akshayd110/modernpython

Sample code for the video course: Modern Python: Big Ideas, Little Code

Modern Python: Big ideas, Little Code

This code is offered as an accompaniment to a Python Video course by Raymond Hettinger.

See Modern Python: Big Ideas, Little Code.

Raymond runs an international Python training and consulting company and is available for basic, intermediate, and advanced python training.

Getting Setup

1. Install Python 3.6.1 or later from https://www.python.org

2. Setup and activate a virtual environment:

    $ python3.6 -m venv modernpython
    $ source modernpython/bin/activate

3. Install the packages used in the examples:

    (modernpython) $ pip install pyflakes
    (modernpython) $ pip install bottle
    (modernpython) $ pip install pytest
    (modernpython) $ pip install hypothesis
    (modernpython) $ pip install mypy

Resampling

This code demonstrates simulations, resampling, bootstrapping, hypothesis testing, and estimating confidence intervals.

Machine Learning

The kmeans.py file implements k-means from scratch. The congress_data directory has CSV files with the voting histories of senators in the 114th U.S. Congress. The congress.py file demonstrates ETL (extract-transfrom-load) and unsupervised machine learning (k-means) to analyze the voting clusters.

Publisher Subscriber

This code implements a simple publisher-subscriber notification service. The pubsub.py implements the data model and core services. The session.py loads sample data. The webapp.py file runs a webserver for the application. The views directory has the Bottle templates and the static directory has the static resources (icons and photos).

To start the service, run:

    (modernpython) $ python webapp.py

Then point your browser to http://localhost:8080/

The login information is in the session.py file.

Testing

The quadratic.py file is a module with a simple function to demonstrate various approaches to testing included in test_quadratic.py.

Validation

The pricing_tool.py file is used to demonstrate the descriptor based data validation tools in validators.py