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Scientific Computing with Python 3

You're reading from   Scientific Computing with Python 3 An example-rich, comprehensive guide for all of your Python computational needs

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Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781786463517
Length 332 pages
Edition 1st Edition
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Authors (4):
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Jan Erik Solem Jan Erik Solem
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Jan Erik Solem
Claus Fuhrer Claus Fuhrer
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Claus Fuhrer
Olivier Verdier Olivier Verdier
Author Profile Icon Olivier Verdier
Olivier Verdier
Claus Führer Claus Führer
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Claus Führer
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Table of Contents (17) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Variables and Basic Types 3. Container Types 4. Linear Algebra – Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Error Handling 11. Namespaces, Scopes, and Modules 12. Input and Output 13. Testing 14. Comprehensive Examples 15. Symbolic Computations - SymPy References

Functions to construct arrays


The usual way to set up an array is via a list. But there are also a couple of convenient methods for generating special arrays, which are given in the following table (Table 4.5):

Methods

Shape

Generates

 zeros((n,m))

(n,m)

Matrix filled with zeros

ones((n,m)) 

(n,m)

Matrix filled with ones

diag(v,k) 

(n,n)

(Sub-, super-) diagonal matrix from a vector v

random.rand(n,m) 

(n,m)

Matrix filled with uniformly distributed random numbers in (0,1)

 arange(n)

(n,)

First n integers

linspace(a,b,n) 

(n,)

Vector with n equispaced points between a and b

Table 4.5: Commands to create arrays

These commands may take additional arguments. In particular, the commands zeros, ones, and arange take dtype as an optional argument. The default type is float, except for arange. There are also methods such as zeros_like and ones_like, which are slight variants of the preceding ones. For instance, the zeros_like(A) method is equivalent...

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