NumPy log()
The numpy.log() function computes the natural logarithm (log base e) of each element in an input array.
It is the inverse operation of the exponential function, satisfying the identity log(exp(x)) = x.
Syntax
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numpy.log(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True)Parameters
| Parameter | Type | Description |
|---|---|---|
x | array_like | Input values. Must be positive since log is undefined for negative numbers. |
out | ndarray, None, or tuple of ndarray and None, optional | Optional output array to store results. If None, a new array is created. |
where | array_like, optional | Boolean mask specifying where to compute the logarithm. Other values retain their original state. |
casting | str, optional | Defines how data is cast during computation. |
order | str, optional | Memory layout order of the output array. |
dtype | data-type, optional | Data type of the output array. |
subok | bool, optional | Determines if subclasses of ndarray are preserved in the output. |
Return Value
Returns an array containing the natural logarithm of the input values, computed element-wise. If the input is a scalar, a scalar is returned.
Examples
1. Computing the Natural Logarithm of a Single Value
We compute the natural logarithm of a single positive value.
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import numpy as np
# Define a positive number
num = np.e # Euler's number (approximately 2.718)
# Compute the natural logarithm
result = np.log(num)
# Print the result
print("Natural logarithm of e:", result)Output:
Natural logarithm of e: 1.0