NumPy cbrt() – Cube Root or Each Element in Array

NumPy cbrt()

The numpy.cbrt() function computes the cube root of each element in an input array, element-wise.

Syntax

numpy.cbrt(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True)

Parameters

Parameter	Type	Description
x	array_like	The values whose cube roots are to be computed.
out	ndarray, None, or tuple of ndarray and None, optional	Optional output array where the result is stored. If None, a new array is created.
where	array_like, optional	Boolean mask specifying which elements to compute. Elements where where=False retain their original value.
casting	str, optional	Defines the casting behavior when computing the cube root function.
order	str, optional	Memory layout order of the output array.
dtype	data-type, optional	Defines the data type of the output array.
subok	bool, optional	Determines if subclasses of ndarray are preserved in the output.

Return Value

Returns an array with the cube root of each element in the input array. If the input is a scalar, a scalar is returned.

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Examples

1. Computing Cube Root of a Single Value

Here, we compute the cube root of a single numeric value.

import numpy as np

# Define a number
num = 27  

# Compute the cube root of the number
result = np.cbrt(num)

# Print the result
print("Cube root of 27:", result)

Output:

Cube root of 27: 3.0

2. Computing Cube Root for an Array

We compute the cube roots for multiple values in an array.

import numpy as np

# Define an array of numbers
numbers = np.array([1, 8, 27, 64, 125])

# Compute the cube root of each number
cbrt_values = np.cbrt(numbers)

# Print the results
print("Input numbers:", numbers)
print("Cube root values:", cbrt_values)

Output:

Input numbers: [  1   8  27  64 125]
Cube root values: [1. 2. 3. 4. 5.]

3. Using the out Parameter

Using an output array to store results instead of creating a new array.

import numpy as np

# Define an array of numbers
numbers = np.array([1, 64, 125, 216])

# Create an output array with the same shape
output_array = np.ndarray(shape=numbers.shape)

# Compute cube root and store in output_array
np.cbrt(numbers, out=output_array)

# Print the results
print("Computed cube root values:", output_array)

Output:

Computed cube root values: [1. 4. 5. 6.]

4. Using the where Parameter

Using a condition to compute cube root only for selected elements.

import numpy as np

# Define an array of numbers
numbers = np.array([1, 64, 125, 216])

# Define a mask (compute cube root only where mask is True)
mask = np.array([True, False, True, False])

# Compute cube root where the mask is True
result = np.cbrt(numbers, where=mask)

# Print the results
print("Computed cube root values with mask:", result)

Output:

Computed cube root values with mask: [1. 0. 5. 0.]

The cube root values are computed only for elements where mask=True. The other values remain unchanged.