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duckdb-parquet-lab-workflow

Query Parquet data with DuckDB and convert results to pandas DataFrames.

5|1|Updated Dec 30, 2024
Search Tags:#sql#pandas#data-analysis#parquet#duckdb#eda#data-processing
One-click install
npx skills add https://github.com/crossxwill/IML4Finance --skill duckdb-parquet-lab-workflow
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: duckdb-parquet-lab-workflow
Source: https://github.com/crossxwill/IML4Finance/tree/main/.github/skills/duckdb-parquet-lab-workflow
Command: npx skills add https://github.com/crossxwill/IML4Finance --skill duckdb-parquet-lab-workflow

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This workflow eliminates the manual steps of loading Parquet files, querying with DuckDB, inspecting schemas, performing joins, and converting results into pandas for analysis.

Core Features & Use Cases

  • Load Parquet data with DuckDB and query it using SQL.
  • Inspect schema with a DESCRIBE command and review metadata.
  • Build robust joins (LEFT/RIGHT) to preserve row counts while combining datasets.
  • Convert query results to pandas DataFrames for modeling and reporting.
  • Use the standard SQL patterns documented in templates/duckdb_snippets.md for consistency.

Quick Start

Load Parquet data with DuckDB, describe the table schema, join relevant tables with explicit join types, and convert the final result to a pandas DataFrame.

Frequently Asked Questions about duckdb-parquet-lab-workflow

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I query Parquet files with DuckDB and convert results to pandas?▼

Query Parquet files with DuckDB using SQL, then convert the query results directly into pandas DataFrames. This workflow handles schema inspection, joins, and dataframe conversion for analysis.

What is the best way to inspect a Parquet schema before joining tables in DuckDB?▼

Inspect a Parquet schema in DuckDB using the DESCRIBE command to review metadata and column types. This ensures your joins and feature engineering steps align with the underlying data structure.

Do I need a specific Python environment to run DuckDB queries on Parquet data?▼

You need a Python environment with both DuckDB and pandas installed to query Parquet data. The workflow references standard SQL patterns to guide data exploration and dataframe conversion.

Can I preserve row counts when joining multiple Parquet datasets in DuckDB?▼

Build robust LEFT or RIGHT joins in DuckDB to preserve row counts while combining Parquet datasets. This prevents data loss during feature engineering and reporting workflows.

Does DuckDB work with pandas for data exploration and reporting on Parquet datasets?▼

DuckDB works with pandas by converting SQL query results into DataFrames for modeling and reporting. This combination streamlines data exploration, schema inspection, and joins on Parquet data.

Are there limitations when converting large DuckDB query results to pandas DataFrames?▼

Converting large DuckDB query results to pandas DataFrames may encounter memory constraints inherent to pandas. Use SQL filtering and aggregation before conversion to manage dataset size effectively.

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