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 :
    • 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 (not covered in this course)

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
  • I still mostly use Python 2 (although I also have Python 3 installed on my laptop) due to such libraries, but as more of them are migrating to Python 3, I'll soon use it full time
  • Marek uses mostly Python 3 as all his libraries are ported
  • Bottom-line: there is no wrong choice, as long as all the libraries you need are supported by the version you choose 
  • In this masterclass: we'll write Python3(-compatible) 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:
    IPython 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!https://www.anaconda.com/distribution/ 
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 IPython Notebook:

$ jupyter notebook

Tutorial

More about Jupyter Notebooks... in this Jupyter Notebook,

and even more in this blog post.

 

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 an IPython Notebook.

TO PRACTICE

Intro to Python -- The Python Programming Language

By utstikkar

Made with Slides.com

Intro to Python -- The Python Programming Language

Second set of slides for the Introduction to Python (and useful libraries) masterclass at the Data Science Retreat.

  • 1,650

More from utstikkar