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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
Author Profile Icon Jan Erik Solem
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

Accessing and changing the shape


The number of dimensions is what distinguishes a vector from a matrix. The shape is what distinguishes vectors of different sizes, or matrices of different sizes. In this section, we examine how to obtain and change the shape of an array.

The shape function

The shape of a matrix is the tuple of its dimensions. The shape of an n × m matrix is the tuple (n, m). It can be obtained by the shape function:

M = identity(3)
shape(M) # (3, 3)

For a vector, the shape is a singleton containing the length of that vector:

v = array([1., 2., 1., 4.])
shape(v) # (4,) <- singleton (1-tuple)

An alternative is to use the array attribute shape, which gives  the same result:

M = array([[1.,2.]])
shape(M) # (1,2)
M.shape # (1,2)

However, the advantage of using  shape as a function is that this function may be used on scalars and lists as well. This may come in handy when code is supposed to work with both scalars and arrays:

shape(1.) # ()
shape([1,2]) # (2...
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