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Numpy

  • Numpy ndarray
    
    # Import numpy
    import numpy as np
    
    data = [[2, 6, 1, 3, 7],
            [5, 10, 4, 9, 8]]
            
    data = np.array(data)
    
    print data
    '''
    Output:
    
    [[2, 6, 1, 3, 7], [5, 10, 4, 9, 8]]
    '''
    
    print data.shape
    '''
    Output:
    
    (2, 5)
    '''
    
    # produce an array of all 0's
    print np.zeros((2,3))
    '''
    Output:
    
    [[ 0.  0.  0.]
     [ 0.  0.  0.]]
    '''
    
    # produce an array of all 1's
    print np.ones((2,3))
    '''
    Output:
    
    [[ 1.  1.  1.]
     [ 1.  1.  1.]]
    '''
    
    array = np.arange(10)
    print array
    '''
    Output:
    
    [0 1 2 3 4 5 6 7 8 9]
    '''
    
    print array[2:5]
    '''
    Output:
    
    [2 3 4]
    '''
    
    array[5:8] = 0
    print array
    '''
    Output:
    
    [0 1 2 3 4 0 0 0 8 9]
    '''
    

    Array operations

    
    # 1. Arithmetic operations
    data = np.array([[5,6,7],
                     [8,9,10]])
    
    print data + 5
    '''
    Output:
    
    [[10 11 12]
     [13 14 15]]
    '''
    
    print data - 3
    '''
    Output:
    
    [[2 3 4]
     [5 6 7]]
    '''
    
    print data * data
    '''
    Output:
    
    [[ 25  36  49]
     [ 64  81 100]]
    '''
    
    # 2. Transposing  and swapping axis
    
    data = np.arange(16).reshape((4,4))
    print data
    '''
    Output:
    
    [[ 0  1  2  3]
     [ 4  5  6  7]
     [ 8  9 10 11]
     [12 13 14 15]]
    '''
    
    # Transpose of array
    print data.T
    '''
    Output:
    
    [[ 0  4  8 12]
     [ 1  5  9 13]
     [ 2  6 10 14]
     [ 3  7 11 15]]
    '''
    
    x = np.random.randn(2,3)
    print x
    '''
    Output:
    
    [[ 0.37993325  0.60038379  0.18884729]
     [ 1.23487005  0.07912221 -1.03242702]]
    '''
    

    Statistical Methods

    
    # Random data
    data = np.random.randn(3,2)
    
    print data.mean()
    '''
    Output: 0.2428949
    '''
    
    print data.min()
    '''
    Output: -0.6057506
    '''
    
    print data.max()
    '''
    Output: 2.2677427
    '''
    
    Matrix Class
    
    X = np.random.randn(3,2)
    X = np.matrix(X)
    
    Y = np.random.randn(2,2)
    Y = np.matrix(Y)
    
    # Multiply the matrix
    print X * Y
    '''
    Output:
    
    [[-0.70717074  2.33580395]
     [ 0.36777429 -1.75207365]
     [ 0.06491743 -0.87368241]]
    '''

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