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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 FREE CHAPTER 2. Statistical Visualizations 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

Components of a Graph


Each graph has a set of common components that can be adjusted. The names that Matplotlib uses for these components are demonstrated in the following graph:

Figure 2.3: Components of a graph

The components of a graph are as follows:

  • Figure: The base of the graph, where all the other components are drawn.

  • Axis: Contains the figure elements and sets the coordinate system.

  • Title: The title gives the graph its name.

  • X-axis label: The name of the x-axis, usually named with the units.

  • Y-axis label: The name of the y-axis, usually named with the units.

  • Legend: A description of the data plotted in the graph, allowing you to identify the curves and points in the graph.

  • Ticks and tick labels: They indicate the points of reference on a scale for the graph, where the values of the data are. The labels indicate the values themselves.

  • Line plots: These are the lines that are plotted with the data.

  • Markers: Markers are the pictograms that mark the point data.

  • Spines: The lines that delimit the...

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