Here's all the code and examples from my book Data Science from Scratch. The code directory contains Python 2.7 versions, and the code-python3 direction contains the Python 3 equivalents. (I tested them in 3.5, but they should work in any 3.x.)

July 2018: I am currently working on the second edition. It will be based on Python 3.6, will have much cleaner code, and will contain expanded coverage of deep learning, NLP, and whatever else I feel like adding. Stay tuned.

Each can be imported as a module, for example (after you cd into the /code directory):

from linear_algebra import distance, vector_mean
v = [1, 2, 3]
w = [4, 5, 6]
print distance(v, w)
print vector_mean([v, w])

Or can be run from the command line to get a demo of what it does (and to execute the examples from the book):

python recommender_systems.py

Additionally, I've collected all the links from the book.

And, by popular demand, I made an index of functions defined in the book, by chapter and page number. The data is in a spreadsheet, or I also made a toy (experimental) searchable webapp.

1. Introduction
2. A Crash Course in Python
3. Visualizing Data
4. Linear Algebra
5. Statistics
6. Probability
7. Hypothesis and Inference
8. Gradient Descent
9. Getting Data
10. Working With Data
11. Machine Learning
12. k-Nearest Neighbors
13. Naive Bayes
14. Simple Linear Regression
15. Multiple Regression
16. Logistic Regression
17. Decision Trees
18. Neural Networks
19. Clustering
20. Natural Language Processing
21. Network Analysis
22. Recommender Systems
23. Databases and SQL
24. MapReduce
25. Go Forth And Do Data Science