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How do you rotate a two dimensional array?
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Closed 7 years ago.
How can rotate multi-dimensional List without using the zip function, I have this list (but it can be longer):
testlist = [[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[10,11,12]]
I want to rotate it 90 degrees clockwise and also 90 degrees counterclockwise.
ps: The reason i don't want to use zip is because this is a homework and i must write it in vanilla python .
Of course, the proper way to rotate the multi-dimensional list would be to use zip on the reversed list. I assume you've already found this in other questions here:
>>> testlist = [[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[10,11,12]]
>>> list(zip(*testlist[::-1]))
[[10, 7, 4, 1], [11, 8, 5, 2], [12, 9, 6, 3]]
If you do not want to do that (and also don't want other builtin functions), you'll basically have to replicate the behaviour of zip. In a very simple form (e.g. just assuming that all the lists have the same length) you can do this in a nested list comprehension. Remember to rotate the list, too:
>>> [[x[i] for x in testlist[::-1]] for i in range(len(testlist[0]))]
[[10, 7, 4, 1], [11, 8, 5, 2], [12, 9, 6, 3]]
Obviously, using zip is much clearer and less error-prone. Of course, you could also spread this out to a few lines using two nested loops. Doing the same for 90 degrees counter-clockwise is left as an excercise to the reader.
I am assuming you mean transpose.
How about this?
import numpy as np
a=np.array(testlist)
print a.T #this is a view
You code would be like:
>>> import numpy as np
>>> s = np.array([[1,2,3], [4,5,6], [7,8,9]], int)
>>> s
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
>>> np.rot90(s)
array([[3, 6, 9],
[2, 5, 8],
[1, 4, 7]])
>>> np.rot90(s, 3) # Rotate counterclockwise 3 times
array([[9, 8, 7],
[6, 5, 4],
[3, 2, 1]])
Since you've stated it's for homework, I'll give you the steps, but not the code.
Rotate 90
Each row becomes a column (you can use 2 for loops and enumerate)
Reverse order of the rows (you can use slices with a negative step)
Rotate -90
Each row from Rotate 90 becomes a column (2 for loops and enumerate)
Reverse order of the rows (slice with a negative step)
Related
It is kind of hard to explain exactly what I mean, therefore I give an example of the function I would like:
a = [[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]
b = [0, 4, 5]
c = np.func(a, b)
print(c)
-->[[0, 0, 0],
[4, 5, 5],
[5, 5, 5]]
In words: every element of an array (a) should be lowered to the closest lower value of another array (b). The values could be floats.
I could do this in a loop, but I'm sure there is a numpy way of doing this.
Any tips much appreciated.
I have a list X which contains a list of lists of lists... e.g.
X = [ [[], [], [], []], [[], [] ,[], []] ]
When i try to convert this list above (X) into a numpy array using
np.array(), I get the following:
array([list([[],[],[],[]]),
list([list([[],[],[],[]]),....)
and the list goes on pun intended*.
What am I doing wrong here? I just need it in a normal array form such that I can index it in ways lists don't allow (i.e. array[:,1])
If your lists are NOT of the same length (in each nested dimension) you CANT do a traditional conversion to a NumPy array because it's necessary for a NumPy array of 2D or above to have the same number of elements in its first dimension.
So you cant convert [[1,2],[3,4,5]] to a numpy array directly. Applying np.array will give you a 2 element numpy array where each element is a list object as - array([list([1, 2]), list([3, 4, 5])], dtype=object). I believe this is the issue you are facing.
You cant create a 2D matrix for example that looks like -
[[1,2,3,?],
[4,5,6,7]]
What you may need to do is pad the elements of each list of lists of lists to a fixed length (equal lengths for each dimension) before converting to a NumPy array.
I would recommend iterating over each of the lists of lists of lists as done in the code I have written below to flatten your data, then transforming it the way you want.
If your lists are of the same length, then should not be a problem with numpy version 1.18.5 or above.
a = [[[1,2],[3,4]],[[5,6],[7,8]]]
np.array(a)
array([[[1, 2],
[3, 4]],
[[5, 6],
[7, 8]]])
However, if you are unable to still work with the list of list of lists, then you may need to iterate over each element first to flatten the list and then change it into a numpy array with the required shape as below -
a = [[[1,2],[3,4]],[[5,6],[7,8]]]
flat_a = [item for sublist in a for subsublist in sublist for item in subsublist]
np.array(flat_a).reshape(2,2,2)
array([[[1, 2],
[3, 4]],
[[5, 6],
[7, 8]]])
Try this:
>>> import numpy as np
>>> a = np.array([[[1,2],[3,4],[5,6]],[[7,8],[9,10],[11,12]]])
>>> a
array([[[ 1, 2],
[ 3, 4],
[ 5, 6]],
[[ 7, 8],
[ 9, 10],
[11, 12]]])
>>> a.reshape(4,-1)
array([[ 1, 2, 3],
[ 4, 5, 6],
[ 7, 8, 9],
[10, 11, 12]])
>>> a.reshape(1,-1)
array([[ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]])
I have two matrices, A and B.
