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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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Toc

Table of Contents (16) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 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

What this book covers

Chapter 1, Getting Started with Python Libraries, gives instructions to install python and fundamental python data analysis libraries. We create a small application using NumPy and draw some basic plots with matplotlib.

Chapter 2, NumPy Arrays, introduces us to NumPy fundamentals and arrays. By the end of this chapter, we will have basic understanding of NumPy arrays and the associated functions.

Chapter 3, The Pandas Primer, introduces us to basic Pandas functionality, data structures and operations.

Chapter 4, Statistics and Linear Algebra, gives a quick overview of linear algebra and statistical functions.

Chapter 5, Retrieving, Processing, and Storing Data, explains how to acquire data in various formats and how to clean raw data and store it.

Chapter 6, Data Visualization, gives an overview of how to plot data with matplotlib and pandas plotting functions.

Chapter 7, Signal Processing and Time Series, contains time series and signal processing examples using sunspot cycles data. The examples use NumPy/SciPy, along with statsmodels.

Chapter 8, Working with Databases, provides information about various databases (relational and NoSQL) and related APIs.

Chapter 9, Analyzing Textual Data and Social Media, analyzes texts for sentiment analysis and topics extraction. A small example is also given of network analysis.

Chapter 10, Predictive Analytics and Machine Learning, explains artificial intelligence with weather prediction as a running example using scikit-learn. Other API are used for algorithms not covered by scikit-learn.

Chapter 11, Environments Outside the Python Ecosystem and Cloud Computing, gives various examples on how to integrate existing code not written in Python. Also, using python in cloud will be demonstrated.

Chapter 12, Performance Tuning, Profiling, and Concurrency, gives hints on improving performance with profiling and Cythoning as key techniques. Relevant frameworks for multicore and distributed systems are also discussed.

Appendix A, Key Concepts, gives key terms and their description.

Appendix B, Useful Functions, provides a list of key functions of the libraries, that can be used as a ready reference.

Appendix C, Online Resources, provides links for the reader to further explore the topics covered in the book.

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