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https://github.com/rhettinger/modernpython
Sample code for the video course: Modern Python: Big Ideas, Little Code
https://github.com/rhettinger/modernpython
Last synced: about 2 months ago
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Sample code for the video course: Modern Python: Big Ideas, Little Code
- Host: GitHub
- URL: https://github.com/rhettinger/modernpython
- Owner: rhettinger
- License: mit
- Created: 2017-04-13T06:04:19.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-11-27T18:28:51.000Z (almost 2 years ago)
- Last Synced: 2024-07-10T22:29:26.239Z (2 months ago)
- Language: Python
- Homepage:
- Size: 184 KB
- Stars: 448
- Watchers: 29
- Forks: 150
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-raymond-hettinger - Code and examples
- my-awesome-github-stars - rhettinger/modernpython - Sample code for the video course: Modern Python: Big Ideas, Little Code (Python)
README
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][1].
Raymond runs an international Python training and consulting
company and is available for basic, intermediate, and advanced
python training.
[1]: http://www.informit.com/store/modern-python-livelessons-big-ideas-and-little-code-9780134743417
Getting Setup
-------------
1) Install Python 3.6.1 or later from https://www.python.org
2) Setup and activate a virtual environment:
```bash
$ python3.6 -m venv modernpython
$ source modernpython/bin/activate
```
3) Install the packages used in the examples:
```bash
(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:
```bash
(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`