NumPy exp2() – 2 Raised to the Power of Each Element in Array

NumPy exp2()

The numpy.exp2() function calculates 2 raised to the power of each element in an input array.

Syntax

numpy.exp2(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True)

Parameters

Parameter	Type	Description
x	array_like	Input values. Each element will be used as an exponent to base 2.
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 power 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 values of \(2^x\) computed element-wise. If the input is a scalar, a scalar is returned.

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Examples

1. Computing 2^x for a Single Value

Here, we compute \(2^x\) for a single value.

import numpy as np

# Define an exponent value
x = 3

# Compute 2^x
result = np.exp2(x)

# Print the result
print("2^3 =", result)

Output:

2^3 = 8.0

2. Computing 2^x for an Array of Values

We compute \(2^x\) for multiple values stored in an array.

import numpy as np

# Define an array of exponent values
x_values = np.array([-2, -1, 0, 1, 2, 3])

# Compute 2^x for each element in the array
exp2_values = np.exp2(x_values)

# Print the results
print("Exponent values:", x_values)
print("2^x values:", exp2_values)

Output:

Exponent values: [-2 -1  0  1  2  3]
2^x values: [0.25 0.5  1.   2.   4.   8.  ]

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 exponent values
x_values = np.array([0, 1, 2, 3])

# Create an output array with the same shape
output_array = np.empty_like(x_values, dtype=float)

# Compute 2^x and store the result in output_array
np.exp2(x_values, out=output_array)

# Print the results
print("Computed 2^x values:", output_array)

Output:

Computed 2^x values: [1. 2. 4. 8.]

4. Using the where Parameter

Using a condition to compute \(2^x\) only for selected elements.

import numpy as np

# Define an array of exponent values
x_values = np.array([0, 1, 2, 3])

# Define a mask (compute 2^x only where mask is True)
mask = np.array([True, False, True, False])

# Compute 2^x values where mask is True
result = np.exp2(x_values, where=mask)

# Print the results
print("Computed 2^x values with mask:", result)

Output:

Computed 2^x values with mask: [1. 0. 4. 0.]

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