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Python Data Analysis, Second Edition

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

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787127487
Length 330 pages
Edition 2nd Edition
Languages
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (16) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 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...
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