1,2,3,4,5])

is the correct way to construct a 1d array from a list of numbers.

Also don't use np.append. I won't even add the 'older and wiser' qualification. It doesn't work in-place; and is slow when used in a loop.

A good way of building a 2 array from 1d arrays is:

alist = []
for i in ....:
    alist.append(<alist or 1d array>)
arr = np.array(alist)

provided all the sublists have the same size, arr should be a 2d array.

This is equivalent to building a 2d array from

np.array([[1,2,3], [4,5,6]])

that is a list of lists.

Or a list comprehension:

np.array([predictions(i) for i in range(10)])

Again, predictions must all return the same length arrays or lists.

answered May 14, 2018 at 20:14

hpaulj

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3 Comments

dr jerry Over a year ago

I'm too old to get any wiser :-) but your answer has helped me. ndarray was not a free choice but a mandatory excercise outcome.

dr jerry Over a year ago

I (aesthetically) like the list comprehension, but not sure how expensive it is? (Performance is not my my top priority though).

hpaulj Over a year ago

List comprehension is generally a bit faster than the equivalent loop with append. The interpreter constructs a tighter set of bytecodes.

bobrobbob · Accepted Answer · 2018-05-14 20:14:58Z

2

append is in the boring section of numpy. here you know the shape in advance

len_predictions = 100

def predictions(degree):
    return np.ones((len_predictions,))

degrees = [1,2,4,8,10]
result = np.empty((len(degrees), len_predictions))
for i, deg in enumerate(degrees):
    result[i] = predictions(deg)

if you want to store the degree somehow, you can use custom dtypes

answered May 14, 2018 at 20:14

bobrobbob

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1 Comment

jpp Over a year ago

This is the way +1. append is expensive (with anything), avoid where possible.

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