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On a smaller scale compared to what I need, here's an example of what I'm looking to do:
>>> a
array([[ 21, 22, 23, 24, 25, 26, 27],
[ 56, 57, 58, 59, 60, 61, 62],
[ 14, 15, 16, 17, 18, 19, 20],
[ 7, 8, 9, 1010, 11, 12, 13],
[ 42, 43, 44, 45, 46, 47, 48],
[ 63, 64, 65, 66, 67, 68, 69],
[ 0, 1, 2, 3, 4, 5, 6],
[ 49, 50, 51, 52, 53, 54, 55],
[ 28, 29, 30, 31, 32, 33, 34],
[ 35, 36, 37, 38, 39, 40, 41]])
>>> indices = a.argmax(axis=0)
>>> indices
array([5, 5, 5, 3, 5, 5, 5])
>>> b = np.zeros(a.shape)
>>> b[indices] = 1.0
>>> b # below is the actual output, not what I want
array([[ 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0.],
[ 1., 1., 1., 1., 1., 1., 1.],
[ 0., 0., 0., 0., 0., 0., 0.],
[ 1., 1., 1., 1., 1., 1., 1.],
[ 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0.]])
But what I actually need is:
>>> b
array([[ 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 1., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0.],
[ 1., 1., 1., 0., 1., 1., 1.],
[ 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0.]])
Numpy indexing can get extremely complicated and it's a little difficult to put the above into words, so hopefully someone can understand what I'm looking for. Essentially it's to set a 1 wherever there's a max of a column and zero elsewhere. How would I go about doing this?
627
Use NumPy array indexing to extract specific columsUse the syntax array[:, [i, j]] to extract the i and j indexed columns from array . Like lists, NumPy arrays use zero-based indexes. Use array[:, i:j+1] to extract the i through j indexed columns from array .
We can access elements of an array using the index operator [] . All you need do in order to access a particular element is to call the array you created. Beside the array is the index [] operator, which will have the value of the particular element's index position from a given array.
You can access an array element by referring to its index number. The indexes in NumPy arrays start with 0, meaning that the first element has index 0, and the second has index 1 etc.
From the docs:
If the number of objects in the selection tuple is less than N , then
:is assumed for any subsequent dimensions.
In your selection there only one array, so you get every row from indices to be equal to 1. To overcome that, you need column indices. I guess this will do the trick:
b[indices, np.arange(a.shape[1])] = 1.0
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