What is Python?

Python is a programming language invented by …

… this guy

… and he named it Python because …

… he was into Monty Python when he wrote it

What can it do?

• Interpreted program, like R or VBA
• Runs on any OS
• Comes standard on Mac and Linux
• General programming
• Access the file system

How does it do it?

The Zen of Python

• Beautiful is better than ugly.
• Explicit is better than implicit.
• Simple is better than complex.
• Complex is better than complicated.
• Flat is better than nested.
• Sparse is better than dense.

More zen

• Readability counts.
• Special cases aren’t special enough to break the rules.
• Although practicality beats purity.
• Errors should never pass silently.
• Unless explicitly silenced.
• In the face of ambiguity, refuse the temptation to guess.
• There should be one– and preferably only one –obvious way to do it.

Analytic data

Visualization

Data from the web

Other cool web stuff

Data science

Similarities between R and Python

• FLOSS - Free, Libre, Open Source Software
• Wide support (take that, Julia!)
• Interpreted, REPL
• Rich package ecosystem
• Not OS dependent
• Fast to program
• Execution not always as fast as C, but possible to use C routines when needed
• great database support - from RDBMS to Spark, Hadoop, etc.

How does it compare with R?

Compare with Excel?

Practicalities

• Integrated Development Environment (IDE)
• Version 2 or 3?
• Sharing your work

Jupyter

scikit-learn

• Built on NumPy, SciPy, and matplotlib
• Open source, commercially usable - BSD license
• Common functional interface for:
  • Data splits, cross validations
  • Data transforms
  • Training, test, scoring
  • Pipelines to manage workflow
  • GridSearch for model tuning

But what algorithms does it support?

Estimators

• Estimators are the building block of scikit-learn. Almost everything is an estimator.
• All estimators have fit() and predict() methods.
• Supervised techniques generally have a score() method as well.
• Some special estimators have a transform() method to pre-process data.
  • Generate new features
  • Clean data
  • Other pre-processing

Pipelines

Pipelines facilitate workflow. Pipelines are estimators too. Each step in a pipeline must be a transformer except for the final step.

Grid Search

• Explore performance of Pipeline across a predefined parameter search space while maintaining separability between training and test sets

Python and you

• If you already know R, check out Python
• If you don’t know R, check out Python
• If you’re an actuary, check out Python
• Jupyter is a great place to start experimenting
• scikit-learn is worth exploring

Thank you for your time!

Questions?

References

• http://scikit-learn.org/stable/
• https://www.python.org/
• https://www.python.org/dev/peps/pep-0020/