Cannot understand the output of this code: python list to 1-d numpy array

Question

I tried to create turn a python list into a numpy array, but got very unintuitive output. Certainly I did something wrong, but out of curiosity I would like to know why I got such an output. Here is my code:

import numpy as np

# Exercise 2
a = [1, 5, 3, 6, 2]
b = np.ndarray(a)
print(b, b.dtype)

the output was

[[[[[0.00000000e+000 6.93284651e-310]
    [6.93284792e-310 6.93284744e-310]
    [6.93284744e-310 6.93284744e-310]
    [6.93284744e-310 6.93284744e-310]
    [6.93284744e-310 6.93284744e-310]
    [6.93284744e-310 6.93284744e-310]]

   [[6.93284744e-310 2.20882835e-314]
    [6.93284743e-310 6.93284743e-310]
    [6.93284743e-310 6.93284650e-310]
    [6.93284744e-310 6.93284744e-310]
    [6.93284744e-310 6.93284744e-310]
    [6.93284744e-310 6.93284744e-310]]

   ... (12 more blocks similar to ones above)

   [[6.93284871e-310 6.93284871e-310]
    [6.93284745e-310 6.93284871e-310]
    [6.93284651e-310 6.93284871e-310]
    [6.93284745e-310 6.93284871e-310]
    [6.93284871e-310 6.93284745e-310]
    [6.93284727e-310 6.93284871e-310]]]]] float64

Does this answer your question? What is the difference between ndarray and array in numpy? — Trenton McKinney
– Trenton McKinney, Commented Jun 30, 2020 at 16:16
Trenton, not exactly, but thank you for the reference. I spotted my mistake, just want to understand why I got this particular output. From the answers I got why the output is of this shape, but now I am also curious about the values. — ryabchenko-a
– ryabchenko-a, Commented Jun 30, 2020 at 16:24
Probably floating point precision. They are all equivalently 0 — Trenton McKinney
– Trenton McKinney, Commented Jun 30, 2020 at 16:27
With ndarray you did not specify the buffer, just the shape. So those values are 'uninitialized', not even to 0. Use np.zeros(shape) if you want an array of 0s with a given shape. — hpaulj
– hpaulj, Commented Jun 30, 2020 at 16:28

Mark Bakker · Accepted Answer · 2020-06-30 16:18:15Z

2

You created a five dimensional array.

a = [1, 5, 3, 6, 2]
b = np.ndarray(a)
print(b.shape)

gives

(1, 5, 3, 6, 2)

The first argument of the np.ndarray is the shape of the array. Your probably wanted

b = np.array(a)
print(b)

which gives

[1 5 3 6 2]

answered Jun 30, 2020 at 16:18

Mark Bakker

8311 gold badge9 silver badges20 bronze badges

Sign up to request clarification or add additional context in comments.

2 Comments

ryabchenko-a Over a year ago

Thank you, this is the clearest answer. Can you also tell me why the most values are 6*10^(-310), while some of them are different?

Mark Bakker Over a year ago

I think it is an empty array, so it is whatever was in memory at the spot that the array was created. Try it again and you'll likely get different numbers.

Rambatino · Accepted Answer · 2020-06-30 16:17:44Z

1

You've created a n-dimensional array whereby n = 5 which is the length of the array passed (which form the dimensions as explained here).

It's likely you're looking for:

np.array(a)

Those numbers are float 0.

answered Jun 30, 2020 at 16:17

Rambatino

4,9522 gold badges37 silver badges57 bronze badges

Comments

Youyoun · Accepted Answer · 2020-06-30 16:18:14Z

1

To create an array from a list, use: b = np.array(a). np.ndarray is another numpy class, and the first argument of the function is the shape of the array (hence the shape of your array being b.shape -> (1, 5, 3, 6, 2))

answered Jun 30, 2020 at 16:18

Youyoun

3381 silver badge5 bronze badges

Comments

Ian · Accepted Answer · 2020-06-30 16:29:14Z

0

np.ndarray(shape, dtype=float....) the argument require shape, so it will create a new array with shape (1, 5, 3, 6, 2) in your example

a = [1, 5, 3, 6, 2]
b = np.ndarray(a)
print(b.shape)

return

(1, 5, 3, 6, 2)

what you want is np.array

a = [1, 5, 3, 6, 2]
b = np.array(a)
print(b.shape)

return

(5,)

answered Jun 30, 2020 at 16:29

Ian

1091 silver badge11 bronze badges

Collectives™ on Stack Overflow

Cannot understand the output of this code: python list to 1-d numpy array

4 Answers 4

2 Comments

Comments

Comments

Comments

Your Answer

Linked

Hot Network Questions

Collectives™ on Stack Overflow

4 Answers 4

2 Comments

Comments

Comments

Comments

Your Answer

Sign up or log in

Post as a guest

Linked

Related