Review new book: Practical Data Analysis

Hi all,

I was invited by the Packtpub team to review the book Practical Data Analysis by Hector Cuesta.  I started reading it for the last two weeks and I really enjoyed the topics approach covered by the book.

The book goes through the data science hot topics by presenting several practical examples of data exploration, analysis and even some machine learning techniques. Python as the main platform for the sample codes was a perfect choice, at my opinion. Since Python has becoming more popular at scientific community. 

The book brings several examples in  several science study fields such as stock prices, sentiment analysis on social networks,  biology modelling scenarios, social graphs,  MapReduce, text classification,  data visualisation, etc.  Many novel libraries and tools are presented including Numpy, Scipy, PIL, Python, Pandas, NLTK, IPython, Wakari ( I really liked a dedicated chapter for this excellent tool for scientific python environment on-line),  etc.  It also covers NoSQL databases such as MongoDB and visualisation libraries like D3.js.

I believe the biggest value proposition of this book is that it brings together in one book several tools and how they can be applied on data science. Many tools mentioned lacks further examples or documentation, which this book can assist any data scientist on this task.

However, the reader must not expect learn machine learning and data science in this book.  The theory is  scarce, and I believe it was not the main goal of the author for this book. For anyone looking to learn data science, this is not the right book. It is focused on who desires an extra resource for sample codes and inspirations, it will be a great pick!

The source code is available on Github, but you can explore them better explained inside the book, including illustrations. To sum up, I have to congratulate Hector for his effort writing this book. For the Scientific community, including the Python group, they will really enjoy this book! I really missed more material about scientific stack software installations, since for the beginners it can be really painful.  But in overall, it was well written focused on practical problems! A guide for any scientists.

For me the best chapters were the Chapter 6: Simulation of Stock Prices, the visualisation using D3.js was great, and the last chapter, 14, about On-line Data Analysis with IPython and Wakari. It's the first time I see Wakari covered in a book! Everyone who works with scientific Python today must give a chance some day to experiment this on-line tool! It's awesome!

Congratulations to PacktPub and Hector for the book!

Regards,

Marcel Caraciolo

Review about the book Learning IPython for Interactive Computing and Data Visualization

Hi all,

I was invited to review a copy of the book recently released titled "Learning IPython for Interactive Computing and Data Visualization" by the author Cyrille Rossant.  The book focus on one of the best tools for working with Python with the interactive incremented shell IPython.  By the way, it was the time to the tool receive a special book about it.

Learning IPython for Interactive Computing and Data Visualization

IPython is covered through the six chapters using several basic examples related to scientific computing  along with another Python tools such as Matplotlib, Numpy, Pandas, etc.  The first chapters explore the IPython basics such as installation, basic commands to get used with the tool.

The next chapters introduces NumPy and Pandas basics with the IPython shell active. Don't expect advanced examples with those tools. The idea is a simple demonstration of what we can do at IPython.

There is a chapter to discuss the visualization data with graphs, plottings with IPython Notebook. However I missed more details abot IPython notebook. It lacks more deep examples related to the topic.  

I really liked the chapter 5 when they showed some basics of MPI (Message Passing Theme), although the topic wasn't vasted explored. But the introduction gives a greate potential of usefulness to the more advanced books about IPython.

The last chapter shows how to create pluggins to IPython, for instance, create an simple extension that introduces a new cell magic (write C++ code directly in the cell, and it will be automatically compiled and executed).

My conclusion about the book is that it achieves the expected goal: a technical introduction to IPython. If you want a book to explore scientific computing or a advanced stuff to IPython, this is not the the book yet. I can say that this book is a first start for much more topics about the use of IPython.  MPI, IPython notebook, etc.  I recommend the book for start exploring the IPython as reference! :D

Regards,

Marcel

Review of the book Learning Scipy for Numerical and Scientific Computing

Hi all,

I've finished reading the book "Learning Scipy for Numerical and Scientific Computing".  This book comes to the scientific python series that PacktPub are bringing to the Python Developers! Congratulations!  As the title informs: it includes Scipy, Numpy and Matplotlib.  I only missed some further information about IPython, but it wasn't the goal of the book, so it goes well even leaved out.

It covers several important topics that are not as commonly covered, specially with several snippets illustrating special functions presented at Scipy library. For the developers it will be another great reference book to complement the native docs that comes with the library.  I enjoyed the author focused more on numerical analysis functions, it is one of the most used functions at the library.  

