Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Python Data Analysis, Second Edition

You're reading from   Python Data Analysis, Second Edition Data manipulation and complex data analysis with Python

Arrow left icon
Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787127487
Length 330 pages
Edition 2nd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Arrow right icon
View More author details
Toc

Table of Contents (16) Chapters Close

Preface 1. Getting Started with Python Libraries 2. NumPy Arrays FREE CHAPTER 3. The Pandas Primer 4. Statistics and Linear Algebra 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources

Summary


In this chapter, the time series examples used annual sunspot cycles data.

You learned that it's common to try to derive a relationship between a value and another data point or combination of data points a fixed number of periods in the past, in the same time series.

A moving average specifies a window of previously seen data, which is averaged each time the window slides forward by one period. In the Pandas API, the DataFrame.rolling() function provides the window functions functionality with different values of the win_type string parameter corresponding to different window functions.

Cointegration is similar to correlation and is a metric to define the relatedness of two time series. In regression setups, we frequently encounter the problem of overfitting. This issue arises when we have a perfect fit for a sample, which performs poorly when we introduce new data points. To evaluate a model, we can compute appropriate evaluation metrics.

Databases are an important tool for data analysis...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image