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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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Toc

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

Performing Topic Modeling

Topic modeling helps us identify the underlying themes or topics within a text. When applied to a corpus, we can easily identify some of the most common topics or themes it covers. Imagine we have a large set of documents, such as news articles, customer reviews or blog posts. The topic modeling algorithms analyzes the words and patterns within the documents to identify recurring themes or topics. It does this by assuming each topic is characterized by a set of words that frequently occur together. By checking the frequency of words which occur together, the algorithm can identify the most likely topics or themes present in a corpus.

Let's see an example with some documents:

Document 1:

"The government needs to consider implementing policies which will stimulate economic growth. The country needs policies aimed at creating jobs and improving overall infrastructure."

Document 2:

"I enjoyed watching the football match over...

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