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EDS 217 Environment Migration Guide: Python 3.11

This guide will help you migrate from the eds217_2024 environment (Python 3.10) to the new eds217_2025 environment (Python 3.11).

Step 1: Export Current Environment (Optional Backup)

If you want to keep a backup of your current environment packages:

# Activate your current environment
conda activate eds217_2024

# Export the environment to a backup file
conda env export > eds217_2024_backup.yml

Step 2: Create New Python 3.11 Environment

Option A: Using the provided environment.yml file

# Create the new environment from the environment.yml file
conda env create -f environment.yml

# Activate the new environment
conda activate eds217_2025

Option B: Manual creation

# Create new environment with Python 3.11
conda create -n eds217_2025 python=3.11

# Activate the new environment
conda activate eds217_2025

# Install core packages with version constraints
conda install \
  "numpy>=2.3,<3.0" \
  "pandas>=2.3,<3.0" \
  "matplotlib>=3.9,<4.0" \
  "seaborn>=0.13,<1.0" \
  jupyter \
  "jupyterlab>=4.4,<5.0" \
  "ipykernel>=6.30" \
  "scipy>=1.11" \
  "scikit-learn>=1.3" \
  "plotly>=5.17" \
  "requests>=2.31" \
  "beautifulsoup4>=4.12" \
  "lxml>=4.9" \
  "openpyxl>=3.1" \
  "xlrd>=2.0" \
  "statsmodels>=0.14"

Step 3: Register the New Environment with Jupyter

# Make sure you're in the new environment
conda activate eds217_2025

# Register the environment as a Jupyter kernel
python -m ipykernel install --user --name eds217_2025 --display-name "Python 3.11 (EDS 217 2025)"

Step 4: Verify Installation

# Check Python version
python --version
# Should show: Python 3.11.x

# Check installed packages
conda list

# Launch JupyterLab to test
jupyter lab

In JupyterLab, you should now see "Python 3.11 (EDS 217 2025)" as an available kernel option.

Step 5: Update Your Workflow

  • When creating new notebooks, select "Python 3.11 (EDS 217 2025)" as your kernel
  • For existing notebooks, you can change the kernel via Kernel → Change Kernel in JupyterLab

Step 6: Remove Old Environment (Optional)

Once you've verified everything works with the new environment:

# Remove the old environment
conda env remove -n eds217_2024

# Remove the old Jupyter kernel
jupyter kernelspec remove eds217_2024

Troubleshooting

If packages are missing:

conda activate eds217_2025
conda install [package-name]

If you need to add the environment to a specific Jupyter installation:

conda activate eds217_2025
python -m ipykernel install --user --name eds217_2025

Check available environments:

conda env list

Check available Jupyter kernels:

jupyter kernelspec list

Key Benefits of Python 3.11

  • Performance: 10-60% faster than Python 3.10
  • Better error messages: More helpful debugging information
  • Enhanced features: Latest Python language improvements
  • Improved compatibility: Better support for modern data science libraries

Package Versions and Rationale

The new environment includes carefully selected packages with version constraints for educational stability:

Core Data Science Stack

  • Python 3.11.x - Latest stable Python with 10-60% performance improvements
  • NumPy ≥2.3 - Enhanced array operations and Python 3.11 optimizations
  • Pandas ≥2.3 - Modern DataFrame operations with copy-on-write improvements
  • Matplotlib ≥3.9 - Latest plotting capabilities with better defaults
  • Seaborn ≥0.13 - Statistical visualizations optimized for modern matplotlib
  • JupyterLab ≥4.4 - Enhanced interface with better debugging tools

Scientific Computing

  • SciPy ≥1.11 - Statistical functions and scientific algorithms
  • Statsmodels ≥0.14 - Advanced statistical modeling capabilities

Data Access and Processing

  • Requests ≥2.31 - HTTP library for API calls and web data access
  • BeautifulSoup4 ≥4.12 - HTML/XML parsing for web scraping
  • lxml ≥4.9 - Fast XML/HTML parsing backend
  • OpenPyXL ≥3.1 - Modern Excel (.xlsx) file support
  • xlrd ≥2.0 - Legacy Excel (.xls) file support

Visualization and Analysis

  • Plotly ≥5.17 - Interactive visualizations for enhanced data exploration
  • Scikit-learn ≥1.3 - Machine learning algorithms for introductory ML concepts

Version Pinning Strategy

All packages include minimum version constraints to ensure:

  • ✅ Educational stability across student environments
  • ✅ Access to modern features and performance improvements
  • ✅ Compatibility with Python 3.11 optimizations
  • ✅ Security updates and bug fixes

Upper bounds prevent breaking changes while allowing patch updates.