Introduction to Python For Scientists and Engineers (Open Source Computing) by Unknown

Author:Unknown
Language: eng
Format: epub, pdf

Dr. Sandeep Nagar

[email protected]

www.bookmuft.com

The answer is quite complex. While array serves most of the general

purposes for matrix algebra, matrix is written to facilitate linear algebra

functionalities. Linear algebra is performed using a submodule of numpy

accessed as numpy.linalg. Issuing command help(numpy.linalg) gives

idea about the purpose of this module. Some of the useful function from

matrix algebra point of view are:

• solve() to solve system of linear equations

• norm() to find norm of matrix

• inv() to find matrix inverse

• pinv() to find pseudo-inverse

• matrix_power() to perform an integer power of a square matrix

To perform linear algebra calculations using matrices, it is suggested that

matrix object is used to avoid errors.

More information about matrix object can be found by issuing the com-

mand help(numpy.matrix) or visiting http://docs.scipy.org/doc/numpy/

reference/generated/numpy.matrix.html.

5.12

Some basic operations

array allows some basic in-built operations which come quite handy while

performing calculations. These functions have been written to optimize time

spent on running the code and minimizing error, hence user can concentrate

on using them for computation rather than writing their own and then

optimizing them. Some of them have been discussed below.

5.12.1

sum

sum() calculates the sum of all elements in the array.

1 >>>a = a r a n g e ( 2 5 )

2 # c r e a t e d

an a r r a y

’ a ’ c o n s i s t i n g

o f

f i r s t

25 numbers

3 >>>a

4

5

a r r a y ( [

0 ,

1 ,

2 ,

3 ,

4 ,

5 ,

6 ,

7 ,

8 ,

9 , 1 0 , 1 1 , 1 2 , 1 3 ,

1 4 , 1 5 , 1 6 ,

69

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