THE PYTHON PROGRAMMING LANGUAGE

WHY PYTHON ?

WHY "PYTHON" ?

  • Monty Python Reference

    "​When he began implementing Python, Guido van Rossum was also reading the published scripts from “Monty Python's Flying Circus”, a BBC comedy series from the 1970s. Van Rossum thought he needed a name that was short, unique, and slightly mysterious, so he decided to call the language Python." -- General Python FAQ
     
  • So nothing to do with reptiles... except for its logo:

WHY LEARNING PYTHON?

  • In general :
    • High-level, general-purpose programming language
    • Easy to learn
    • Elegant yet simple and compact syntax 
    • Dynamic typing + interpreted language
    • High-level data structures
    • Object-Oriented Programming
    • Many libraries 
    • Great for both scripting and software/app development

    • Free and open-source software, with vibrant community

    • Extensible: add new built-in modules to Python (written in C)
       

  • For Data Scientists :
    • Very rich scientific computing libraries
    • All DS tasks can be performed with Python:
      • accessing, collecting, cleaning, analysing, visualising data
      • modelling, evaluating models, integrating in prod, scaling

PYTHON for ds
Components

  • Python​
    • Python language
    • Modules of the standard library
    • A large number of specialised modules and apps written in Python
    • Development tools
  • IPython
    • ​an interactive Python shell
    • Jupyter Notebook: web application to create and share documents that contain live code, equations, visualizations and explanatory text
  • NumPy: p​rovides powerful numerical array objects, and routines to manipulate them
  • SciPy: high-level data processing routines. Optimisation, regression, interpolation, etc.
  • Matplotlib: 2- and 3-D visualisations and plots​
  • Pandas: powerful data structures especially to deal with time series
  • Scikit-learn: Machine Learning toolbox
  • statsmodels: statistical modelling toolbox

PYTHON @DSR08

SCHEDULE & TOC

  • (L) Mon Sept 19: python intro, py2-py3, Jupyter, hands-on 

 

  • (L) Thu Sept 22: pandas                                                             

 

  • (M) Mon Sept 26: numpy, strings and file processing          

 

  • (M) Tue Sept 27: advanced python -- OOP, scripts, modules, packages, cython

 

  • (M) Wed Sept 28: basic data structures and algorithms for data science

PYTHON 2 vs. PYthon 3

WHAT IS IT ABOUT?

  • 2 major versions of Python in widespread use:
    Python 2.x and Python 3.x
  • Some features in Python 3 are not backward compatible with Python 2
  • Some Python 2 libraries have not been updated to work with Python 3
  • There's pressure from the community to enforce the    py2->py3 transition, but many important projects are not yet there 
  • Bottom-line: there is no wrong choice per, as long as all the libraries you need are supported by the version you choose - but it'd be nice to support the transition
  • In this masterclass: we'll write Python3 code

Examples

Text

# Python 2 only:
print 'Hello'

# Python 2 and 3:
print('Hello')

# Python 2 only:
print 'Hello', 'World'

# Python 2 and 3:
from __future__ import print_function
print('Hello', 'World')
  • From print statements (Py2) to print function calls (Py3):






     
  • From integer division to true division:
     
# Python 2 only:
assert 2 / 3 == 0

# Python 2 and 3:
assert 2 // 3 == 0

# Python 3 only:
assert 3 / 2 == 1.5

# Python 2 and 3:
from __future__ import division    # (at top of module)
assert 3 / 2 == 1.5

MORE on THIS

  • To explore and test more differences:
    Jupyter Notebook by Sebastian Raschka
     
  • To keep your code Python 2 and 3 compatible:
    a comprehensive Cheat Sheet

INSTALLING PYthon
and all useful packages

several options

  • [RECOMMENDED] Anaconda (Windows, Linux, OSX) : 
    • For new Python users who want to install a full Python environment for Data Science
    • Download it here
    • Follow the simple installation instructions
  • Manual installation with Python package manager :
    • (Only) if you already have been using Python on your computer for some time 
    • Make sure to install:  Python, pip and virtualenv 
    • Follow installation instructions for: OS X, Linux or Windows
    • Once in your dedicated virtualenv for this masterclass, use pip to install of the necessary packages: 

      Warning: this might require some external dependencies to be installed. When in doubt: Google your error messages!
pip install -U numpy, scipy, ipython, matplotlib, pandas, scikit-learn

RUNNING
THE IPYTHON interpreter
and a python file

your first python file

  • Open your favorite editor
  • Create a file hello_world.py
  • Inside write the following:

     
  • Now from your shell/terminal/console run:
     
  • Then simply run the IPython interpreter:
     
  • And from there run again your hello_world.py file:
     
  • Explore further:
s = 'Hello World!'
print(s)
$ ipython hello_world.py
$ ipython
In [1]: %run hello_world.py
In [2]: s
In [3]: i = 2
In [4]: s2 = 'Hi'
In [5]: s+s2

JUPYTER notebook

in action

Start Jupyter Notebook:

$ jupyter notebook

Tutorial

More about Jupyter Notebooks... in this Jupyter Notebook

 

Tip: Try cloning or downloading it and then opening it.
Run and modify its code cells.

PYthon basics

TUTORIAL

Read and test for yourself the examples provided in:

The SciPy Lectures -- The Python Language

Tip: Practice those examples using alternatively python files, the IPython interpreter and a Jupyter Notebook.

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