I am trying two array like this. It's different from column_stack so I am not able to find how to do it from documentation or google search.
I have arrays a and b. How can I make c from them ?
a = [[1, 2],[3, 4]]
b = [[5 , 6]]
c = [[[1, 2],[5]],
[3, 4],[6]]]
I need this to input the values to theanets.
In [54]:
a = np.array([[1, 2],[3, 4]])
a
Out[54]:
array([[1, 2],
[3, 4]])
In [55]:
b = np.array([[5 , 6]])
b
Out[55]:
array([[5, 6]])
In [96]:
c = [[a[n].tolist() , b[:,n].tolist()] for n in range(len(a))]
c
Out[96]:
[[[1, 2], [5]], [[3, 4], [6]]]
Related
Is there anyway to add two numpy arrays of different length in a Descartian fashion without iterating over columns a? See example below.
a = np.array([[1, 2], [3, 4]])
b = np.array([[1, 1], [2, 2], [3, 3]])
c = dec_sum(a, b) # c = np.array([[[2, 3], [3, 4], [3, 5]], [[4, 4], [5, 6], [6, 7]]])
Given a 2x2 numpy array a and 3x2 numpy array b, c= dec_sum(a, b) and c is 2x3x2.
I have a 1 x 2 x 3 array:
>>> a = np.array([[[1,2,3],[4,5,6]]])
>>> a
array([[[1, 2, 3],
[4, 5, 6]]])
>>> a.shape
(1, 2, 3)
I want to reshape it to (3,1,2), but so that the elements along original dim 3 are now along dim 1. I want the result to look like this:
>>> new_a
array([[[1, 4]],
[[2, 5]],
[[3, 6]]])
and when I just use reshape, I get the the right shape, but the elements are in the same order, not what I want:
>>> a.reshape((3,1,2))
array([[[1, 2]],
[[3, 4]],
[[5, 6]]])
How can I achieve this?
Simply use np.transpose -
a.transpose(2,0,1)
Sample run -
In [347]: a
Out[347]:
array([[[1, 2, 3],
[4, 5, 6]]])
In [348]: a.transpose(2,0,1)
Out[348]:
array([[[1, 4]],
[[2, 5]],
[[3, 6]]])
Alternatively :
With np.moveaxis -
np.moveaxis(a,2,0)
With np.rollaxis -
np.rollaxis(a,2,0)
There are a few ways, but transpose() is probably the easiest:
array.transpose(2,0,1)
import einops
einops.rearrange(x, 'x y z -> z x y')
And better use some meaningful axes names instead of x, y, z (like width, height, etc.)
Is there a nice quick way to do the following selection for numpy arrays?
>>> A=np.array([[1,2], [2,2], [3,5], [4,5]])
>>> A
array([[1, 2],
[2, 2],
[3, 5],
[4, 5]])
I would like to select the first rows when the second column changes value. For the above array, the result would be:
array([[1, 2],
[3, 5]])
>>> xs = np.array([[1,2], [2,2], [3,5], [4,5]])
>>> j = scipy.r_[True, xs[:-1,1] != xs[1:,1]] # or np.concatenate here
>>> xs[j,:]
array([[1, 2],
[3, 5]])
Let a be a list in python.
a = [1,2,3]
When matrix transpose is applied to a, we get:
np.matrix(a).transpose()
matrix([[1],
[2],
[3]])
I am looking to generalize this functionality and will next illustrate what I am looking to do with the help of an example. Let b be another list.
b = [[1, 2], [2, 3], [3, 4]]
In a, the list items are 1, 2, and 3. I would like to consider each of [1,2], [2,3], and [3,4] as list items in b, only for the purpose of performing a transpose. I would like the output to be as follows:
array([[[1,2]],
[[2,3]],
[[3,4]]])
In general, I would like to be able to specify what a list item would look like, and perform a matrix transpose based on that.
I could just write a few lines of code to do the above, but my purpose of asking this question is to find out if there is an inbuilt numpy functionality or a pythonic way, to do this.
EDIT: unutbu's output below matches the output that I have above. However, I wanted a solution that would work for a more general case. I have posted another input/output below. My initial example wasn't descriptive enough to convey what I wanted to say. Let items in b be [1,2], [2,3], [3,4], and [5,6]. Then the output given below would be of doing a matrix transpose on higher dimension elements. More generally, once I describe what an 'item' would look like, I would like to know if there is a way to do something like a transpose.
Input: b = [[[1, 2], [2, 3]], [[3, 4], [5,6]]]
Output: array([[[1,2], [3,4]],
[[2,3], [5,6]]])
Your desired array has shape (3,1,2). b has shape (3,2). To stick an extra axis in the middle, use b[:,None,:], or (equivalently) b[:, np.newaxis, :]. Look for "newaxis" in the section on Basic Slicing.
In [178]: b = np.array([[1, 2], [2, 3], [3, 4]])
In [179]: b
Out[179]:
array([[1, 2],
[2, 3],
[3, 4]])
In [202]: b[:,None,:]
Out[202]:
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
Another userful tool is np.swapaxes:
In [222]: b = np.array([[[1, 2], [2, 3]], [[3, 4], [5,6]]])
In [223]: b.swapaxes(0,1)
Out[223]:
array([[[1, 2],
[3, 4]],
[[2, 3],
[5, 6]]])
The transpose, b.T is the same as swapping the first and last axes, b.swapaxes(0,-1):
In [226]: b.T
Out[226]:
array([[[1, 3],
[2, 5]],
[[2, 4],
[3, 6]]])
In [227]: b.swapaxes(0,-1)
Out[227]:
array([[[1, 3],
[2, 5]],
[[2, 4],
[3, 6]]])
Summary:
Use np.newaxis (or None) to add new axes. (Thus, increasing the dimension of the array)
Use np.swapaxes to swap any two axes.
Use np.transpose to permute all the axes at once. (Thanks to #jorgeca for pointing this out.)
Use np.rollaxis to "rotate" the axes.
I have two arrays of the form:
a = np.array([1,2,3])
b = np.array([4,5,6])
Is there a NumPy function which I can apply to these arrays to get the followng output?
[[1,4],[2,5][3,6]]
np.vstack((a,b)).T
returns
array([[1, 4],
[2, 5],
[3, 6]])
and
np.vstack((a,b)).T.tolist()
returns exactly what you need:
[[1, 4], [2, 5], [3, 6]]