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Solving the Traveling Salesperson Problem in Python

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1.What the Traveling Salesperson Problem Is About

Getting Started Introduction to the Traveling Salesperson Problem Background on the Traveling Salesperson Problem Understand Geospatial Data Quiz about the Traveling Salesperson Problem

2.Preprocessing of Traveling Salesperson Data

Import Data Inspect Data: Data Analysis Inspect Data: Plotting Test Your Data Inspection Knowledge Manipulate Data Test Your Data Manipulation Knowledge

3.Solving the Traveling Salesperson Problem

Line Distance Measurement Test Your Distance Knowledge Measurement of Road Distance Traversing Data Exploring openpyxl Road Distance: Google Maps Shortest Overall Distance Brute Force Genetic Algorithm Traveling Salesperson Problem: Package Operations Research Tools User Form: Select Start Point

4.Traveling Salesperson Data Mining

Network Graph Test Your Network Graph Knowledge Data Clustering Key Performance Indicator Data Enrichment Unlocking Hidden Patterns Enhancing Interactivity Storytelling

5.Building the Traveling Salesperson Dashboard

Dashboard: Shortest Route Dashboard: Shortest Route and Data Mining

6.Scalability

Handling Large-Scale Traveling Salesperson Problems Cloud-Powered TSP

7.Conclusion

8.Appendix

Python and Development Environment Data Import: Different Data File Types Python Data Structures Error Handling PyInstaller: Bundle Python Applications Dockerizing the Application Dashboard Cloud Deployment OpenStreetMap (OSM): An Alternative to Commercial Maps Useful Tools

Project

Use Simulated Annealing for the Traveling Salesman Problem

Solving the Traveling Salesperson Problem in Python

Test Your Data Manipulation Knowledge

Learn to use list and zip for geocoding.

We'll cover the following...

Reverse geocoding

In the previous code widget, we derived geom using map(str).

Python 3.8

df['geom'] = df['lat'].map(str) + ',' + df['lon'].map(str)

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