Introduction to Python Workshop

  • Date : 23rd March 2018
  • Time : 1:00pm to 5:00pm
  • Location : T1, Sir Louis Matheson Library, Clayton, VIC
  • Instructors : Anup Shah, Kirill Tsyganov
  • Helpers : Simon Michnowicz, Michael See

This hands-on workshop aims to equip participants with the fundamentals of programming and give them skills needed to apply data analysis approaches to their research questions.

General Information

The workshop will be taught in a similar style to Data Carpentry workshops. Data Carpentry’s mission is to trains researchers in the core data skills for efficient, shareable, and reproducible research practices.

Who: The course is aimed at beginners to programming. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

Setup Requirements: BYO Laptop. No software installation required. The course will be taught online, so active internet connection is required. Tools and material will be provided on the day.

Schedule

Time Programme
13:00 Welcome
13:15 Session 1
15:15 Break
15:30 Session 2
16:45 Wrap-up

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Syllabus

Intro to python

01 Programming in Python (Introduction)

The Basics of Python Python is a general purpose programming language that supports rapid development of scripts and applications. Python’s main advantages: Open Source software, supported by Python Software Foundation Available on all platforms (ie. Windows, Linux and MacOS) It is a general-purpose programming language Supports multiple programming paradigms Very large community with a rich ecosystem of third-party packages Interpreter Python is an interpreted language which can be used in two ways:
Intro to python

02 Data Analysis in Python

Working With Pandas DataFrames We can automate the process of performing data manipulations in Python. It’s efficient to spend time building the code to perform these tasks because once it’s built, we can use it over and over on different datasets that use a similar format. This makes our methods easily reproducible. We can also easily share our code with colleagues and they can replicate the same analysis. The Dataset For this lesson, we will be using the Portal Teaching data, a subset of the data from Ernst et al Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA
Intro to python

03 Indexing, Slicing and Subsetting

In lesson 02, we read a CSV into a Python pandas DataFrame. We learned: How to save the DataFrame to a named object, How to perform basic math on the data, How to calculate summary statistics, and How to create basic plots of the data. In this lesson, we will explore ways to access different parts of the data using: Indexing, Slicing, and Subsetting Loading our data We will continue to use the surveys dataset that we worked with in the last lesson.
Intro to python

04 Automation with Loops

An example task that we might want to repeat is printing each character in a word on a line of its own. word = 'lead' We can access a character in a string using its index. For example, we can get the first character of the word 'lead', by using word[0]. One way to print each character is to use four print statements: print(word[0]) print(word[1]) print(word[2]) print(word[3]) Gives output
Intro to python

05 Making Plots With ggplot

Introduction Python has powerful built-in plotting capabilities such as matplotlib, but with great power comes great complexity. For this exercise, we are going to use different python library, plotnine. There are a number of different libraries to choose from, but we are setting on plotnine as this is python port of original ggplot2 an R library (package), which is a very nice way to create publication quality plots and syntax is preserved, meaning you can take your python ggplot code and run it in R if you want it.