**Win Free e-copies of NumPy Beginner’s Guide Second Edition **

Readers would be pleased to know that I have teamed up with Packt Publishing to organize a Giveaway of the NumPy Beginner’s Guide ( 2^{nd} edition ).

And 3 lucky winners stand a chance to win e-copies of this new book. Keep reading to find out how you can be one of the Lucky Winners.

# Overview:

- Perform high performance calculations with clean and efficient NumPy code.
- Analyze large data sets with statistical functions
- Execute complex linear algebra and mathematical computations

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**How to Enter?**

All you need to do is head on over to the book page and look through the product description of the book and drop a line via the **comments below this post to let us know what interests you the most about this book**. It’s that simple.

**Deadline**

**The contest will close on May 20, 2013. Winners will be contacted by email, so be sure to use your real email address when you comment!**

I have been using numpy for years now, and I still feel like I don’t take advantage of the built-in convenience functions and cool data-structures enough — that aspect of the book sounds pretty intriguing.

In my job, everything I do is about relatively simple numerical analysis, but it’s always different, different data sets, with some small amount of shared or similar work. I’ve looked into NumPy a bit and it looks like a great way to power through many of my tasks. But it’s been pretty tough finding a good single reference on it – everyone presumes knowledge of other libraries or wants to teach NumPy with other libraries etc. so this book likes a fantastic solution for me to pick up just this piece of the puzzle in a pretty short tme.

All the best

I’m a student and am new into the research field. I have my fist couple papers in the pipeline and I am using python/numpy for all of my data analysis. I have found it to be an extremely useful tool, but a better understanding of how to optimize my code would be useful.

I’m pretty much interested in the entire book! I’ve been trying to get more and more into Python development lately, and I read tons of blog post titles with various permutations of “Python”, “NumPy”, “pandas”, “data analysis”, leading me to believe that knowing NumPy would be useful in my Python projects. I’d love to be equipped to handle large data sets without having to reinvent the wheel.

I am an FPGA design and verification engineer from Australia. So I do not really expect it will be shipped here. But a well organized book and plenty example and explanations will be great helpful for my first graphic processing moduling project in python with numpy.

Starting a math’s degree in September this book looks really helpful, the documentation on the numpy pages is a little daunting and getting a book aimed at getting started and explanations and reasoning behind code looks really helpful. Pretty sure I’m going to buy this anyway.

I have always wanted to start using Numpy but don’t have the slightest idea as to what to use it for. It sounds like this book could at least give me example data, etc.

Being a PhD. scholar working in Machine Learning and Systems, Python is the language of choice. Now, moving to Python from Matlab, you want to be assured that you don’t miss on the vector routines and a lot of other features. Having used Numpy for a few years now, i feel that learning from a book can give you the edge and you then know where to look when stuck, so that you don’t end up reinventing the wheel. For example, i posted this question on SO http://stackoverflow.com/questions/16330971/efficiently-computing-element-wise-product-of-transition-matrices-mm-nn , only to realize that Numpy has an inbuilt routine for it. So this book would address these issues and presents the whole ecosystem of Scipy, Matplotlib and Numpy. Enough for a researcher!

I’m taking a class at Coursera about Machine Learning and I wolud like to translate the work I’m doing with Octave to Python.

I have just begun using Numpy as an alternative to Matlab for matrix based calculations. This eBook appears to provide some straightforward demonstrations on the type of calculations I am accustomed to using Matlab. Its hard to break the habit of just jumping into Matlab to work scientific data but I believe Numpy and Python together are a solid alternative.

Thanks!

I have spent many years in my life doing research (numerical simulations, data analysis, plotting) using C and a heterogeneous set of tools. Now I see in your book that I can do everything in a single and much more friendly framework. A true eye opener. Thank you.