frankthetank123/mfe-coding-workshop

Topics

• Version Control (git)
• Advanced Python
• Production Python

Version Control

• Why use version control tools?
• git vs. GitHub
• Sourcetree
• git on white board (distributed version control flow)
• basic git commands:
  • git init and git clone
  • git status, git add and git commit
  • git push
  • git pull
  • git checkout and git branch
  • git merge and resolve conflicts
  • git rebase
  • git stash
• Github specials
  • fork
  • pull requests

Advanced Python

Setup

0. Pro tip: use Python3

Anaconda

Anaconda is very good Python package manager + environment manager + additional scientific libraries. Highly recommend for local development. Not recommend for servers, as its ~3GB.

1. Download and install Anaconda Python 3.6 here
2. Create environment conda create --name test --file conda-requirements.txt
3. source activate test (vs. source deactivate)
4. whichp python

Note that the test environment folder is usually stored with your anaconda. To check, try which conda.

pip + virtualenv

pip is light-weigt Python pacakge manager. virtualenv is light-weight Python environment manager.

1. Install Python3
2. Go to the folder where you want to install your env, usually the repo.
3. Setup environment virtualenv env -p python3
4. source env/bin/activate (vs. deactivate)
5. pip install -r requirements.txt
6. which python

Fluent Python

Please refer to src/fluent_python/*.py for the code.

• List Comprehension
• Generator, yield, yield from
• Decorator
• Functional programming
• Variable Scope
• Operator overloading
• Interface and abc
• Inheritance vs. Composition (no .py, draw on board and look at Sklearn)
• Python function call-by

Production Python

Development Environment

• Local/Development: your laptop or a sandbox on a development server
• Testing: for all the tests, shares the same environement as staging and production
• Staging: an environment for final testing immediately prior to deploying to production. A mirror of production, but not facing to the users.
• Production: the environment that users/clients directly interact with

Python Debug

• Why bother debugging?
• You need to debug when:
  • you see a Shape ratio of 40
  • you are trying to understand your teammate's code
• How many of you debug via:
  • print(...)
  • Binary search
  • pdb/ipdb
• Examples
  • debug in ipdb (make sure you pip install ipdb)
  • debug in ipython notebook (use the magic command %debug after you see an error)

Python Tests

• Why bother testings?
  • Collborations
  • Too big to fail
  • Know who to blame
• Different types of tests
  • lint checks
  • unit test
  • integration test
  • regression test
  • ...

Lint

• Why we need Lint?
• flake8: try flake8 src/production_code
• pylint: try pylint src/production_code
• mypy: type hint (only), try mypy src/mypy_examples

Unit Test

• Why do we need unit tests?
• try: pytest src/production_code

Continuous Integration

Continuous Integration (CI) tools help you stick to your team’s quality standards by running tests every time you push a new commit and reporting the results to a pull request.

Examples:

• Travis CI
• Jenkins

makefile

• Make your life easier
• make lint instead of running flake8 + pylint + mypy
• make install <- who doesn't like 1-click setup?
• checkout Makefile

docker (very brief, no code)

• ref: A Beginner-Friendly Introduction to Containers, VMs and Docker