The bool also brings chapters on more specific applications: signal processing, data mining and computational geometry.  There is an extra chapter about the integration with another languages, but I found it not dense enough to explain those integrations. I really missed more start-off examples showing how to install the f2py or how to use Scipy with C/C++. 

Overall the Learning Scipy for Numerical and Scientific Computing book is a good book on Scipy covering  lots of mathematics with examples in Python. The book has a good size and it helps the scientists and scientific developers (by the way the non-developers will face some difficulties due to the heavy math that comes with the examples) to have a good overview on the library before exploring the reference material.

Thanks Kenny for the invitation to review this book, and congratulations to Francisco for bringing one more technical book for scientific python computing to the series!

Regards,

Marcel Caraciolo

Review about the book Numpy Cookbook!

Hi all,

This year I had the opportunity to review the book Numpy Cookbook by Ivan Idris and published by Pack Publishing. The goal of the book is to present the numpy library through several examples.  The author refers them as recipes.

For my first impression taking a right look into the bok before reading it I could say that the book is not only about Numpy but it also covers another related libraries such as Scipy, Scikit, Matplotlib, Cython, Pandas, Rpy, which I think for the book it's quite better.  Even the title is not well suited to it (it could be such as Scientific Python Cookbook for example), the book is a perfect book for scientific developers and for anyone not much familiar with the libraries, so it offers a fast way to explore it.

I believe the audience of this book if for people who knows how Python works and has some experience on scientific side.

Content

The book is well structured and generally easy to read and digest (congratulations to all Packt books that I read has this same organization).  I like the way the author introduces the examples by using IPython shell instead the regular one. The first experience with Scientific python with IPython must be presented for anyone who works in this field.

The first chapter covers the Ipython and the feature notebook.  It supports Matplotlib and it's a good way os sharing scripts between people.  Great points (even he didn't mention that) when the examples that he shows through the book runs on the Ipython Notebook! :)

The next two chapters he tackles the Numpy aspects: array indexing and several numpy functions.  I didn't like the little mess with different instalations of the packages. However I could install all the packages provided based on the links in the book.  The recipes were quite interesting and I liked the examples with trading stocks. For financial developers those examples are quite attractive!

The fourth chapter handles the Numpy comunication  with other Python modules and other programming languages.  There are several examples covering Matlab, octave, R, Java and even Google App Engine! All of them are simple recipes, so don't expect much of real action or deeper use (Unfortunately!).  You still need to read the docummentations of those interfaces to go beyond of the very basics provided in the book.

I'd like to give an extra credit for the author mentioning the picloud enviroment, a distributed cloud computing provider, which offers pre-installed Python software including numpy for distributed scientific  computing!

The next chapter covers audio and image signal processing and I think it's one of the best scientific recipes approached in the book. Even there's no in-depth details, but creating an audio filter was interesting! Some advanced functions covered in this chapter needs more details such as memmap, clip, etc.   

The Chapter 6 presents more special Numpy classes that can be useful such as masked arrays and recarrays. Those advanced structures can be quite useful in complex algorithms.

The main streams of the book are the next two chapters. Many scientists usually forget: profiling, debugging and quality insurance. The author covers all those topics showing several recipes using several tools ( I believe many of the examples uses nosetests - the author should mention it).  I really appreciate the BDD and the mock objects! I didn't know that!  My opinion for those chapters is that it worths the reading specially about those overlooked topics on scientific books covered in this book.

The chapter 9 gives some interesting recipes on optimizing your code with Cython. By the way it was excellent chapter since we don't have many books handling about those topics. Extra points for tackling a common problem with almost scientists: Call C functions with Python! However, it does not mention Fortran (Fpy) integration.

Finally the last chapter covers the scikit and pandas, that for me they are one of the best frameworks on top of numpy/scipy nowadays covering machine learning, image processing and statistics research fields! Although the recipes are not deeper you can have at a glance of how powerful each framework is and makes you curious to explore each one of them.

Conclusions

The book is well written and it's really easy to read digest. I liked the approach that the author uses by several examples familiar to scientists and scientific developers. It's not a book for Python beginners and it's focused on showing with recipes what the Scientific Python enviroment is capable of. And it does quite well!  I recommend this book for everyone looking to study profoundly with learning experiments the Numpy/Scipy/Matplotlib frameworks. I only miss some deeper explanations about some topics but it does not compromise the book.  Congratulations Packt and Ivan for this great contribution to the scientific python community!

