numpy.log1p() in Python

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

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    # 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)

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    Output : 
    

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

    

     
    
Code 2 : Graphical representation
    

    

    

    

    

    
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    # 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()   
    

    
    		

    
    

    

    

    

    

    

    
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    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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