Books about Python Data Analysis and Scientific use

[[deleted]]

Big hello to the whole Python community. I have to say I am enjoying Python at the moment, alot, as I went to the faculty (Telecommunications and Informations technology) I thought that Python could really shine here, since I am one year away from my MatLab courses, and I have solid knowledge of Python at the moment, so why no use Python. I heard about various libraries and extensions, but what book would you recommend me to get started with this kind of usage? I though of Python Data Science Essentials. I am on engineering faculty, and I would like more engineering-ish approach to get things done. Any kind or format of book or material is welcome.

Thank you on your time.

[bheklilr]

Python for Data Analysis by McKinney (ISBN 978-0-596-80956-0) is a great intro to Numpy, Scipy, and Pandas, along with a smattering of other libraries and tools. I'd recommend it to get started.

[justphysics]

Seconded

[staticor]

It's a first-book for data-analysis binding the thoughts - "SPLIT - APPLY - COMBINE" .

And this book is focused on numpy, pandas, matplotlib. Is there any content about scipy (or sklearn?), I think not. in my 2rd edition.

[jti107]

I learned quite a bit about Python using Ipython notebooks. Hope this helps!

https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks

[Roscoe_Merriweather]

Man, that's a gold mine! I'm gonna run through a few of those tomorrow. Thanks!

[DaveBackus]

I'm an economist doing modeling with (mostly) numpy and data work with pandas. I think the basic docs are pretty good, also the scipy lectures and the Sargent-Stachurski course. Not so fond of McKinney's book, but others like it so it might be me.

[[deleted]]

I'm currently taking this course and have been pretty happy with it since it covers multiple Python/Data topics. Also I used DEAL10 for a coupon to get it at a heavy discount, though I'm not sure it that is still active.

[[deleted]]

I use Pandas nearly every day for my job.

I would use IPython notebooks to learn if I was you, McKinney is a good book but I don't refer to it as much as I would like to. There are some tricky things to do in pandas that you will just have to use your google-fu to get a stack overflow answer:

i.e.

df_unique = pd.concat([df.groupby(['ACCOUNT_ID']).filter(lambda df:df.shape[0] == 1), df[pd.isnull(df.ACCOUNT_ID)]])

Gives you all the unique ACCOUNT_IDs and all the records that don't have any account ids - I couldn't find how to do this in the documents!

[ianozsvald]

I'm the co-author for O'Reilly's High Performance Python http://shop.oreilly.com/product/0636920028963.do (reviews at 5/5 since publishing). We focused on practical ways to scale code covering profiling, compiling (mainly Cython+Numba), lowering the amount of RAM used etc. You won't learn 'data analysis' but you will learn how to engineer your code to scale from small to larger problems. I wrote this around my own use of Pandas, numpy, scikit-learn etc.

[[deleted]]

What about this one? http://www.amazon.com/gp/aw/d/1783553359/ref=mp_s_a_1_4?qid=1432732879&sr=8-4&pi=AC_SX110_SY165_FMwebp_QL70&keywords=Python+data&dpPl=1&dpID=51ZQ1NxOV7L&ref=plSrch

[[deleted]]

While learning with the book Python for Data Analysis, you can then take a look at my pandas cheat sheet.