Advanced Data Analysis
by mochalatte
Automate Python-based data cleaning, statistical analysis, and visualization for uploaded files in the code interpreter.
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Works with the AI tools you already use
See it in action
You say
I uploaded q4_sales.csv. Clean the revenue column, group by region, and give me a bar chart of the totals saved as a PNG. Also, run a regression of revenue against units.
Your agent does
Python script executed successfully.
- Cleaned 'revenue' (removed symbols, cast to float).
- Grouped by 'region'.
- Generated 'q4_revenue_region.png'.
- Stats: slope=12.45, r=0.89, p=1.2e-05. [Download q4_revenue_region.png] [Download summary.csv]
What you get
About this skill
The problem
Vague data prompts often result in broken Python code, unhandled null values, or memory errors. Manual data cleaning and visualization in a sandbox environment is time-consuming and prone to silent failures.
What it does
- Cleans and transforms raw uploads using
pandas, handling currency formats, whitespace, and duplicates. - Generates professional visualizations with
matplotlibandseaborn, including automatic axis labeling and file exports. - Performs statistical analysis including linear regressions, t-tests, and correlation matrices.
- Orchestrates multi-sheet Excel processing and joins disparate datasets on shared keys.
- Implements memory-efficient chunking and explicit
dtypepinning to prevent environment crashes.
Frameworks & tools
Python, pandas, numpy, scipy, matplotlib, and seaborn.
Why this beats prompting it yourself
Standard LLM prompts often ignore data types and fail to save chart files for download. This skill enforces rigorous data validation and export recipes, ensuring you get runnable code and tangible files instead of just text descriptions.
Use cases
- Clean and join customer orders with CRM data for a unified reporting view.
- Generate publication-ready PNG charts from messy CSV sales exports.
- Run A/B test significance checks and linear regressions on experiment data.
- Reshape daily logs into monthly pivot tables for executive summaries.
Known limitations
Limited to the Python sandbox environment. Cannot access external databases, live APIs, or the web. Large files exceeding 2GB require sampling or chunking to stay within RAM limits.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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