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Mastering pandas

You're reading from   Mastering pandas A complete guide to pandas, from installation to advanced data analysis techniques

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Product type Paperback
Published in Oct 2019
Publisher
ISBN-13 9781789343236
Length 674 pages
Edition 2nd Edition
Languages
Tools
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Author (1):
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Ashish Kumar Ashish Kumar
Author Profile Icon Ashish Kumar
Ashish Kumar
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Table of Contents (21) Chapters Close

Preface 1. Section 1: Overview of Data Analysis and pandas FREE CHAPTER
2. Introduction to pandas and Data Analysis 3. Installation of pandas and Supporting Software 4. Section 2: Data Structures and I/O in pandas
5. Using NumPy and Data Structures with pandas 6. I/Os of Different Data Formats with pandas 7. Section 3: Mastering Different Data Operations in pandas
8. Indexing and Selecting in pandas 9. Grouping, Merging, and Reshaping Data in pandas 10. Special Data Operations in pandas 11. Time Series and Plotting Using Matplotlib 12. Section 4: Going a Step Beyond with pandas
13. Making Powerful Reports In Jupyter Using pandas 14. A Tour of Statistics with pandas and NumPy 15. A Brief Tour of Bayesian Statistics and Maximum Likelihood Estimates 16. Data Case Studies Using pandas 17. The pandas Library Architecture 18. pandas Compared with Other Tools 19. A Brief Tour of Machine Learning 20. Other Books You May Enjoy

Reading feather files

The feather format is a binary file format for storing data that makes use of Apache Arrow, an in-memory columnar data structure. It was developed by Wes Mckinney and Hadley Wickham, chief scientists at RStudio as an initiative for a data sharing infrastructure across Python and R. The columnar serialization of data in feather files makes way for efficient read and write operations, making it far faster than CSV and JSON files where storage is record-wise.

Feather files have the following features:

  • Fast I/O operations.
  • Feather files can be read and written in languages other than R or Python, such as Julia and Scala.
  • They have compatibility with all pandas datatypes, such as Datetime and Categorical.

Feather currently supports the following datatypes:

  • All numeric datatypes
  • Logical
  • Timestamps
  • Categorical
  • UTF-8 encoded strings
  • Binary

Since feather is merely...

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