Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Python Data Analysis, Second Edition

You're reading from   Python Data Analysis, Second Edition Data manipulation and complex data analysis with Python

Arrow left icon
Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787127487
Length 330 pages
Edition 2nd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Arrow right icon
View More author details
Toc

Table of Contents (16) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. The Pandas Primer 4. Statistics and Linear Algebra 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources

IPython Parallel

IPython Parallel is the IPython API for parallel computing. We will set it up to use MPI for message passing. We may have to set environment variables as follows:

$ export LC_ALL=en_US.UTF-8
$ export LANG=en_US.UTF-8

Issue the following command at the command line:

$ ipython3 profile create --parallel --profile=mpi

The preceding command will create several files in the .ipython/profile_mpi folder located in your home directory.

Start a cluster that uses the MPI profile as follows:

$ ipcluster start --profile=mpi --engines=MPI --debug

The preceding command specifies that we are using the mpi profile and MPI engine with debug-level logging. We can now interact with the cluster from an IPython notebook. Start a notebook with plotting enabled and with NumPy, SciPy, and matplotlib automatically imported, as follows:

$ jupyter-notebook --profile=mpi --log-level=DEBUG 

The preceding command uses the mpi profile with debug log level. The notebook for this example is stored in the IPythonParallel...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image