# How To Eda Codex > Run a trust-first exploratory data analysis (EDA) with Codex + Python on any schema, assembling an open-source profiling / integrity / forensic toolbox before writing code. Use when handed a new dataset from an unproven pipeline and asked to "can we trust this data", "profile this", "run EDA", "check data quality", "find integrity issues", or before building any dashboard/model on unfamiliar tables. Walks the same 6-step pipeline as how-to-eda (the Claude edition) but leads with referential integrity, entity resolution, and forensic checks - and outputs findings ranked by dollar and trust impact. - Skill: `phoebefu6/how-to-eda-codex` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add phoebefu6/how-to-eda-codex` - Raw SKILL.md: https://api.skillmd.com/api/skills/phoebefu6/how-to-eda-codex/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: phoebefu6 (https://skillmd.com/u/phoebefu6) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/phoebefu6/how-to-eda-codex --- # how-to-EDA-using-codex-python Trust-first EDA: the output is a ranked list of integrity and business findings an executive can act on, with charts as evidence - not a wall of describe() output. Sibling of `how-to-eda` (Claude edition): same spine, a toolbox that Codex assembles from OpenAI/GitHub agent skills plus open-source data libraries, and a sharper focus on the defects that hide across joins. Showcase walkthrough (Everrest retail case, real executed charts): https://github.com/phoebefu6/phoebe-data-skills - `docs/how-to-eda-using-codex-python/` ## The 6 steps ### 1. Input Collect the schema (tables, columns, types, one-line descriptions) and business context. When the data comes from a fresh or unproven pipeline, the objective shifts from "what's growing" to "can this be trusted yet". Note grain and row counts. Never trust a foreign key until it has been proven to resolve. ### 2. Sample data (only when real data isn't available yet) Write a seeded generator (`numpy.random.default_rng()`) at realistic scale. Plant integrity-flavored quirks on purpose - orphan foreign keys, stale price snapshots, timezone/timestamp bugs, dirty categorical labels, fabricated (round-number / Benford-breaking) amounts, duplicate identities - and document each in the docstring. With real data, skip generation; the defects are already in there. ### 3. Objective Frame ONE trust question ("can this data be trusted for this quarter's decisions, and which defect would embarrass the team if shipped?"), then 3-5 sub-questions covering keys, values, entities, and the cleaned business read. Every chart must serve one; anything else is decoration. ### 4. Find-skills - assemble the toolbox Before writing code, activate agent skills to find the right tools, then pull the libraries that serve this objective: - **Agent layer**: OpenAI Codex CLI (authoring), superpowers / `find-skills` (route to specialists), grill-me (sharpen the objective). - **Profiling**: sweetviz or ydata-profiling (one-shot report - the 10-minute head start), missingno (missingness matrix). - **Validation**: pandera (schema + referential-integrity assertions as code), Great Expectations (promote findings to permanent CI gates). - **Forensic**: scipy + Benford's-law first-digit test, round-number heaping. - **Discipline**: dataviz (one anomaly color across every chart), schema-to-insights (DQ gate first, rank by $ / trust). Reuse beats rebuilding - the point of a shelf is that the next analysis starts here. ### 5. Code + charts One script, sectioned to mirror the sub-questions, in this order: 1. **Profiling head start**: run the profile report first; read off the free signals (missing-on-join, distinct-count blowups, negative-tailed gaps). 2. **Data-quality & integrity gate**: referential integrity (orphan FKs), duplicate identities, dirty-label normalization, timestamp sanity (no negative latency), value reconciliation (payments vs line items), Benford. Quantify each in $ or trust terms and clean before analysis. 3. **Business questions on the CLEAN data**: concentration (pareto), distributions (log scale when skewed), seasonal decomposition, day-of-week x hour, cohorts, category small-multiples (on normalized labels), geography. Rules: fixed seed stated in output; every chart titled with its FINDING, not its technique; one consistent anomaly color (amber) means "this is the problem"; name anomalous entities on the chart; 300 DPI PNGs; aim for 16+ visuals when the data supports them; kill any chart with no action attached. ### 6. Expert review Convene a panel of 4-6 senior reviewer agents, each with 10+ years in data, data insights, and business. Review the actual code + outputs through these lenses, apply the fixes, and re-run: - **Pipeline & integrity**: are foreign keys checked before joining? orphans quantified? - **Methodology**: are dirty labels normalized before rollups? DQ gate separated from analysis? - **Decision framing**: does every chart answer a sub-question? technique titles cut? - **Business impact**: is every finding quantified and ranked? would the board be misled without it? - **QA & reproducibility**: does a clean re-run reproduce output and recover every planted quirk? Keep the before/after - review that changes nothing is theater. ## Output format `findings.md`: findings ranked by $ / trust impact, each 2-3 sentences: what, evidence (which chart), recommended action. Charts in a `charts/` folder. Script(s) runnable top-to-bottom from a fresh shell. ## Baseline script (start here, then tune) This skill ships a runnable baseline in `baseline/` - the real code behind the Everrest showcase. Read it as the starting point, then tune it to the user's schema and question: - `${CLAUDE_SKILL_DIR}/baseline/eda_codex_v2.py` - trust-first EDA (integrity + profiling + forensic checks) - `${CLAUDE_SKILL_DIR}/baseline/profile_report.py` - one-shot profiling head start (sweetviz) - `${CLAUDE_SKILL_DIR}/baseline/generate_everrest_codex.py` - seeded sample data (skip when real data exists) Run it in a Python env with pandas + matplotlib (plus any extras noted). Point it at the user's data, then change what the use case needs - new columns, a different decision question, their industry. The showcase page walks the full example.