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numpy.exp() in Python

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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")


Output : References : .

numpy.exp() is a function in the Python NumPy library that calculates the exponential value of an input array. It returns an array with the exponential value of each element of the input array.

The syntax for using numpy.exp() is as follows:

import numpy as np


Here, x is the input array or scalar value whose exponential value is to be calculated. The function returns an array with the same shape as x, with the exponential value of each element.



import numpy as np
# Calculating the exponential value of a scalar
exp_val = np.exp(3)
print(exp_val) # Output: 20.085536923187668
# Calculating the exponential value of an array
x = np.array([1, 2, 3])
exp_arr = np.exp(x)
print(exp_arr) #


Output: [ 2.71828183  7.3890561  20.08553692]

In the above example, we calculate the exponential value of a scalar 3 using np.exp(3), which returns 20.085536923187668. We also calculate the exponential value of an array [1, 2, 3] using np.exp(x), which returns [2.71828183, 7.3890561, 20.08553692].

Advantages of numpy.exp() function in Python:

  1. Fast computation: numpy.exp() function is highly optimized for fast computation, which makes it suitable for handling large datasets and complex calculations in scientific computing and data analysis.
  2. Versatility: numpy.exp() function can be used with a wide range of input types, including scalars, arrays, and matrices.
  3. Mathematical accuracy: numpy.exp() function provides high mathematical accuracy for calculating exponential values, which makes it useful in numerical simulations and scientific experiments.
  4. Integration with other NumPy functions: numpy.exp() function can be easily integrated with other NumPy functions and libraries, allowing for more complex calculations and data analysis.

Disadvantages of numpy.exp() function in Python:

  1. Potential for numerical overflow or underflow: Since numpy.exp() calculates exponential values, it has the potential for numerical overflow or underflow when dealing with very large or very small numbers. This can lead to inaccurate results or errors in some cases.
  2. Limited functionality: While numpy.exp() function is useful for calculating exponential values, it has limited functionality compared to other more specialized libraries and functions for mathematical operations and data analysis.
  3. Requires NumPy library: To use numpy.exp() function, you need to have the NumPy library installed and imported in your Python environment, which can add some overhead to your code and may not be suitable for certain applications.

Last Updated : 29 Mar, 2023
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