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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
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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 array entries


Array entries are accessed by indexes. In contrast to vector coefficients two indexes are needed to access matrix coefficients. These are given in one pair of brackets. This distinguishes the array syntax from a list of lists. There, two pairs of brackets are needed to access elements.

M = array([[1., 2.],[3., 4.]])
M[0, 0] # first row, first column: 1.
M[-1, 0] # last row, first column: 3.

Basic array slicing

Slices are similar to those of lists except that there might now be in more than one dimension:

  • M[i,:] is a vector filled by the row i of M.
  • M[:,j] is a vector filled by the column i of M.
  • M[2:4,:] is a slice of 2:4 on the rows only.
  • M[2:4,1:4] is a slice on rows and columns.

The result of matrix slicing is given in the following figure (Figure 4.1):

Figure 4.1: The result of matrix slicing

Note

Omitting a dimension

If you omit an index or a slice, NumPy assumes you are taking rows only. M[3] is a vector that is a view on the third row of and M[1:3] is a...

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