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Query: Getting Information from Data with the Wolfram Language

by Seth J. Chandler (Author)
5.0 5.0 out of 5 stars 6 ratings
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Mathematica and the Wolfram Language provide an integrated environment for sophisticated programming, making them the ideal tools for calculus, data visualization, machine learning, and more. But how about using Wolfram as the linchpin for organizing data? In this book, Professor Seth J. Chandler, an award-winning, 30-year user of the Wolfram Language, guides intermediate users from simple cases to data in the wild and shows how each can be fed into the full range of Wolfram Language functionality. It's both a conceptual and practical approach filled with annotated examples.

Want to know more about associations or the organization and presentation of datasets? This book shows how it's done. Want to organize or reduce data in the Wolfram Language similarly to the way one might use SQL, pandas in Python, or dplyr in R's tidyverse? This book shows how to simplify data processing and go beyond what is possible with other tools using the Query function. It also explores how to import information from the places real-world data is most likely to be found: CSV, Excel, or JSON files.

Use Query both as a tutorial and as a reference. Read it cover-to-cover doing the exercises or dip in to look up specific data-wrangling tasks. Augmenting the underlying power of the Wolfram Language with the techniques taught in this book puts you at the forefront of innovative data analysis.

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  1. ISBN-10
    1579550851
  2. ISBN-13
    978-1579550851
  3. Publisher
    Wolfram Media, Inc.
  4. Publication date
    October 3, 2023
  5. Language
    English
  6. Dimensions
    7 x 0.9 x 9.9 inches
  7. Print length
    368 pages
  8. See all details
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Editorial Reviews

About the Author

Seth J. Chandler is a Law Foundation Professor of Law at the University of Houston Law Center (UHLC). He maintains a dual life as a law professor, teaching subjects such as constitutional law and insurance law, and as an expert on the Wolfram Language, presenting at numerous Wolfram conferences and winning a Wolfram Innovator Award in 2011. He has combined both of his passions in the course Analytic Methods for Lawyers, which is taught at UHLC and is available via his YouTube channel (@sethjchandler). He has made many contributions to the Wolfram Demonstrations Project, Function Repository, and Data Repository and to Wolfram Community. Professor Chandler received his undergraduate degree in 1979 from Princeton University and his Juris Doctorate in 1983 from Harvard Law School, where he served as Managing Editor of the Harvard Law Review. His knowledge of math and of the Wolfram Language is generally self-taught, much of it from listening to and reading the works of Stephen Wolfram and others. He has never taken a college-level programming course; the last math course on his transcript is a linear algebra class from 1975.

Product details

  • Publisher ‏ : ‎ Wolfram Media, Inc. (October 3, 2023)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 368 pages
  • ISBN-10 ‏ : ‎ 1579550851
  • ISBN-13 ‏ : ‎ 978-1579550851
  • Item Weight ‏ : ‎ 1.65 pounds
  • Dimensions ‏ : ‎ 7 x 0.9 x 9.9 inches
  • Customer Reviews:
    5.0 5.0 out of 5 stars 6 ratings

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Customer reviews

5 out of 5 stars
5 out of 5
6 global ratings

Top reviews from the United States

Kambiz Homayounfar
5.0 out of 5 stars A golden egg from an odd duck
Reviewed in the United States on November 23, 2023
Verified Purchase
This book helps to liberate your computation from messy (unstructured) data sources and teaches you how to clean up the data you get from public sites or open-source projects.

Chandler highlights the three expressions (Association, Query, and Dataset) in the Wolfram Language (WL, aka Mathematica) to analyze data.
Association is a tool for giving structure to data (like Python's dictionaries or Java's objects).
Query is your Swiss Army knife for data manipulation. You can use it to probe all nooks and crannies of your data, extract specific parts, or rearrange it in various ways.
Dataset organizes and analyzes structured data in a tabular format, like a spreadsheet (whereas Association is a key-value pair structure for storing and accessing individual elements based on unique keys.)

