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Julia for Data Science
Julia for Data Science

Julia for Data Science: high-performance computing simplified

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Julia for Data Science

Chapter 2. Data Munging

It is said that around 50% of the data scientist's time goes into transforming raw data into a usable format. Raw data can be in any format or size. It can be structured like RDBMS, semi-structured like CSV, or unstructured like regular text files. These contain some valuable information. And to extract that information, it has to be converted into a data structure or a usable format from which an algorithm can find valuable insights. Therefore, usable format refers to the data in a model that can be consumed in the data science process. This usable format differs from use case to use case.

This chapter will guide you through data munging, or the process of preparing the data. It covers the following topics:

  • What is data munging?
  • DataFrames.jl
  • Uploading data from a file
  • Finding the required data
  • Joins and indexing
  • Split-Apply-Combine strategy
  • Reshaping the data
  • Formula (ModelFrame and ModelMatrix)
  • PooledDataArray
  • Web scraping

What is data munging?

Munging comes from the term "munge," which was coined by some students of Massachusetts Institute of Technology, USA. It is considered one of the most essential parts of the data science process; it involves collecting, aggregating, cleaning, and organizing the data to be consumed by the algorithms designed to make discoveries or to create models. This involves numerous steps, including extracting data from the data source and then parsing or transforming the data into a predefined data structure. Data munging is also referred to as data wrangling.

The data munging process

So what's the data munging process? As mentioned, data can be in any format and the data science process may require data from multiple sources. This data aggregation phase includes scraping it from websites, downloading thousands of .txt or .log files, or gathering the data from RDBMS or NoSQL data stores.

It is very rare to find data in a format that can be used directly by the data...

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...

Summary

In this chapter, we learned what data munging is and why it is necessary for data science. Julia provides functionalities to facilitate data munging with the DataFrames.jl package, with features such as these:

  • NA: A missing value in Julia is represented by a specific data type, NA.
  • DataArray: DataArray provided in the DataFrames.jl provides features such as allowing us to store some missing values in an array.
  • DataFrame: DataFrame is 2-D data structure like spreadsheets. It is very similar to R or pandas's dataframes, and provides many functionalities to represent and analyze data. DataFrames has many features well suited for data analysis and statistical modeling.
  • A dataset can have different types of data in different columns.
  • Records have a relation with other records in the same row of different columns of the same length.
  • Columns can be labeled. Labeling helps us to easily become familiar with the data and access it without the need to remember their numerical indices.

We learned...

References

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Key benefits

  • An in-depth exploration of Julia's growing ecosystem of packages
  • Work with the most powerful open-source libraries for deep learning, data wrangling, and data visualization
  • Learn about deep learning using Mocha.jl and give speed and high performance to data analysis on large data sets

Description

Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century). This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game. This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning. This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia.

Who is this book for?

This book is aimed at data analysts and aspiring data scientists who have a basic knowledge of Julia or are completely new to it. The book also appeals to those competent in R and Python and wish to adopt Julia to improve their skills set in Data Science. It would be beneficial if the readers have a good background in statistics and computational mathematics.

What you will learn

  • Apply statistical models in Julia for data-driven decisions
  • Understanding the process of data munging and data preparation using Julia
  • Explore techniques to visualize data using Julia and D3 based packages
  • Using Julia to create self-learning systems using cutting edge machine learning algorithms
  • Create supervised and unsupervised machine learning systems using Julia. Also, explore ensemble models
  • Build a recommendation engine in Julia
  • Dive into Julia's deep learning framework and build a system using Mocha.jl

Product Details

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Publication date : Sep 30, 2016
Length: 346 pages
Edition : 1st
Language : English
ISBN-13 : 9781785289699
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Publication date : Sep 30, 2016
Length: 346 pages
Edition : 1st
Language : English
ISBN-13 : 9781785289699
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Concepts :

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Table of Contents

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

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RareComplexCollectionOfMatter Aug 17, 2020
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Good
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Deepankar A. Jan 25, 2017
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The way the book is written is really amazing with various practical examples. This actually gives a good insights of how to use Julia with Data Science. The language is easy by comparing the complexity the book is dealing with so it is easy for starters to start with. It is a must read if one is adopting Julia as a language for Data Science.
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Amazon Customer Dec 27, 2016
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I would definitely recommend this book. I have worked on many projects in the past and have used Python,R and Scala, this book has added an entire new area for me to work on. It is well structured and was easy to go through, a good job by the author.
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Amazon Customer Dec 22, 2016
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Learning a new language is always a difficult task, but this book has covered all the crucial topics and thus makes learning very smooth and easy.
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Rahul Dec 17, 2017
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As someone who knew nothing about data science and machine learning models, this book proves to be a great asset for people looking out for serious and immersive content over the topic. The author explains each and every topic in detail and uses Julia code, which btw is something that every modern data scientist should be looking out for. Overall, this is a great book and I would highly recommend it.
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