 # numpy.log1p () in Python

numpy.log1p (arr, out = None, *, where = True, cast = & # 39; same_kind & # 39 ;, order = & # 39; K & # 39 ;, dtype = None, ufunc & # 39; log1p & # 39;):
This math function helps the user to compute the natural logarithmic value of x + 1, where x belongs to all elements of the input array.

• log1p is the opposite of exp (x) — 1 .
• Parameters :

`  array:  [array_like] Input array or object.  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 natural logarithmic value of x + 1; where x belongs to all elements of input array. `

Code 1: Working

 ` # Python program explaining ` ` # log1p () function ` ` import ` ` numpy as np ````   in_array = [ 1 , 3 , 5 ] print ( "Input array:" , in_array)    out_array = np.log1p (in_array) ```` print ` ` (` ` "Output array:" ` ` , out_array) `

Output:

` Input array: [1, 3, 5] Output array: [0.69314718 1.38629436 1.79175947] `

Code 2: Graphic representation

 ` # Show Python program ` ` # Graphical view ` ` # log1p () 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.log1p (in_array) `   ` print ` ` (` ` "out_array:" ` `, out_array) `   ` y ` ` = ` ` [` ` 1 ` `, ` ` 1.2 ` `, ` ` 1.4 ` `, ` ` 1.6 ` `, ` ` 1.8 ` `, ` ` 2 ` `] ` ` plt.plot (in_array, y, color ` ` = ` ` `blue` ` `, marker ` ` = ` ` "*" ` `) `   ` # red for numpy.log1xp () ` ` plt.plot (out_array, y, color ` ` = ` ` `red` ` `, marker ` ` = "o" ) ```` plt.title ( "numpy.log1p ()" ) plt .xlabel ( "X" ) plt.ylabel ( " Y " ) plt.show ()  ```

Output:

` out_array: [0.69314718 0.78845736 0.87546874 0.95551145 1.02961942 1.09861229] `