Mastering the NumPy Stack Method

The Syntax Behind the Stack Method

The NumPy stack method is a powerful tool for combining arrays efficiently. It takes in a sequence of arrays to be joined, along with several optional arguments: axis, out, and dtype.

numpy.stack(arrays, axis=-1, out=None, dtype=None)

The axis defines the dimension in which the arrays are joined, while out specifies the destination to store the result. The dtype, on the other hand, determines the data type of the resultant array.

Important Notes to Keep in Mind

• Only one of out and dtype arguments can be passed.
• The shape of all arrays in the input tuple should be the same.

Stacking Arrays: Examples and Applications

Let’s dive into some practical examples to illustrate the versatility of the stack method.

Example 1: Stacking Three Arrays

By default, the stack method joins arrays row-wise (vertically) when no axis is specified. This results in a 3-D array, as seen below.

import numpy as np

array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])
array3 = np.array([7, 8, 9])

stacked_array = np.stack((array1, array2, array3))
print(stacked_array.shape)  # Output: (3, 3)

Example 2: Stacking Arrays in Different Dimensions

By specifying the axis, you can control how the arrays are stacked. When axis is 0, the arrays are stacked row-wise, while axis 1 stacks them column-wise.

import numpy as np

array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])
array3 = np.array([7, 8, 9])

stacked_array_row_wise = np.stack((array1, array2, array3), axis=0)
print(stacked_array_row_wise.shape)  # Output: (3, 3)

stacked_array_col_wise = np.stack((array1, array2, array3), axis=1)
print(stacked_array_col_wise.shape)  # Output: (3, 3)

Example 3: Returning an Existing Array as a Stacked Array

Instead of generating a new array, you can use the out argument to store the output in an existing array. However, the shape of the output array must match the shape of the stacked array to avoid errors.

import numpy as np

array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])
array3 = np.array([7, 8, 9])

existing_array = np.empty((3, 3))

np.stack((array1, array2, array3), out=existing_array)
print(existing_array)

Example 4: Specifying the Datatype of a Stacked Array

Need to change the data type of the stacked array? Simply pass the dtype argument to achieve this.

import numpy as np

array1 = np.array([1, 2, 3], dtype=float)
array2 = np.array([4, 5, 6], dtype=float)
array3 = np.array([7, 8, 9], dtype=float)

stacked_array = np.stack((array1, array2, array3), dtype=int)
print(stacked_array.dtype)  # Output: int64

The Difference Between Stack and Concatenate

While both methods combine arrays, they differ in their approach. The stack method adds a new dimension and combines arrays into a higher-dimensional array, whereas concatenate joins arrays along an existing axis without introducing a new dimension.

import numpy as np

array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])

stacked_array = np.stack((array1, array2))
print(stacked_array.shape)  # Output: (2, 3)

concatenated_array = np.concatenate((array1, array2))
print(concatenated_array.shape)  # Output: (6,)