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Python Data Analysis - Third Edition

You're reading from  Python Data Analysis - Third Edition

Product type Book
Published in Feb 2021
Publisher Packt
ISBN-13 9781789955248
Pages 478 pages
Edition 3rd Edition
Languages
Authors (2):
Avinash Navlani Avinash Navlani
Profile icon Avinash Navlani
Ivan Idris Ivan Idris
Profile icon Ivan Idris
View More author details
Toc

Table of Contents (20) Chapters close

Preface 1. Section 1: Foundation for Data Analysis
2. Getting Started with Python Libraries 3. NumPy and pandas 4. Statistics 5. Linear Algebra 6. Section 2: Exploratory Data Analysis and Data Cleaning
7. Data Visualization 8. Retrieving, Processing, and Storing Data 9. Cleaning Messy Data 10. Signal Processing and Time Series 11. Section 3: Deep Dive into Machine Learning
12. Supervised Learning - Regression Analysis 13. Supervised Learning - Classification Techniques 14. Unsupervised Learning - PCA and Clustering 15. Section 4: NLP, Image Analytics, and Parallel Computing
16. Analyzing Textual Data 17. Analyzing Image Data 18. Parallel Computing Using Dask 19. Other Books You May Enjoy

Recognizing entities

Entity recognition means extracting or detecting entities in the given text. It is also known as Named Entity Recognition (NER). An entity can be defined as an object, such as a location, people, an organization, or a date. Entity recognition is one of the advanced topics of NLP. It is used to extract important information from text.

Let's see how to get entities from text using spaCy:

# Import spacy
import spacy

# Load English model for tokenizer, tagger, parser, and NER
nlp = spacy.load('en')

# Sample paragraph
paragraph = """Taj Mahal is one of the beautiful monuments. It is one of the wonders of the world. It was built by Shah Jahan in 1631 in memory of his third beloved wife Mumtaj Mahal."""

# Create nlp Object to handle linguistic annotations in documents.
docs=nlp(paragraph)
entities=[(i.text, i.label_) for i in docs.ents]
print(entities)

This results in the following output:

[('Taj Mahal', 'PERSON'),...
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