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Learning Julia

You're reading from   Learning Julia Build high-performance applications for scientific computing

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781785883279
Length 316 pages
Edition 1st Edition
Languages
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Authors (2):
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Rahul Lakhanpal Rahul Lakhanpal
Author Profile Icon Rahul Lakhanpal
Rahul Lakhanpal
Anshul Joshi Anshul Joshi
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Anshul Joshi
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Table of Contents (11) Chapters Close

Preface 1. Understanding Julia's Ecosystem FREE CHAPTER 2. Programming Concepts with Julia 3. Functions in Julia 4. Understanding Types and Dispatch 5. Working with Control Flow 6. Interoperability and Metaprogramming 7. Numerical and Scientific Computation with Julia 8. Data Visualization and Graphics 9. Connecting with Databases 10. Julia’s Internals

Understanding DataFrames


A DataFrame is a data structure that has labeled columns, which may individually have different data types. Like a SQL table or a spreadsheet, it has two dimensions. It can also be thought of as a list of dictionaries, but fundamentally, it is different.

DataFrames are the recommended data structure for statistical analysis. Julia provides a package called DataFrames.Jl, which has all the necessary functions to work with DataFrames.

Julia's package, DataFrames, provides three data types:

  • NA: A missing value in Julia is represented by a specific data type, NA.
  • DataArray: The array type defined in the standard Julia library, though it has many features, doesn't provide any specific functionalities for data analysis. The DataArray type provided in DataFrames.jl provides such features (for example if we needed to store some missing values in the array).
  • DataFrame: This is a two-dimensional data structure, such as spreadsheets. It is much like R or Pandas DataFrames and provides...
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