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Mastering Julia

You're reading from   Mastering Julia Enhance your analytical and programming skills for data modeling and processing with Julia

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781805129790
Length 506 pages
Edition 2nd Edition
Languages
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Author (1):
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Malcolm Sherrington Malcolm Sherrington
Author Profile Icon Malcolm Sherrington
Malcolm Sherrington
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Table of Contents (14) Chapters Close

Preface 1. Chapter 1: The Julia Environment 2. Chapter 2: Developing in Julia FREE CHAPTER 3. Chapter 3: The Julia Type System 4. Chapter 4: The Three Ms 5. Chapter 5: Interoperability 6. Chapter 6: Working with Data 7. Chapter 7: Scientific Programming 8. Chapter 8: Visualization 9. Chapter 9: Database Access 10. Chapter 10: Networks and Multitasking 11. Chapter 11: Julia’s Back Pages 12. Index 13. Other Books You May Enjoy

Structured datasets

Structured datasets include simple delimited files, such as the familiar comma-separated-values (CSV), and files incorporating more descriptive metadata, such as XML and HDF5.

In this section, we will discuss the important topic of Julia’s DataFrames. This will be familiar to all R users. They are also implemented in Python via the pandas module.

CSV and other delimited (DLM) files

Data is often presented in table form as a series of rows representing individual records and fields corresponding to a data value for that particular record, rather than the relatively unstructured forms we have seen in the previous files.

Columns in the table are consistent, in the sense that they may all be integers, floats, dates, and so on, and are to be considered as the same “class” of data.

This might be familiar to you as it maps directly to the way data is held in a spreadsheet.

CSV file format

One of the oldest forms of representing...

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