I have a numpy array: a = [[1, 999, 3], [-1, 1, 3], [2, 999, 6]]
I want to find every instance of number 999 and replace it with the average of the two neighbouring numbers (999 is always in the middle).
I used the following code to try and make this work: np.where(a == 999, .5 * (a[0] + a[2]), a)
But the output I get appends the value I calculate for the first array: [[1, 2, 3], [-1, 1, 3], [2, 2, 6]] instead of:[[1, 2, 3], [-1, 1, 3], [2, 4, 6]]
How can I fix that?
You can get the row indices where the second column equals 999, and replace with the mean of the respective first and third columns. I'm using np.ix_ here to avoid integer based indexing, this will instead create a mesh from the input sequences:
a = np.array([[1, 999, 3], [-1, 1, 3], [2, 999, 6]])
ix = a[:,1] == 999
a[ix, 1] = a[np.ix_(ix, [0,2])].mean(1)
print(a)
array([[ 1, 2, 3],
[-1, 1, 3],
[ 2, 4, 6]])
np.ix_ does here is create an array containing all the pairs of indexes. ix is a list containing the index of the rows where there is a 999. So by giving np.ix_ ix and [0, 2] we are asking np.ix_ to generate the indexes of the values. In that case: ix => [0, 2], np.ix_(ix, [0, 2]) => [[0], [2]], [[0, 2]], a[np.ix_(ix, [0, 2])] => [[a[0, 0], a[0, 2]], [a[2, 0], a[2, 2]]]