A=np.matrix([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
B=np.matrix([[1,1,1],[2,2,2],[3,3,3],[4,4,4]])
I want to substract some of B'rows (namely 0,2 and 3) from A. I tried to use
Index=np.array([0,2,3])
for i in Index:
A[i,:]=A[i,:]-B[i,:]
but it didn't work because matriz A should look like
matrix([[0, 1, 2],
[1, 2, 3],
[4, 5, 6],
[6, 7, 8]])
and I got
matrix([[ 1, 2, 3],
[ 2, 3, 4],
[ 7, 8, 9],
[10, 11, 12]])
What's the correct way to do this operation? I took me a long time to realize this problem (the real problem I'm trying to solve has more variables) and can't seem to figure it out.
If you do mean substract, then your should use
A[i,:]=A[i,:]-B[i,:]
instead of
A[i,:]=A[i,:]+B[i,:]
Numpy has element-wise subtraction, so something like:
import numpy as np
A=np.matrix([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
B=np.matrix([[1,1,1],[2,2,2],[3,3,3],[4,4,4]])
indices = [0,2,3]
for i in indices:
A[i,:]=np.subtract(A[i,:], B[i,:])
Will give you this matrix for A:
[[0, 1, 2],
[4, 5, 6],
[4, 5, 6],
[6, 7, 8]])
Is this what you are after? For better performance you could also just change the particular rows of A:
A[indices]=np.subtract(A[indices],B[indices])
Which will give the same answer.
I have a 3 dimensional numpy array. The dimension can go up to 128 x 64 x 8192. What I want to do is to change the order in the first dimension by interchanging pairwise.
The only idea I had so far is to create a list of the indices in the correct order.
order = [1,0,3,2...127,126]
data_new = data[order]
I fear, that this is not very efficient but I have no better idea so far
You could reshape to split the first axis into two axes, such that latter of those axes is of length 2 and then flip the array along that axis with [::-1] and finally reshape back to original shape.
Thus, we would have an implementation like so -
a.reshape(-1,2,*a.shape[1:])[:,::-1].reshape(a.shape)
Sample run -
In [170]: a = np.random.randint(0,9,(6,3))
In [171]: order = [1,0,3,2,5,4]
In [172]: a[order]
Out[172]:
array([[0, 8, 5],
[4, 5, 6],
[0, 0, 2],
[7, 3, 8],
[1, 6, 3],
[2, 4, 4]])
In [173]: a.reshape(-1,2,*a.shape[1:])[:,::-1].reshape(a.shape)
Out[173]:
array([[0, 8, 5],
[4, 5, 6],
[0, 0, 2],
[7, 3, 8],
[1, 6, 3],
[2, 4, 4]])
Alternatively, if you are looking to efficiently create those constantly flipping indices order, we could do something like this -
order = np.arange(data.shape[0]).reshape(-1,2)[:,::-1].ravel()
For example, I have a ndarray that is:
a = np.array([1, 3, 5, 7, 2, 4, 6, 8])
Now I want to split a into two parts, one is all numbers <5 and the other is all >=5:
[array([1,3,2,4]), array([5,7,6,8])]
Certainly I can traverse a and create two new array. But I want to know does numpy provide some better ways?
Similarly, for multidimensional array, e.g.
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[2, 4, 7]])
I want to split it according to the first column <3 and >=3, which result is:
[array([[1, 2, 3],
[2, 4, 7]]),
array([[4, 5, 6],
[7, 8, 9]])]
Are there any better ways instead of traverse it? Thanks.
import numpy as np
def split(arr, cond):
return [arr[cond], arr[~cond]]
a = np.array([1,3,5,7,2,4,6,8])
print split(a, a<5)
a = np.array([[1,2,3],[4,5,6],[7,8,9],[2,4,7]])
print split(a, a[:,0]<3)
This produces the following output:
[array([1, 3, 2, 4]), array([5, 7, 6, 8])]
[array([[1, 2, 3],
[2, 4, 7]]), array([[4, 5, 6],
[7, 8, 9]])]
It might be a quick solution
a = np.array([1,3,5,7])
b = a >= 3 # variable with condition
a[b] # to slice the array
len(a[b]) # count the elements in sliced array
1d array
a = numpy.array([2,3,4,...])
a_new = a[(a < 4)] # to get elements less than 5
2d array based on column(consider value of column i should be less than 5,
a = numpy.array([[1,2],[5,6],...]
a = a[(a[:,i] < 5)]
if your condition is multicolumn based, then you can make a new array applying the conditions on the columns. Then you can just compare the new array with value 5(according to my assumption) to get indexes and follow above codes.
Note that, whatever i have written in (), returns the index array.