Data wrangling in Python

FLAMES - Flanders’ Training Network for Methodology and Statistics

View the Project on GitHub jorisvandenbossche/FLAMES-python-data-wrangling

Data wrangling in Python

Introduction

The handling of data is a recurring task for most scientists. Reading in experimental data, checking its properties, and creating visualisations may become tedious tasks. Hence, increasing the efficiency in this process is beneficial for many scientists. Spreadsheet-based software lacks the ability to properly support this process, due to the lack of automation and repeatability. The usage of a high-level scripting language such as Python is ideal for these tasks.

This course trains students to use Python effectively to do these tasks. The course focuses on data manipulation and cleaning, explorative analysis and visualisation using some important packages such as Pandas, Numpy and Matplotlib.

The course does not cover statistics, data mining, machine learning, or predictive modelling. It aims to provide researchers the means to effectively tackle commonly encountered data handling tasks in order to increase the overall efficiency of the research.

The course has been developed as a course for the Flanders’ Training Network for Methodology and Statistics (Flames), but can be taught to others upon request.

Course info

Aim & scope

This course is intended for researchers that have at least basic programming skills. A basic (scientific) programming course that is part of the regular curriculum should suffice. For those who have experience in another programming language (e.g. Matlab, R, …), following a Python tutorial prior to the course is advised.

It is intended for researchers that want to enhance their general data manipulation and analysis skills in Python. The course is NOT intended to be a course on statistics or machine learning.

Program

The course is organized as a two day course with following program:

Getting started

The course uses Python 3 and some data analysis packages such as Pandas, Numpy and Matplotlib. To install the required libraries, we highly recommend Anaconda or miniconda (https://www.anaconda.com/download/) or another Python distribution that includes the scientific libraries (this recommendation applies to all platforms, so for both Window, Linux and Mac).

For detailed instructions to get started on your local machine , see the setup instructions.

In case you do not want to install everything and just want to try out the course material, use the environment setup by Binder Binder and open de notebooks rightaway (inside the notebooks directory).

Slides

For the course slides, click here.

Contributing

Found any typo or have a suggestion, see how to contribute.

Meta

Authors: Joris Van den Bossche, Stijn Van Hoey