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Python Data Analysis, Second Edition

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

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787127487
Length 330 pages
Edition 2nd Edition
Languages
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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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Table of Contents (16) Chapters Close

Preface 1. Getting Started with Python Libraries 2. NumPy Arrays FREE CHAPTER 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...

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