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Scientific Computing with Python

You're reading from   Scientific Computing with Python High-performance scientific computing with NumPy, SciPy, and pandas

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781838822323
Length 392 pages
Edition 2nd Edition
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Authors (4):
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Olivier Verdier Olivier Verdier
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Olivier Verdier
Jan Erik Solem Jan Erik Solem
Author Profile Icon Jan Erik Solem
Jan Erik Solem
Claus Führer Claus Führer
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Claus Führer
Claus Fuhrer Claus Fuhrer
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Claus Fuhrer
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Table of Contents (23) Chapters Close

Preface 1. Getting Started 2. Variables and Basic Types FREE CHAPTER 3. Container Types 4. Linear Algebra - Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Series and Dataframes - Working with Pandas 11. Communication by a Graphical User Interface 12. Error and Exception Handling 13. Namespaces, Scopes, and Modules 14. Input and Output 15. Testing 16. Symbolic Computations - SymPy 17. Interacting with the Operating System 18. Python for Parallel Computing 19. Comprehensive Examples 20. About Packt 21. Other Books You May Enjoy 22. References

13.1 Namespaces

Names of Python objects, such as the names of variables, classes, functions, and modules, are collected in namespaces. Modules and classes have their own named namespaces with the same name as these objects. These namespaces are created when a module is imported or a class is instantiated. The lifetime of a namespace of a module is as long as the current Python session. The lifetime of a namespace of a class instance is until the instance is deleted.

Functions create a local namespace when they are executed (invoked). It is deleted when the function stops the execution with a regular return or an exception. Local namespaces are unnamed.

The concept of namespaces puts a variable name in its context. For example, there are several functions with the name sin and they are distinguished by the namespace they belong to, as shown in the following code:

import math
import numpy
math.sin
numpy.sin

They are indeed different, as numpy.sin is a universal...

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