NumPy log1p() Function

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The NumPy log1p() function is used to compute the natural logarithm of one plus each element in the input array. It calculates loge(1 + x) for each element x in the array.

This function can be applied to scalars, lists, or NumPy arrays and will return an array of the same shape with the logarithm of each element plus one.

Syntax

Following is the syntax of the NumPy log1p() function −

numpy.log1p(x, /, out=None, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])

Parameters

This function accepts the following parameters −

• x: The input array or scalar. The function computes the natural logarithm of (1 + x) for each element of the array or scalar.
• out (optional): A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.
• where (optional): This condition is broadcast over the input. At locations where the condition is True, the result will be computed. Otherwise, the result will retain its original value.
• casting (optional): Controls what kind of data casting may occur. Defaults to 'same_kind'.
• order (optional): Controls the memory layout order of the result. 'C' means C-order, 'F' means Fortran-order, 'A' means 'F' if inputs are all F, 'C' otherwise, 'K' means match the layout of the inputs as closely as possible.
• dtype (optional): The type of the returned array and of the accumulator in which the elements are processed. The dtype of x is used by default unless dtype is specified.
• subok (optional): If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array.

Return Value

This function returns an array where each element is the natural logarithm of (1 + the corresponding element in the input array x). If out is provided, it returns a reference to out.

Example: Basic Usage of log1p() Function

In the following example, we use the log1p() function to compute the natural logarithm of 1 plus each element in a 1-dimensional array −

import numpy as np

# Creating a 1-dimensional array
arr = np.array([1, 2, 3, 4])

# Applying log1p to each element
result = np.log1p(arr)
print(result)

The output obtained will be −

[0.69314718 1.09861229 1.38629436 1.60943791]

Example: log1p() Function with Broadcasting

In this example, we demonstrate the use of broadcasting with the log1p() function. We create a 2-dimensional array and apply log1p on it −

import numpy as np

# Creating a 2-dimensional array
arr = np.array([[1, 2, 3], [4, 5, 6]])

# Applying log1p to each element
result = np.log1p(arr)
print(result)

This will produce the following result −

[[0.69314718 1.09861229 1.38629436]
 [1.60943791 1.79175947 1.94591015]]

Example: log1p() Function with Scalar

In this example, we apply the log1p() function to a scalar value −

import numpy as np

# Scalar value
scalar = 10

# Applying log1p to the scalar
result = np.log1p(scalar)
print(result)

The output obtained is −

2.3978952727983707

Example: log1p() Function with Negative Values (Warning)

In this example, we apply the log1p() function to an array with negative values. The logarithm of (1 + x) will not be defined for values where x is less than -1, so a warning may be raised −

import numpy as np

# Creating a 1-dimensional array with values less than -1
arr = np.array([-1.5, -2, -3])

# Applying log1p to each element (will raise a warning for values where 1 + x <= 0)
result = np.log1p(arr)
print(result)

This will produce the following warning −

/home/cg/root/673acdd6238d1/main.py:7: RuntimeWarning: invalid value encountered in log1p
  result = np.log1p(arr)
[nan nan nan]