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Julia for Data Science

You're reading from   Julia for Data Science high-performance computing simplified

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785289699
Length 346 pages
Edition 1st Edition
Languages
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Author (1):
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Anshul Joshi Anshul Joshi
Author Profile Icon Anshul Joshi
Anshul Joshi
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Table of Contents (12) Chapters Close

Preface 1. The Groundwork – Julia's Environment 2. Data Munging FREE CHAPTER 3. Data Exploration 4. Deep Dive into Inferential Statistics 5. Making Sense of Data Using Visualization 6. Supervised Machine Learning 7. Unsupervised Machine Learning 8. Creating Ensemble Models 9. Time Series 10. Collaborative Filtering and Recommendation System 11. Introduction to Deep Learning

What is a DataFrame?

A DataFrame is a data structure that has labeled columns, which individually may 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 have all 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. DataArray provided in DataFrames.jl provides such features (for example, if we required to store in an array some missing values).
  • DataFrame: DataFrame is 2-D data structure, like spreadsheets. It is much like R or pandas's DataFrames, and...
You have been reading a chapter from
Julia for Data Science
Published in: Sep 2016
Publisher: Packt
ISBN-13: 9781785289699
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