Introduction

While pip install has been the go-to package installer for Python developers, UV brings game-changing performance improvements to dependency management. UV achieves significantly faster installation speeds through several clever optimizations:

  • Parallel Downloads: Unlike pip’s sequential approach, UV downloads multiple packages simultaneously, dramatically reducing wait times for large dependency sets.
  • Wheel-First Strategy: UV prioritizes pre-built wheels over source distributions, avoiding time-consuming compilation steps when possible.
  • Rust-Based Implementation: Built with Rust’s memory safety and concurrent processing capabilities, UV handles package resolution more efficiently than pip’s Python-based implementation.

In real-world testing, UV often installs packages 5-10x faster than pip, particularly in environments with many dependencies. For data professionals working with complex libraries like pandas, numpy, scikit-learn, or pyspark, this speed difference isn’t just convenient – it’s transformative for workflow efficiency.

But UV’s capabilities extend beyond just faster installations. Today, we’ll explore one of its most powerful features: inline metadata for dependencies. This feature is particularly valuable for data engineers who need to quickly spin up isolated environments for testing transformations, validating data quality, or debugging pipeline issues.



The Power of Self-Contained Scripts

Let’s dive into a practical example. Imagine you’re working on a data quality check that needs to validate JSON structures across your data lake. Here’s how you can create a self-contained script using UV:

# /// script
# dependencies = [
#   "pandas>=2.0.0",
#   "pyspark>=3.4.0",
#   "great_expectations>=0.17.15"
# ]
# ///

import pandas as pd
from pyspark.sql import SparkSession
import great_expectations as ge

def validate_json_structure(data_path):
    # Initialize Spark session
    spark = SparkSession.builder \
        .appName("JSON Validator") \
        .getOrCreate()
    
    # Read JSON data
    df = spark.read.json(data_path)
    
    # Convert to pandas for Great Expectations
    pdf = df.toPandas()
    
    # Create Great Expectations DataFrame
    ge_df = ge.from_pandas(pdf)
    
    # Add your validation logic here
    validation_result = ge_df.expect_column_values_to_not_be_null("id")
    
    return validation_result

if __name__ == "__main__":
    result = validate_json_structure("path/to/your/data.json")
    print(f"Validation Results: {result}")

To run this script, you simply need:

uv run validate_json.py

No more worrying about virtual environments or dependency conflicts - UV handles it.



Testing Data Transformations and Performance

One of UV’s most powerful applications is enabling reproducible performance testing of different data transformation approaches. Instead of just explaining an issue or sharing code snippets, you can provide a complete, self-contained script that others can run to reproduce results exactly in an isolated environment.

Here’s a practical example comparing two different approaches to transforming sales data - one using pandas and another using polars:

# /// script
# dependencies = [
#   "pandas>=2.0.0",
#   "polars>=0.20.0",
#   "pytest>=7.0.0",
#   "numpy>=1.24.0",
#   "pytest-benchmark>=4.0.0"
# ]
# ///

import pandas as pd
import polars as pl
import numpy as np
import pytest
from pytest_benchmark.fixture import BenchmarkFixture

# Generate larger test dataset for meaningful performance comparison
def create_test_data(size: int = 1_000_000):
    """Create a large test dataset"""
    np.random.seed(42)
    return pd.DataFrame({
        'date': pd.date_range('2024-01-01', periods=size, freq='1min'),
        'product_id': np.random.randint(1, 1000, size),
        'quantity': np.random.randint(1, 100, size),
        'price': np.random.uniform(10.0, 1000.0, size)
    })

# Pandas implementation
def transform_with_pandas(df: pd.DataFrame) -> pd.DataFrame:
    """Transform sales data using pandas"""
    return df.assign(
        revenue=df['quantity'] * df['price'],
        date=pd.to_datetime(df['date'])
    ).groupby(['date', 'product_id']).agg({
        'revenue': 'sum',
        'quantity': 'sum'
    }).reset_index()

# Polars implementation
def transform_with_polars(df: pd.DataFrame) -> pd.DataFrame:
    """Transform sales data using polars"""
    pl_df = pl.from_pandas(df)
    result = pl_df.with_columns([
        (pl.col('quantity') * pl.col('price')).alias('revenue'),
        pl.col('date').cast(pl.Datetime)
    ]).groupby(['date', 'product_id']).agg([
        pl.col('revenue').sum(),
        pl.col('quantity').sum()
    ]).sort('date')
    return result.to_pandas()

def test_transformations_match():
    """Verify both implementations produce the same results"""
    test_data = create_test_data(1000)  # Smaller dataset for equality testing
    
    pandas_result = transform_with_pandas(test_data)
    polars_result = transform_with_polars(test_data)
    
    # Ensure both results match
    pd.testing.assert_frame_equal(
        pandas_result.sort_values(['date', 'product_id']).reset_index(drop=True),
        polars_result.sort_values(['date', 'product_id']).reset_index(drop=True),
        check_dtype=False  # Polars might return slightly different dtypes
    )

def test_pandas_performance(benchmark: BenchmarkFixture):
    """Benchmark pandas transformation"""
    test_data = create_test_data()
    benchmark(transform_with_pandas, test_data)

def test_polars_performance(benchmark: BenchmarkFixture):
    """Benchmark polars transformation"""
    test_data = create_test_data()
    benchmark(transform_with_polars, test_data)

if __name__ == "__main__":
    pytest.main([__file__, '--benchmark-only'])

Running this script is as simple as:

uv run test_transform.py

When sharing transformation code or debugging performance issues, this approach allows teams to:

  • Quickly reproduce and verify issues in isolated environments
  • Compare different implementation approaches objectively
  • Share complete, runnable examples instead of code fragments
  • Maintain consistent testing environments across team members

The results might look something like this:

------------------------- benchmark: 2 tests ------------------------
Name (time in ms)          Mean                Median              
-------------------------------------------------------------------
test_polars_performance    289.8261 (1.0)      288.9921 (1.0)     
test_pandas_performance    892.3182 (3.08)     891.8901 (3.09)    
-------------------------------------------------------------------

This makes it clear which approach performs better and by how much, all while ensuring the results are reproducible by anyone with the script.

This testing approach can be extended to compare various scenarios:

  1. Memory Usage Comparison

    • Add memory profiling to compare RAM usage between approaches
    • Test with different dataset sizes to understand scaling characteristics

  2. Different Data Types

    • Compare performance with string operations vs numeric operations
    • Test date/time manipulation efficiency
    • Evaluate handling of missing values

  3. Parallel Processing

    • Compare single-threaded vs multi-threaded performance
    • Test different chunk sizes for parallel processing
    • Evaluate scaling across CPU cores

  4. I/O Operations

    • Compare reading/writing performance with different file formats
    • Test compression ratio impacts
    • Evaluate network I/O effects

By leveraging UV’s isolated environments, you can ensure these comparisons are fair and reproducible, making it easier to make informed decisions about which approaches to use in production.



Conclusion

UV’s inline metadata feature represents a significant step forward in making data engineering scripts more portable and reproducible. By allowing dependencies to be specified directly within scripts, it eliminates the hassle of environment management and makes sharing code snippets much more practical.

For data engineers, this means:

  • Faster debugging and testing cycles
  • More reliable code sharing across teams
  • Easier reproduction of data quality checks
  • Simplified environment management for different transformation tasks

As we continue to deal with increasingly complex data pipelines and transformations, tools like UV that simplify dependency management become invaluable parts of our toolkit.



Resources and Further Reading