March 12 and 14 (2025): Introduction to Python

upcoming
Get introduced to the Python from simple basics to functions, dataframes and plotting.
Author

Martijn Wehrens

Published

March 12, 2025

This workshop is full.

12 people have registered, we do not have place for more participants. Unfortunately, you cannot register any more.

Please send us an email in case you were interested in signing up, such that we know to organize more workshops soon.

An introduction to Python

The bioDSC organizes an introductory workshop that will cover basic functionalities of Python.

Python is a computer script language that has become a workhorse of data analysis throughout many fields and disciplines. It can process and analyze big data sets, perform image analysis, and create great visualizations.

See below for small overview of what types of tasks can be done using Python.

Workshop goals

After this workshop, you’ll be familiar with Python basics. We’ll discuss and let you practice on:

  • Using Python (Jupyter notebooks, VS Studio code, Spyder)
    • Throughout the workshop, we’ll use an online version of Jupyter notebook to avoid installation issues)
  • Variables, types, basic commands, functions, libraries.
  • Working with tabular data (Pandas).
  • Plotting (matplotlib).
  • Using loops and conditionals.
  • Writing functions.
  • Good practices in programming and software engineering.

This workshop will largely (>95%) follow the Carpentries training material “Plotting and programming in Python”. All training material can be found at:

See this workshop page for a summary of desired learning results. The materials we’ll use do not use examples specific to biology, but instead use the “Gapminder” dataset (see [1] and [2]). We think this offers thorough insights into how Python works. Follow-up courses, e.g. into image analysis, will be delve into more biological datasets.

If you have any questions regarding whether this course is relevant for you, please send us an email (info@biodsc.nl) or walk by our desks.

Requirements

This workshop assumes no prior knowledge on Python or programming!

Workshop logistics

The course will be given by Martijn Wehrens and Misha Paauw from the bioDSC. There is room for 4-12 participants. You have to bring your own laptop, we’ll use an online version of Python, so there are no software installation requirements.

As mentioned, the contents of this workshops are almost completely based on: https://swcarpentry.github.io/python-novice-gapminder/

Workshop schedule

Date Time Location Topic
12 March 12:30 - 17:30 SP A1.06 Introduction to Python, episodes 1-9
14 March 12:30 - 17:30 SP B0.209 Introduction to Python, episodes 11-18

Sign up

This workshop is full.

12 people have registered, we do not have place for more participants. Unfortunately, you cannot register any more.

Please send us an email in case you were interested in signing up, such that we know to organize more workshops soon.

Typical use cases for Python

Python is a scripting language, meaning that your write a file with commands for the computer to execute. This allows you to perform complicated tasks, such as:

  • Plotting and analyzing large data sets
    • With the pandas, matplotlib, and seaborn library, large datasets can be imported, manipulated, and visualized
  • Image analysis
    • Python has become a great open-source tool for image analysis
    • Multiple libraries are now available that can do pretty much any “classical” image operations to analyze images. You can:
      • Segment your image (identify cells, or other parts of your image that are of interest)
      • Extract summary parameters relevant for your analysis.
      • Related libraries: scikit-image, SciPy, OpenCV, ..
    • Multiple tools exist to allow smooth user-interaction, such as Napari
  • Machine learning, neural networks, LLM, AI
    • Python has become the go-to tool for working with machine learning related tech
      • Libraries: E.g. Keras and PyTorch
  • High-throughput data analysis and automation
    • Python is often used to process large amounts of data for further processing (see bio-informatics below)
  • Bioinformatics
    • Can be used to handle, manipulate, process, quantify raw sequence data or similar.
    • Perform single cell analysis, e.g. using the SCANPY lirbary.
    • Not the tool for RNA-seq statistics and gene expression analysis, for which R is superior.
  • Much more ..

See also the post I wrote earlier to decide whether you should Python or R.