numpy.log() in Python

numpy.log(x[, out] = ufunc ‘log1p’) : This mathematical function helps user to calculate Natural logarithm of x where x belongs to all the input array elements.
Natural logarithm log is the inverse of the exp(), so that log(exp(x)) = x. The natural logarithm is log in base e.

Parameters :

array : [array_like] Input array or object.
out : [ndarray, optional] Output array with same dimensions as Input array, placed with result.

Return :
An array with Natural logarithmic value of x; where x belongs to all elements of input array.

Code #1 : Working

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# Python program explaining
# log() function
import numpy as np
  
in_array = [1, 3, 5, 2**8]
print ("Input array : ", in_array)
  
out_array = np.log(in_array)
print ("Output array : ", out_array)
  
  
print("\nnp.log(4**4) : ", np.log(4**4))
print("np.log(2**8) : ", np.log(2**8))

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Output :

Input array :  [1, 3, 5, 256]
Output array :  [ 0.          1.09861229  1.60943791  5.54517744]

np.log(4**4) :  5.54517744448
np.log(2**8) :  5.54517744448

 
Code #2 : Graphical representation

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# Python program showing
# Graphical representation  
# of log() 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.log(in_array)
  
print ("out_array : ", out_array)
  
plt.plot(in_array, in_array, 
         color = 'blue', marker = "*")
  
# red for numpy.log()
plt.plot(out_array, in_array, 
         color = 'red', marker = "o")
           
plt.title("numpy.log()")
plt.xlabel("out_array")
plt.ylabel("in_array")
plt.show() 

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Output :

out_array :  [ 0.          0.18232156  0.33647224  0.47000363  0.58778666  0.69314718]


References : https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.log.html#numpy.log
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