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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

Dealing with stop words

Stop words are words that occur very frequently in a language. They generally do not add significant meaning to the text. Some common stop words include pronouns, prepositions, conjunctions, and articles. In the English language, examples of stop words include a, an, the, and, is, was, of, for, and not. This list may vary based on the language or context.

Before analyzing text, we should remove stop words so that we can focus on more relevant words in the text. Stop words typically do not have significant information and can cause noise within our dataset. Therefore, removing them helps us find insights easily and focus on what is most relevant.

However, the removal of stop words is highly dependent on the goal of our analysis and the type of task we perform. For example, the outcome of a sentiment analysis task can be misleading due to the removal of key stop words. This is highlighted here:

  • Sample sentence: The food was not great.
  • Sample...
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