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

Understanding attributes and their types

Data is the collection of raw facts and statistics such as numbers, words, and observations. An attribute is a column or data field or series that represents the characteristics of an object and is also known as a variable, a feature, or a dimension. Statisticians use the term variable, while machine learning engineers prefer the term feature. The term dimension is used in data warehousing, while database professionals use the term attribute.

Types of attributes

The data type of attributes is more crucial for data analysis because certain situations require certain data types. The data type of attributes helps analysts select the correct method for data analysis and visualization plots. The following list shows the various attributes:

  1. Nominal attributes: Nominal refers to names or labels of categorized variables. The value of a nominal attribute can be the symbol or name of items. The values are categorical, qualitative, and unordered in nature...