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

Text normalization

Text normalization converts text into standard or canonical form. It ensures consistency and helps in processing and analysis. There is no single approach to the normalization process. The first step in normalization is the lower case all the text. It is the simplest, most applicable, and effective method for text pre-processing. Another approach could be handling wrongly spelled words, acronyms, short forms, and the use of out-of-vocabulary words; for example, "super," "superb," and "superrrr" can be converted into "super". Text normalization handles the noise and disturbance in test data and prepares noise-free data. We also apply stemming and lemmatization to normalize the words present in the text.

Let's perform a basic normalization operation by converting the text into lowercase:

# Input text
paragraph="""Taj Mahal is one of the beautiful monuments. It is one of the wonders of the world. It was built...