skills.rest
vuralserhat86
vuralserhat86COMMUNITY
Explore all skills by this author →
GitHub

python_data_stack

Build Python data pipelines with Polars, DuckDB, Statsmodels, and Prefect.

43|11|Updated Dec 27, 2025
Search Tags:#python#data analysis#data engineering#statistics#duckdb#polars#statsmodels
One-click install
npx skills add https://github.com/vuralserhat86/antigravity-agentic-skills --skill python-data-stack
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python_data_stack
Source: https://github.com/vuralserhat86/antigravity-agentic-skills/tree/main/skills/python_data_stack
Command: npx skills add https://github.com/vuralserhat86/antigravity-agentic-skills --skill python-data-stack

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires polars, duckdb, statsmodels, altair, ydata-profiling, prefect, dagster, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for efficient and robust data analysis, modeling, and pipeline construction using Python's most powerful libraries, enabling rigorous statistical inference and scalable data processing.

Core Features & Use Cases

  • Statistical Modeling: Fit OLS, GLM, ARIMA, and other advanced statistical models with detailed diagnostics.
  • Data Processing: Leverage Polars for high-performance data manipulation on large datasets and DuckDB for in-process SQL analytics.
  • Visualization & Profiling: Create informative visualizations with Altair/Plotnine and generate data quality reports with ydata-profiling.
  • Pipeline Orchestration: Build reliable ETL pipelines using Prefect or Dagster.
  • Use Case: Analyze a multi-gigabyte CSV dataset by loading it into Polars, performing complex SQL queries with DuckDB, generating statistical models using Statsmodels, visualizing results with Altair, and orchestrating the entire process with Prefect.

Quick Start

Use the python_data_stack skill to analyze the 'sales_data.parquet' file using Polars and DuckDB, then generate a statistical summary of the 'revenue' column.

Frequently Asked Questions about python_data_stack

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

FAQPage Schema
How do I process multi-gigabyte CSV datasets for statistical modeling in Python?▼

You can process multi-gigabyte CSV datasets by loading them into Polars for high-performance data manipulation, querying with DuckDB for in-process SQL analytics, and fitting statistical models using Statsmodels for rigorous inference and diagnostics.

What is the best way to build ETL pipelines for data analysis using DuckDB and Polars?▼

Building ETL pipelines for data analysis is best handled by using DuckDB and Polars for scalable data processing, then orchestrating the automated workflows using integrated tools like Prefect or Dagster for reliable pipeline execution.

Can I run in-process SQL analytics on large Parquet files without a separate database server?▼

Yes, you can run in-process SQL analytics directly on large Parquet files using DuckDB, which integrates seamlessly with Polars dataframes to efficiently query and manipulate large-scale datasets without needing a separate database server.

How do I generate statistical summaries and data quality reports for a dataframe?▼

Generating statistical summaries and data quality reports is done using ydata-profiling to automatically inspect dataframes, while Statsmodels fits OLS or GLM models to provide detailed statistical inference and diagnostics for your analysis.

Does this Python data stack support visualizing statistical model results with Altair?▼

Yes, this Python data stack supports visualizing statistical model results using Altair, allowing you to create informative visualizations directly from the outputs of Statsmodels and the processed Polars or DuckDB data.

When should I use Polars instead of DuckDB for large-scale data manipulation?▼

You should use Polars for high-performance dataframe transformations and complex data manipulation operations, whereas DuckDB is best utilized when you need to run in-process SQL analytics directly against large-scale datasets or Parquet files.

Related Skills

Explore More Data & Analytics Skills→
AngelP17AngelP17
1

ml-pipeline-workflow

Automate end-to-end ML pipelines from data ingestion to deployment.

Community
Advanced
BagelHoleBagelHole
46

ai-pipeline-orchestration

Orchestrate AI/ML pipelines for ingestion, training, inference, and RAG indexing.

Community
Advanced
WynterJonesWynterJones
5

data-analyst

Analyze datasets to produce statistical summaries and data-driven reports.

Community
Intermediate
TeiNamTeiNam
12

python-data-analysis

Automate Python data analysis workflows with pandas, polars, and duckdb.

Community
Advanced