Summary and Schedule

The best way to learn how to program is to do something useful, so this introduction to Python is built around a common scientific task: data analysis.

Scenario: A Miracle Arthritis Inflammation Cure

Our imaginary colleague “Dr. Maverick” has invented a new miracle drug that promises to cure arthritis inflammation flare-ups after only 3 weeks since initially taking the medication! Naturally, we wish to see the clinical trial data, and after months of asking for the data they have finally provided us with a CSV spreadsheet containing the clinical trial data.

The CSV file contains the number of inflammation flare-ups per day for the 60 patients in the initial clinical trial, with the trial lasting 40 days. Each row corresponds to a patient, and each column corresponds to a day in the trial. Once a patient has their first inflammation flare-up they take the medication and wait a few weeks for it to take effect and reduce flare-ups.

To see how effective the treatment is we would like to:

  1. Calculate the average inflammation per day across all patients.
  2. Plot the result to discuss and share with colleagues.
3-step flowchart shows inflammation data records for patients moving to the Analysis stepwhere a heat map of provided data is generated moving to the Conclusion step that asks thequestion, How does the medication affect patients?

Data Format

The data sets are stored in comma-separated values (CSV) format:

  • each row holds information for a single patient,
  • columns represent successive days.

The first three rows of our first file look like this:

0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0
0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1
0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1

Each number represents the number of inflammation bouts that a particular patient experienced on a given day.

For example, value “6” at row 3 column 7 of the data set above means that the third patient was experiencing inflammation six times on the seventh day of the clinical study.

In order to analyze this data and report to our colleagues, we’ll have to learn a little bit about programming.

Prerequisite

Prerequisites

You need to understand the concepts of files and directories and how to start a Python interpreter before tackling this lesson. This lesson sometimes references Jupyter Notebook although you can use any Python interpreter mentioned in the Setup.

The commands in this lesson pertain to any officially supported Python version. Newer versions usually have better error printouts, so using newer Python versions is recommend if possible.

Getting Started

To get started, follow the directions on the Setup page to download data and install a Python interpreter.

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.

Overview

This lesson is designed to be run on a personal computer. All of the software and data used in this lesson are freely available online, and instructions on how to obtain them are provided below.

Environment file for this set of lesions

  1. Download aaib26.yaml

Install Python

In this lesson, we will be using Python 3 with some of its most popular scientific libraries. One can install a plain-vanilla Python and all required libraries by hand. If you’ve followed the Carpentry workshop installation instructions you will have installed miniforge and the required libraries in an environment called carpentries. Before starting you’ll want to type conda activate carpentries in order to activate that environment.

Obtain lesson materials

  1. Download python-novice-inflammation-data.zip, python-novice-inflammation-code.zip, iris.csv and additional_stuff.zip.
  2. Create a folder called swc-python on your Desktop.
  3. Move downloaded files to swc-python.
  4. Unzip the files.

You should see three folders called data, code, and additional stuff in the swc-python directory on your Desktop. You should also see the iris dataset in that folder.

Launch Python interface

To start working with Python, we need to launch a program that will interpret and execute our Python commands. Below we list several options. If you don’t have a preference, proceed with the top option in the list that is available on your machine. Otherwise, you may use any interface you like.

Option A: Jupyter Notebook

A Jupyter Notebook provides a browser-based interface for working with Python. You can launch a notebook from the command line:

1. Navigate to the data directory:

If you’re using a Unix shell application, such as Terminal app in macOS, Console or Terminal in Linux, or Git Bash on Windows, execute the following command:

BASH 

cd ~/Desktop/swc-python/data

On Windows, you can use its native Command Prompt program. The easiest way to start it up is pressing Windows Logo Key+R, entering cmd, and hitting Return. In the Command Prompt, use the following command to navigate to the data folder:

cd /D %userprofile%\Desktop\swc-python\data

2. Start Jupyter server

BASH 

jupyter notebook
python -m notebook

3. Launch the notebook by clicking on the “New” button on the right and selecting “Python 3” from the drop-down menu: Anaconda Navigator Notebook directory

 

Option B: IPython interpreter

IPython is an alternative solution situated somewhere in between the plain-vanilla Python interpreter and Jupyter Notebook. It provides an interactive command-line based interpreter with various convenience features and commands. You can install IPython on your system system.

