Python | stacking-arrays

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Stacking arrays

Stacking arrays means combining them into a single array, either by appending them along a new axis or by concatenating them along an existing axis. In NumPy and Dask, there are several functions for stacking arrays, including np.stack(), np.concatenate(), da.stack(), and da.concatenate().

Here is an example of how to stack two NumPy arrays along a new axis using np.stack():

import numpy as np

# Create two 2D arrays with the same shape

arr1 = np.array([[1, 2], [3, 4]])

arr2 = np.array([[5, 6], [7, 8]])

# Stack the arrays along a new axis

stacked = np.stack([arr1, arr2], axis=0)

In this example, we create two NumPy arrays with the same shape and stack them along a new axis using np.stack(). The resulting array stacked has a shape of (2, 2, 2).

Here is an example of how to concatenate two Dask arrays along an existing axis using da.concatenate():

import dask.array as da

# Create two 2D Dask arrays with the same shape

arr1 = da.from_array([[1, 2], [3, 4]], chunks=1)

arr2 = da.from_array([[5, 6], [7, 8]], chunks=1)

# Concatenate the arrays along the rows

concatenated = da.concatenate([arr1, arr2], axis=0)

In this example, we create two Dask arrays with the same shape and concatenate them along the rows using da.concatenate(). The resulting array concatenated has a shape of (4, 2).

Note that when concatenating arrays in Dask, it is important to ensure that the chunks of the resulting array are well-aligned to avoid performance issues.