In the context of this exercise, what is an axis in Numpy?
An axis is similar to a dimension. For a 2-dimensional array, there are 2 axes: vertical and horizontal.
When applying certain Numpy functions like
np.mean(), we can specify what axis we want to calculate the values across.
axis=0, this means that we apply a function along each “column”, or all values that occur vertically.
axis=1, this means that we apply a function along each “row”, or all values horizontally.
# Given the following 2-dimensional array values = np.array([ [10, 20, 30, 40], [50, 60, 70, 80], ]) # Axis=0 # along each "column" print np.mean(values, axis=0) # [30, 40, 50, 60] # Axis=1 # along each "row" print np.mean(values, axis=1) # [25, 65]