Overview

Why scientific coding?

A research workflow often involves collecting data, cleaning and organising them, analysing them, and presenting the results. Scientific coding lets us connect these steps in a workflow that can be repeated when new data arrive or when our methods change.

For example, imagine a field study with 40 outcrops, around 10 samples per outcrop, and seven measurement methods per sample. Repeating the same processing manually quickly becomes time-consuming. With Python, we can describe the processing once and apply it consistently across the measurements.

In this class, we start with tabular data and pandas: the tools we need to organise observations and select the data relevant to a research question.

We will start our data science journey by learning a bit about the most useful Python library for this class: Pandas. As a reminder, a library is a set of tools we load on top of Python that provides new functionalities for a specific problem or type of analysis. Here, Pandas provides functions for data manipulation and analysis, handling structured data like tables or time series and facilitating numerous tasks you might encounter as a scientist. These include:

Reading/writing data from various commonly-used formats (CSV, Excel, SQL, JSON, etc.)
Handling missing data
Filtering, sorting, reshaping and grouping data
Aggregating data (sum, mean, count, etc.)
Time series support (date ranges, frequency conversions)
Statistical operations

Today’s objectives

The objective of this class is by no way to make you an expert in Pandas and data science. Rather, the objective is to take you through the most basic manipulations in order to build the confidence to keep on exploring the use of scientific coding and to include it into your research pipeline. The objectives of this module are to review:

What is a Pandas DataFrame and its basic anatomy
How to load data in a DataFrame
How to access data (e.g., query by label/position)
How to filter data (e.g., comparison and logical operators)
How to rearrange data (e.g., sorting values)
How to operate on data (e.g., arithmetic and string operations)

We first start by reviewing the data structure behind Pandas, then we will move on to a few coding exercises to make you familiar with some basic functionalities.

Connect this class to your research

As you work through the examples, think about a dataset you already use—or might use—in your research:

What would one row represent: a sample, a measurement, a location, or an event?
What variables would appear in the columns?
What question would you like to answer?
Which observations would you need to select to investigate that question?

If you have a suitable dataset, bring a small example to class. Otherwise, identify an open dataset relevant to your interests. You do not need to know how to analyse it yet: start by describing what it contains and what you would like to learn from it.

Today’s dataset

We will use dummy_volcanoes.csv, the small synthetic teaching dataset of selected volcanic eruptions introduced in the Pandas slides. It contains 20 rows and six columns, with dates ranging from 1980 to 2023. Its small size lets us inspect the results of our code and check whether they match what we expect.

Download the dataset

What does a row represent?

Each row describes one selected eruption, rather than the complete history of a volcano. Each column records one property of that entry:

Column	Stored values	Meaning
`Name`	Text	Name of the volcano associated with the eruption.
`Country`	Text / category	Country in which the volcano is located.
`Date`	Date written as `YYYY-MM-DD`	Date assigned to the eruption in this teaching dataset.
`VEI`	Integer	Volcanic Explosivity Index: an ordered class describing eruption explosivity.
`Latitude`	Decimal number	Latitude in degrees; positive values are north of the equator and negative values are south.
`Longitude`	Decimal number	Longitude in degrees; positive values are east of Greenwich and negative values are west.

The first five rows, also shown in the slides, give us a preview:

Name	Country	Date	VEI	Latitude	Longitude
St. Helens	USA	1980-05-18	5	46.1914	-122.1956
Pinatubo	Philippines	1991-04-02	6	15.1501	120.3465
El Chichón	Mexico	1982-03-28	5	17.3559	-93.2233
Galunggung	Indonesia	1982-04-05	4	-7.2567	108.0771
Nevado del Ruiz	Colombia	1985-11-13	3	4.895	-75.322

What is the VEI?

The Volcanic Explosivity Index (VEI) classifies the size of explosive volcanic eruptions on a scale from 0 to 8. It uses the volume of erupted tephra—ash and other rock fragments ejected into the air—together with eruption-column height and observations of the eruption (USGS explanation).

The scale is logarithmic: from VEI 2 upwards, each increase of one class corresponds to a tenfold increase in the erupted-volume thresholds. For example, VEI 4 covers approximately 0.1–1 km³ of tephra, while VEI 5 covers approximately 1–10 km³. The lowest classes do not follow exactly the same tenfold spacing.

Figure 1: The Volcanic Explosivity Index, showing erupted tephra volumes and example eruptions. Figure reproduced from the class slides; graphic: AFP, based on USGS/Newhall and Self (1982). The Tonga estimate shown is explicitly preliminary.

For our dataset, remember that VEI describes an eruption, not a permanent property of a volcano. Mount St. Helens, for example, appears at several VEI classes in the figure because it has produced eruptions of different sizes. Since VEI is a class rather than a directly measured volume, a VEI 6 eruption is not simply “twice as large” as a VEI 3 eruption.