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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 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

Using Fortran code through f2py

Fortran (derived from Formula Translation) 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, and 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 the 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...

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