CODEWITHSUNDEEP
pythonarraysnumpyindexingvectorization
akilat90
akilat90

Reputation: 5696

Using a numpy array to assign values to another array

I have the following numpy array matrix ,

matrix = np.zeros((3,5), dtype = int)

array([[0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0]])

Suppose I have this numpy array indices as well

indices = np.array([[1,3], [2,4], [0,4]])

array([[1, 3],
       [2, 4],
       [0, 4]])

Question: How can I assign 1s to the elements in the matrix where their indices are specified by the indices array. A vectorized implementation is expected.

For more clarity, the output should look like:

    array([[0, 1, 0, 1, 0], #[1,3] elements are changed
           [0, 0, 1, 0, 1], #[2,4] elements are changed
           [1, 0, 0, 0, 1]]) #[0,4] elements are changed

Upvotes: 2

Views: 3257

Answers (2)

Siraj S.
Siraj S.

Reputation: 3751

this involves loop and hence may not be very efficient for large arrays

for i in range(len(indices)):
    matrix[i,indices[i]] = 1

> matrix
 Out[73]: 
array([[0, 1, 0, 1, 0],
      [0, 0, 1, 0, 1],
      [1, 0, 0, 0, 1]])

Upvotes: 0

Divakar
Divakar

Reputation: 221524

Here's one approach using NumPy's fancy-indexing -

matrix[np.arange(matrix.shape[0])[:,None],indices] = 1

Explanation

We create the row indices with np.arange(matrix.shape[0]) -

In [16]: idx = np.arange(matrix.shape[0])

In [17]: idx
Out[17]: array([0, 1, 2])

In [18]: idx.shape
Out[18]: (3,)

The column indices are already given as indices -

In [19]: indices
Out[19]: 
array([[1, 3],
       [2, 4],
       [0, 4]])

In [20]: indices.shape
Out[20]: (3, 2)

Let's make a schematic diagram of the shapes of row and column indices, idx and indices -

idx     (row) :      3 
indices (col) :  3 x 2

For using the row and column indices for indexing into input array matrix, we need to make them broadcastable against each other. One way would be to introduce a new axis into idx, making it 2D by pushing the elements into the first axis and allowing a singleton dim as the last axis with idx[:,None], as shown below -

idx     (row) :  3 x 1
indices (col) :  3 x 2

Internally, idx would be broadcasted, like so -

In [22]: idx[:,None]
Out[22]: 
array([[0],
       [1],
       [2]])

In [23]: indices
Out[23]: 
array([[1, 3],
       [2, 4],
       [0, 4]])

In [24]: np.repeat(idx[:,None],2,axis=1) # indices has length of 2 along cols
Out[24]: 
array([[0, 0],  # Internally broadcasting would be like this
       [1, 1],
       [2, 2]])

Thus, the broadcasted elements from idx would be used as row indices and column indices from indices for indexing into matrix for setting elements in it. Since, we had -

idx = np.arange(matrix.shape[0]),

Thus, we would end up with -

matrix[np.arange(matrix.shape[0])[:,None],indices] for setting elements.

Upvotes: 8

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