Introduction to the Krembil Centre for Neuroinformatics (KCNI) and the Scientific Computing Working Group (SCWG) Python Workshop

Workshop Overview

This workshop is designed for beginners who want to learn Python for data analysis and scientific computing. By the end of this session, you'll be comfortable with Python basics and ready to work with real-world datasets.

Topics Covered

Part 1: Basic Python Operations and Syntax

• Arithmetic operations (addition, subtraction, multiplication, division)
• Floor division and modulo operators
• Variable assignment and naming conventions
• String operations and methods

Part 2: Data Structures and Control Flow

• Lists (ordered, mutable collections)
• Dictionaries (key-value pairs)
• Tuples (immutable sequences)
• Sets (unique collections)
• Understanding mutable vs immutable types
• For loops and iteration
• Booleans and logical operators

Part 3: Package Management

• Installing packages with pip
• Importing modules and packages
• Introduction to NumPy (numerical computing)
• Introduction to Pandas (data manipulation)
• Introduction to Scikit-learn (scientific computing)

Part 4: Pandas for Data Manipulation

• Loading data from CSV, TSV, and TXT files
• Selecting columns and rows
• Filtering data with conditions
• Handling missing values (drop NA)
• Dropping unwanted columns
• Grouping and aggregation
• Joining DataFrames (left, right, inner, outer joins)

Part 5: Data Analysis and Modeling (If Time Permits)

• Summary statistics
• Data visualization with Matplotlib and Seaborn
• Correlation analysis
• Building linear regression models
• Model evaluation and interpretation

Getting Started

Using GitHub Codespaces

This workshop is optimized for GitHub Codespaces, which provides a complete containderized Python environment in your browser.

1. Open in Codespaces: Click the "Code" button and select "Create codespace on main"
2. Wait for setup: The environment will automatically install all required packages
3. Open notebooks: Navigate to the notebooks/ folder and start with 01_basics_and_syntax.ipynb

Local Setup (Alternative)

If you prefer to work locally:

# Clone the repository
git clone <repository-url>
cd python_workshop

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Launch Jupyter
jupyter notebook

Repository Structure

python_workshop/
├── .devcontainer/
│   └── devcontainer.json      # GitHub Codespaces configuration

├── notebooks/
│   ├── 01_basics_and_syntax.ipynb
│   ├── 02_data_structures_and_control_flow.ipynb
│   ├── 03_packages_and_libraries.ipynb
│   ├── 04_pandas_data_manipulation.ipynb
│   └── 05_analysis_and_modeling.ipynb
├── requirements.txt           # Python package dependencies
└── README.md                  # This file

How to Use This Workshop

1. Work through notebooks sequentially: Each notebook builds on concepts from the previous one
2. Run all code cells: Execute each cell to see the output and understand the behavior
3. Complete exercises: Each notebook includes practice exercises at the end
4. Experiment: Modify the code and try your own variations
5. Ask questions: Don't hesitate to explore beyond the provided examples

Packages

All packages are pre-installed in the Codespace. The workshop uses:

• pandas (>=2.0.0): Data manipulation and analysis
• numpy (>=1.24.0): Numerical computing
• scikit-learn (>=1.3.0): Machine learning
• matplotlib (>=3.7.0): Plotting and visualization
• seaborn (>=0.12.0): Statistical data visualization
• jupyter (>=1.0.0): Interactive notebooks

Sample Data Note

The dataset we are using for this course has been generated randomly using different python scripts. All data is simulated and not representative of any real or actual dataset

Getting Help

• Official Documentation:
  • Python Docs
  • Pandas Docs
  • NumPy Docs
  • Scikit-learn Docs

Next Steps After This Workshop

1. Build small projects: Apply what you learned to real problems
2. Learn more advanced topics:
   • Advanced Pandas operations
   • More machine learning algorithms
   • Data visualization with Plotly
   • Web scraping with BeautifulSoup

Free Beginner Python Resources

• HarvardX: CS50's Introduction to Programming with Python
• Google Crash Course on Python
• Corey Schafer Pandas Tutorials

Contact Information

If you have any questions, feel free to reach out:

• Hassan Abdurasul - Hassan.Abdulrasul@camh.ca
• Bailey Ng - bailey.ng@mail.utoronto.ca

License

This workshop material is provided for educational purposes. Feel free to use and modify for your learning.

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