NumPy Recipes
NumPy Recipes
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NumPy Recipes

Last updated on 2020-04-18

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.

About the Author

Martin McBride
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.

Table of Contents

  • Foreword
    • Who is this book for?
    • About the author
    • Keep in touch
  • 1 Introduction to NumPy
    • 1.1 Installing NumPy
    • 1.2 What is NumPy?
    • 1.3 NumPy vs Python lists
    • 1.4 Advantages of NumPy
    • 1.5 NumPy universal functions
    • 1.6 Compatibility with other libraries
  • 2 Anatomy of a NumPy array
    • 2.1 NumPy arrays compared to lists
    • 2.2 Printing the characteristics of an array
      • 2.2.1 Rank
      • 2.2.2 Shape
      • 2.2.3 Size
      • 2.2.4 Data type
      • 2.2.5 Item size
      • 2.2.6 Data location
    • 2.3 Array rank examples
    • 2.4 Data types
      • 2.4.1 Integers
      • 2.4.2 Unsigned integers
      • 2.4.3 Floating point values
      • 2.4.4 Complex number formats
      • 2.4.5 Boolean
      • 2.4.6 List of main data types
  • 3 Creating arrays
    • 3.1 Creating an array of zeroes
    • 3.2 Creating other fixed content arrays
    • 3.3 Choosing the data type
    • 3.4 Creating multi-dimensional arrays
    • 3.5 Creating like arrays
    • 3.6 Creating an array from a Python list
    • 3.7 Controlling the type with the array function
    • 3.8 array function anti-patterns
    • 3.9 Creating a value series with arange
    • 3.10 Rounding error problem with arange
    • 3.11 Create a sequence of a specific length with linspace
    • 3.12 Making linspace more like arange using the endpoint parameter
    • 3.13 Obtaining the linspace step size
    • 3.14 Other sequence generators
    • 3.15 Creating an identity matrix
    • 3.16 Creating an eye matrix
    • 3.17 Using vectorisation
  • 4 Vectorisation
    • 4.1 Performing simple maths on an array
    • 4.2 Vectorisation with other data types
    • 4.3 Vectorisation with multi-dimensional arrays
    • 4.4 Expressions using two arrays
    • 4.5 Expressions using two multi-dimensional arrays
    • 4.6 More complex expressions
    • 4.7 Using conditional operators
    • 4.8 Combining conditional operators
  • 5 Universal functions
    • 5.1 Example universal function - sqrt
    • 5.2 Example universal function of two arguments - power
    • 5.3 Summary of ufuncs
      • 5.3.1 Maths operations
      • 5.3.2 Trigonometric functions
      • 5.3.3 Bit manipulation
      • 5.3.4 Comparison functions
      • 5.3.5 Logical functions
      • 5.3.6 Min and max
      • 5.3.7 Float functions
    • 5.4 ufunc methods
      • 5.4.1 Reduce
      • 5.4.2 Accumulation
    • 5.5 Optional keyword arguments for ufuncs
      • 5.5.1 out
      • 5.5.2 where
  • 6 Indexing, slicing and broadcasting
    • 6.1 Indexing an array
      • 6.1.1 Indexing in 1 dimension
      • 6.1.2 Indexing in 2 dimensions
      • 6.1.3 Picking a row or column in 2-dimensions
      • 6.1.4 Indexing in 3 dimensions
      • 6.1.5 Picking a row or column in a 3D array
      • 6.1.6 Picking a matrix in a 3D array
    • 6.2 Slicing an array
      • 6.2.1 Slicing lists - a recap
      • 6.2.2 Slicing 1D NumPy arrays
      • 6.2.3 Slicing a 2D array
      • 6.2.4 Slicing a 3D array
      • 6.2.5 Full slices
    • 6.3 Slices vs indexing
    • 6.4 Views
    • 6.5 Broadcasting
      • 6.5.1 Broadcasting from 1 to 2 dimensions
      • 6.5.2 Broadcasting 1 to 3 dimensions
      • 6.5.3 Broadcasting 2 to 3 dimensions
    • 6.6 Broadcasting rules
    • 6.7 Broadcasting a column vector
    • 6.8 Broadcasting a row vector and a column vector
    • 6.9 Broadcasting scalars
    • 6.10 Efficient broadcasting
    • 6.11 Fancy indexing
  • 7 Array manipulation functions
    • 7.1 Copying an array
    • 7.2 Changing the type of an array
    • 7.3 Changing the shape of an array
    • 7.4 Splitting arrays
      • 7.4.1 Splitting along different axes
      • 7.4.2 Unequal splits
      • 7.4.3 Alternative functions
    • 7.5 Stacking arrays
      • 7.5.1 Stacking 2-dimensional arrays
  • 8 File input and output
    • 8.1 CSV format
    • 8.2 Writing CSV data
      • 8.2.1 Adding a header or footer
      • 8.2.2 Changing the line separator
      • 8.2.3 Compressing the output file
    • 8.3 Reading CSV data
      • 8.3.1 Skipping header or footer
      • 8.3.2 Reading compressing file
  • 9 Using Matplotlib with NumPy
    • 9.1 Installing Matplotlib
    • 9.2 Plotting a histogram
    • 9.3 Plotting functions
    • 9.4 Plotting functions with NumPy
    • 9.5 Creating a heatmap
  • 10 Reference
    • 10.1 Data types
      • 10.1.1 Unsigned integer sizes and ranges
      • 10.1.2 Signed integer sizes and ranges
      • 10.1.3 Integer wrap-around
      • 10.1.4 Float characteristics
      • 10.1.5 Complex characteristics

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