# Programmatic Eda

Systematic exploratory data analysis.

- Repository: https://github.com/nimrodfisher/data-analytics-skills (512 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-09-25
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add nimrodfisher/data-analytics-skills --skill programmatic-eda -g -a claude-code -y`
Codex: `npx skills add nimrodfisher/data-analytics-skills --skill programmatic-eda -g -a codex -y`
Cursor: `npx skills add nimrodfisher/data-analytics-skills --skill programmatic-eda -g -a cursor -y`
Gemini CLI: `npx skills add nimrodfisher/data-analytics-skills --skill programmatic-eda -g -a gemini-cli -y`

## Description

Systematic exploratory data analysis. Activate when a dataset needs profiling, structure check, nulls, outliers, distributions, correlations, before deeper analysis begins.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# When to use
- You receive a new dataset and need to understand its shape and quality before analysis
- An analysis produces surprising numbers and you want to verify the underlying data first
- A stakeholder asks "is this data reliable?" or "what's in this table?"
- You're about to run a model or statistical test and need data-quality assurance

# Process
1. **Load and overview**, run `scripts/data_overview.py` to get row count, dtypes, memory usage, and a sample. Confirm grain (what one row represents).
2. **Null profile**, run `scripts/null_profiler.py`; compare output against thresholds in `references/quality_thresholds.md` and flag columns above limits.
3. **Outlier detection**, run `scripts/outlier_detector.py` (IQR + z-score) on numeric columns; document flagged values and decide: real signal or data error?
4. **Distribution summary**, run `scripts/distribution_summary.py` for descriptive stats and univariate histograms on each numeric column.
5. **Correlation exploration**, run `scripts/correlation_explorer.py`; flag pairs with |r| > 0.8 as potential multicollinearity or redundancy.
6. **EDA checklist sign-off**, work through `references/eda_checklist.md` and confirm each item before declaring the dataset profiled.
7. **Write findings**, fill `assets/eda_report_template.md` with full profiling output; distil top issues into `assets/findings_summary.md`.

For pattern recipes (e.g. polars vs pandas equivalents, chunked reads for large files), see `references/pandas_polars_recipes.md`.

# Inputs the skill needs
- Required: dataset path (CSV / Parquet / Excel) or a DataFrame already in scope
- Required: business context, what does one row represent?
- Optional: quality threshold overrides (defaults in `references/quality_thresholds.md`)
- Optional: columns to skip (PII, binary blobs, high-cardinality IDs)

The rest: https://github.com/nimrodfisher/data-analytics-skills/blob/HEAD/01-data-quality-validation/programmatic-eda/SKILL.md