Polars: Blazing Fast Python Framework for Clinical Trial Data

High-performance data exploration with Apache Arrow

Python

Clinical Reporting

Advanced

Authors

Michael Chow (Principal Software Engineer, Posit)

Jeroen Janssens (Head of Developer Relations, Posit PBC)

Overview

Intermediate Python Performance

Polars is a cutting-edge Python DataFrame library with high-performance backend and Apache Arrow columnar format for blazingly fast data manipulation. Learn how it accelerates clinical trial workflows from database querying to TFL preparation.

What You’ll Learn

⚡ Polars fundamentals - Lightning-fast data operations
🏗️ Apache Arrow - Columnar data format
📊 Clinical workflows - CDISC data processing
🔗 Pharmaverse-py integration
📈 Great Tables - Data presentation

Prerequisites

Required Knowledge:

Intermediate Python
Basic pandas experience helpful
Understanding of clinical trial data

Key Technologies

Polars

Apache Arrow

Great Tables

pharmaverse-py

Workshop Materials

Resources

GitHub Examples: github.com/machow/examples-great-tables-pharma

Why Polars?

Performance Advantages

🚀 10-100x faster than pandas for large datasets
💾 Memory efficient with lazy evaluation
🔄 Parallel processing out of the box
📦 Apache Arrow native format

Polars vs Pandas

# Pandas (traditional)
df = pd.read_csv("adsl.csv")
result = df[df['AGE'] > 65].groupby('ARM')['AGE'].mean()

# Polars (fast)
result = (
    pl.scan_csv("adsl.csv")
    .filter(pl.col('AGE') > 65)
    .group_by('ARM')
    .agg(pl.col('AGE').mean())
    .collect()  # Lazy evaluation
)

Clinical Trial Applications

1. Database Querying

Fast SQL-like operations
Efficient joins across SDTM domains
Lazy evaluation for large queries

2. Complex Data Wrangling

Grouping and aggregation
Window functions for time-series
Pivoting and reshaping

3. TFL Preparation

Data summarization
Creating analysis datasets
Integration with Great Tables

Learning Outcomes

✅ Master Polars DataFrame operations
✅ Leverage Apache Arrow for performance
✅ Process clinical trial data efficiently
✅ Integrate with pharmaverse-py ecosystem
✅ Create TFLs with Great Tables

Similar Workshops

Python for CSR and Submission - Complete Python workflow
pointblank: Data Validation - R equivalent for data quality

Next Steps

Full Python workflow: Python CSR workshop
High-performance in R: See duckplyr presentation

Last updated: November 2025 | R/Pharma 2025 Conference

--- title: "Polars: Blazing Fast Python Framework for Clinical Trial Data" subtitle: "High-performance data exploration with Apache Arrow" author: - "Michael Chow (Principal Software Engineer, Posit)" - "Jeroen Janssens (Head of Developer Relations, Posit PBC)" categories: [Python, Clinical Reporting, Advanced] --- ## Overview [Intermediate]{.badge .badge-intermediate} [Python]{.badge .badge-category} [Performance]{.badge .badge-category} Polars is a cutting-edge Python DataFrame library with high-performance backend and Apache Arrow columnar format for blazingly fast data manipulation. Learn how it accelerates clinical trial workflows from database querying to TFL preparation. ### What You'll Learn - ⚡ **Polars fundamentals** - Lightning-fast data operations - 🏗️ **Apache Arrow** - Columnar data format - 📊 **Clinical workflows** - CDISC data processing - 🔗 **Pharmaverse-py integration** - 📈 **Great Tables** - Data presentation ## Prerequisites ::: requirements **Required Knowledge:** - Intermediate Python - Basic pandas experience helpful - Understanding of clinical trial data ::: ## Key Technologies ::: tool-tag Polars ::: ::: tool-tag Apache Arrow ::: ::: tool-tag Great Tables ::: ::: tool-tag pharmaverse-py ::: ## Workshop Materials ::: callout-note ## Resources **GitHub Examples:** [github.com/machow/examples-great-tables-pharma](https://github.com/machow/examples-great-tables-pharma) ::: ## Why Polars? ### Performance Advantages - 🚀 **10-100x faster** than pandas for large datasets - 💾 **Memory efficient** with lazy evaluation - 🔄 **Parallel processing** out of the box - 📦 **Apache Arrow** native format ### Polars vs Pandas ``` python # Pandas (traditional) df = pd.read_csv("adsl.csv") result = df[df['AGE'] > 65].groupby('ARM')['AGE'].mean() # Polars (fast) result = ( pl.scan_csv("adsl.csv") .filter(pl.col('AGE') > 65) .group_by('ARM') .agg(pl.col('AGE').mean()) .collect() # Lazy evaluation ) ``` ## Clinical Trial Applications ### 1. Database Querying - Fast SQL-like operations - Efficient joins across SDTM domains - Lazy evaluation for large queries ### 2. Complex Data Wrangling - Grouping and aggregation - Window functions for time-series - Pivoting and reshaping ### 3. TFL Preparation - Data summarization - Creating analysis datasets - Integration with Great Tables ## Learning Outcomes ✅ Master Polars DataFrame operations\ ✅ Leverage Apache Arrow for performance\ ✅ Process clinical trial data efficiently\ ✅ Integrate with pharmaverse-py ecosystem\ ✅ Create TFLs with Great Tables ------------------------------------------------------------------------ ### Similar Workshops - [Python for CSR and Submission](../specialized/python-csr-submission.qmd) - Complete Python workflow - [pointblank: Data Validation](pointblank.qmd) - R equivalent for data quality ### Related Presentations - [Polars workshop presentation](../presentations/europe-us-sessions.qmd) - Context ### Next Steps - **Full Python workflow:** [Python CSR workshop](../specialized/python-csr-submission.qmd) - **High-performance in R:** See [duckplyr presentation](../presentations/europe-us-sessions.qmd#duckplyr-analyze-large-data-with-duckdb) ------------------------------------------------------------------------ *Last updated: November 2025 \| R/Pharma 2025 Conference*