Data exploration with Python
Registration OPEN please register HERE
Time:
9am - 5pm each day
Venue:
Online
Places:
25 (first come, first served)
Registration fee:
University of Edinburgh Staff/Students - £405
Other University Staff/Students - £425
Industry Staff - £446
Information:
This course focusses on using Python's scientific software libraries to manipulate and visualise large datasets. It's intended for researchers with a basic knowledge of Python who need to explore large datasets and quickly visualise patterns and relationships.
Python is a dynamic, readable language that is a popular platform for all types of data analysis work, from simple one-off scripts to large, complex software projects. One of the strengths of the Python language is the availability of mature, high-quality libraries for working with scientific data. Integration between the most popular libraries has lead to the concept of a "scientific Python stack": a collection of packages which are designed to work well together. In this workshop we will see how to leverage these libraries to efficiently work with and visualise large volumes of data.
Instructor
Dr. Martin Jones (Founder, Python for Biologists)
Who should attend
This course is intended for researchers with a basic knowledge of Python who need to explore large datasets and quickly visualise patterns and relationships. Rather than attempting to give a comprehensive overview of Python, we will instead concentrate on how best to use existing libraries to accomplish a lot while writing a very small amount of code! There will be opportunities to use your own data throughout, and the final day is set aside as workshop time for you to work on your own datasets with help from the instructor. If in any doubt as to whether the workshop is suitable for you, take a look at the detailed session content below or drop Martin Jones (martin@pythonforbiologists.com) an email.
Workshop session content
The workshop is delivered over five day-long sessions (see the detailed curriculum below). Each session consists of roughly a one hour lecture followed by two hours of practical exercises, with breaks at the organizer’s discretion. There will also be plenty of time for students to discuss their own problems and data.
1. Introduction and datasets
Jupyter (formerly iPython) is a programming environment that is rapidly becoming the de facto standard for scientific data analysis. In this session we'll learn why Jupyter is so useful, covering its ability to mix notes and code, to render inline plots, charts and tables, to use custom styles and to create polished web pages. We'll also take a look at the datasets that we'll be investigating during the course and discuss the different types of data we encounter in bioinformatics work.
2. Introduction to pandas
In this session we introduce the first part of the scientific Python stack: the pandas data manipulation package. We'll learn about Dataframes — the core data structure that much of the rest of the course will rely on — and how they allow us to quickly select, sort, filter and summarize large datasets. We'll also see how to extend existing Dataframes by writing functions to create new columns, as well as how to deal with common problems like missing or inconsistent values in datasets. We'll get our first look at data visualisation by using pandas' built in plotting ability to investigate basic properties of our datasets.
3. Grouping and pivoting with pandas
This session continues our look at pandas with advanced uses of Dataframes that allow us to answer more complicated questions. We'll look two very powerful tools: grouping, which allows us to aggregate information in datasets, and pivoting/stacking, which allows us to flexibly rearrange data (a key step in preparing datasets for visualisation). In this session we'll also go into more detail about pandas indexing system.
4. Advanced manipulation with pandas
In this final session on the pandas library we'll look at a few common types of data manipulation — binning data (very useful for working with time series), carrying out principal component analysis, and creating networks. We'll also cover some features of pandas designed for working with specific types of data like timestamps and ordered categories.
5. Introduction to seaborn
This session introduces the seaborn charting library by showing how we can use it to investigate relationships between different variables in our datasets. Initially we concentrate on showing distributions with histograms, scatter plots and regressions, as well as a few more exotic chart types like hexbins and KDE plots. We also cover heatmaps, in particular looking at how they lend themselves to displaying the type of aggregate data that we can generate with pandas.
6. Categories in seaborn
This session is devoted to seaborn's primary use case: visualising relationships across multiple categories in complex datasets. We see how we can use colour and shape to distinguish categories in single plots, and how these features work together with the pandas tools we have already seen to allow us to very quickly explore a dataset. We continue by using seaborn to build small multiple or facet plots, separating categories by rows and columns. Finally, we look at chart types that are designed to show distributions across categories: box and violin plots, and the more exotic swarm and strip plots.
7. Customisation with seaborn
For the final session on seaborn, we go over some common types of customisation that can be tricky. To achieve very fine control over the style and layout of our plots, we'll learn how to work directly with axes and chart objects to implement things like custom heatmap labels, log axis scales, and sorted categories.
8. Matplotlib
In the final teaching session, we look at the library that both pandas and seaborn rely on for their charting tools: matplotlib. We'll see how by using matplotlib directly we can do things that would be impossible in pandas or seaborn, such as adding custom annotations to our charts. We'll also look at using matplotlib to build completely new, custom visualisation by combining primitive shapes.
9. / 10. Data workshop
The two sessions on the final day are set aside for a data workshop. Students can practice applying the tools they've learned to their own datasets with the help of an instructor, or continue to work on exercises from the previous day. There may also be time for some demonstrations of topics of particular interest, such as interactive visualisation tools and animations.