Manual NumPy Beginners Guide - Second Edition

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With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more. Start Free Trial No credit card required. View table of contents. Book Description An action packed guide using real world examples of the easy to use, high performance, free open source NumPy mathematical library.

NumPy Beginner's Guide (Second Edition)

Perform high performance calculations with clean and efficient NumPy code. Analyze large data sets with statistical functions Execute complex linear algebra and mathematical computations In Detail NumPy is an extension to, and the fundamental package for scientific computing with Python. History Why use NumPy? 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.

Pop quiz — the shape of ndarray Have a go hero — create a three-by-three matrix Selecting elements NumPy numerical types Data type objects Character codes dtype constructors 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?

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 — doing simple statistics What just happened?

Numpy Beginner's Guide Second Edition

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 — 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 — calculating the exponential moving average What just happened? Bollinger bands Time for action — enveloping with Bollinger bands What just happened? Have a go hero — switching to exponential moving average Linear model Time for action — predicting price with a linear model What just happened?


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Trend lines Time for action — drawing trend lines What just happened? Methods of ndarray Time for action — clipping and compressing arrays What just happened? Factorial Time for action — calculating the factorial What just happened? Pop quiz — calculating covariance Polynomials Time for action — fitting to polynomials What just happened? Have a go hero — improving the fit On-balance volume Time for action — balancing volume What just happened? Simulation Time for action — avoiding loops with vectorize What just happened?

Have a go hero — analyzing consecutive wins and losses Smoothing Time for action — smoothing with the hanning function What just happened? Have a go hero — smoothing variations Summary 5. Working with Matrices and ufuncs Matrices Time for action — creating matrices What just happened? Creating a matrix from other matrices Time for action — creating a matrix from other matrices What just happened? Pop quiz — defining a matrix with a string Universal functions Time for action — creating universal function What just happened?

NumPy Beginner's Guide (Second Edition) - Ivan Idris - Google Books

Universal function methods Time for action — applying the ufunc methods on add What just happened? Arithmetic functions Time for action — dividing arrays What just happened? Fibonacci numbers Time for action — computing Fibonacci numbers What just happened? Have a go hero — timing the calculations Lissajous curves Time for action — drawing Lissajous curves What just happened?

Square waves Time for action — drawing a square wave What just happened? Have a go hero — getting rid of the loop Sawtooth and triangle waves Time for action — drawing sawtooth and triangle waves What just happened? Have a go hero — getting rid of the loop Bitwise and comparison functions Time for action — twiddling bits What just happened? Pop quiz — creating a matrix Have a go hero — inverting your own matrix Solving linear systems Time for action — solving a linear system What just happened?

Finding eigenvalues and eigenvectors Time for action — determining eigenvalues and eigenvectors What just happened? Singular value decomposition Time for action — decomposing a matrix What just happened? Pseudoinverse Time for action — computing the pseudo inverse of a matrix What just happened? Determinants Time for action — calculating the determinant of a matrix What just happened? Fast Fourier transform Time for action — calculating the Fourier transform What just happened?

Shifting Time for action — shifting frequencies What just happened? Random numbers Time for action — gambling with the binomial What just happened? Hypergeometric distribution Time for action — simulating a game show What just happened?

NumPy Beginner - SciPy 2016 Tutorial - Alexandre Chabot LeClerc

Continuous distributions Time for action — drawing a normal distribution What just happened? Lognormal distribution Time for action — drawing the lognormal distribution What just happened? Have a go hero — trying a different sort order Complex numbers Time for action — sorting complex numbers What just happened? Pop quiz — generating random numbers Searching Time for action — using searchsorted What just happened? Array elements' extraction Time for action — extracting elements from an array What just happened? These ideas are applied to a classic, Fibonacci numbers but also to Lissajous curves and the drawing of sawtooth and triangle waves, and also how to work with bitwise comparison operations.

Up to this point this is the perfect companion for a crash course on the uses of Python and libraries like NumPy; however is also strong enough to be used as part of a college lecture or as the tool that should not be absent in your tool box and desk. Is hard to summarize further chapters; is a very good book because Idris shows his ability to explain in clear and concise sentences and then he applies immediately to exercises in a variety of areas. In chapter 6 he explains applications to Linear Algebra including eigenvalues and eigenvectors, Fourier Transform, random numbers and applications to a gambling show.

In chapter 7 he works with complex numbers, including sorting and searching, and even plays with Bessel functions. Chapter 8 deals on Assuring Quality with Testing and how to assert and compare arrays. Chapter 9 is one of my favorites, because Idris shows excellent examples on how to plot using Matplotlib, including animations!

If you cannot impress your audience with these tools, likely they are dead. Finally in Chapter 11, he talks about PyGame along with Matplotlib, Numpy and applications to … Artificial Intelligence, and simulations of life. However, is up to us to get the most of a tool. If you work on Engineering or Science and are in need to deal with data, vectors, matrices, arrays, sounds or images, if you need to plot and to make things evident to others, if you need a data handling Swiss tool in your toolkit, this is it, clear, concise, full of many examples in so many areas that you will always have something to talk with others even from areas different to yours.

