The formula C=numpy.add(A, B) adds the two matrices A and B and stores the result in C. • To subtract matrix B from matrix A, enter C=numpy.subtract(A, B). The result is stored in C. • C=numpy.divide(A, B): Split matrix A into two equal parts and store the result in C. • C stands for numpy. multiply(A, B): Multiply matrices A and B, then store the result in matrices C. • C=numpy.sum(A): Calculate the sum of each element in the matrix A, then store the result in c R. • C=numpy.sum(A, axis = 0): Create a vector C by adding the columns of the matrix A. • C=numpy.sum(A, axis = 1): Summarize matrix A row-by-row and store the result in the vector C. Create Python code that demonstrates the use of these techniques in a sample matrix.

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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The formula C=numpy.add(A, B) adds the two matrices A and B and stores the result in C.
• To subtract matrix B from matrix A, enter C=numpy.subtract(A, B). The result is stored in C.
• C=numpy.divide(A, B): Split matrix A into two equal parts and store the result in C.
• C stands for numpy.
multiply(A, B): Multiply matrices A and B, then store the result in matrices C.
• C=numpy.sum(A): Calculate the sum of each element in the matrix A, then store the result in c R.
• C=numpy.sum(A, axis = 0): Create a vector C by adding the columns of the matrix A.
• C=numpy.sum(A, axis = 1): Summarize matrix A row-by-row and store the result in the vector C.

Create Python code that demonstrates the use of these techniques in a sample matrix.

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