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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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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

Logarithmic plots


Logarithmic plots (or log plots) are plots that use a logarithmic scale. A logarithmic scale shows the value of a variable which uses intervals that match orders of magnitude, instead of a regular linear scale. There are two types of logarithmic plots. The log-log plot employs logarithmic scaling on both axes and is represented in matplotlib by the matplotlib.pyplot.loglog() function. The semi-log plots use linear scaling on one axis and logarithmic scaling on the other axis. These plots are represented in the matplotlib API by the semilogx() and semilogy() functions. On log-log plots, power laws appear as straight lines. On semi-log plots, straight lines represent exponential laws.

Moore's law is such a law. It's not a physical, but more of an empirical observation. Gordon Moore discovered a trend of the number of transistors in integrated circuits doubling every two years. On http://en.wikipedia.org/wiki/Transistor_count#Microprocessors, a table can be found with transistor...

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