ELSTE Data science

Class 2: Exploratory data analysis

Sébastien Biass

sebastien.biasse@unige.ch

Earth Sciences

Stéphane Guerrier

Stephane.Guerrier@unige.ch

Earth Sciences

Pharmaceutical Sciences

October 21, 2026

Today’s objectives

Last week: We learned how to handle data using Pandas
- Load, access, query, filter, sort and operate on data

This week: You received / compiled / produced a new dataset
Review common tasks / method to get accustomed to the dataset
- Understand its structure → what type of data does it contain
- Explore it visually → plotting with dedicated libraries
- Describe single variables → univariate analyses
- Assess coupled behaviours of two variables → bivariate analyses
- First attempt at modelling these behaviours → linear regressions

Sample vs population

The dataset

Case study

Campi Flegrei: Home to 1.5 million people
Caldera-forming eruptions
- Campanian Ignimbrite (CI), ~39 ka ago
- Neapolitan Yellow Tuff (NYT), ~15 ka ago

Dataset

Tephrostratigraphy and glass compositions of post-15 kyr Campi Flegrei eruptions

Smith et al. (2011)
Major and trace elements

Loading the dataset

Import pandas
Load an excel file using pandas
Specify which sheet to read from using the sheet_name argument
- Supp_majors → df_majors
- Supp_traces → df_traces

# Load Pandas
import pandas as pd # Import pandas

# Import the dataset specifying which Excel sheet name to load the data from
df_majors = pd.read_csv('https://raw.githubusercontent.com/ELSTE-Master/Data-Science/main/Data/Smith_glass_post_NYT_data_majors.csv')
df_traces = pd.read_csv('https://raw.githubusercontent.com/ELSTE-Master/Data-Science/main/Data/Smith_glass_post_NYT_data_traces.csv')

Inspecting the dataset

df_traces.head(5)

	Analysis no.	Strat. Pos.	Eruption	controlcode	Sample	Epoch	Crater size	Date of analysis	Si/bulk cps	SiO2* (EMP)	...	Ho	Er	Tm	Yb	Lu	Hf	Ta	Pb	Th	U
0	1915	63	Astroni 7	1	79	three-b	20	100210am	20.21	59.27	...	1.11	3.26	0.47	2.80	0.43	7.84	2.96	60.93	35.02	9.20
1	1916	63	Astroni 7	1	79	three-b	20	100210am	11.92	59.27	...	1.08	2.27	0.46	3.14	0.46	7.33	3.52	59.89	34.46	10.46
2	1917	63	Astroni 7	1	79	three-b	20	100210am	17.06	59.27	...	1.25	3.69	0.61	3.51	0.63	8.43	3.05	49.87	29.22	8.73
3	1918	63	Astroni 7	1	79	three-b	20	100210am	24.52	59.27	...	1.24	3.72	0.46	3.04	0.44	8.95	3.08	59.59	30.71	9.79
4	1919	63	Astroni 7	1	79	three-b	20	100210am	14.35	59.27	...	1.08	2.68	0.46	2.79	0.41	7.24	2.67	60.70	32.13	9.01

5 rows × 37 columns

df_traces.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 370 entries, 0 to 369
Data columns (total 37 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   Analysis no.      370 non-null    int64  
 1   Strat. Pos.       370 non-null    int64  
 2   Eruption          370 non-null    object 
 3   controlcode       370 non-null    int64  
 4   Sample            370 non-null    object 
 5   Epoch             370 non-null    object 
 6   Crater size       370 non-null    int64  
 7   Date of analysis  370 non-null    object 
 8   Si/bulk cps       370 non-null    float64
 9   SiO2* (EMP)       370 non-null    float64
 10  Sc                370 non-null    float64
 11  Rb                370 non-null    int64  
 12  Sr                369 non-null    float64
 13  Y                 370 non-null    float64
 14  Zr                370 non-null    int64  
 15  Nb                370 non-null    float64
 16  Cs                370 non-null    float64
 17  Ba                370 non-null    float64
 18  La                370 non-null    float64
 19  Ce                370 non-null    float64
 20  Pr                370 non-null    float64
 21  Nd                370 non-null    float64
 22  Sm                370 non-null    float64
 23  Eu                370 non-null    float64
 24  Gd                370 non-null    float64
 25  Tb                370 non-null    float64
 26  Dy                370 non-null    float64
 27  Ho                370 non-null    float64
 28  Er                370 non-null    float64
 29  Tm                370 non-null    float64
 30  Yb                370 non-null    float64
 31  Lu                369 non-null    float64
 32  Hf                370 non-null    float64
 33  Ta                370 non-null    float64
 34  Pb                368 non-null    float64
 35  Th                370 non-null    float64
 36  U                 370 non-null    float64
dtypes: float64(27), int64(6), object(4)
memory usage: 107.1+ KB

Types of data

Basic types of data

int: Integer numbers → 0, 1, 2, …
float: Decimal numbers → 1.1, 1.2, 1.3, …
object: Strings → “Campi Flegrei”

A note on Bytes

int and float numbers are followed by 8, 16, 32 or 64 → Byte → control how much memory a variable uses
A Bit is a binary digit → 0/1 → smallest possible unit of data
int8 can store \(2^8\) digits = 256
int16 can store \(2^16\) digits = 65’563

Families of data

Numerical data

Represent quantities or measurable values
Quantitative
- Discrete: Earthquake counts
- Continuous: Temperature
Stored as numerical values
Operations → arithmetic

Categorical data

Represent categories or groups of data
Qualitative / semi-quantitative
- Nominal: No order → Landslide type
- Ordinal: Order → rank
Stored as strings or integer
Operations → counting, grouping

Families of data - cheat sheet

Feature	Categorical Data	Numerical Data
Definition	Categories or groups of data	Quantities or measurable values
Nature	Qualitative (describes qualities)	Quantitative (describes amounts or measurements)
Data Type	Non-numeric (often text or labels)	Numeric (numbers only)
Examples	Eruption type	Element concentration
Possible Operations	Counting, grouping, mode	Arithmetic operations
Measurement Scale	Nominal or Ordinal	Interval or Ratio
Visualization Tools	Bar chart, Pie chart	Histogram, Box plot, Scatter plot
Subtypes	Nominal: Categories with no order (e.g., color); Ordinal: Categories with order (e.g., rank)	Discrete: Countable numbers (e.g., number of earthquakes); Continuous: Measurable values (e.g., temperature)
Examples of Statistical Summary	Frequency, mode	Mean, median, standard deviation
Storage Format	Strings or labels	Integers or floats

Today

Exploratory data analysis

Can fractional crystallisation explain the chemical variability of Campi Flegrei glasses, or do we need magma mixing?

Univariate: Are element concentrations narrowly distributed, broadly spread or multimodal?
Bivariate: Are relationships such as Sr–Zr consistent with Sr removal by feldspar crystallisation while Zr becomes enriched in the residual melt?
Interpretation: Can one differentiation process explain the observations, or are multiple magma components needed?
- Correlation ≠ mechanistic explanation
Sample vs population: Do the analysed glasses capture the compositional variability within each eruption?

Objectives

Understand data structure
- Type and completeness of dataset
Basics of plotting with matplotlib and seaborn
Univariate statistics
- Position, spread, shape
- Quantification + visualisation
Bivariate statistics
- Covariance & Correlation
- Intro to regression + residual analysis

Your turn!

Go to https://elste-master.github.io/Data-Science/
- Class 2
- Explore the structure of the glass geochemistry or your dataset
- Get used to the various functions