ericmjl

UPDATE: I have baked the ideas in this file inside a Python CLI tool called pyds-cli. Please find it here: https://github.com/ericmjl/pyds-cli

How to organize your Python data science project

Having done a number of data projects over the years, and having seen a number of them up on GitHub, I've come to see that there's a wide range in terms of how "readable" a project is. I'd like to share some practices that I have come to adopt in my projects, which I hope will bring some organization to your projects.

Disclaimer: I'm hoping nobody takes this to be "the definitive guide" to organizing a data project; rather, I hope you, the reader, find useful tips that you can adapt to your own projects.

Disclaimer 2: What I’m writing below is primarily geared towards Python language users. Some ideas may be transferable to other languages; others may not be so. Please feel free to remix whatever you see here!

tasdikrahman

A Typical directory structure for running tests using unittest

Ref : stackoverflow

The best solution in my opinion is to use the unittest [command line interface][1] which will add the directory to the sys.path so you don't have to (done in the TestLoader class).

For example for a directory structure like this:

new_project

├── antigravity.py

spalladino

# Backup
docker exec CONTAINER /usr/bin/mysqldump -u root --password=root DATABASE > backup.sql

# Restore
cat backup.sql | docker exec -i CONTAINER /usr/bin/mysql -u root --password=root DATABASE

raymondchua

import java.util.PriorityQueue;
import java.util.List;
import java.util.ArrayList;
import java.util.Collections;



public class DijkstraAlgo{
/* Dijkstra Algorithm
 *