What is the difference between these two numpy objects?
import numpy as np
np.array([[0,0,0,0]])
np.array([0,0,0,0])
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1The dimensions.Ignacio Vazquez-Abrams– Ignacio Vazquez-Abrams2016-09-26 03:39:13 +00:00Commented Sep 26, 2016 at 3:39
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Look at this similar question from earlier today: stackoverflow.com/questions/39687960/…hpaulj– hpaulj2016-09-26 04:02:34 +00:00Commented Sep 26, 2016 at 4:02
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3 Answers
In [71]: np.array([[0,0,0,0]]).shape
Out[71]: (1, 4)
In [72]: np.array([0,0,0,0]).shape
Out[72]: (4,)
The former is a 1 x 4 two-dimensional array, the latter a 4 element one-dimensional array.
5 Comments
bkr879
What does the notation (4,) mean? 4 rows and undefined number of columns? Why is the result of the second operation not (,4)?
Jan Christoph Terasa
The
shape attribute returns a standard python tuple, where the numbers are the number of elements per dimension. So (4,) is a standard 1-element python tuple, with the element having the value 4. (,4) is not a valid tuple.Simon
I'm not sure but doesn't (4,) mean that there is just 4 rows and one column, ie, it is a one dimensional array? The second output of (1,4) would mean 1 row and 4 column, so shouldn't take mean the output has 4 dimensions as it has 4 columns?
Jan Christoph Terasa
The latter
(4,) array does not have any "columns", since it is 1d. Having columns implies that it is 2d.
toto'
(4,) it is a standard way to represent 1-dim vector. Here a vector with 4 elems.
The difference between single and double brackets starts with lists:
In [91]: ll=[0,1,2]
In [92]: ll1=[[0,1,2]]
In [93]: len(ll)
Out[93]: 3
In [94]: len(ll1)
Out[94]: 1
In [95]: len(ll1[0])
Out[95]: 3
ll is a list of 3 items. ll1 is a list of 1 item; that item is another list. Remember, a list can contain a variety of different objects, numbers, strings, other lists, etc.
Your 2 expressions effectively make arrays from two such lists
In [96]: np.array(ll)
Out[96]: array([0, 1, 2])
In [97]: _.shape
Out[97]: (3,)
In [98]: np.array(ll1)
Out[98]: array([[0, 1, 2]])
In [99]: _.shape
Out[99]: (1, 3)
Here the list of lists has been turned into a 2d array. In a subtle way numpy blurs the distinction between the list and the nested list, since the difference between the two arrays lies in their shape, not a fundamental structure. array(ll)[None,:] produces the (1,3) version, while array(ll1).ravel() produces a (3,) version.
In the end result the difference between single and double brackets is a difference in the number of array dimensions, but we shouldn't loose sight of the fact that Python first creates different lists.
1 Comment
TheMaster
@anonymous user
ll1 It's still a list of 1 item only(but that item is a list of 3 items)When you defined an array with two brackets, what you were really doing was declaring an array with an array with 4 0's inside. Therefore, if you wanted to access the first zero you would be accessing
your_array[0][0] while in the second array you would just be accessing your array[0]. Perhaps a better way to visualize it is
array: [
[0,0,0,0],
]
vs
array: [0,0,0,0]
2 Comments
hpaulj
With
np.array, it is better to index with x[0, 0]. numpy arrays have more powerful indexing methods than nested lists.
Avi Mosseri
true but for someone asking this kind of a question I thought that this might be an easier way to explain it