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numpy.log1p() in Python
  • Last Updated : 29 Nov, 2018

numpy.log1p(arr, out = None, *, where = True, casting = ‘same_kind’, order = ‘K’, dtype = None, ufunc ‘log1p’) :
This mathematical function helps user to calculate natural logarithmic value of x+1 where x belongs to all the input array elements.

  • log1p is reverse 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 : Graphical representation




    # Python program showing
    # Graphical representation of 
    # 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]

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

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