Part 1: Introduction¶

Introduction to Python¶

Tom Paskhalis¶

2022-07-27¶
Data Science Summer School 2022¶

Source: xkcd

Source: Reddit

About me¶

  • Assistant Professor in Political Science and Data Science, Trinity College Dublin
    • Before: Postdoctoral Fellow, New York University
    • PhD in Social Research Methods, London School of Economics and Political Science
  • My research:
    • Political communication, social media, interest groups
    • Text analysis, machine learning, record linkage, data visualization
  • Contact
    • tom.paskhalis@tcd.ie
    • tom.paskhal.is
    • @tpaskhalis

Source: Tumblr

About you¶

Go to www.menti.com
Enter the code: 4525 3763

R/Stata/SPSS is great, why learn Python?¶

  • Python is free and open source
  • Python is a truly versatile programming language
  • Python offers a great library ecosystem (>300K)
  • Python is widely used in the industry
  • Python is well-known outside academia/data science

Popularity of programming languages¶

Source: TIOBE

Popularity of data analysis software¶

Source: Kaggle 2021 State of Data Science and Machine Learning survey

Python and Development Enviroments¶

  • There is a number of integrated development environments (IDEs) available for Python (IDLE, Spyder, PyCharm)
  • As well code editors with Python-specific extensions (Visual Studio Code, Atom, Sublime Text, Vim)
  • Try different ones and choose what works best for you!

Python and Jupyter Notebook¶

  • Jupyter Notebook is language-agnostic web-based interactive computational environment
  • Is available with backends (kernels) for different programming languages (Julia, Python, R = Jupyter)
  • Can be used both locally and remotely
  • Good for ad-hoc data analysis and visualization

Jupyter Notebook¶

  • Notebooks allow writing, executing and viewing the output of Python code within the same environment
  • All notebook files have .ipynb extension for interactive python notebook
  • The main unit of notebook is cell, a text input field (Python, Markdown, HTML)
  • Output of a cell can include text, table or figure

Jupyter Notebook online¶

  • For this workshop I recommend using one of the online platforms for working with Jupyter Notebooks:
    • Google Colab, a cloud platform for hosting Jupyter Notebooks. You need to have a Google account, but it does not require any local installations.
    • Kaggle Code, a platform for sharing and exploring data-science-focussed Jupyter Notebooks. Although technically owned by Google, you can register just for Kaggle website.

Jupyter Notebook installation¶

  • If you would prefer to install Jupyter Notebook on your local machine, there are two main ways to do this:
    • pip
    • conda
  • Unless you have prior experience with Python, I recommend installing Anaconda distribution, which contains all the packages required for this course.

Jupyter Notebook demonstration¶

Jupyter Notebook 1

Jupyter Notebook demonstration¶

Jupyter Notebook 2

Course outline¶

Date Time (CEST) Topic
27 July 15:00-16:45 Introduction to Python objects and data types
16:45-17:00 Exercise I
17:00-17:15 Break
17:15-18:00 Introduction to Pandas
18:00-19:00 Exploratory data analysis and data visualization
19:00-19:15 Exercise II

Materials¶

  • All materials for this workshop can be found:
    • In this GitHub repository: github.com/tpaskhalis/DS3_Introduction_Python
    • Alternative shortlink: bit.ly/DS3-Python
  • For your convenience you might want to choose to clone this repository to your local macihine.
  • It is worth noting that all slides and exercises were created using Python and Jupyter Notebooks.

Additional materials¶

  • There are many great online resources and published books on programming in Python.
  • Some of them also provide a good coverage of using Python for data analysis.
  • Here are some pointers to start from.

Books¶

  • Guttag, John. 2021 Introduction to Computation and Programming Using Python: With Application to Computational Modeling and Understanding Data. 3rd ed. Cambridge, MA: The MIT Press

  • McKinney, Wes. 2017. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. 2nd ed. Sebastopol, CA: O'Reilly Media

  • Sweigart, Al. 2019. Automate the Boring Stuff with Python. 2nd ed. San Francisco, CA: No Starch Press

Online¶

  • Python For You and Me

  • Python Wikibook

  • Python 3 Documentation (intermediate and advanced)

Next¶

  • Basic Python types
  • Operations
  • Object manipulations