Building a Modular Data Analysis Pipeline with Lilac: Functional Programming for Clean, Scalable Insights

Setting Up the Environment

The tutorial begins by installing essential libraries including Lilac, Pandas, and NumPy to facilitate data handling and analysis. Running the installation command ensures access to the full Lilac suite and powerful data manipulation tools.

!pip install lilac[all] pandas numpy

Importing Libraries

The setup includes importing core Python libraries such as json, uuid, pandas, and pathlib, alongside functional programming utilities from functools and the Lilac library for dataset management.

Functional Utilities for Data Transformation

Key functional programming utilities are defined to streamline data processing:

• pipe allows chaining of multiple functions in a clear sequence.
• map_over and filter_by provide functional wrappers for mapping and filtering iterables.

A realistic sample dataset is created with entries containing fields like id, text, category, score, and tokens, simulating real-world data for analysis.

Initializing the Lilac Project and Dataset

Functions are provided to set up a Lilac project directory with a unique name and to create a dataset from the sample data by writing it to a JSONL file. The dataset configuration ties the data source into Lilac’s framework.

Extracting and Filtering Data

Data is extracted from the Lilac dataset into a Pandas DataFrame for flexible manipulation. Multiple filters are applied functionally to create subsets based on score thresholds, categories, token counts, duplicate removal, and combined quality criteria.

Data Quality Analysis

The dataset undergoes quality assessment measuring metrics such as total records, unique texts, duplicate rates, average scores, category distributions, and token statistics.

Data Transformations

Various transformations are defined to enrich the data:

• Normalizing scores relative to the maximum.
• Categorizing token lengths.
• Assigning quality tiers based on scores.
• Ranking categories by scores.

These transformations can be selectively applied to the dataset to enhance analysis.

Exporting Filtered Data

Filtered datasets are exported as JSONL files into a dedicated directory, enabling organized storage of data subsets for further use.

Main Analysis Pipeline

The complete workflow is encapsulated in a main pipeline function that:

• Sets up the project.
• Creates and loads sample data.
• Extracts and analyzes data quality.
• Applies transformations and filters.
• Exports the filtered datasets.
• Prints summaries of filtered results and top-quality entries.

This demonstrates how Lilac combined with functional programming facilitates a modular, scalable, and reusable data analysis pipeline.

Summary

This guide provides a practical approach to building a reproducible data analysis workflow that leverages Lilac’s dataset abstractions and Python’s functional programming tools. It covers all essential steps from data creation to export, highlighting the advantages of a clean, testable, and extensible pipeline architecture.