A beginner-friendly, open-source guide designed to teach Python programming through real-world biological datasets, lab calculations, and bioinformatics concepts.
Traditional programming courses often rely on abstract examples like managing shopping carts or calculating circle areas. This repository takes a practical approach: core Python concepts are introduced using biological data, including DNA/RNA sequences, enzyme kinetics, cell populations, and patient clinical variables.
Whether you are a wet-lab biologist transitioning to computational research, a student learning data analysis, or a researcher aiming to automate daily workflows, this guide provides a structured foundation.
🖼️ Browse all 6 slides
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Perfect for:
- Life science students needing Python for coursework, data analysis, or thesis projects.
- Lab researchers aiming to automate routine calculations, sequence processing, and plotting.
- Biologists transitioning from Excel to reproducible, script-based workflows.
- Absolute beginners with zero prior programming experience.
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Not intended for:
- Experienced bioinformaticians looking for advanced NGS pipeline engineering or structural modeling architectures.
- Python Fundamentals: Variables, dynamic typing, input processing, dynamic casting, and core syntax rules.
- Control Flow & Logic: Decision structures (
if-elif-else), Walrus operator (:=), and repetitive task automation using loops. - Data Structures: Lists, tuples, dictionaries (e.g., codon translation maps), sets (e.g., unique sequence extraction), and string manipulation.
- Bioinformatics & Data Handling: Reading/parsing FASTA records via Biopython, modeling exponential cell growth, and creating publication-ready line plots.
| Chapter | Markdown Source | Interactive Colab Notebook | Key Life Science Applications |
|---|---|---|---|
| 01 | 01_introduction.md |
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PySam, Biopython, DeepChem setup & execution lifecycle |
| 02 | 02_python_basics.md |
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Calculating exponential DNA amplification from PCR inputs |
| 03 | 03_operations.md |
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Evaluating patient BMI, glucose thresholds, & enzyme logic |
| 04 | 04_control_flow.md |
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Cholesterol classification & tracking cell growth loops |
| 05 | 05_functions.md |
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Modularizing BMI tools & sequence length evaluation |
| 06 | 06_data_structures.md |
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Codon maps, expression vectors, & unique DNA base extraction |
| 07 | 07_bioinformatics.md |
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FASTA parsing, DNA-to-RNA conversion, & bacterial growth plotting |
Here is a preview showing how basic Python string manipulation and math translate into sequence analysis:
# DNA Sequence Analysis & Transcription Shorthand
dna_seq = "ATGCTTGCCA"
# Transcribe DNA to RNA
rna_seq = dna_seq.replace("T", "U")
# Calculate GC Content Percentage
gc_count = dna_seq.count('G') + dna_seq.count('C')
gc_content = (gc_count / len(dna_seq)) * 100
print(f"RNA Sequence: {rna_seq}")
print(f"GC Content: {gc_content:.2f}%")
- Clone this repository:
git clone https://github.com/samavinasir/python-basics-for-life-scientists.git
cd python-for-life-scientists- Create and activate a virtual environment:
# Create environment named 'env'
python -m venv env
# Activate on macOS/Linux:
source env/bin/activate
# Activate on Windows:
env\Scripts\activate- Install dependencies:
pip install -r requirements.txt
Distributed under the MIT License. Feel free to use, adapt, and distribute for educational or research purposes.