Learning Numpy Array : Supercharge your scientific Python computations by understanding how to use the NumPy library effectively

**Supercharge your scientific Python computations by understanding how to use the NumPy library effectively** About This Book* 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 Who This Book Is ForThis book is for programmers, scientists, or engineers, who have basic Python knowledge and would like to be able to do numerical computations with Python. What You Will Learn* 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 DetailNumPy 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. Cover Copyright Credits About the Author About the Reviewers www.PacktPub.com Table of Contents Preface 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 Analysing 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 Chapter 7: The Scientific Python Ecosystem Numerical integration Interpolation Using Cython with NumPy Clustering stocks with scikit-learn Detecting corners Comparing NumPy to Blaze Summary Index

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.