Why Python?

🐍The Story of Python

1. 1989A Christmas hobby

   Guido van Rossum starts Python as a hobby project during the Christmas break at CWI in Amsterdam.

2. 1991 – 2005The scripting era

   Python establishes itself as a simple, readable scripting language for automation and system administration.

3. 2006 – 2011Scientific foundation

   NumPy (2006) and SciPy lay the groundwork for scientific computing. Pandas (2008) follows — academic researchers start to adopt Python.

4. 2012 – 2015The breakthrough

   The data-science explosion. Jupyter notebooks and scikit-learn make Python the default in research labs.

5. 2016 – 2020AI / ML dominance

   TensorFlow (2015) and PyTorch (2016) cement Python as the lingua franca of deep learning.

6. 2020 – nowTotal dominance

   COVID-19 accelerates data-science adoption. Python usage nearly doubles — 8M+ active developers worldwide.

Guido van Rossum

BDFL — Benevolent Dictator for Life

“I was looking for a hobby programming project that would keep me occupied during the week around Christmas.”

🎩Named after Monty Python's Flying Circus — not the snake!

▶ Watch the Python Documentary

How to Learn Programming

Using AI Responsibly

Generative AI tools must only be used in an assistive role in this module.

See Canvas for full details of permitted use of AI for assessments.

How You'll Learn

Every component builds towards your final project — from your first variable to building a working analysis pipeline.

Your Main Resources

Weeks 1–4: Python fundamentals

1

Data Types, Variables & Operations

Calculate the GC content and length of one DNA sequence

Today

2

Strings & Data Structures

Store many sequences; find and translate codons

3

Loops & Control Flow

Process all 10,000 sequences in one go

4

Functions, Files & Error Handling

Package the pipeline into a reusable program — Claudia's tool

Weeks 5–8: Python for Data Science

5

NumPy & Pandas

Wrangle large biological datasets — gene expression matrices, clinical tables

6

Data Visualisation

Build publication-ready plots: histograms, scatterplots, heatmaps

7

Image Analysis

Segment cells from microscopy images and extract quantitative features

8

Statistics & ML

Test hypotheses and build predictive models from your data

How You'll Be Assessed

Three pieces, each building on the one before — nothing arrives without a run-up.

Full details of the projects will be covered in the Week 5 Project Practical. Projects are submitted via Canvas.

Improving this Module

How We'll Work With Python

Three places you'll run Python on this course — each hands you more control and flexibility.

Session 1 — What You Will Be Able To Do

By the end of today you'll have written a working GC-content calculator — the first step of Claudia's pipeline.

Try it now — edit the code and hit Run.

Variables — labels for your data

The = sign means assignment, not equality — read it as “put this value into this name”. Once stored, you reuse the value by its name.

Run it — nothing appears. The values are stored; showing them is the next step.

Naming Variables

print() — see your data

Hand any value to print() and Python displays it. Pass several values separated by commas and it prints them on one line, with spaces in between.

This is how you check a variable holds what you expect.

Run it, then try printing your own values:

f-strings — variables inside text

Prefix a string with f and wrap any variable name in { } — the value is swapped in, with the spacing exactly as you typed it.

f-strings are straightforward, versatile, and easy to understand.

Built-in Functions

A built-in function works out of the box — no import needed. The pattern is always the same: function(value).

Forgotten what one does? Pass it to help() and Python prints the official documentation — for any function. Try round(2.5) too: Python rounds half to even, so the answer is 2, not 3.

Errors Are Normal

When Python hits something it can't do, it stops and prints a traceback.

Don't panic — read the last line. It names the error type and tells you exactly what went wrong. Every programmer reads these all day long.

Run your code — did you get an error? Read the last line of the traceback for the error type. Above that, Python tells you exactly which line the error occurred on.

Three Errors You'll Meet Constantly

Try it Yourself — Fix the errors

The Four Core Data Types

type() — ask Python what it is

Pass any value to type() and Python reports its type. The classic trap: "16" (a string) is not 16 (an integer) — and you can't do maths on text.

Run it — then try 9 / 16 vs 16 > 0:

Arithmetic Operators

Integers and Floats work with mathematical operators, either directly or when assigned to a variable

This is the first half of the GC calculation we'll finish in a moment.

Division & Decimals

9 / 3       # 3.0   (a float, not 3!)

gc_count = 9
length   = 16
gc_frac  = gc_count / length    # 0.5625

gc_frac * 100              # 56.25
round(gc_frac * 100, 1)    # 56.2 percent  (1 decimal place)

Counting Characters — .count()

Beyond function(value), every value carries its own toolkit — methods, written value.method().

A sneak preview for now — we'll discover more methods as we progress through the module. Try using .count() for different substrings — how many "GC"s are there in sequence?

Putting It All Together — Claudia's First Report

No new concepts — every line uses something we've seen today.

Try it: swap in a new sequence (e.g. "GGGGAAAATTTTCCCC") and re-run.

Question 1 — f-strings

Which line prints Sample S-014 has pH 7.4?

sample = "S-014"
ph = 7.4

Question 2 — Types

A reading arrives from a file as "3.14", quotes included. What is it?

print(type("3.14"))

Question 3 — Naming

Which of these will Python accept as a variable name?

Recap — Variables, Types and Operators

Today you learned

• ✓Variables & assignment
• ✓print() and f-strings
• ✓Built-in functions — len(), round(), type()
• ✓Reading errors — NameError, SyntaxError, TypeError
• ✓Four data types — str, int, float, bool
• ✓Arithmetic — +, -, *, /
• ✓.count() — your first string method
• ✓A working GC content calculator

One step at a time — and you've already written a real bioinformatics program. Take a break, grab a coffee. ☕

What's Next