rand vs normal in Numpy.random in Python

  • About random: For random we are taking .rand()
    numpy.random.rand(d0, d1, …, dn) :
    creates an array of specified shape and
    fills it with random values.
    Parameters :

    d0, d1, ..., dn : [int, optional]
    Dimension of the returned array we require, 
    
    If no argument is given a single Python float 
    is returned.
    

    Return :

    Array of defined shape, filled with random values.
    
  • About normal: For random we are taking .normal()
    numpy.random.normal(loc = 0.0, scale = 1.0, size = None) : creates an array of specified shape and fills it with random values which is actually a part of Normal(Gaussian)Distribution. This is Distribution is also known as Bell Curve because of its characteristics shape.
    Parameters :

    loc   : [float or array_like]Mean of 
    the distribution. 
    scale : [float or array_like]Standard 
    Derivation of the distribution. 
    size  : [int or int tuples]. 
    Output shape given as (m, n, k) then
    m*n*k samples are drawn. If size is 
    None(by default), then a single value
    is returned. 
    

    Return :

    Array of defined shape, filled with 
    random values following normal 
    distribution.
    
  • Code 1 : Randomly constructing 1D array

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    # Python Program illustrating
    # numpy.random.rand() method
       
    import numpy as geek
       
    # 1D Array
    array = geek.random.rand(5)
    print("1D Array filled with random values : \n", array)

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    Output :

    1D Array filled with random values : 
     [ 0.84503968  0.61570994  0.7619945   0.34994803  0.40113761]
    
    

    Code 2 : Randomly constructing 1D array following Gaussian Distribution

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    # Python Program illustrating
    # numpy.random.normal() method
       
    import numpy as geek
       
    # 1D Array
    array = geek.random.normal(0.0, 1.0, 5)
    print("1D Array filled with random values "
          "as per gaussian distribution : \n", array)
    # 3D array
    array = geek.random.normal(0.0, 1.0, (2, 1, 2))
    print("\n\n3D Array filled with random values "
          "as per gaussian distribution : \n", array)

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    Output :

    1D Array filled with random values as per gaussian distribution : 
     [-0.99013172 -1.52521808  0.37955684  0.57859283  1.34336863]
    
    3D Array filled with random values as per gaussian distribution : 
     [[[-0.0320374   2.14977849]]
    
     [[ 0.3789585   0.17692125]]]
    


    Code3 : Python Program illustrating graphical representation of random vs normal in NumPy

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    # Python Program illustrating
    # graphical representation of 
    # numpy.random.normal() method
    # numpy.random.rand() method
       
    import numpy as geek
    import matplotlib.pyplot as plot
       
    # 1D Array as per Gaussian Distribution
    mean = 0 
    std = 0.1
    array = geek.random.normal(0, 0.1, 1000)
    print("1D Array filled with random values "
          "as per gaussian distribution : \n", array);
      
    # Source Code : 
    # generated/numpy-random-normal-1.py
    count, bins, ignored = plot.hist(array, 30, normed=True)
    plot.plot(bins, 1/(std * geek.sqrt(2 * geek.pi)) *
              geek.exp( - (bins - mean)**2 / (2 * std**2) ),
              linewidth=2, color='r')
    plot.show()
      
      
    # 1D Array constructed Randomly
    random_array = geek.random.rand(5)
    print("1D Array filled with random values : \n", random_array)
      
    plot.plot(random_array)
    plot.show()

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    Output :

    1D Array filled with random values as per gaussian distribution : 
     [ 0.12413355  0.01868444  0.08841698 ..., -0.01523021 -0.14621625
     -0.09157214]
    
    
    
    1D Array filled with random values : 
     [ 0.72654409  0.26955422  0.19500427  0.37178803  0.10196284]
    
    
    

    Important :
    In code 3, plot 1 clearly shows Gaussian Distribution as it is being created from the values generated through random.normal() method thus following Gaussian Distribution.
    plot 2 doesn’t follow any distribution as it is being created from random values generated by random.rand() method.

    Note :
    Code 3 won’t run on online-ID. Please run them on your systems to explore the working.
    .
    This article is contributed by Mohit Gupta_OMG 😀. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

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