NumPy float_power()

NumPy float_power()

The numpy.float_power() function raises each base in x1 to the positionally corresponding power in x2, element-wise. Unlike the regular power() function, it ensures a minimum floating-point precision of float64 for accurate results, even when using lower-precision data types.

Syntax

numpy.float_power(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True)

Parameters

Parameter	Type	Description
x1	array_like	Base values to be raised to the power.
x2	array_like	Exponent values. Must be broadcastable with x1.
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 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 where each element is the base from x1 raised to the exponent in x2. If both x1 and x2 are scalars, a scalar is returned.

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Examples

1. Computing Power of Single Values

Here, we compute 2.5 raised to the power of 3.

import numpy as np

# Define base and exponent values
base = 2.5
exponent = 3

# Compute power
result = np.float_power(base, exponent)

# Print the result
print("2.5^3 =", result)

Output:

2.5^3 = 15.625

2. Computing Power for Arrays

We compute the power for an array of bases and an array of exponents.

import numpy as np

# Define base and exponent arrays
bases = np.array([1, 2, 3, 4, 5])
exponents = np.array([2, 3, 4, 5, 6])

# Compute power
result = np.float_power(bases, exponents)

# Print the results
print("Bases:", bases)
print("Exponents:", exponents)
print("Result:", result)

Output:

Bases: [1 2 3 4 5]
Exponents: [2 3 4 5 6]
Result: [   1.    8.   81. 1024. 15625.]

3. Using the out Parameter

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

import numpy as np

# Define base and exponent arrays
bases = np.array([2, 3, 4])
exponents = np.array([3, 2, 1])

# Create an output array with the same shape
output_array = np.empty_like(bases, dtype=np.float64)

# Compute power and store the result in output_array
np.float_power(bases, exponents, out=output_array)

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

Output:

Computed power values: [8. 9. 4.]