The numpy exp() function is used to calculate the exponential of an array or scalar value. It returns an array of the same shape as the input array, with each element replaced by its exponential value.
Numpy exp() function example
Simple example code.
import numpy as np
from matplotlib import pyplot as plt
# single element
print(np.exp(3))
# multiple elements of 1-d array
arr = [2, 5, 8]
res = np.exp(arr)
print(res)
# 2-D numpy array elements
arr = np.array([[4, 6, 3, 7], [8, 5, 2, 9]])
res = np.exp(arr)
print(res)
Output:
Use numpy.exp() function to the graphical representation
import numpy as np
from matplotlib import pyplot as plt
# Use numpy.exp() function to graphical representation
arr = [1, 1.4, 1.8, 2, 2.6, 3]
out_array = np.exp(arr)
arr2 = [1, 1.3, 1.6, 2.3, 2.8, 3]
plt.plot(arr, arr2, color='green', marker="*")
# Yellow for numpy.exp()
plt.plot(out_array, arr2, color='yellow', marker="o")
plt.title("numpy.exp()")
plt.xlabel("X")
plt.ylabel("Y")
plt.show()
Output:
What exactly does numpy.exp() do?
Answer: The exponential function is e^x where e is a mathematical constant called Euler’s number, approximately 2.718281. This value has a close mathematical relationship with pi and the slope of the curve e^x is equal to its value at every point. np.exp() calculates e^x for each value of x in your input array.
exp(x) = e^x where e= 2.718281(approx)
Do comment if you have any doubts or suggestions on this Python Numpy function
Note: IDE: PyCharm 2021.3.3 (Community Edition)
Windows 10
Python 3.10.1
All Python Examples are in Python 3, so Maybe its different from python 2 or upgraded versions.