NumPy exp() Function:
Numpy.exp: To determine e raised to the power of a given value(ex), use the exp() function of the Numpy module. Here, e is the natural logarithm’s base, and its value is about 2.718282.
Syntax:
numpy.exp(a, out=None)
Parameters
a: This is required. It is the array or an object given as input.
out: This is optional. It is the location where the result will be stored. It must have a shape that the inputs broadcast to if it is provided. If None or not given, a newly allocated array is returned.
Return Value:
Numpy exp: The exponential value of each element of the given array(a) is returned by the exp() function of the NumPy module.
NumPy exp() Function in Python
Example1
Approach:
- Import numpy module using the import keyword
- Pass some random list as an argument to the array() function of the numpy module to create an array.
- Store it in a variable.
- Print the above-given array.
- Pass the above-given array as an argument to the exp() function of the numpy module to get the exponential(ex) values of each element of the given array.
- Store it in another variable.
- Print the exponential values of each element of the given array
- The Exit of the Program.
Below is the implementation:
# Import numpy module using the import keyword
import numpy as np
# Pass some random list as an argument to the array() function
# of the Numpy module to create an array.
# Store it in a variable.
gvn_arry = np.array([3, 6, 4, 10, 1])
# Print the above given array.
print("The above given array is:")
print(gvn_arry)
print()
# Pass the above given array as an argument to the exp() function of the
# numpy module to get the exponential values of each element of
# the given array
# Store it in another variable.
rslt = np.exp(gvn_arry)
# Print the exponential values of each element of the given array
print("The exponential values of each element of the given array:")
print(rslt)
Output:
The above given array is: [ 3 6 4 10 1] The exponential values of each element of the given array: [2.00855369e+01 4.03428793e+02 5.45981500e+01 2.20264658e+04 2.71828183e+00]
Example2(Plotting a Graph)
Approach:
- Import numpy module using the import keyword
- Import pyplot from the matplotlib module using the import keyword
- Give some random list as static input and store it in a variable.
- Pass the above-given list as an argument to the exp() function of the numpy module to get the exponential(ex) values of each element of the given array.
- Store it in another variable.
- Store the above input array in another variable for plotting the input array vs input array.
- Plot the input array versus input array with some random color and marker values using the plot() function of the matplotlib module
- Plot the output array(exponential values) versus input array with some other random color and marker values using the plot function of the matplotlib module
- Give the title of the plot using the title() function of the matplotlib module
- Display the plot using the show() function of the matplotlib module.
- The Exit of the Program.
Below is the implementation:
# Import numpy module using the import keyword
import numpy as np
# Import pyplot from the matplotlib module using the import keyword
import matplotlib.pyplot as plt
# Give some random list as static input and store it in a variable.
gvn_arry = [1.3, 1, 2.4, 3.2, 4]
# Pass the above-given list as an argument to the exp() function of the numpy module to
# get the exponential(ex) values of each element of the given array.
# Store it in another variable.
rslt_arry = np.exp(gvn_arry)
# Store the above input array in another variable for plotting the input array vs input array.
temp_inputarry = [1.3, 1, 2.4, 3.2, 4]
# Plot the input array versus input array with some random color and marker values using
# the plot() function of the matplotlib module
plt.plot(gvn_arry, temp_inputarry, color = 'green', marker = "*")
# Plot the output array(exponential values) versus input array with some other random color
# and marker values using the plot function of the matplotlib module
plt.plot(rslt_arry, temp_inputarry, color = 'orange', marker = "o")
# Give the title of the plot using the title() function of the matplotlib module
plt.title("Exponential values")
plt.xlabel("X")
plt.ylabel("Y")
# Display the plot using the show() function of the matplotlib module.
plt.show()
Output:
