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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 (22) Chapters Close

Python Data Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
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 Key Concepts
Useful Functions Online Resources
Index

Summary


In this chapter, we discussed visualizing data with Python by plotting. We used matplotlib, pandas, and Plot.ly. We covered box plots, scatter plots, bubble charts, logarithmic plots, autocorrelation plots, lag plots, three-dimensional plots, legends, and annotations.

Logarithmic plots (or log plots) are plots that use a logarithmic scale. The semi-log plots use linear scaling on one axis and logarithmic scaling on the other axis. Scatter plots plot two variables against each other. A bubble chart is a special type of scatter plot. In a bubble chart, the value of a third variable is relatively represented by the size of the bubble surrounding a data point. Autocorrelation plots graph autocorrelations of time series data for different lags. A box plot visualizes data based on the data's quartiles.

The next chapter, Chapter 7, Signal Processing and Time Series is about a special type of data: time series. Time series are ordered data points that have been timestamped. A lot of the physical...

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