NumPy By Example
NumPy By Example
A Beginner's Guide to Learning NumPy
About the Book
The material is suitable for the NumPy beginner, although some basic programming knowledge -- preferably Python -- is assumed. It's also assumed you have at least a rudimentary understanding of running shell commands from a terminal on your system of choice. Many courses, especially in data science, have modules or bridging lessons for learning NumPy as a pre-requisite. This book would certainly be suitable as support material for this kind of introductory course.
About the Author
Table of Contents
- Preface
- About the Book
- About DAT Linux
- Chapter 1. Introduction
- 1.1 What is NumPy?
- 1.2 Why NumPy is Important
- 1.3 NumPy as a Base or Integration Library
- 1.4 Why Learn NumPy?
- Chapter 2. Getting Started
- 2.1 Python 3
- 2.2 Using
pip
- 2.3 Installing NumPy
- 2.4 Execution Environment
- 2.5 NumPy First Steps
- Chapter 3. Array Basics
- 3.1 Dimensions & Axes
- 3.2 Data Types
- 3.3 Compound Types
- 3.4 Mathematical Constants
- 3.5 Exercises
- Chapter 4. Array Creation
- 4.1 Create Arrays using Python Lists
- 4.2 Create Empty Arrays
- 4.3 Create Arrays Filled with Preferred Values
- 4.4 Create Arrays Filled with Incremental Sequences
- 4.5 Create Arrays Filled with Random Values using
numpy.random
- 4.6 Array-like objects
- 4.7 Create Arrays from Other Arrays or Array-like Objects
- 4.8 Creating Common Matrices (2-D arrays)
- 4.9 Structured Arrays
- 4.10 Record arrays
- 4.11 Other Ways to Create Arrays
- 4.13 Exercises
- Chapter 5. Array Inspection
- 5.1 Shape & Size Information
- 5.2 Truth Evaluation
- 5.3 Type Properties
- 5.4 String Representation
- 5.5 Exercises
- Chapter 6. Input & Output
- 6.1 Persisting & Loading a Single Array
- 6.2 Persisting & Loading Multiple Arrays
- 6.3 Write Data to a CSV (text) File
- 6.4 A Note About File Paths
- 6.5 Exercises
- Chapter 7. Array Selection & Modification
- 7.1 Common Indexing & Slicing: 1-D Arrays
- 7.2 Common Indexing & Slicing: n-D Arrays
- 7.3 Fancy Indexing
- 7.4 Exercises
- Chapter 8. Array Computation
- 8.1 Unary Ufuncs — Operating on a Single Array
- 8.2 Binary Ufuncs
- 8.3 Broadcasting — Binary Operations with Arrays of Dissimilar Dimension
- 8.4 Matrix Operations
- 8.5 Set Operations
- 8.6 Other Logic Operations
- 8.7 Statistical Operations
- 8.8 Exercises
- Chapter 9. Array Transformation
- 9.1 Transposing
- 9.2 Reshaping
- 9.3 Flattening
- 9.4 Rotating
- 9.5 Combining & Splitting
- 9.6 Sorting
- 9.7 Exercises
- Appendix A. Virtual Environments
- A.1
virtualenv
- A.2 Docker Containers
- A.1
- Appendix B. Python Lists &
arrayVs NumPy Arrays- B.1 Python Lists
- B.2 The
arrayArray
- B.3 The Case for (or Against) NumPy Arrays
- Appendix C. NumPy Function & Property Reference
- C.1
numpy
- C.2
numpy.ndarray
- C.3
numpy.dtype
- C.4
numpy.linalg
- C.5
numpy.fft
- C.6
numpy.random
- C.1
- Appendix D. Solutions to Exercises
- D.1 Chapter 3
- D.2 Chapter 4
- D.3 Chapter 5
- D.4 Chapter 6
- D.5 Chapter 7
- D.6 Chapter 8
- D.7 Chapter 9
- Preface
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