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### NumPy By Example

###### A Beginner's Guide to Learning NumPy

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.

• ### Categories

• Computers and Programming
• Data Structures
• Python
• Data Science
• ### Feedback

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DAT Linux

DAT Linux is a Linux distribution for data science. It brings together all your favourite open-source data science tools and apps into a ready-to-run desktop environment. It’s based on Ubuntu 22.04, so it’s easy to install and use. The custom DAT Linux Control Panel provides a centralised one-stop-shop for running and managing dozens of data science programs.

• Preface
• 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
• 6.3  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
• Appendix B. Python Lists & `array` Vs NumPy Arrays
• B.1  Python Lists
• B.2  The `array` Array
• 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`
• 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

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