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Numerical Computing with Python

You're reading from   Numerical Computing with Python Harness the power of Python to analyze and find hidden patterns in the data

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Product type Course
Published in Dec 2018
Publisher Packt
ISBN-13 9781789953633
Length 682 pages
Edition 1st Edition
Languages
Concepts
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Authors (5):
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Pratap Dangeti Pratap Dangeti
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Pratap Dangeti
Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
Allen Yu Allen Yu
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Allen Yu
Aldrin Yim Aldrin Yim
Author Profile Icon Aldrin Yim
Aldrin Yim
Claire Chung Claire Chung
Author Profile Icon Claire Chung
Claire Chung
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Table of Contents (21) Chapters Close

Title Page
Contributors
About Packt
Preface
1. Journey from Statistics to Machine Learning FREE CHAPTER 2. Tree-Based Machine Learning Models 3. K-Nearest Neighbors and Naive Bayes 4. Unsupervised Learning 5. Reinforcement Learning 6. Hello Plotting World! 7. Visualizing Online Data 8. Visualizing Multivariate Data 9. Adding Interactivity and Animating Plots 10. Selecting Subsets of Data 11. Boolean Indexing 12. Index Alignment 13. Grouping for Aggregation, Filtration, and Transformation 14. Restructuring Data into a Tidy Form 15. Combining Pandas Objects 1. Other Books You May Enjoy Index

Chapter 8. Visualizing Multivariate Data

When we have big data that contains many variables, the plot types in Chapter 7, Visualizing Online Data may no longer be an effective way of data visualization. We may try to cramp as many variables in a single plot as possible, but the overcrowded or cluttered details would quickly reach the boundary of a human's visual perception capabilities.

In this chapter, we aim to introduce multivariate data visualization techniques; they enable us to better understand the distribution of data and the relationships between variables. Here is the outline of this chapter:

  • Getting End-of-Day (EOD) stock data from Quandl
  • Two-dimensional faceted plots:
    • Factor plot in Seaborn
    • Faceted grid in Seaborn
    • Pair plot in Seaborn
  • Other two-dimensional multivariate plots:
    • Heatmap in Seaborn
    • Candlestick plot in matplotlib.finance:
      • Visualizing various stock market indicators
    • Building a comprehensive stock chart
  • Three-dimensional plots:
    • Scatter plot
    • Bar chart
    • Caveats of using Matplotlib...
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