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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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Toc

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

Which Tool Should Be Used?


Seaborn tries to make the creation of some common analysis graphs easier than using Matplotlib directly. Matplotlib can be considered more low-level than Seaborn, and although this makes it a bit more cumbersome and verbose, it gives analysts much more flexibility. Some graphs, which with Seaborn are created with one function call, would take several lines of code to achieve using Matplotlib.

There is no rule to determine whether an analyst should use only the pandas plotting interface, Matplotlib directly, or Seaborn. Analysts should keep in mind the visualization requirements and the level of configuration required to create the desired graph.

Pandas' plotting interface is easier to use but is more constrained and limited. Seaborn has several graph patterns ready to use, including common statistical graphs such as pair plots and boxplots, but requires that the data is formatted into a tidy format and is more opinionated on how the graphs should look. Matplotlib...

You have been reading a chapter from
Big Data Analysis with Python
Published in: Apr 2019
Publisher: Packt
ISBN-13: 9781789955286
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