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Practical Data Analysis Using Jupyter Notebook

You're reading from   Practical Data Analysis Using Jupyter Notebook Learn how to speak the language of data by extracting useful and actionable insights using Python

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Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781838826031
Length 322 pages
Edition 1st Edition
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Author (1):
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Marc Wintjen Marc Wintjen
Author Profile Icon Marc Wintjen
Marc Wintjen
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Data Analysis Essentials
2. Fundamentals of Data Analysis FREE CHAPTER 3. Overview of Python and Installing Jupyter Notebook 4. Getting Started with NumPy 5. Creating Your First pandas DataFrame 6. Gathering and Loading Data in Python 7. Section 2: Solutions for Data Discovery
8. Visualizing and Working with Time Series Data 9. Exploring, Cleaning, Refining, and Blending Datasets 10. Understanding Joins, Relationships, and Aggregates 11. Plotting, Visualization, and Storytelling 12. Section 3: Working with Unstructured Big Data
13. Exploring Text Data and Unstructured Data 14. Practical Sentiment Analysis 15. Bringing It All Together 16. Works Cited
17. Other Books You May Enjoy

Counting words and exploring results

Counting word frequency will provide initial metadata about the unstructured source text. Exposing the count of the occurrence of a word or when specific words are missing within a body of text is known as text mining. Text mining will provide analytics about the data, so a data analyst can determine the value of a data asset along with how it can be used to answer business questions. Likewise, you can identify keyword patterns that occur during unexpected outages that impact users by looking at application system logs. Once those words or phrases are identified, you can work with the developers to identify the root cause and reduce the impact on your application users.

A popular option available for text analysis is the use of regular expressions or regex for short. The regex concept is when you use a combination of rules and search patterns to extract features from very large, unstructured text. Regex becomes useful when reading the...

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