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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
Author Profile Icon Allen Yu
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

Getting End-of-Day (EOD) stock data from Quandl


Since we are going to discuss stock data extensively, note that we do not guarantee the accuracy, completeness, or validity of the content presented; nor are we responsible for any errors or omissions that may have occurred. The data, visualizations, and analyses are provided on an “as is” basis for educational purposes only, without any representations, warranties, or conditions of any kind. Therefore, the publisher and the authors do not accept liability for your use of the content. It should be noted that past stock performance may not predict future performance. Readers should also be aware of the risks involved in stock investments and should not take any investment decisions based on the content in this chapter. In addition, readers are advised to conduct their own independent research into individual stocks before making an investment decision.

We are going to adapt the Quandl JSON API code in Chapter 7Visualizing Online Data to get...

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