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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

Changing Plot Design: Modifying Graph Components


So far, we've looked at the main graphs used in analyzing data, either directly or grouped, for comparison and trend visualization. But one thing that we can see is that the design of each graph is different from the others, and we don't have basic things such as a title and legends.

We've learned that a graph is composed of several components, such as a graph title, x and y labels, and so on. When using Seaborn, the graphs already have x and y labels, with the names of the columns. With Matplotlib, we don't have this. These changes are not only cosmetic.

The understanding of a graph can be greatly improved when we adjust things such as line width, color, and point size too, besides labels and titles. A graph must be able to stand on its own, so title, legends, and units are paramount. How can we apply the concepts that we described previously to make good, informative graphs on Matplotlib and Seaborn?

The possible number of ways that plots can...

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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