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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

Analyzing ngrams

An n-gram is a continuous sequence of n items of a given text. These items can be words, letters, or syllables. N-grams help us extract useful information about the distribution of words, syllables, or letters within a given text. The n stands for positive numerical values, starting from 1 to n. The most common n-grams are unigram, bigram, and trigram, where n is 1, 2, and 3 respectively.

Analyzing n-grams involves checking the frequency or distribution of an n-gram within a text. We typically split the text into the respective n-gram and count the frequency of each one in the text data. This will help us identify the most common words, syllables, or phrases in our data.

For example, in the sentence “The boy threw the ball,” the n-grams would be as follows:

  • 1-gram (or unigram): ["The", "boy", "threw", "the", "ball"]
  • 2-gram (or bigram): ["The boy", "boy threw", &quot...
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