NumPy: Beginner's Guide - Third Edition

Ivan Idris

• Éditeur: Packt Publishing
• Année: 2015
• ISBN:
  9781785288838
• Édition: 3
• Sujet: Informatica
• Sous-sujet: Programmazione in Python

About This Book

• Analyze, benchmark, and profile your Node.js application to find slow spots, and push it to the limit by eliminating performance bottlenecks
• Learn the basis of performance analysis using Node.js
• Explore the high performance capabilities of Node.js, along with best practices

Who This Book Is For

This book is for Node.js developers who want a more in-depth knowledge of the platform to improve the performance of their applications. Whether you have a base Node.js background or you are an expert who knows the garbage collector and wants to leverage it to make applications more robust, the examples in this book will benefit you.

What You Will Learn

• Develop applications using well-defined and well-tested development patterns
• Explore memory management and garbage collection to improve performance
• Monitor memory changes and analyze heap snapshots
• Profile CPU and improve your code to avoid patterns that force intensive processor usage
• Understand the importance of data and when you should cache information
• Educate yourself to always test your code and benchmark when needed
• Extend your application's scope and know what other elements can influence performance

In Detail

Node.js is a tool written in C that allows you to use JavaScript on the server-side. High performance on a platform such as Node.js is to take advantage of every aspect of your hardware, helping memory management act at its best. Spot the memory leaks and solve them fast with Node.js by monitoring and stopping them before they become an issue.

Starting with performance analysis concepts and their importance in helping Node.js developers eliminate performance bottlenecks, this book will take you through development patterns to avoid performance penalties. You will learn the importance of garbage collection and its behavior, and discover how to profile your processor, allowing better performance and scalability.

NumPy Beginner's Guide Third Edition

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Table of Contents

NumPy Beginner's Guide Third Edition

Credits

About the Author

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

Preface

What is NumPy?

History

Why use NumPy?

Limitations of NumPy

What this book covers

What you need for this book

Who this book is for

Sections

Time for action – heading

What just happened?

Pop quiz – heading

Have a go hero – heading

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. NumPy Quick Start

Python

Time for action – installing Python on different operating systems

What just happened?

The Python help system

Time for action – using the Python help system

What just happened?

Basic arithmetic and variable assignment

Time for action – using Python as a calculator

What just happened?

Time for action – assigning values to variables

What just happened?

The print() function

Time for action – printing with the print() function

What just happened?

Code comments

Time for action – commenting code

The if statement

Time for action – deciding with the if statement

What just happened?

The for loop

Time for action – repeating instructions with loops

What just happened?

Python functions

Time for action – defining functions

What just happened?

Python modules

Time for action – importing modules

What just happened?

NumPy on Windows

Time for action – installing NumPy, matplotlib, SciPy, and IPython on Windows

What just happened?

NumPy on Linux

Time for action – installing NumPy, matplotlib, SciPy, and IPython on Linux

NumPy on Mac OS X

Time for action – installing NumPy, SciPy, matplotlib, and IPython with MacPorts or Fink

What just happened?

Building from source

Arrays

Time for action – adding vectors

What just happened?

Pop quiz – Functioning of the arange() function

Have a go hero – continue the analysis

IPython – an interactive shell

Online resources and help

Summary

2. Beginning with NumPy Fundamentals

NumPy array object

Time for action – creating a multidimensional array

What just happened?

Pop quiz – the shape of ndarray

Have a go hero – create a three-by-three array

Selecting elements

NumPy numerical types

Data type objects

Character codes

The dtype constructors

The dtype attributes

Time for action – creating a record data type

What just happened?

One-dimensional slicing and indexing

Time for action – slicing and indexing multidimensional arrays

What just happened?

Time for action – manipulating array shapes

What just happened?

Stacking

Time for action – stacking arrays

What just happened?

Splitting

Time for action – splitting arrays

What just happened?

Array attributes

Time for action – converting arrays

What just happened?

Summary

3. Getting Familiar with Commonly Used Functions

File I/O

Time for action – reading and writing files

What just happened?

Comma-seperated value files

Time for action – loading from CSV files

Volume Weighted Average Price

Time for action – calculating Volume Weighted Average Price

What just happened?

The mean() function

Time-weighted average price

Pop quiz – computing the weighted average

Have a go hero – calculating other averages

Value range

Time for action – finding highest and lowest values

What just happened?

Statistics

Time for action – performing simple statistics

What just happened?

Stock returns

Time for action – analyzing stock returns

What just happened?

Dates

Time for action – dealing with dates

What just happened?

Have a go hero – looking at VWAP and TWAP

Time for action – using the datetime64 data type

What just happened?

Weekly summary

Time for action – summarizing data

What just happened?

Have a go hero – improving the code

Average True Range

Time for action – calculating the Average True Range

What just happened?

Have a go hero – taking the minimum() function for a spin

Simple Moving Average

Time for action – computing the Simple Moving Average

What just happened?

Exponential Moving Average

Time for action – cal...