Stacking two-dimensional arrays is a common operation when working with data analysis and machine learning. In NumPy and Dask, there are several functions for stacking two-dimensional arrays, including np.hstack(), np.vstack(), np.dstack(), da.hstack(), da.vstack(), and da.stack().
Here is an example of how to stack two NumPy two-dimensional arrays horizontally using np.hstack():
import numpy as np# Create two two-dimensional arrays with the same number of rowsarr1 = np.array([[1, 2], [3, 4], [5, 6]])arr2 = np.array([[7, 8], [9, 10], [11, 12]])# Stack the arrays horizontallystacked = np.hstack([arr1, arr2]) |
In this example, we create two NumPy two-dimensional arrays with the same number of rows and stack them horizontally using np.hstack(). The resulting array stacked has a shape of (3, 4).
Here is an example of how to stack two Dask two-dimensional arrays vertically using da.vstack():
import dask.array as da# Create two two-dimensional Dask arrays with the same number of columnsarr1 = da.from_array([[1, 2], [3, 4], [5, 6]], chunks=(1, 2))arr2 = da.from_array([[7, 8], [9, 10], [11, 12]], chunks=(1, 2))# Stack the arrays verticallystacked = da.vstack([arr1, arr2]) |
In this example, we create two Dask two-dimensional arrays with the same number of columns and stack them vertically using da.vstack(). The resulting array stacked has a shape of (6, 2).
Note that when stacking two-dimensional arrays in Dask, it is important to ensure that the chunks of the resulting array are well-aligned to avoid performance issues. Additionally, da.stack() can stack arrays along a new axis, while np.dstack() can stack arrays depth-wise.