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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

Using Fortran code through f2py

Fortran (from Formula Translation System) is a mature programming language mostly used for scientific computing. It was developed in the 1950s with newer versions emerging such as Fortran 77, Fortran 90, Fortran 95, Fortran 2003, and Fortran 2008 (refer to http://en.wikipedia.org/wiki/Fortran). Each version added features and new programming paradigms. We will need a Fortran compiler for this example. The gfortran compiler is a GNU Fortran compiler, which can be downloaded from http://gcc.gnu.org/wiki/GFortranBinaries.

The NumPy f2py module serves as an interface between Fortran and Python. If a Fortran compiler is present, we can create a shared library from Fortran code using this module. We will write a Fortran subroutine that is intended to sum rain amount values as given in the previous examples. Define the subroutine and store it in a Python string. After that, we can call the f2py.compile() function to produce a shared library from the Fortran code...

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