I have a list of matrices:
arr = [array([[1, 2, 3], [7, 8, 9]]), array([[4, 5, 6], [0, 0, 1]])]
I want to flatten them in the following way:
[[1, 2, 3], [7, 8, 9], [4, 5, 6], [0, 0, 1]]
numpy.flatten flattens it into a single array of numbers.
I tried this: flattened_list = [y for x in arr for y in x]
It does the job, but all rows of the matrix are numpy arrays.
Is there any way to flatten numpy arrays upto a certain depth?
You should use reshape:
out = arr.reshape((4,3))
What you want is the vstack function from numpy. It takes a tuple of ndarrays and returns a new ndarray which is the result of stacking them vertically with the first ndarray being on top and so on.
For example:
import numpy as np
>>> a = np.array([1, 2])
>>> b = np.array([3, 4])
>>> c = np.array([5, 6])
>>> np.vstack(a, b)
array([[1, 2],
[3, 4],
[5, 6]])
In your case you can easily call the tuple function on your list of ndarrays
>>> arr = [array([[1, 2, 3], [7, 8, 9]]), array([[4, 5, 6], [0, 0, 1]])]
>>> np.vstack(tuple(arr))
array([[1, 2, 3],
[7, 8, 9],
[4, 5, 6],
[0, 0, 1]])
If you want your answer as a python list then just call numpy's ndarray.tolist function on the result like so:
>>> np.ndarray.tolist(np.vstack(arr))
[[1, 2, 3], [7, 8, 9], [4, 5, 6], [0, 0, 1]]
Related
I have tried to find a similar question but so far it seems only half my question can be answered.
I have a 2D numpy array, e.g.:
a= np.array([[6, 4, 5],
[4, 7, 8],
[2, 8, 9]])
And i also have 2 further numpy arrays, indicating the rows, and columns where i would like to rearrange (or not):
rows= np.array([[0, 0, 0],
[1, 0, 1],
[2, 2, 2]])
cols= np.array([[0, 1, 2],
[0, 0, 2],
[0, 1, 2]])
now i would like to rearrange the array "a" based on these indices, so that the result is:
result= np.array([[6, 4, 5],
[4, 6, 8],
[2, 8, 9]])
Doing this only for columns or only for rows is easy, e.g. see this Thread:
np.array(list(map(lambda x, y: y[x], cols, a)))
This is a typical case of fancy/array indexing:
result = a[rows, cols]
Output:
array([[6, 4, 5],
[4, 6, 8],
[2, 8, 9]])
Let's say I have this numpy matrix:
>>> mat = np.matrix([[3,4,5,2,1], [1,2,7,6,5], [8,9,4,5,2]])
>>> mat
matrix([[3, 4, 5, 2, 1],
[1, 2, 7, 6, 5],
[8, 9, 4, 5, 2]])
Now let's say I have some indexes in this form:
>>> ind = np.matrix([[0,2,3], [0,4,2], [3,1,2]])
>>> ind
matrix([[0, 2, 3],
[0, 4, 2],
[3, 1, 2]])
What I would like to do is to get three values from each row of the matrix, specifically values at columns 0, 2, and 3 for the first row, values at columns 0, 4 and 2 for the second row, etc. This is the expected output:
matrix([[3, 5, 2],
[1, 5, 7],
[5, 9, 4]])
I've tried using np.take but it doesn't seem to work. Any suggestion?
This is take_along_axis.
>>> np.take_along_axis(mat, ind, axis=1)
matrix([[3, 5, 2],
[1, 5, 7],
[5, 9, 4]])
This will do it: mat[np.arange(3).reshape(-1, 1), ind]
In [245]: mat[np.arange(3).reshape(-1, 1), ind]
Out[245]:
matrix([[3, 5, 2],
[1, 5, 7],
[5, 9, 4]])
(but take_along_axis in #user3483203's answer is simpler).
I have 2d and 1d numpy and I want to join them with Cartesian product.
For example the numpy's are:
td = np.array([[1,2,3],
[4,5,6],
[7,8,9]])
od = np.array([1,2,3])
The expected result should be:
[[1,2,3,1],
[1,2,3,2],
[1,2,3,3],
[4,5,6,1],
[4,5,6,2],
[4,5,6,3],
[7,8,9,1],
[7,8,9,2],
[7,8,9,3]]
The following code does not achieve the desired result:
import numpy as np
rslt = np.transpose([np.tile(td, len(od)), np.repeat(od , len(td))])
What needs to change?
Give this a try:
np.c_[np.repeat(td,3,axis=0),np.tile(od,3).reshape((-1,1))]
output:
array([[1, 2, 3, 1],
[1, 2, 3, 2],
[1, 2, 3, 3],
[4, 5, 6, 1],
[4, 5, 6, 2],
[4, 5, 6, 3],
[7, 8, 9, 1],
[7, 8, 9, 2],
[7, 8, 9, 3]])
Assume I have the following numpy array :
a = np.array([[4, 5, 8],
[7, 2, 9],
[1, 5, 3]])
and I want to extract points from the array 'a' to have this array :
b = array([[4, 8],
[1, 3]])
How can I do this ?
PS : In my real case I have 13*13 matrix and I want to create a 3*3 matrix from the first one
You can use np.ix_() for this to create a map of which values you want by location.
>>> a = np.arange(1,10).reshape(3,3)
>>> a
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
>>> b=np.ix_([0,2],[0, 2])
>>> a[b]
array([[1, 3],
[7, 9]])
I have a 2D array containing values and a 1D array with index values where I would like to split the 2D matrix, where the splitted sub-arrays include the 'split-point'.
I know I can use the numpy.split function to split by indices and I know I can use stride_tricks to split an array for creating consecutive overlapping subset-views.
But it seems the stride_ticks only applies if we want to split an array into equal sized sub-arrays.
Minimal example, I can do the following:
>>> import numpy as np
>>> array = np.random.randint(0,10, (10,2))
>>> indices = np.array([2,3,8])
>>> array
array([[8, 1],
[1, 0],
[2, 0],
[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7],
[6, 4]])
>>> split_array = np.split(array, indices, axis=0)
>>> split_array
[array([[8, 1],
[1, 0]]),
array([[2, 0]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4]]),
array([[6, 7],
[6, 4]])]
But I'm merely looking for an option within the split function where I could define include_split_point=True, which would give me a result as such:
[array([[8, 1],
[1, 0],
[2, 0]]),
array([[2, 0],
[8, 8]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7]]),
array([[6, 7],
[6, 4]])]
Create a new array with the index elements repeated
new_indices = np.zeros(array.shape[0], dtype = int)
new_indices[indices] = 1
new_indices += 1
new_array = np.repeat(array, new_indices, axis = 0)
Update indices to account for the changed array
indices = indices + np.arange(1, len(indices)+1)
Split using the indices as usual
np.split(new_array, indices, axis = 0)
output:
[array([[8, 1],
[1, 0],
[2, 0]]),
array([[2, 0],
[8, 8]]),
array([[8, 8],
[1, 6],
[7, 8],
[4, 4],
[9, 4],
[6, 7]]),
array([[6, 7],
[6, 4]])]