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

Defining cointegration

Cointegration is just like a correlation that can be viewed as a superior metric to define the relatedness of two time series. Cointegration is the stationary behavior of the linear combination of two time series. In this way, the trend of the following equation must be stationary:

y(t) - a x(t)

Consider a drunk man and his dog out on a walk. Correlation tells us whether they are going in the same direction. Cointegration tells us something about the distance over time between the man and his dog. We will show cointegration using randomly generated time-series and real data. The Augmented Dickey-Fuller (ADF) test tests for a unit root in a time series and can be used to determine the stationarity of time series.

Let's see an example to understand the cointegration of two time series.

You can check out the full code for this example at the following GitHub link: