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Frank Kane's Taming Big Data with Apache Spark and Python

You're reading from   Frank Kane's Taming Big Data with Apache Spark and Python Real-world examples to help you analyze large datasets with Apache Spark

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787287945
Length 296 pages
Edition 1st Edition
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Author (1):
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Frank Kane Frank Kane
Author Profile Icon Frank Kane
Frank Kane
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Table of Contents (8) Chapters Close

Preface 1. Getting Started with Spark FREE CHAPTER 2. Spark Basics and Spark Examples 3. Advanced Examples of Spark Programs 4. Running Spark on a Cluster 5. SparkSQL, DataFrames, and DataSets 6. Other Spark Technologies and Libraries 7. Where to Go From Here? – Learning More About Spark and Data Science

Improving the word-count script with regular expressions


The main problem with the initial results from our word-count script is that we didn't account for things such as punctuation and capitalization. There are fancy ways to deal with that problem in text processing, but we're going to use a simple way for now. We'll use something called regular expressions in Python. So let's look at how that works, then run it and see it in action.

Text normalization

In the previous section, we had a first crack at counting the number of times each word occurred in our book, but the results weren't that great. We had each individual word that had different capitalization or punctuation surrounding it being counted as a word of its own, and that's not what we want. We want each word to be counted only once, no matter how it's capitalized or what punctuation might surround it. We don't want duplicate words showing up in there. There are toolkits you can get for Python such as NLTK (Natural Language Toolkit...

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