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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

Checking sentiments

When analyzing text data, we may be interested in understanding and analyzing the sentiments conveyed in text. Sentiment analysis helps us to achieve this. It helps to identify the emotional tone expressed within our text. This emotional tone or expressed sentiment is typically classified as a positive, negative, or neutral sentiment. Sentiment analysis is very useful because it typically yields actionable insights about a specific topic that our text covers.

Let’s check out some examples:

  • I really like the dress
  • I didn’t get value for my money
  • I paid for the dress

By analyzing each text, we can easily identify that the first text expresses a positive sentiment, the second expresses a negative sentiment, and the last text expresses a neutral sentiment. In terms of the insights gleaned, the first points to a satisfied customer, while the second points to a dissatisfied customer who likely needs intervention. When performing...

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