To start using IPython, execute:

ipython

 

Option C: plain-vanilla Python interpreter

To launch a plain-vanilla Python interpreter, execute:

python

If you are using Git Bash on Windows, you have to call Python via winpty:

winpty python

Your anaconda environment

What is an python environment

A Python environment (often called a virtual environment) is an isolated workspace where you can install and manage Python packages independently from other projects.

Think of it like this:

Imagine each project you work on has its own “bubble”:

  • It has its own Python version
  • Its own libraries (packages)
  • Its own dependencies

This prevents conflicts between projects.

Why Python environments are important

Without environments:

  • One project might need pandas version 1.5
  • Another might need pandas version 2.0
  • These would clash and break things

With environments:

  • Each project gets exactly what it needs

Activate a Conda environment

As part of the installation, you should already have installed Miniforge. On Windows, open the Miniforge Prompt. On macOS or Linux, open a terminal.

You only need to create the environment once, but you need to activate it each time you start a new session.

To activate the carpentries environment, type:

conda activate carpentries

To check that the environment has activated correctly, you should either see that your prompt now displays the environment name or you can type:

conda info --envs

The active environment will have a * next to it.

Install packages with Conda

Most of the packages you need should already be installed in the carpentries environment. Here, we will see how to install packages using Conda in Miniforge or the terminal. Later, we will also see examples of how to install packages from within Jupyter Notebook.

conda install numpy

Another option if conda is problematic is to use, then you can install packages via pip

pip install numpy

Make sure that, when you run these commands, you are in the correct environment (carpentries).

Callout

Safe guarding “base”

In Anaconda, the default environment is called “base.” It serves as the core foundation for everything within Anaconda. If this environment becomes corrupted or broken, you may lose the ability to create or install new environments. For this reason, it’s recommended that you only use the base environment to create new environments or update Conda, and avoid making other changes to it.

Jupyter Notebook

What is Jupyter Notebook?

Jupyter Notebook is an interactive tool that lets you write and run code in small sections, while also adding notes, explanations, and visual outputs, all in one place.

It’s widely used in:

  • Data science
  • Machine learning
  • Research and education

Think of it as a combination of a coding environment and a document.

Understanding Cells

A notebook is made up of cells, which are the building blocks of your work.

Types of cells: * Code cells → where you write and run Python code * Markdown cells → where you write text, explanations, or instructions

Using Jupyter Notebook

Click the red box to create a new notebook and then select python 3 (ipykernal)

Click the red box to create a new notebook and then select python 3 (ipykernel)

Blue: the cells where you run code, Orange: is the output from a cell above, Pink: is the current cell selected, Red: remove selected cell, Green: Run select cell and Black: Name of worksheet, change by clicking it.

Blue: the cells where you run code, Orange: is the output from a cell above, Pink: is the current cell selected, Red: remove selected cell, Green: Run select cell and Black: Name of worksheet, change by clicking it.

Red: run selected cell, Blue: run all if needing to check of errors and Green is to start and run cells with a blank canvas.

Red: run selected cell, Blue: run all if needing to check of errors and Green is to start and run cells with a blank canvas.

Why Use Notebooks Instead of Terminal or IDE?

Jupyter Notebooks are used as a starting point because they are:

  • Beginner-friendly
  • No need to manage multiple files
  • Immediate feedback when running code
  • Interactive, run small pieces of code step-by-step
  • Experiment or test small sections of code
  • Visual Outputs, charts, and explanations appear together
  • Great for learning
  • You can mix code + commenting + results in one place

A Quick Note on Terminal & IDEs

More advanced workflows often use:

  • The command line (terminal)
  • Full development environments (IDEs)
  • High-performance computing (HPC) systems for large-scale workloads

These are powerful, but:

  • Require more setup
  • Are less interactive for beginners

Why We Start with Notebooks

Notebooks provide the most accessible entry point because they:

  • Reduce complexity
  • Encourage experimentation
  • Help you understand code step-by-step

Once you’re comfortable, you can then move on to:

  • Scripts
  • Terminals
  • Larger systems like HPC