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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
Author Profile Icon Claus Fuhrer
Claus Fuhrer
Olivier Verdier Olivier Verdier
Author Profile Icon Olivier Verdier
Olivier Verdier
Claus Führer Claus Führer
Author Profile Icon Claus Führer
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

List filling patterns

In this section we will compare different ways to fill lists. They are different in computational  efficiency and also in code readability.

List filling with the append method

A ubiquitous programming pattern is to compute elements and store them in a list:

L = []
for k in range(n):
    # call various functions here
    # that compute "result"
    L.append(result)

This approach has a number of disadvantages:

  • The number of iterations is decided in advance. If there is a break instruction, then the preceding code takes care of both generating values and deciding when to stop. This is not desirable and lacks flexibility.
  • It makes the assumption that the user wants the whole history of the computation, for all the iterations. Suppose we are only interested in the sum of all the computed values. If there are many computed values, it does not make sense to store them, as it is much more efficient to add them one at a...
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