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
Conferences
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

Lag plots


A lag plot is a scatter plot for a time series and the same data lagged. With such a plot, we can check whether there is a possible correlation between CPU transistor counts this year and the counts of the previous year, for instance. The lag_plot() Pandas function in pandas.tools.plotting can draw a lag plot. Draw a lag plot with the default lag of 1 for the CPU transistor counts, as follows:

lag_plot(np.log(df['trans_count'])) 

Refer to the following plot for the end result:

The following code for the lag plot example can also be found in the ch-06.ipynb file in this book's code bundle:

import matplotlib.pyplot as plt 
import numpy as np 
import pandas as pd 
from pandas.tools.plotting import lag_plot 
 
df = pd.read_csv('transcount.csv') 
df = df.groupby('year').aggregate(np.mean) 
 
gpu = pd.read_csv('gpu_transcount.csv') 
gpu = gpu.groupby('year').aggregate(np.mean) 
 
df = pd.merge(df, gpu, how='outer', left_index...
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