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Python Data Analysis, Second Edition

You're reading from   Python Data Analysis, Second Edition Data manipulation and complex data analysis with Python

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787127487
Length 330 pages
Edition 2nd Edition
Languages
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (16) Chapters Close

Preface 1. Getting Started with Python Libraries 2. NumPy Arrays FREE CHAPTER 3. The Pandas Primer 4. Statistics and Linear Algebra 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources

Appendix A. Key Concepts

This appendix gives a brief overview and glossary of technical concepts used throughout the book.

Amdahl's law predicts the maximum possible speedup due to parallelization. The number of processes limits the absolute maximum speedup. Some parts of any given Python code might be impossible to parallelize. We also have to take into account overhead from parallelization setup and related interprocess communication. Amdahl's law states that there is a linear relationship between the inverse of the speedup, the inverse of the number of processes, and the portion of the code that cannot be parallelized.

ARMA models combine autoregressive and moving average models. They are used to forecast future values of time series.

Artificial Neural Networks (ANN) are models inspired by the animal brain. A neural network is a network of neurons--units with inputs and outputs. The output of a neuron can be passed to a neuron and so on, thus creating a multilayered...

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