Trying to teach myself some python and I am super confused from the docs what the where function does. Can somebody explain the example from the documentation below step by step please?
>>> np.where([[True, False], [True, True]],
... [[1, 2], [3, 4]],
... [[9, 8], [7, 6]])
array([[1, 8],
[3, 4]])
The basic syntax is np.where(x, a, b) Wherever x is true, take that element of a, and wherever it's false, take an element of b. It's equivalent to something like this:
x = . . [[1, 0], [1, 1]]), not x =[[0, 1], [0, 0 ]]
array([[1,
2], [3, 4]]) + array([[7, 8], [9,10]])array([[1, 0], [3, 4]]) + array([[0, 8], [0, 0 ]]) = array([[1, 8], [3, 4]])
Basically used as follows:
np.where(condition, value if condition is True, value if condition is False)
In this case:
condition is [[True, False], [True, True]]
value if condition is True is [[1, 2], [3, 4]].
value if condition is False is [[9, 8], [7, 6]].
The final result of array([[1, 8], [3, 4]]) is equal to the array from 'value if condition is True', except for the one location in condition where it is False. In this case, the value of 8 comes from the second array.
I think it becomes pretty clear when you add linebreaks to arrange the inputs to look like matrices:
np.where( # First argument
[[True, False],
[True, True]],
# Second argument
[[1, 2],
[3, 4]],
# Third argument
[[9, 8],
[7, 6]])
You can see the first argument as a mask that determines from which of the two following inputs elements should be taken.
The result
array([[1, 8],
[3, 4]])
contains elements from the second argument wherever the mask is True and elements from the third argument where it is False.
