NumPy exp()

 import numpy as np array1 = np.array([1, 2, 3, 4, 5]) 
# use of exp() to calculate the exponential values of each elements in array1 result = np.exp(array1)
 print(result) # Output : [ 2.71828183 7.3890561 20.08553692 54.59815003 148.4131591 ]

exp() Syntax

The syntax of exp() is:

 numpy.exp(array)

exp() Arguments

The exp() function takes one argument:

• array - the input array

exp() Return Value

The exp() function returns an array that contains the exponential values of the elements in the input array.

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

 import numpy as np # create a 2-D array array1 = np.array([[1, 2, 3], [4, 5, 6]]) 
# use exp() to calculate the exponential values each element in array1
 result = np.exp(array1) print(result)

Output

 [[ 2.71828183 7.3890561 20.08553692] [ 54.59815003 148.4131591 403.42879349]]

Here, we have used the np.exp() function to calculate the exponential values of each element in the 2-D array named array1.

The resulting array result contains the exponential values.

Example 2: Graphical Representation of exp()

To provide a graphical representation of the exponential function, let's plot the exponential 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 -5 to 5 with a step of 0.1 x = np.arange(-5, 5, 0.1) # compute the exponential values of x y = np.exp(x) # Plot the exponential curve plt.plot(x, y) plt.xlabel('x') plt.ylabel('exp(x)') plt.title('Exponential Function') plt.grid(True) plt.show()

Output

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