Aggregating multidimensional arrays involves computing summary statistics across one or more dimensions of the array. This can be done using various NumPy functions that are designed for this purpose, such as sum(), mean(), std(), max(), min(), and argmax().
To illustrate, let's consider a 2-dimensional NumPy array arr:
import numpy as nparr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) |
To compute the sum of all elements in the array, we can use the sum() function:
>>> arr.sum()45 |
To compute the sum along a particular axis, we can specify the axis parameter. For example, to compute the sum of each row, we can use:
>>> arr.sum(axis=1)array([ 6, 15, 24]) |
Here, axis=1 specifies that the sum should be computed along the second axis (i.e., the columns).
Similarly, to compute the mean of each column, we can use:
>>> arr.mean(axis=0)array([4., 5., 6.]) |
Here, axis=0 specifies that the mean should be computed along the first axis (i.e., the rows).
These functions can also be used with multidimensional arrays. For example, consider a 3-dimensional NumPy array arr:
import numpy as nparr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) |
To compute the sum along the third axis (i.e., the "depth" dimension), we can use:
>>> arr.sum(axis=2)array([[ 3, 7], [11, 15]]) |
Here, axis=2 specifies that the sum should be computed along the third axis.
These functions can also be used with Dask arrays, which allows us to compute aggregates on large, distributed arrays. However, because Dask arrays are split into chunks, it is important to specify the axis parameter carefully to ensure that the aggregates are computed correctly.