Numpy: How masking in index works?
Question:
I’m a new learner of Python.
May I get some help on NumPy ?
How does the Masking in index selection works?
My first masking is like
array[mask][:,0]
# array([1905., 1920., 1929., 1938., 1948., 1965., 2002., 2008., 2016.,2022.]
Which I understand how it works using index
But my second
numpy.array[mask1][:,mask2]
# [[1905. 18.9]
# [1920. 20.6]
# [1929. 18.9]
# [1938. nan]
# [1948. 19.9]
# [1965. nan]
# [2002. nan]
# [2008. 19.5]
# [2016. 19.4]
# [2022. nan]]
# [[1905. 18.9]
# [1920. 20.6]
# [1929. 18.9]
# [1938. nan]
# [1948. 19.9]
# [1965. nan]
# [2002. nan]
# [2008. 19.5]
# [2016. 19.4]
# [2022. nan]]
I don’t understand why it returns a (10,2) shape array
I hope I asking questions in the right way, sorry for any inconvenience.
Answers:
Masks basically say select that column, row, or axis. There are two main ways that are used in numpy.
- Boolean [True, False, True, …] with the same shape as the array or an axis
- Indices [2,2,0] of arbitrary shape that say, select axis/value 2, 2 and 0 in that order.
np.array([1,2,3])[[2,2,0,0]] -> [3,3,1,1] note the double brackets, it says select indices [2,2,0,0] from axis 0
In case of boolean masks you get a shape the sum of the True values in each mask.
In case of index masks your results are in the shape of the masks.
numpy.array[mask1][:,mask2] will have roughly the shape (mask1.shape, mask2.shape), but of course these could be more than 2 dimensions.
I’m a new learner of Python.
May I get some help on NumPy ?
How does the Masking in index selection works?
My first masking is like
array[mask][:,0]
# array([1905., 1920., 1929., 1938., 1948., 1965., 2002., 2008., 2016.,2022.]
Which I understand how it works using index
But my second
numpy.array[mask1][:,mask2]
# [[1905. 18.9]
# [1920. 20.6]
# [1929. 18.9]
# [1938. nan]
# [1948. 19.9]
# [1965. nan]
# [2002. nan]
# [2008. 19.5]
# [2016. 19.4]
# [2022. nan]]
# [[1905. 18.9]
# [1920. 20.6]
# [1929. 18.9]
# [1938. nan]
# [1948. 19.9]
# [1965. nan]
# [2002. nan]
# [2008. 19.5]
# [2016. 19.4]
# [2022. nan]]
I don’t understand why it returns a (10,2) shape array
I hope I asking questions in the right way, sorry for any inconvenience.
Masks basically say select that column, row, or axis. There are two main ways that are used in numpy.
- Boolean [True, False, True, …] with the same shape as the array or an axis
- Indices [2,2,0] of arbitrary shape that say, select axis/value 2, 2 and 0 in that order.
np.array([1,2,3])[[2,2,0,0]] -> [3,3,1,1] note the double brackets, it says select indices [2,2,0,0] from axis 0
In case of boolean masks you get a shape the sum of the True values in each mask.
In case of index masks your results are in the shape of the masks.
numpy.array[mask1][:,mask2] will have roughly the shape (mask1.shape, mask2.shape), but of course these could be more than 2 dimensions.