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Data Analysis with Python

You're reading from   Data Analysis with Python A Modern Approach

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789950069
Length 490 pages
Edition 1st Edition
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Author (1):
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David Taieb David Taieb
Author Profile Icon David Taieb
David Taieb
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Table of Contents (14) Chapters Close

Preface 1. Programming and Data Science – A New Toolset FREE CHAPTER 2. Python and Jupyter Notebooks to Power your Data Analysis 3. Accelerate your Data Analysis with Python Libraries 4. Publish your Data Analysis to the Web - the PixieApp Tool 5. Python and PixieDust Best Practices and Advanced Concepts 6. Analytics Study: AI and Image Recognition with TensorFlow 7. Analytics Study: NLP and Big Data with Twitter Sentiment Analysis 8. Analytics Study: Prediction - Financial Time Series Analysis and Forecasting 9. Analytics Study: Graph Algorithms - US Domestic Flight Data Analysis 10. The Future of Data Analysis and Where to Develop your Skills A. PixieApp Quick-Reference Other Books You May Enjoy Index

Introduction to graphs


The introduction of graphs and the associated graph theory is widely attributed to Leonhard Euler in 1736 when he worked on the problem of the Seven Bridges of Königsberg (https://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg).

The city was divided by the Pregel river which at some point formed two islands, and seven bridges were built according to the layout shown in the following diagram. The problem was to find a way for a person to walk across each and every bridge once and only once and come back to the starting point. Euler proved that there was no solution to this problem and while doing this gave birth to graph theory. The fundamental idea was to transform the city diagram into a graph where each land mass is a vertex, and each bridge is an edge that linked two vertices (that is, land mass). The problem was then reduced to finding a path, which is a continuous sequence of edges and vertices, that contains each and every bridge only once.

The following...

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