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Big Data Analysis with Python

You're reading from   Big Data Analysis with Python Combine Spark and Python to unlock the powers of parallel computing and machine learning

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789955286
Length 276 pages
Edition 1st Edition
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Authors (3):
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Ivan Marin Ivan Marin
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Ivan Marin
Sarang VK Sarang VK
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Sarang VK
Ankit Shukla Ankit Shukla
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Ankit Shukla
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Table of Contents (11) Chapters Close

Big Data Analysis with Python
Preface
1. The Python Data Science Stack 2. Statistical Visualizations FREE CHAPTER 3. Working with Big Data Frameworks 4. Diving Deeper with Spark 5. Handling Missing Values and Correlation Analysis 6. Exploratory Data Analysis 7. Reproducibility in Big Data Analysis 8. Creating a Full Analysis Report Appendix

Code Practices and Standards


Writing code with a set of practices and standards is important for code reproducibility, as is explaining the workflow of the process descriptively in a step-wise manner.

This is universally applicable across any coding tool that you may use, not just with Jupyter. Some coding practices and standards should be followed strictly and a few of these will be discussed in the next section.

Environment Documentation

For installation purposes, you should maintain a snippet of code to install the necessary packages and libraries. The following practices help with code reproducibility:

  • Include the versions used for libraries/packages.

  • Download the original version of packages/libraries used and call the packages internally for installation in a new setup.

  • Effective implementation by running it in a script that automatically installs dependencies.

Writing Readable Code with Comments

Code commenting is an important aspect. Apart from the markdown option available on Jupyter,...

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