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NumPy Recipes

A practical introduction to NumPy

Martin McBride

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About the Book

NumPy Recipes takes practical approach to the basics of NumPy

This book is primarily aimed at developers who have at least a small amount of Python experience, who wish to use the NumPy library for data analysis, machine learning, image or sound processing, or any other mathematical or scientific application. It only requires a basic understanding of Python programming.

Detailed examples show how to create arrays to optimise storage different types of information, and how to use universal functions, vectorisation, broadcasting and slicing to process data efficiently. Also contains an introduction to file i/o and data visualisation with Matplotlib.

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About the Author

Martin McBride

Martin McBride is a software developer, specialising in computer graphics, sound, and mathematical programming. He has been writing code since the 1980s in a wide variety of languages from assembler through to C++, Java and Python. He writes for PythonInformer.com and is the author of Functional Programming in Python. He is interested in generative art and works on the generativepy open source project.

Contents

Table of Contents

Foreword

  1. Who is this book for?
  2. About the author
  3. Keep in touch

1 Introduction to NumPy

  1. 1.1 Installing NumPy
  2. 1.2 What is NumPy?
  3. 1.3 NumPy vs Python lists
  4. 1.4 Advantages of NumPy
  5. 1.5 NumPy universal functions
  6. 1.6 Compatibility with other libraries

2 Anatomy of a NumPy array

  1. 2.1 NumPy arrays compared to lists
  2. 2.2 Printing the characteristics of an array
  3. 2.2.1 Rank
  4. 2.2.2 Shape
  5. 2.2.3 Size
  6. 2.2.4 Data type
  7. 2.2.5 Item size
  8. 2.2.6 Data location
  9. 2.3 Array rank examples
  10. 2.4 Data types
  11. 2.4.1 Integers
  12. 2.4.2 Unsigned integers
  13. 2.4.3 Floating point values
  14. 2.4.4 Complex number formats
  15. 2.4.5 Boolean
  16. 2.4.6 List of main data types

3 Creating arrays

  1. 3.1 Creating an array of zeroes
  2. 3.2 Creating other fixed content arrays
  3. 3.3 Choosing the data type
  4. 3.4 Creating multi-dimensional arrays
  5. 3.5 Creating like arrays
  6. 3.6 Creating an array from a Python list
  7. 3.7 Controlling the type with the array function
  8. 3.8 array function anti-patterns
  9. 3.9 Creating a value series with arange
  10. 3.10 Rounding error problem with arange
  11. 3.11 Create a sequence of a specific length with linspace
  12. 3.12 Making linspace more like arange using the endpoint parameter
  13. 3.13 Obtaining the linspace step size
  14. 3.14 Other sequence generators
  15. 3.15 Creating an identity matrix
  16. 3.16 Creating an eye matrix
  17. 3.17 Using vectorisation

4 Vectorisation

  1. 4.1 Performing simple maths on an array
  2. 4.2 Vectorisation with other data types
  3. 4.3 Vectorisation with multi-dimensional arrays
  4. 4.4 Expressions using two arrays
  5. 4.5 Expressions using two multi-dimensional arrays
  6. 4.6 More complex expressions
  7. 4.7 Using conditional operators
  8. 4.8 Combining conditional operators

5 Universal functions

  1. 5.1 Example universal function - sqrt
  2. 5.2 Example universal function of two arguments - power
  3. 5.3 Summary of ufuncs
  4. 5.3.1 Maths operations
  5. 5.3.2 Trigonometric functions
  6. 5.3.3 Bit manipulation
  7. 5.3.4 Comparison functions
  8. 5.3.5 Logical functions
  9. 5.3.6 Min and max
  10. 5.3.7 Float functions
  11. 5.4 ufunc methods
  12. 5.4.1 Reduce
  13. 5.4.2 Accumulation
  14. 5.5 Optional keyword arguments for ufuncs
  15. 5.5.1 out
  16. 5.5.2 where

6 Indexing, slicing and broadcasting

  1. 6.1 Indexing an array
  2. 6.1.1 Indexing in 1 dimension
  3. 6.1.2 Indexing in 2 dimensions
  4. 6.1.3 Picking a row or column in 2-dimensions
  5. 6.1.4 Indexing in 3 dimensions
  6. 6.1.5 Picking a row or column in a 3D array
  7. 6.1.6 Picking a matrix in a 3D array
  8. 6.2 Slicing an array
  9. 6.2.1 Slicing lists - a recap
  10. 6.2.2 Slicing 1D NumPy arrays
  11. 6.2.3 Slicing a 2D array
  12. 6.2.4 Slicing a 3D array
  13. 6.2.5 Full slices
  14. 6.3 Slices vs indexing
  15. 6.4 Views
  16. 6.5 Broadcasting
  17. 6.5.1 Broadcasting from 1 to 2 dimensions
  18. 6.5.2 Broadcasting 1 to 3 dimensions
  19. 6.5.3 Broadcasting 2 to 3 dimensions
  20. 6.6 Broadcasting rules
  21. 6.7 Broadcasting a column vector
  22. 6.8 Broadcasting a row vector and a column vector
  23. 6.9 Broadcasting scalars
  24. 6.10 Efficient broadcasting
  25. 6.11 Fancy indexing

7 Array manipulation functions

  1. 7.1 Copying an array
  2. 7.2 Changing the type of an array
  3. 7.3 Changing the shape of an array
  4. 7.4 Splitting arrays
  5. 7.4.1 Splitting along different axes
  6. 7.4.2 Unequal splits
  7. 7.4.3 Alternative functions
  8. 7.5 Stacking arrays
  9. 7.5.1 Stacking 2-dimensional arrays

8 File input and output

  1. 8.1 CSV format
  2. 8.2 Writing CSV data
  3. 8.2.1 Adding a header or footer
  4. 8.2.2 Changing the line separator
  5. 8.2.3 Compressing the output file
  6. 8.3 Reading CSV data
  7. 8.3.1 Skipping header or footer
  8. 8.3.2 Reading compressing file

9 Using Matplotlib with NumPy

  1. 9.1 Installing Matplotlib
  2. 9.2 Plotting a histogram
  3. 9.3 Plotting functions
  4. 9.4 Plotting functions with NumPy
  5. 9.5 Creating a heatmap

10 Reference

  1. 10.1 Data types
  2. 10.1.1 Unsigned integer sizes and ranges
  3. 10.1.2 Signed integer sizes and ranges
  4. 10.1.3 Integer wrap-around
  5. 10.1.4 Float characteristics
  6. 10.1.5 Complex characteristics

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