Book Image

Python Data Analysis - Third Edition

By : Avinash Navlani, Ivan Idris
5 (1)
Book Image

Python Data Analysis - Third Edition

5 (1)
By: Avinash Navlani, Ivan Idris

Overview of this book

Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you’ll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you’ll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You’ll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you’ll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you’ll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you’ll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.
Table of Contents (20 chapters)
Section 1: Foundation for Data Analysis
Section 2: Exploratory Data Analysis and Data Cleaning
Section 3: Deep Dive into Machine Learning
Section 4: NLP, Image Analytics, and Parallel Computing

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.
entities=[(i.text, i.label_) for i in docs.ents]

This results in the following output:

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