COM6018 Data Science with Python

Week 4: Introducing Pandas

Jon Barker

Copyright © 2023–2025 Jon Barker, University of Sheffield. All rights reserved.

In this lab

• Reading datasets from CSV files
• Dealing with missing data
• Merging datasets
• Plotting data
• Comparing Pandas with previous Python-only approaches

The Task

• You will analyse atmospheric gas concentration data.

• Extending our previous analysis to include more greenhouse gases.

• We will be using Pandas to make the data processing easier.

The Data

We will be reusing the co2.csv and ch4.csv files from the previous lab.

Introducing two new datasets:

• data/sf6.csv (Sulphur Hexafluoride) and
• data/n2o.csv (Nitrous Oxide).

The Aim

We aim to estimate the combined global warming potential of all four gases.

We will produce a figure like the one below.

Obtaining the Jupyter Notebook

If you have cloned and pulled the module's GitHub repository, you should see,

materials/labs/
├── 040_working_with_pandas.ipynb
|-- ... etc
├── data
│   ├── ch4.csv
│   ├── co2.csv
│   ├── n2o.csv
│   ├── sf6.csv
│   ├── ... etc

The lab is 040_working_with_pandas.ipynb and it will need the files data/ch4.csv, data/co2.csv, data/n2o.csv, and data/sf6.csv.

Alternatively, you can download the notebook and data via links on Blackboard.

Getting Help

• If you are stuck, just raise a hand to ask for help.

• Feel free to discuss the lab with your neighbours.

• Re-read the Pandas tutorial notes

  • In the Git repo at tutorials/040_Introducing_Pandas.ipynb
  • Or online at https://uos-com-6018.github.io/COM6018

• Use the Pandas documentation for reference, https://pandas.pydata.org/