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Exploratory Data Analysis with Python Cookbook

You're reading from   Exploratory Data Analysis with Python Cookbook Over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data

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Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781803231105
Length 382 pages
Edition 1st Edition
Languages
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Author (1):
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Ayodele Oluleye Ayodele Oluleye
Author Profile Icon Ayodele Oluleye
Ayodele Oluleye
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Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Generating Summary Statistics 2. Chapter 2: Preparing Data for EDA FREE CHAPTER 3. Chapter 3: Visualizing Data in Python 4. Chapter 4: Performing Univariate Analysis in Python 5. Chapter 5: Performing Bivariate Analysis in Python 6. Chapter 6: Performing Multivariate Analysis in Python 7. Chapter 7: Analyzing Time Series Data in Python 8. Chapter 8: Analysing Text Data in Python 9. Chapter 9: Dealing with Outliers and Missing Values 10. Chapter 10: Performing Automated Exploratory Data Analysis in Python 11. Index 12. Other Books You May Enjoy

Preparing text data

Text data usually must go through some processing before it can be effectively analyzed. This is because text data is often messy. It could contain irrelevant information and sometimes isn’t in a structure that can be easily analyzed. Some common steps for preparing text data include the following:

  • Expanding contractions: A contraction is a shortened version of a word. It is formed by removing some letters and replacing them with apostrophes. Examples include don’t instead of do not, and would’ve instead of would have. Usually, when preparing text data, all contractions should be expanded to their original form.
  • Removing punctuations: Punctuations are useful for separating sentences, clauses, or phrases. However, they are mostly not needed for text analysis because they do not convey any significant meaning.
  • Converting to lowercase: Text data is typically a combination of capital and lowercase letters. However, this needs to...
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