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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
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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

4.3.1 Array properties

Arrays are essentially characterized by the three properties, described in Table 4.2:

Name

Description

shape

This describes how the data should be interpreted, as a vector, a matrix, or a higher-order tensor, and it gives the corresponding dimension. It is accessed with the attribute shape.

dtype

This gives the type of the underlying data (float, complex, integer, and so on).

strides

This attribute specifies in which order the data should be read. For instance, a matrix could be stored in memory contiguously column by column (FORTRAN convention), or row by row (C convention). The attribute is a tuple with the numbers of bytes that have to be skipped in memory to reach the next row and the number of bytes to be skipped to reach the next column. It even allows for a more flexible interpretation of the data in memory, which is what makes array views possible.

Table 4.2: The three...
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