 # 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 * * 8  )) ```

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] `