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] 

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    Links:
    https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.exp.html
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