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

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)`.