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Bioinformatics with Python Cookbook

You're reading from   Bioinformatics with Python Cookbook Use modern Python libraries and applications to solve real-world computational biology problems

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Product type Paperback
Published in Sep 2022
Publisher Packt
ISBN-13 9781803236421
Length 360 pages
Edition 3rd Edition
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Author (1):
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Tiago Antao Tiago Antao
Author Profile Icon Tiago Antao
Tiago Antao
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Table of Contents (15) Chapters Close

Preface 1. Chapter 1: Python and the Surrounding Software Ecology 2. Chapter 2: Getting to Know NumPy, pandas, Arrow, and Matplotlib FREE CHAPTER 3. Chapter 3: Next-Generation Sequencing 4. Chapter 4: Advanced NGS Data Processing 5. Chapter 5: Working with Genomes 6. Chapter 6: Population Genetics 7. Chapter 7: Phylogenetics 8. Chapter 8: Using the Protein Data Bank 9. Chapter 9: Bioinformatics Pipelines 10. Chapter 10: Machine Learning for Bioinformatics 11. Chapter 11: Parallel Processing with Dask and Zarr 12. Chapter 12: Functional Programming for Bioinformatics 13. Index 14. Other Books You May Enjoy

Performing R magic with Jupyter

Jupyter provides quite a few extra features compared to standard Python. Among those features, it provides a framework of extensible commands called magics (actually, this only works with the IPython kernel of Jupyter since it is actually an IPython feature, but that is the one we are concerned with). Magics allow you to extend the language in many useful ways. There are magic functions that you can use to deal with R. As you will see in our example, it makes R interfacing much easier and more declarative. This recipe will not introduce any new R functionalities, but hopefully, it will make it clear how IPython can be an important productivity boost for scientific computing in this regard.

Getting ready

You will need to follow the previous Getting ready steps of the Interfacing with R via rpy2 recipe. The notebook is Chapter01/R_magic.py. The notebook is more complete than the recipe presented here and includes more chart examples. For brevity...

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