numpy.exp() in Python

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

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# 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)

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Output : 


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



 

Code 2 : Graphical representation











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# 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()   



















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Output : 

Image


References : 

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

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