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

Dependency parsing

Dependency parsing finds the relationship among words – how words are related to each other. It helps computers to understand sentences for analysis; for example, "Taj Mahal is one of the most beautiful monuments." We can't understand this sentence just by analyzing words. We need to dig down and understand the word order, sentence structure, and parts of speech:

# Import spacy
import spacy

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

# Create nlp Object to handle linguistic annotations in a documents.

# Visualize the using render function
displacy.render(docs, style="dep", jupyter= True, options={'distance': 150})

This results in the following output:

In the preceding example, we have imported the display class and called its render() method with a NLP text object, style as 'dep', jupyter as True, and options as a dictionary with a distance key and a value...