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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
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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

Encapsulating code with functions

Functions are useful for gathering similar pieces of code in one place. Consider the following mathematical function:

Encapsulating code with functions

The Python equivalent is as follows:

def f(x):
    return 2*x + 1

In Figure 1.4 Anatomy of a function the  elements of a function block are explained. 

  • The keyword def   tells Python we are defining a function.
  • f is the name of the function.
  • x is the argument, or input of the function.
  • What is after return is called the output of the function.

Encapsulating code with functions

Figure 1.4: Anatomy of a function

Once the function is defined, it can be called using the following code:

f(2) # 5
f(1) # 3
You have been reading a chapter from
Scientific Computing with Python 3
Published in: Dec 2016
Publisher: Packt
ISBN-13: 9781786463517
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