Review: Fundamentals of Data Analytics in Python

I massively use Python for data analysis and when I was offered to review the video tutorial with the title “Fundamentals of Data Analytics in Python LiveLessons”, I couldn't refuse.

The tutorial starts from the basics showing how to install Python and its data analysis libraries. Then it continues explaining the main uses that data scientists and engineers practice during their analysis: importing and cleaning data, vectorial computing, visualization and data summarization.

Most of the videos are commented sessions of IPython notebook sometimes supported by some slides. The authors go deep into the explanation of how to use the libraries for the manipulation of the data (Numpy, Scipy and Pandas), while they summarize the potential of the other complementary libraries. In particular, the last video is a survey of various visualization tools.

In conclusion, this video tutorial provides a solid introduction to the main tools for data analysis in Python and a clear view of the open source Python tools relevant to scientific and engineering programming. This tutorial seems perfect for people who need to learn the technical methodologies for data analysis and for people who already know Python but want to acquire skills about data analysis.

Book review: Learning IPython for Interactive Computing and Data Visualization

I use IPython almost every day and I am very happy to review Learning IPython for Interactive Computing and Data Visualization by Cyrille Rossant, and published by Packt Publishing.

The book introduces the IPython basics and then focuses on how to combine IPython with some of the most useful libraries for data analysis such as Numpy, Matplotlib, Basemap and Pandas. Every topic is covered with examples and the code presented is also available online. The references proposed are always up-to-date and give the reader the opportunity to discovery resources not covered in the book.

Favorite chapter

The Chapter 5 is a little gem. Here, you can find an introduction on how to use IPython to write high performance code through Cython and the parallel programming facilities of IPython. The attention paid by the author on how to write efficient code is remarkable.

Conclusions

This book definitely achieves its goal to provide a technical introduction to IPython. It is intended for Python users who want an easy to follow introduction to IPython, but also experienced users will find this book useful. It is to notice that, at the moment, this is the only book about IPython.

Book review: NumPy Cookbook

This year I have the chance to review the book NumPy Cookbook written by Ivan Idris and published by Packt Publishing. It introduces the numpy library by examples (which the author refers as recipes :). It is written with a simple language and it covers a wide range of topics, from the istallation of numpy to the combination with Cython.

My impression of the book was good and, in particular, I liked the structure of the book. Every chapter face a series of problem related to the a specific topic through examples. Each example comes with an introduction to the problem that will be solved, the code commented line by line and a short recap of the techniques applied to solve the problem. Most of the examples are about practical problems and the code is made to be adapted in your own projects.

Favorite chapters

Chapters 5 and 10 are my favorite. The first one is about audio end image processing and explains some basic operation about the manipulation, the generation and the filtering of audio and video signals. The second is about the combination of numpy with some scikits,like scikits-learn, scikits-statsmodels and pandas. I loved these chapters because they cover some topics related to complex fields, such as machine learning and data analysis, in a very straightforward fashion.

Favorite example

Some examples presented by the book kept my attention. In particular, I found very interesting the one about the generation of the Mandelbrot. This example contains an explanation of the mathematical formula behind the fractal and the combination of the image generated using the formula and a simpler one. It is my favorite because provides one of the most practical explanation of the Mandelbrot fractal I have ever seen.

Conclusions

This book could be a good starting point for who want to begin with numpy using a gentle approach. It can be used also as a manual which can help you in the development of small parts of more complex projects.

Waiting for NumPy Cookbook

Also this year the Packt Publishing gives me the opportunity to review an interesting book about scientific computing in Python. The last year I had the pleasure to review Numpy 1.5 Beginner's Guide from Ivan Idris and I was surprised about the number of code examples and the straightforward approach to the explanation of the topics. Now I am waiting for Numpy Cookbook, which is from the same author and I can't wait to write the review. Stay tuned ;)

Book review: Numpy 1.5 Beginner's Guide

I got the chance to read the book NumPy 1.5 Beginner's Guide written by Ivan Idris and published by Packt Publishing last month. My impression of the book was quite positive. It's a book based on examples, which incrementally introduce all the main features of the library. It is written with a simple language, and it is always easy to understand.

Contents and structure

The organization and flow are good. The ten chapters contain well thought out examples that you can use as building blocks for your scientific computing projects. Every example is structured in this way:

• An introduction to the problem that the example will solve.
• The code, commented line by line.
• The result of the code.
• A short recap of how the problem has been solved.
• And, sometimes, a multiple choice question to help the reader to test his own understanding.

There is no attempt at teaching the mathematics behind the examples. Every example is a "how to" that can help you to learn how to use the library and can save hours of searching through the official documentation and more complicated texts.

The chapters 1,2 and 3 contain the starting points to use NumPy. They explain how to install NumPy, how to handle the NumPy arrays and how to use some of the basic mathematical/statistical functions provided by the library. Chapters 4 through 7 cover the basics about handling matrices, how to load and write data, how to write universal functions and cover some of the basic modules that are discussed. Chapter 8 explains how to use the unit test functions provided by NumPy. Finally, chapters 9 and 10 (my favorites!) introduce how to integrate NumPy with Matplotlib and SciPy.

Who is this book for?

This book is aimed at people who know Python and need to start using scientific computing in their programs. It is also suitable for people who use another scientific computing environment, such as Matlab, and want quick-start introduction to NumPy.

Waiting for NumPy 1.5 Beginner’s Guide

I'm waiting to receive a copy of NumPy 1.5 Beginner’s Guide. Lately, I have read a sample chapter of the book and I was surprised because it contains a lot of code examples and clearly explains how to use the code and what it means.

I found on the publisher web site that the book is supposed to cover a lot of topics, from the NumPy installation to the integration of NumPy in real scientific computing projects. If the entire book is like the sample chapter, it will be one of my favorite resources about the NumPy. I can't wait to have the book!

Stay tuned for the complete review ;)