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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Tools
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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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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 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
Index

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 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 lag_plot.py 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=True, right_index=True)
df = df.replace(np.nan, 0)
lag_plot(np.log(df['trans_count']))...
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