Read it, use it, enjoy it. Jun 07, Zoran Bogicevic rated it it was amazing. Having in mind that NumPy is library for scientific computing, allowing rapid interactive prototyping, this book makes effort of deep understanding of NumPy's nature much more interesting, being written in learn-by-doing manner.

Initially intended for beginners, at it's very beginning, book guides you th NumPy Beginner's Guide, Second Edition, by Ivan Idris, is realy valuable resource for efficient leveraging NumPy derived from Numerical Python - high performance mathematical library for Python. So, even developers and scientists having no prior experience with Python can easily catch up.

NumPy Beginner's Guide is structured as action guide. At the beginning of each chapter, there is just enough theory to get you introduced to given subject, and then you encounter plethora of "Time for action" exercises with expected results shown. There is pop-quiz in each chapter, with short, multiple choice answers questions, convenient for easier memorizing and refreshing what's learned.

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Throughout book's eleven chapters, it starts with NumPy fundamentals and commonly used functions; continues with matrices, introduces different NumPy modules and some special routines, quality assurance, plotting histograms and drawing distributions with Mathplotlib, and eventually ends with SciPy, MATLAB, Octave and Pygame. Mentioning Pygame seemed a bit odd, but it makes perfect sense after working out through this chapter. Pygame and NumPy go together very well, because games involve lots of computation, and that's where NumPy and SciPy blend in.

Plus, learning is much more fun when laid around playing games. Especially interesting are "Time for action" exercises, step-by-step recipes, involving arrays manipulation, analyzing stock returns, calculating the exponential moving average, dealing with Fibonacci array, decomposing matrices, shifting frequencies, simulating a game show, mortgage and loan calculations, plotting stock volume, 3D plotting, animating objects and creating a simple game.

If you are worried about not knowing anything about Hamming or Kaiser window, Bollinger bands or Lissajous curves - fear no more, it will be so easy to comprehend after working out through exercises in this book. The book is very easy to read and follow and all concepts aren't hard to understand. Full table of contents is available here http: You can have a look at some sample chapters on publisher's web page Packt Publishing http: Complete source code is available for download from the book's page, but it's strongly recommended not just to copy and paste it.

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You'll learn much more efficiently if actually type the code in by hand. Jun 15, Tamir Lousky rated it it was amazing. The book is very well written, and tries quite successfully to address this specialized, complicated subject in a light, simplified manner without losing depth and oversimplifying. The book includes many down to earth examples and sample code, and many interesting exercises that help implement the material. It starts with a nice, gradual introduction to NumPy, its functions, data structures and uses and of course, how to install it. Then, the book addresses how to handle, analyze and manipulate larger datasets with examples from stock market data.

Around half way, the book focuses on matrices and linear systems. All in all, a very good book, fun to read and useful. Recommended to anyone interested in manipulating arrays, matrices and large datasets in python efficiently. For further info, please see the mini-review on my blog: May 28, Tuukka Turto rated it really liked it Shelves: The book is relatively thick, a bit over pages and packed with content.

As usual with Packt books, it starts by introducing the tools and giving detailed instructions on installing them, before diving into actual subject. The book starts easy, teaching how to create arrays and manipulate vectors. Soon more concepts are introduced starting from slicing and ending to SciPy. There is even a chapter about testing, which I found especially interesting to read. I liked how there are pop quizzes to help the reader to check if he understood what he just read.

They aren't really hard, but still quite fun. Layout of the book is clear and makes the books easy to read. There are plenty of examples and graphs in the book that help to explain the concepts. The book is very suited for a person who is not familiar with NumPy and wants to learn it. It covers lot of ground in sufficient detail. I felt that reading this book was good investment of time and enjoyed it. The book is available at Packt Publishing: Jun 14, Kashyap rated it it was amazing.

Numpy Beginner's Guide is a great book for computer science students, data scientists or analysts of any kind. I was particularly impressed by the author's technique of dividing concepts into small, easily digestible chunks, followed by NumPy implementations of each concept. The book provides a comprehensive explanation of the methods of numerical computation and representation available in NumPy, which is especially useful for those data analysts who are looking to translate their knowledge of nu Numpy Beginner's Guide is a great book for computer science students, data scientists or analysts of any kind.

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The book provides a comprehensive explanation of the methods of numerical computation and representation available in NumPy, which is especially useful for those data analysts who are looking to translate their knowledge of numerical analysis to the computer. The introductory material is also great at helping newbies to the field learn to get comfortable with working with large amounts of data in files and process them for analysis.

NumPy is available from the Packt Publishing's website. May 09, Duane Kaufman added it Recommends it for: Python Programmers, Engineers, Scientists. Jun 13, Nipun rated it really liked it. I was given a review copy of this book and liked it. I have added a detailed chapter wise review of the book here http: USP Talks about varieties of topics ranging from finance to signal processing and introduces functions through practical examples.

Aug 11, Altrovideo rated it it was amazing. Great book, let's you discover how arrays are stored efficiently, such as list of lists, the more efficency in large arrays. Danyel Lawson rated it liked it Nov 28, Ttt rated it it was amazing Jul 05, Anderson rated it really liked it Feb 13, Alexander Matyasko rated it liked it Sep 11, Stas Sajin rated it really liked it Mar 08, Mohamad Ismail rated it really liked it Oct 27,