# 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))

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

# 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() 

out_array :  [ 0.          0.18232156  0.33647224  0.47000363  0.58778666  0.69314718]