NumPy ldexp()

The numpy.ldexp() function computes the value of x1 * 2**x2 for each element in the input arrays. It is useful for constructing floating-point numbers using a mantissa (x1) and an exponent (x2).

Syntax

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

Parameters

Parameter	Type	Description
`x1`	array_like	Array of multipliers (mantissas).
`x2`	array_like, int	Array of two’s exponents. If `x1.shape != x2.shape`, they must be broadcastable to a common shape.
`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 or scalar containing the computed values of x1 * 2**x2. If both x1 and x2 are scalars, the result is a scalar.

Examples

1. Computing ldexp for Single Values

Here, we compute ldexp for a single mantissa and exponent.

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import numpy as np

# Define a mantissa and an exponent
mantissa = 3.5
exponent = 2

# Compute the ldexp result
result = np.ldexp(mantissa, exponent)

# Print the result
print("3.5 * 2**2 =", result)

Output:

3.5 * 2**2 = 14.0