Learning NumPy Array


Learning NumPy Array
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Overview
Table of Contents
Author
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Sample Chapters
  • Improve the performance of calculations with clean and efficient NumPy code
  • Analyze large data sets using statistical functions and execute complex linear algebra and mathematical computations
  • Perform complex array operations in a simple manner

Book Details

Language : English
Paperback : 164 pages [ 235mm x 191mm ]
Release Date : June 2014
ISBN : 1783983906
ISBN 13 : 9781783983902
Author(s) : Ivan Idris
Topics and Technologies : All Books, Big Data and Business Intelligence, Open Source


Table of Contents

Preface
Chapter 1: Getting Started with NumPy
Chapter 2: NumPy Basics
Chapter 3: Basic Data Analysis with NumPy
Chapter 4: Simple Predictive Analytics with NumPy
Chapter 5: Signal Processing Techniques
Chapter 6: Profiling, Debugging, and Testing
Chapter 7: The Scientific Python Ecosystem
Index

  • Chapter 1: Getting Started with NumPy
    • Python
    • Installing NumPy, Matplotlib, SciPy, and IPython on Windows
    • Installing NumPy, Matplotlib, SciPy, and IPython on Linux
    • Installing NumPy, Matplotlib, and SciPy on Mac OS X
    • Building from source
    • NumPy arrays
      • Adding arrays
    • Online resources and help
    • Summary
  • Chapter 2: NumPy Basics
    • The NumPy array object
      • The advantages of using NumPy arrays
    • Creating a multidimensional array
    • Selecting array elements
    • NumPy numerical types
      • Data type objects
      • Character codes
      • dtype constructors
      • dtype attributes
    • Creating a record data type
    • One-dimensional slicing and indexing
    • Manipulating array shapes
      • Stacking arrays
      • Splitting arrays
      • Array attributes
      • Converting arrays
    • Creating views and copies
    • Fancy indexing
    • Indexing with a list of locations
    • Indexing arrays with Booleans
    • Stride tricks for Sudoku
    • Broadcasting arrays
    • Summary
  • Chapter 3: Basic Data Analysis with NumPy
    • Introducing the dataset
    • Determining the daily temperature range
    • Looking for evidence of global warming
    • Comparing solar radiation versus temperature
    • Analyzing wind direction
    • Analyzing wind speed
    • Analyzing precipitation and sunshine duration
    • Analyzing monthly precipitation in De Bilt
    • Analyzing atmospheric pressure in De Bilt
    • Analyzing atmospheric humidity in De Bilt
    • Summary
  • Chapter 4: Simple Predictive Analytics with NumPy
    • Examining autocorrelation of average temperature with pandas
    • Describing data with pandas DataFrames
    • Correlating weather and stocks with pandas
    • Predicting temperature
      • Autoregressive model with lag 1
      • Autoregressive model with lag 2
    • Analyzing intra-year daily average temperatures
    • Introducing the day-of-the-year temperature model
    • Modeling temperature with the SciPy leastsq function
    • Day-of-year temperature take two
    • Moving-average temperature model with lag 1
    • The Autoregressive Moving Average temperature model
    • The time-dependent temperature mean adjusted autoregressive model
    • Outliers analysis of average De Bilt temperature
    • Using more robust statistics
    • Summary
  • Chapter 5: Signal Processing Techniques
    • Introducing the Sunspot data
      • Sifting continued
    • Moving averages
    • Smoothing functions
    • Forecasting with an ARMA model
    • Filtering a signal
      • Designing the filter
    • Demonstrating cointegration
    • Summary
  • Chapter 6: Profiling, Debugging, and Testing
    • Assert functions
      • The assert_almost_equal function
      • Approximately equal arrays
      • The assert_array_almost_equal function
    • Profiling a program with IPython
    • Debugging with IPython
    • Performing Unit tests
    • Nose tests decorators
    • Summary

 

Ivan Idris

Ivan Idris was born in Bulgaria from Indonesian parents. He moved to the Netherlands and graduated from university with a degree in Experimental Physics.

His graduation thesis had a strong emphasis on Applied Computer Science. After graduating, he worked for several companies as a Java Developer, Data Warehouse Developer, and QA Analyst.

His main professional interests are Business Intelligence, big data, and cloud computing. He enjoys writing clean, testable code and interesting technical articles. He is the author of NumPy Beginner’s Guide, NumPy Cookbook, and Learning NumPy.

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What you will learn from this book

  • Install NumPy and discover its arrays and features
  • Perform data analysis and complex array operations with NumPy
  • Analyze time series and perform signal processing
  • Understand NumPy modules and explore the scientific Python ecosystem
  • Improve the performance of calculations with clean and efficient NumPy code
  • Analyze large data sets using statistical functions and execute complex linear algebra and mathematical computations

In Detail

NumPy is an extension of Python, which provides highly optimized arrays and numerical operations. NumPy replaces a lot of the functionality of Matlab and Mathematica specifically vectorized operations, but in contrast to those products is free and open source. In today's world of science and technology, it is all about speed and flexibility.

This book will teach you about NumPy, a leading scientific computing library. This book enables you to write readable, efficient, and fast code, which is closely associated to the language of mathematics. Save thousands of dollars on expensive software, while keeping all the flexibility and power of your favorite programming language.

You will learn about installing and using NumPy and related concepts. At the end of the book we will explore related scientific computing projects. This book will give you a solid foundation in NumPy arrays and universal functions. Learning NumPy Array will help you be productive with NumPy and write clean and fast code.

Approach

A step-by-step guide, packed with examples of practical numerical analysis that will give you a comprehensive, but concise overview of NumPy.

Who this book is for

This book is for programmers, scientists, or engineers, who have basic Python knowledge and would like to be able to do numerical computations with Python.

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