numpy.log () in Python

numpy.log (x [, out] = ufunc & # 39; log1p & # 39;): this math function helps the user to compute the natural logarithm of x, where x belongs to all elements of the input array. 
The natural logarithm of log is inverse of exp () , so log (exp (x)) = x . Natural logarithm — this is the logarithm of 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: Work

# 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 ( "np.log (4 ** 4):" , np.log ( 4 * * 4 ))

print ( "np.log (2 ** 8):" , np.log ( 2 * * ))

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

# Display the Python program
# Graphical representation
The 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:

 out_array: [0. 0.18232156 0.33647224 0.47000363 0.58778666 0.693147 18] 

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