Stacking one-dimensional arrays is a common operation when working with data analysis and machine learning. In NumPy and Dask, there are several functions for stacking one-dimensional arrays, including np.hstack(), np.vstack(), da.hstack(), and da.vstack().
Here is an example of how to stack two NumPy one-dimensional arrays horizontally using np.hstack():
import numpy as np# Create two one-dimensional arraysarr1 = np.array([1, 2, 3])arr2 = np.array([4, 5, 6])# Stack the arrays horizontallystacked = np.hstack([arr1, arr2]) |
In this example, we create two NumPy one-dimensional arrays and stack them horizontally using np.hstack(). The resulting array stacked has a shape of (6,).
Here is an example of how to stack two Dask one-dimensional arrays vertically using da.vstack():
import dask.array as da# Create two one-dimensional Dask arraysarr1 = da.from_array([1, 2, 3], chunks=1)arr2 = da.from_array([4, 5, 6], chunks=1)# Stack the arrays verticallystacked = da.vstack([arr1, arr2]) |
In this example, we create two Dask one-dimensional arrays and stack them vertically using da.vstack(). The resulting array stacked has a shape of (6,).
Note that when stacking one-dimensional arrays in Dask, it is important to ensure that the chunks of the resulting array are well-aligned to avoid performance issues. Additionally, some functions like np.hstack() and da.hstack() can stack arrays only if they have compatible shapes, which means they must have the same number of rows or the same number of elements in each row.