NumPy Recipes ebook


Martin McBride, 2020-04-18
Tags arrays, data types, vectorisation, 2d arrays, efficiency, index, slice
Categories book, numpy

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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.

Contents:

  • 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
  • 2.3 Array rank examples
  • 2.4 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
  • 5.4 ufunc methods
  • 5.5 Optional keyword arguments for ufuncs
  • 6 Indexing, slicing and broadcasting
  • 6.1 Indexing an array
  • 6.2 Slicing an array
  • 6.3 Slices vs indexing
  • 6.4 Views
  • 6.5 Broadcasting
  • 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
  • 8 File input and output
  • 8.1 CSV format
  • 8.2 Writing CSV data
  • 8.3 Reading CSV data
  • 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

More information:

If you have any comments or questions you can get in touch by any of the following methods:

  • Joining the Python Informer forum at http://pythoninformer.boards.net/. Follow the NumPy board in the Books section.
  • The book page on the Leanpub website.
  • Signing up for the Python Informer newsletter at pythoninformer.com
  • Following @pythoninformer on twitter.
  • Contacting me directly by email (info@axlesoft.com).

Buy now from Leanpub, or download a free preview.

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