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Hands-On Data Science with Anaconda

You're reading from   Hands-On Data Science with Anaconda Utilize the right mix of tools to create high-performance data science applications

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788831192
Length 364 pages
Edition 1st Edition
Languages
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Authors (2):
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James Yan James Yan
Author Profile Icon James Yan
James Yan
Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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Table of Contents (15) Chapters Close

Preface 1. Ecosystem of Anaconda 2. Anaconda Installation FREE CHAPTER 3. Data Basics 4. Data Visualization 5. Statistical Modeling in Anaconda 6. Managing Packages 7. Optimization in Anaconda 8. Unsupervised Learning in Anaconda 9. Supervised Learning in Anaconda 10. Predictive Data Analytics – Modeling and Validation 11. Anaconda Cloud 12. Distributed Computing, Parallel Computing, and HPCC 13. References 14. Other Books You May Enjoy

Using IPython

For a Windows version, after navigating to Anaconda3 from All Programs and then clicking on IPython, we can see the following:

You can also do the the same by launching Anaconda Prompt and then typing ipython.

This tells us that the version of Python is 3.6.3 and that the version of IPython is 6.1.0. We could type a few commands to test it. For example, if we invest $100 today for an investment horizon of 5 years, and the annual return is 10%, what is our expected future value? The future value formula for a given present value is provided here:

where FV is the future value, PV is the present value, R is the period return, and n is the number of periods:

The first line of command of pv=100 assigns a value of 100 to pv. For the fifth line, we typed a command line of pv*(1+r)^n. However, we received an error message of TypeError: unsupported operand type(s) for...

You have been reading a chapter from
Hands-On Data Science with Anaconda
Published in: May 2018
Publisher: Packt
ISBN-13: 9781788831192
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