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numpy.exp() in Python
  • Last Updated : 29 Nov, 2018

numpy.exp(array, out = None, where = True, casting = ‘same_kind’, order = ‘K’, dtype = None) :
This mathematical function helps user to calculate exponential of all the elements in the input array.

Parameters :

array    : [array_like]Input array or object whose elements, we need to test.
out      : [ndarray, optional]Output array with same dimensions as Input array, 
            placed with result.
**kwargs : Allows you to pass keyword variable length of argument to a function. 
           It is used when we want to handle named argument in a function.
where    : [array_like, optional]True value means to calculate the universal 
           functions(ufunc) at that position, False value means to leave the  
           value in the output alone.

Return :

An array with exponential of all elements of input array. 

 
Code 1 : Working




# Python program explaining
# exp() function
import numpy as np
  
in_array = [1, 3, 5]
print ("Input array : ", in_array)
  
out_array = np.exp(in_array)
print ("Output array : ", out_array)

Output :



Input array :  [1, 3, 5]
Output array :  [   2.71828183   20.08553692  148.4131591 ]

 
Code 2 : Graphical representation




# Python program showing
# Graphical representation of 
# exp() function
import numpy as np
import matplotlib.pyplot as plt
  
in_array = [1, 1.2, 1.4, 1.6, 1.8, 2]
out_array = np.exp(in_array)
  
y = [1, 1.2, 1.4, 1.6, 1.8, 2]
plt.plot(in_array, y, color = 'blue', marker = "*")
  
# red for numpy.exp()
plt.plot(out_array, y, color = 'red', marker = "o")
plt.title("numpy.exp()")
plt.xlabel("X")
plt.ylabel("Y")
plt.show()  

Output :

References :
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.exp.html
.

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