Python Support for Dr. D’s Data Science

This site now supports both R and Python code execution and rendering in Quarto documents, using uv for fast and reliable Python environment management.

Prerequisites

• uv (install from: https://docs.astral.sh/uv/getting-started/installation/)
• Quarto (install from: https://quarto.org/docs/get-started/)

Setup

Option 1: Automated Setup (Recommended)

python setup_python.py

Option 2: Manual Setup

# Create virtual environment
uv venv

# Install all data science packages
uv pip install pandas numpy matplotlib seaborn scipy statsmodels scikit-learn sqlalchemy pyodbc psycopg2-binary folium geopandas jupyter ipykernel python-dotenv requests plotly altair

# Verify Quarto can detect Python
source .venv/bin/activate && quarto check jupyter

Using the Environment

With uv (Recommended)

# Render your site (automatically activates environment)
uv run quarto render

# Preview your site
uv run quarto preview

# Run Jupyter
uv run jupyter lab

Manual Activation

# Activate environment
source .venv/bin/activate

# Then use quarto normally
quarto render
quarto preview

Using Python in Blog Posts

Basic Python Code Block

import pandas as pd
import numpy as np

# Your Python code here
data = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
print(data)

Python Code Block with Options

#| label: my-analysis
#| echo: true
#| eval: true
#| fig-cap: "My Figure Caption"

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5, 6])
plt.show()

Common Code Block Options

• #| echo: true/false - Show/hide code
• #| eval: true/false - Execute/don’t execute code
  
• #| output: true/false - Show/hide output
• #| warning: false - Hide warnings
• #| message: false - Hide messages
• #| fig-cap: "Caption" - Add figure caption
• #| fig-width: 8 - Set figure width
• #| fig-height: 6 - Set figure height

Python Utilities

The python_utils.py file contains Python equivalents of the R functions used in your analyses:

• custom_summary() - Comprehensive statistics (equivalent to your R function)
• extract_datetime_features() - Extract date/time components
• create_response_time_plots() - Generate analysis plots
• create_geographic_map() - Interactive maps with Folium
• load_sql_data() - Database connectivity

Example Usage in a Blog Post

#| label: load-utilities
from python_utils import custom_summary, create_response_time_plots
import pandas as pd

# Load your data
df = pd.read_csv('cardiac_arrests_cy.csv')

# Get comprehensive statistics
stats = custom_summary(df['call_entry_time'])
print(stats)

# Create visualizations
fig = create_response_time_plots(df, 'call_entry_time')
fig.show()

Database Connections

For SQL Server connections (equivalent to your R setup):

import sqlalchemy as sa
from python_utils import load_sql_data, CARDIAC_ARREST_CURRENT_YEAR

# Connection string for SQL Server
conn_str = "mssql+pyodbc://server/database?driver=ODBC+Driver+17+for+SQL+Server"

# Load data using your SQL queries
df = load_sql_data(conn_str, CARDIAC_ARREST_CURRENT_YEAR)

Rendering

To render your site with Python support:

quarto render

To preview with live reload:

quarto preview

The site will automatically detect and execute both R and Python code blocks based on the language specified in the fenced code block.