After a customary overview in Chapter 1, the book flows as follows.

Chapter 2 teaches WL programming at an intermediate level. You’ll learn how to put to good use functions like MapIndexed, ReverseSortBy, GatherBy.
Chandler devotes chapters 3, 4, and 5 to three expressions: Association, Query, and Dataset.
Chapter 6 provides basic recipes for using the Wolfram Data Repository: a platform for accessing (or publishing your own} curated data in diverse fields, from agriculture to transportation.
Chapter 7 provides advanced recipes: these are sophisticated models with many valuable tips on handling missing data.
Chapter 8 is about importing and exporting data generated other computer languages, such as Excel, Tableau, Julia, Python, R, C, or Google Sheets. Chandler calls these “wild” data. I find Chapter 8 to be the most helpful part of the book.
Chapter 9 gives the answers to exercises in previous chapters and helps you to confirm your own solutions.

I've been a fan of Seth Chandler's work on Mathematica since 2011, when I came across his contribution to Wolfram Demonstrations, where he developed a model of Shakespearean Networks. Therein, Chandler provided the structure of eight of Bard's famous plays by creating links between characters who speak during each scene. My background (former life) is in computational physics, so Chandler's work opened my eyes to WL's utility beyond purely numerical calculations. Since then, I've benefitted from his contributions to insurance law and microeconomics, and I now use WL extensively for quantitative analysis of venture capital projects.

To decide if the book will be helpful, ask how much of your time (and money) is spent on getting good data. In machine learning projects, approximately 60% to 80% of the time goes to data collection and cleaning. This involves sourcing relevant data, resolving inconsistencies, and preprocessing it into a usable format. The quality of your data directly influences your model's accuracy and effectiveness.
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Surbiton
5.0 out of 5 stars A Readable Textbook
Reviewed in the United States on January 18, 2024
Verified Purchase
The book gives a good framework of using Query function. The Query function simplifies the code for many data tasks. It also provides good examples of presenting data. Another useful feature is small exercises with solutions so that you may internalize the concepts that are introduced. Using Lists and standard commands, my code is unwieldy, Query (along with Association and other "data functions") tames the dataset.
Query function is sufficiently outlined in Wolfram documentation, this book provides examples and exercises. I think the content could be an excellent Wolfram U course.
Definitely worth the time invested so far.
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Top reviews from other countries

ShoreRoadMan
5.0 out of 5 stars Should be a "the missing manual" book. Absolutely, without peer, fantastic.
Reviewed in Canada on December 8, 2023
Verified Purchase
I have been a hobbyist user of Mathematica since version 3, circa 1996. With version 10 and the emphasis on the Wolfram Language (circa 2013-14), Wolfram introduced the Entity, Dataset, Query and so much more. To my mind these changes ushered in a monumental shift toward seamless computational knowledge. And this continues unabated to today.

However, unlearning the habits of eight years of pre-version 10 programming was difficult for me and a much as I tried I eventually defaulted back to Lists, standard function composition, and nested lists of functions for my hobby programing endeavors. I often felt left behind as I tried to decipher and mimic the code of more accomplished/sophisticated WL users. Until today, that was a stretch for me.

This book opens wide an access to the rich post-Mathematica 10 WL revolution. Whereas I used to often forsake Mathematica for the TidyVerse of R or the Pandas realm of Python to munge datasets into a form amenable for statistical analysis (my real hobby); this is no longer necessary!

Only today, I used my first Query of NFLVerse play-by-play data to replicate a Tidyverse R query in Mathematica. And, my code was terser, as 'user-friendly', and as fast (using a CSV file) as R code.

I simply cannot praise this book enough. And while you are at it check out Seth Chandler's YouTube lectures on Mathematica on Analytic Methods for Lawyers.

Query Dataset with Right Composition pipelines that make the hard work of data munging fun and accessible while at the same time building you competence for the computational knowledge future.

Invest in this book. It is a gem.
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