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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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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 (17) Chapters Close

Preface 1. Getting Started with Python Libraries 2. NumPy Arrays FREE CHAPTER 3. Statistics and Linear Algebra 4. pandas Primer 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
Index

Chapter 5. Retrieving, Processing, and Storing Data

Data can be found everywhere in all shapes and forms. We can get it from the Web, by e-mail and FTP, or create it ourselves in a lab experiment or marketing poll. An exhaustive overview of how to acquire data in various formats will require many more pages than what we have available. Sometimes, we need to store data before we can analyze it or after we are done with our analysis. We will also discuss storing data in this chapter. Chapter 8, Working with Databases, gives information about various databases (relational and NoSQL) and related APIs. The following is a list of the topics that we are going to cover in this chapter:

  • Writing CSV files with NumPy and pandas
  • The binary .npy and pickle formats
  • Reading and writing to Excel with pandas
  • JSON
  • REST web services
  • Parsing RSS feeds
  • Scraping the Web
  • Parsing HTML
  • Storing data with PyTables
  • HDF5 pandas I/O
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