Exploring Data

Exploring Data

0. Route by size FIRST

ls -la <filepath>   # or: wc -l for row estimate

• < 200MB and < ~5M rows → ydata-profiling path (section A). Exact stats, interactive HTML.
• Larger → large-file path (section B). ydata-profiling loads everything into pandas and will crawl or OOM; the DuckDB/sketch path runs in fixed memory at any size.
• Task-specific ops (any size): duplicates, join feasibility, drift → section C.

A. Standard path (ydata-profiling)

1. Check if installed (instant)

bash /mnt/skills/user/exploring-data/scripts/check_install.sh

Returns: installed or not_installed

2. Install if needed (one-time, ~19s)

if [ "$(bash /mnt/skills/user/exploring-data/scripts/check_install.sh)" = "not_installed" ]; then
    bash /mnt/skills/user/exploring-data/scripts/install_ydata.sh
fi

3. Run analysis (always generates JSON + HTML by default)

bash /mnt/skills/user/exploring-data/scripts/analyze.sh <filepath> [minimal|full] [html|json]

Defaults: minimal + html (also generates JSON)

Output:

• eda_report.html - Interactive report for user
• eda_report.json - Machine-readable for Claude analysis

4. If Claude needs to analyze (user asks "what do you think?" etc.)

python /mnt/skills/user/exploring-data/scripts/summarize_insights.py /mnt/user-data/outputs/eda_report.json

Claude should read the stdout markdown summary, NOT the full JSON report.

5. Present findings visually (don't just hand over the ydata HTML)

The ydata report is exhaustive but dense; a link to it is a weak deliverable. Turn the JSON into a compact dashboard of the findings that matter:

python3 /mnt/skills/user/exploring-data/scripts/visualize_findings.py \
    /mnt/user-data/outputs/eda_report.json
# → /mnt/user-data/outputs/eda_findings.html

Emits a single self-contained HTML file (Chart.js from cdnjs, dark-mode aware): missingness by column (tiered good/bad), the most skewed or zero-inflated numeric distributions as small-multiple histograms, and the largest categorical breakdowns. --top N caps charts per category (default 6). Also reads profile_large.py --json output, so the large-file path gets the same treatment.

Present BOTH files: eda_findings.html for the headline read, eda_report.html for the full drill-down. In a chat surface that renders inline visuals, prefer rendering the two or three findings that actually answer the user's question as inline charts over linking a file — a link the user has to open is the weakest form of "showing" data.

Modes

Minimal (default, 5-10s): overview, variable analysis, correlations, missing values, alerts Full (10-20s): minimal + scatter matrices, sample data, character analysis

Full-mode triggers: "comprehensive analysis", "detailed EDA", "full profiling", "deep analysis". Otherwise minimal.

Time series

If the data has a datetime index/column and the user cares about temporal behavior (gaps, trends, seasonality, autocorrelation), pass tsmode=True to ProfileReport — run the venv python directly instead of analyze.sh:

ProfileReport(df, tsmode=True, sortby="<datetime_col>", title=...)

This adds gap detection, stationarity and seasonality checks that the default report omits.

Small-file drift

Comparing two versions of a dataset that BOTH fit in memory: use ydata's native compare — ProfileReport(df_a).compare(ProfileReport(df_b)).to_file(...). For files too big to load, or comparing against a months-old file you no longer have, use the sketch snapshot/drift ops in section C.

B. Large-file path (DuckDB, fixed memory)

1. Install deps (idempotent, ~10s first time)

bash /mnt/skills/user/exploring-data/scripts/install_large.sh

2. Profile

python3 /mnt/skills/user/exploring-data/scripts/profile_large.py <file> [--json out.json]

Streams the file through DuckDB: per-column null%, approximate distinct counts (HLL), min/max/mean, approximate quantiles (t-digest) for numerics, top-5 values for strings, plus quality flags (mostly-null, constant, id-like columns). Markdown lands on stdout — read it directly, no summarize step needed. Handles csv/tsv/parquet/json/ndjson. 1M rows profiles in seconds; memory is flat regardless of file size.

For ad-hoc follow-up queries on the same large file, use DuckDB SQL directly (duckdb.connect().execute("SELECT ... FROM read_csv_auto('...')")) rather than loading pandas.

C. Sketch ops (any file size, fixed memory)

All via scripts/sketch_ops.py (deps from install_large.sh). These answer questions profilers don't:

Near-duplicate rows

python3 sketch_ops.py dups <file> [--threshold 0.9] [--cols a,b,c] [--unweighted]

Exact duplicates counted by hash; near-duplicates via MinHash LSH over row tokens. --threshold is a weighted Jaccard cutoff: a token occurring c times in a row counts c times, so new york new york and york new score 0.5 rather than 1.0. Pass --unweighted for set semantics, where repeats are discarded. Use --cols to restrict to the columns that define identity.

Key overlap / join feasibility

python3 sketch_ops.py overlap <fileA> <fileB> --key <col> [--key-b <col>]

Theta sketches per key column → estimated intersection, Jaccard, and "% of A's keys in B" both ways — answers "will this join hold?" without loading either file.

Drift vs stored baseline

python3 sketch_ops.py snapshot <file> --out baseline.sketch.json   # ~20KB
python3 sketch_ops.py drift <newfile> --baseline baseline.sketch.json

Snapshot serializes HLL (all columns) + KLL quantile sketches (numeric columns) to a small JSON. Drift reports schema changes, >10% shifts in distinct counts, and IQR-relative quantile movement. The snapshot is a few KB — store it (repo, memory) and diff next month's delivery against it without keeping the original file.

Note: snapshot/dups stream rows through Python (~1M rows in a few seconds); profile_large is pure DuckDB and faster. For a quick look at a big file, profile first, sketch ops only when the question calls for them.