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


In this chapter, we explored text analysis using NLTK and spaCy. The main focus was on text preprocessing, sentiment analysis, and text similarity. The chapter started with text preprocessing tasks such as text normalization, tokenization, removing stopwords, stemming, and lemmatization. We also focused on how to create a word cloud, recognize entities in a given text, and find dependencies among tokens. In later sections, we focused on BoW, TFIDF, sentiment analysis, and text classification.

The next chapter, Chapter 13, Analyzing Image Data, focuses on image processing, basic image processing operations, and face detection using OpenCV. The chapter starts with image color models, and image operations such as drawing on an image, resizing an image, and flipping and blurring an image. In later sections, the focus will be on face detection in a given input image.