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Python Data Analysis

Python Data Analysis

By : Ivan Idris
3.9 (16)
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Python Data Analysis

Python Data Analysis

3.9 (16)
By: Ivan Idris

Overview of this book

This book is for programmers, scientists, and engineers who have knowledge of the Python language and know the basics of data science. It is for those who wish to learn different data analysis methods using Python and its libraries. This book contains all the basic ingredients you need to become an expert data analyst.
Table of Contents (22 chapters)
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Python Data Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Key Concepts
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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