Returns a matrix from an array-like object, or from a string of data. A matrix is a specialized 2-D array that retains its 2-D nature through operations. It has certain special operators, such as * …

You can pass Python lists of lists to create a 2-D array (or “matrix”) to represent them in NumPy. >>> data = np . array ([[ 1 , 2 ], [ 3 , 4 ], [ 5 , 6 ]]) >>> data array([[1, 2], [3, 4], [5, 6]])

The 2D array creation functions e.g. numpy.eye, numpy.diag, and numpy.vander define properties of special matrices represented as 2D arrays. np.eye(n, m) defines a 2D identity matrix. The …

numpy.array# numpy. array (object, dtype = None, *, copy = True, order = 'K', subok = False, ndmin = 0, like = None) # Create an array. Parameters: object array_like. An array, any object …

Compute the sign and (natural) logarithm of the determinant of an array. trace (a[, offset, axis1, axis2, dtype, out]) Return the sum along diagonals of the array. linalg.trace (x, /, *[, offset, …

Matrix operators @ and @= were introduced in Python 3.5 following PEP 465, and the @ operator has been introduced in NumPy 1.10.0. Further information can be found in the …

The matmul function implements the semantics of the @ operator introduced in Python 3.5 following PEP 465. It uses an optimized BLAS library when possible (see numpy.linalg). …

numpy. asmatrix (data, dtype = None) [source] # Interpret the input as a matrix. Unlike matrix , asmatrix does not make a copy if the input is already a matrix or an ndarray.

isin is an element-wise function version of the python keyword in. isin(a, b) is roughly equivalent to np.array([item in b for item in a]) if a and b are 1-D sequences. element and test_elements are …

Indexing x['field-name'] returns a new view to the array, which is of the same shape as x (except when the field is a sub-array) but of data type x.dtype['field-name'] and contains only the part …