Regards,

Marcel Caraciolo

Review of the book Numpy 1.5 - Beginner's Guide

Hi all,

I'd like to share my review of the book Numpy 1.5 the Beginner's Guide by Ivan Idris, which is one of the latest books in a series of manuals covering scientific computing libraries written in Python.  This book covers the Numpy library for manipulating vectors and matrices and support for mathematical libraries.

Numpy 1.5  from Packt Publisher

Quick Review

The book is a great and useful resource for anyone who wants to explore further the Numpy scientific library since it covers almost all of the modules available at Numpy 1.5.  It comes with several examples, specially for finantial researchers and developers that work with finantial data. The author explored several modules using stocks and historical price data.  The authors explains each function or operation with code and the expected results, so the reader can follow precisely what's happening when he presents the modules. One of the values of the book is how it is organized: the step-by-step guide when he presents complex functions at Numpy, for example: add.reduceat, add.accumulate and add.reduce operators.

The part that I didn't like was about the exercises which was quite simple. I'd like to see deep exercises exploring the resources given at the book and I missed more information about NaN values. Also, I didn't  see information also about the functions squeeze, choose and about more complex structured arrays (arrays with tuples, etc.).

To sum up, I recommend this book for anyone whishing to learn about scientific computing with Python using the mathematical library Numpy which is a great alternative (and free !) for Matlab, Mathematica and other packages. I expect quite soon a book covering Scipy library also!  By the way, the finantial fans will love this book since it covers almost of the entire book with examples using finance data!

Review

The book starts with a step-by-step installation process of Numpy as also giving a litte introduction about what is Numpy, its history, etc.   I'd like to mention that even all the platforms covered at the book, Numpy is not so easy as mentioned to install at Mac OS.  The problem is that generally the developers don't use the built-in Python that comes with the Mac, since it is outdated (my Snow leopard comes with the Python 2.6.1). So when you install the new Python, that the problems come! Several compilation errors, messages that you can't understand, etc.  But if you go by using the MacPorts,  you will free of all these errors! ( After all the nightmare of the installation, I discovered the MacPorts :P).

The following chapters 2-4 presents the Numpy Fundamentals covering the array manipulations and most commonly used operations.  The books goes into a cyclic process, where each function that the author presents goes through an introduction about the problem to solve, the actions (how you with Numpy can solve), auxiliar numpy functions and operations and finally what just happened, that is, explain what he has done after showing the solution. The examples covered at book, most of them, are from finantial data and stock market values. An interesting choice since he used the same examples through the chapters in a progression and logical way.  Having each function and numpy featured described and explained made the book a good reference guide for someone using the library.  There were minor issues  related to the imports, he doesn't mention the imports in some examples,  for instance the numpy.loadtxt function when he uses the datetime module.  For a beginner that is studying Python for the first time, it may be harder to them to follow the examples, since he could not always tell where the functions or modules were coming from.

The second part of the book includes the matrices, universal functions, some scipy modules and the use of matplotlib and testing.  The chapter 5 covers the matrix module and universal functions such as add, divide, prod, sum and so on.  I missed some functions that weren't covered at this chapter such as numpy.choose or numpy.squeeze.  I believe the author didn't remember or didn't have space to mention these specific functions, but it does not prejudice at all the quality of the book. The chapter that I liked the most at the book was about testing. Several developers, special the scientific researchers are not used to test their code, so I believe it is a great chapter for anyone who wants to assure quality and avoid future bugs using Numpy testing modules.  The chapter should be more bigger and include more examples even creating test cases and tips for scientific developers.
Finally the last two chapters focus on plotting and Scipy integration. I think the plotting chapter should be at the beginning of the book, because he already uses lots of examples at the previous chapters with matplotlib and only at the end explain further about the library. The chapter is well-written and gives you sufficient content for beginning with Matplotlib. The last chapter covers the use of several scipy functions but it does not give deeper explanations about how it works as he did at the previous chapters with Numpy. However it gives several useful examples to work with integration, image processing and even optimization. Many developers will enjoy this extra-chapter covering the use of scipy+numpy.

Conclusions

 My overall impression of this book is that it could make a useful reference guide for Numpy. For finantial researchers and developers it will be a great book since it also covers lots of examples using finance data to present the numpy fundamentals.  There were minor issues related to Scipy and Matplotlib that should be more explained. For anyone who wants to start using Numpy it can a be an excellent book to begin, since it covers all the fundamentals steps with a cyclic progressive introduction of using the scientific packages in Python.

Regards,

Marcel Caraciolo