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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries 2. NumPy Arrays FREE CHAPTER 3. Statistics and Linear Algebra 4. pandas Primer 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
Index

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:

$ ipython profile create --parallel --profile=mpi

The preceding command will create a file in our home directory, which can be found at .ipython/profile_mpi/iplogger_config.py.

Add the following line in this file:

c.IPClusterEngines.engine_launcher_class = 'MPIEngineSetLauncher'

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:

$ ipython notebook --profile=mpi --log...
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