Linear Algebra With Python 1

Linear Algebra With Python 1 – Basic Operations

Outline

    Original Post

    Linear Algebra With Python 1 – Basic Operations


    Shape

    >>> a = np.array([[1, 2, 3], [4, 5, 6]])
    >>> b = np.array([[7, 8, 9, 23], [10, 11, 12, 1]])
    >>> a.shape
    (2, 3)
    >>> b.shape
    (2, 4)

    Addition And Scalar

    >>> a = np.array([[1, 2, 3], [4, 5, 6]])
    >>> b = np.array([[7, 8, 9], [10, 11, 12]])
           
    >>> a + b
    array([[ 8, 10, 12],
           [14, 16, 18]])
    >>> a - b
    array([[-6, -6, -6],
           [-6, -6, -6]])
           
    >>> 5 * a
    array([[ 5, 10, 15],
           [20, 25, 30]])     

    Multiplication

    >>> a = np.array([[1, 2, 5], [4, 5, 9]])
    >>> b = np.array([[1, 0, 1, 2], [2, 1, 4, 3], [5, 1, 0, 6]])
    >>> np.dot(a, b)
    array([[20,  5,  9, 26],
           [44, 11, 24, 59]])
    

    Transpose

    >>> a = np.array([[1, 2, 5], [4, 5, 9]])
    >>> a.T
    array([[1, 4],
           [2, 5],
           [5, 9]])

    Check two matrix equal or not

    
    >>> a = np.array([1, 2, 5, 7])
    >>> b = np.array([1, 2, 5, 7])
    # Method 1: use array_equal()
    >>> np.array_equal(a, b)
    True
    # Method 2: use == operator and all()
    >>> print((a == b).all())
    True
    # Method 3: use numpy.allcloses()
    >>> np.allclose(a, b)
    True
    # Method 4: user numpy.array_equiv()
    >>> np.array_equiv(a, b)
    True


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    Aron

    A data scientist working in a retail company, with experience in web design and marketing. Recently dives into cryptocurrency and quantitative investing.

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