NumPy log()

The numpy.log() function is used to calculate the natural logarithm of the elements in an array.

Example

import numpy as np

# create a NumPy array
array1 = np.array([1, 2, 3, 4, 5])

# calculate the natural logarithm # of each element in array1 result = np.log(array1)
print(result) # Output: [0. 0.69314718 1.09861229 1.38629436 1.60943791]

log() Syntax

The syntax of the numpy.log() method is:

numpy.log(array)

log() Arguments

The numpy.log() method takes one argument:

  • array - the input array

log() Return Value

The numpy.log() method returns an array that contains the natural logarithm of the elements in the input array.


Example 1: Use of log() to Calculate Natural Logarithm

import numpy as np

# create a 2-D array
array1 = np.array([[0.5, 1.0, 2.0, 10.0], 
                                [3.4, 1.5, 6.8, 4.12]])

# calculate the natural logarithm # of each element in array1 result = np.log(array1)
print(result)

Output

[[-0.69314718  0.          0.69314718  2.30258509]
 [ 1.22377543  0.40546511  1.91692261  1.41585316]]

Here, we have used the np.log() method to calculate the natural logarithm of each element in the 2-D array named array1.

The resulting array result contains the natural logarithm values.


Example 2: Graphical Representation of log()

To provide a graphical representation of the logarithm function, let's plot the logarithm curve using matplotlib, a popular data visualization library in Python.

To use matplotlib, we'll first import it as plt.

import numpy as np
import matplotlib.pyplot as plt
# generate x values from 0.1 to 5 with a step of 0.1 x = np.arange(0.1, 5, 0.1) # compute the logarithmic values of x y = np.log(x) # plot the logarithmic curve plt.plot(x, y) plt.xlabel('x') plt.ylabel('log(x)') plt.title('Logarithmic Function') plt.grid(True) plt.show()

Output

Graphical Representation of log()
Graphical Representation of log()

In the above example, we plot the x array on the x-axis and the y array, which contains the natural logarithm values, on the y-axis using plt.plot(x, y).

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