Book Image

Python Data Visualization Cookbook (Second Edition)

Book Image

Python Data Visualization Cookbook (Second Edition)

Overview of this book

Python Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that will guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts. Python Data Visualization Cookbook starts by showing how to set up matplotlib and the related libraries that are required for most parts of the book, before moving on to discuss some of the lesser-used diagrams and charts such as Gantt Charts or Sankey diagrams. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python.
Table of Contents (16 chapters)
Python Data Visualization Cookbook Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Importance of autocorrelation


Autocorrelation represents the degree of similarity between a given time series and a lagged (that is, delayed in time) version of itself over successive time intervals. It occurs in time series studies when the errors associated with a given time period carry over into future time periods. For example, if we are predicting the growth of stock dividends, an overestimate in 1 year is likely to lead to overestimates in the succeeding years.

The time series analysis data arise in lots of different scientific applications and in lots of financial processes. Some of the examples include: generated reports of financial performance, prices over time, computing volatility, and others.

If we are analyzing unknown data, autocorrelation can help us detect if the data is random or not. For that, we can use a correlogram. It can help provide answers to questions such as: is the data random, is this time series data a white noise, is it sinusoidal, is it autoregressive, what...