May 19, 21 and 26 (2025): Introduction to Python

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

Martijn Wehrens

Published

May 19, 2025

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 and Seaborn).
  • Using loops and conditionals.
  • Writing functions.
  • Good practices in programming and software engineering.

Workshop materials

Contents

This workshop will largely (>80%) follow the Carpentries training material “Plotting and programming in Python”. We customized materials for lessons 8 and 9 (dataframes and plotting, see below) to include biological examples. We are working to publish these materials online too. The course does not address specific programming solutions for common biological challenges. Follow-up courses, e.g. about RNA-seq analysis and image analysis, will delve into more biological programming topics.

Datasets

These materials use the “Gapminder” dataset (see [1] and [2]). Programming examples from this dataset will not relate to biological problems, but the examples offer good insights in how Python works. Additionally, we included custom materials based on single cell sequencing data from Kohela et al. (GSE149331), which will highlight Python functionalities through biological examples.

Changes compared previous workshops

This workshop will be similar to previous Python workshops [1], [2]. We changed the scheduling (3 shorter timeslots instead of 2), and edited the materials to include more biologically relevant examples (and plotting with Seaborn).

Questions

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!
  • You have to bring your own laptop.

Workshop logistics

The course will be given by Misha Paauw, Martijn Wehrens and Frans van der Kloet from the bioDSC. There is room for 4-20 participants. You have to bring your own laptop, we’ll use an online version of Python, so there are no software installation requirements.

Workshop schedule

Date Time Location Topic
May 19 (Mon) 13:00 - 17:00 SP L1.13* The basics, episodes 1-6
May 21 (Wed) 13:00 - 17:00 SP L1.13* Dataframes and plotting, episodes 7-9
May 26 (Mon) 13:00 - 17:00 SP L1.12* Programming concepts, episodes 11-16

*) These rooms are in the “Lab 42” building!

Sign up

Sign up using this link. Sign up deadline: 28th of April. First come, first serve.

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
    • Not the tool for RNA-seq statistics and gene expression analysis, for which R is superior.
    • Can be used to handle, manipulate, process, quantify raw sequence data or similar.
    • Perform single cell analysis, e.g. using the SCANPY lirbary.
  • Much more ..

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

Identifier

This meeting’s identifier: 2025-05-19-UvA-bioDSC.