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Julia 1.0 Programming

You're reading from   Julia 1.0 Programming Dynamic and high-performance programming to build fast scientific applications

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Product type Paperback
Published in Sep 2018
Publisher
ISBN-13 9781788999090
Length 196 pages
Edition 2nd Edition
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Author (1):
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Ivo Balbaert Ivo Balbaert
Author Profile Icon Ivo Balbaert
Ivo Balbaert
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Table of Contents (12) Chapters Close

Preface 1. Installing the Julia Platform FREE CHAPTER 2. Variables, Types, and Operations 3. Functions 4. Control Flow 5. Collection Types 6. More on Types, Methods, and Modules 7. Metaprogramming in Julia 8. I/O, Networking, and Parallel Computing 9. Running External Programs 10. The Standard Library and Packages 11. Other Books You May Enjoy

Using DataFrames


If you measure n variables (each of a different type) of a single object, then you get a table with n columns for each object row. If there are m observations, then we have m rows of data. For example, given the student grades as data, you might want to know compute the average grade for each socioeconomic group, where grade and socioeconomic group are both columns in the table, and there is one row per student.

DataFrame is the most natural representation to work with such a (m x n) table of data. They are similar to Pandas DataFrames in Python or data.frame in R. DataFrame is a more specialized tool than a normal array for working with tabular and statistical data, and it is defined in the DataFrames package, a popular Julia library for statistical work. Install it in your environment by typing in add DataFrames in the REPL. Then, import it into your current workspace with using DataFrames. Do the same for the DataArrays and RDatasets packages (which contain a collection...

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