safedata-guard 1.0.6

A lightweight framework for safely enabling LLMs to analyze pandas/Polars data without exposing raw data or blindly executing generated code.

safedata-guard

A lightweight framework for safely letting LLMs analyze pandas/Polars data without exposing raw rows or blindly running the code they generate.

Most "chat with your data" tools send the whole table to the model and run whatever code it writes, unchecked. safedata-guard fixes both halves: it sends a compact, quality-aware summary instead of raw rows, and runs the model's code behind guardrails on a copy of your data.

What it does

1. Summarises before the data reaches the model. Instead of 100,000 rows, it sends columns, types, a few sample values, basic stats, and warnings about common data traps: numbers stored as text ("$500"), the same category written several ways ("North"/"north "), dates-as-text and Excel serial dates (45292), non-unique IDs, empty/mostly-empty/constant columns, duplicate column names, and unexpected negatives.

2. Runs the model's code behind an AST screen. Before running, a static screen refuses anything outside in-memory analysis:

• imports beyond a small set (pandas, numpy, math, statistics, datetime, re)
• introspection/dunder tricks and dangerous builtins
• file/data readers and writers, however reached: read_*/to_*/write_* methods, file-backed classes (ExcelFile, ExcelWriter, HDFStore), aliases (w = df.to_csv), direct imports (from numpy import save), SQL readers, and internal helpers behind pd.io.* / np.lib.* / np.ctypeslib / np.f2py
• the df.eval() / df.query() string channels the screen can't inspect

It then runs on a copy of your data in a separate process with a timeout. The model may add/transform columns freely; afterwards the guardrail checks it didn't silently drop rows (unless allow_row_reduction=True) or return an empty result, and feeds any error back so the model fixes its own code.

Scope: please read honestly

This is defense in depth for cooperative or semi-trusted model output: it stops the destructive accidents an honest model makes and the obvious escape attempts. It is not a sandbox for deliberately malicious code. In-process Python screening can be defeated, and a child process still shares your filesystem permissions, so isolation here means timeout + crash safety, not a filesystem jail. For untrusted code, run inside OS-level isolation (container, locked-down user, or VM). PII masking and quality checks are best-effort heuristics, not a compliance guarantee.

Install

pip install safedata-guard
pip install "safedata-guard[polars]"   # optional, for Polars support

pandas or Polars frames work everywhere; the library detects the type.

Quick start

import safedata, pandas as pd

df = pd.DataFrame({"date": ["2025-01-01", "2024-05-01", "2025-08-01"],
                   "amount": [100.0, 50.0, 200.0]})

def my_model(prompt):          # plug in any LLM: text in, code out
    return "result = df[df['date'].str.startswith('2025')]['amount'].sum()"

agent = safedata.Agent(model=my_model)
out = agent.ask(df, "What were total sales in 2025?")
print(out.answer)              # 300.0
print(out.blocked, out.attempts, out.tokens)

Connecting a real model

Real models return messy text (Markdown fences, chatter, occasional failures). safedata.wrap() takes any text-in/text-out function, extracts the bare code, and raises a clear ModelError on failure, so you're not tied to one provider.

def my_call(prompt):
    return some_model_that_takes_and_returns_text(prompt)   # OpenAI, local, ...

agent = safedata.Agent(model=safedata.wrap(my_call))
out = agent.ask(df, "What were total sales in 2025?")

A stronger model just means good code on the first try and fewer retries; the safety guarantees do not depend on it.

Token saving

Sending a whole table costs tokens per row; the summary is far smaller. Measured against OpenAI's own counter, a 1,000-row table was 18,180 → 229 input tokens (98.7%) for one question; on millions of rows the saving approaches 99.99%.

print(safedata.token_savings(df))    # readable sentence
safedata.token_stats(df)             # {summary_tokens, raw_tokens, saved_*}

The raw-data figure is estimated from a small row sample (never by serialising the whole table), so it stays cheap even on huge frames; exact counts vary by provider.

PII masking

The summary includes a few real sample values, which can contain personal data. By default safedata masks obvious PII (emails, cards, phones, SSNs, IPs) before the summary leaves your machine and notes which columns were masked.

safedata.summarize(df)                    # PII masked by default
safedata.summarize(df, redact_pii=False)  # raw samples, if you are sure

Regex masking cannot catch names or addresses; build_safe_prompt(..., privacy= "mask") (below) goes further and fully withholds every detected PII column.

Data quality & AI-readiness API

The same findings are also available as structured objects you can act on, each with a rule id, severity, confidence, column, evidence, and (where possible) ready-to-run fix code.

import safedata as sd

sd.validate(df)          # list[Issue]: rule_id, severity, confidence, evidence...
sd.suggest_fixes(df)     # [{issue, column, suggested_code}], runnable pandas
sd.explain_issue(issue)  # plain-language explanation
sd.quality_score(df)     # {score 0..100, breakdown, privacy_risk}
sd.ai_readiness(df)      # {ready_for_summary, safe_to_send_raw, needs_review, ...}
sd.privacy_report(df)    # {pii_columns, high_risk, medium_risk, actions}
sd.infer_columns(df)     # {col: "identifier"|"date"|"money"|"pii_email"|...}
sd.build_safe_prompt(df, "What are the top trends?", privacy="mask")

validate() is read-only and never runs code. quality_score().privacy_risk is driven by the kind of PII found (one email column = High), kept separate from the data-quality number. build_safe_prompt(privacy="mask") withholds all PII columns, including the name/address columns regex cannot see, so they never reach the model.

Command line

safedata check sales.csv                     # summary + quality score + tokens
safedata check data.xlsx --report out.html   # also write an HTML report
safedata check sales.csv --no-redact --samples 5
safedata check sales.csv --json              # machine-readable for automation
safedata check customer.csv --fail-on pii    # exit 2 if PII present
safedata check sales.csv --fail-on high      # exit 2 on any high-severity issue

--json emits quality_score, privacy_report, ai_readiness, issues, pii_columns, tokens. --fail-on (low/medium/high/pii/any) turns safedata into a gate for CI/CD, Airflow, or pre-refresh checks. The CLI only reads and summarises; it never executes model code. Supported formats: .csv, .tsv, .xlsx, .xls, .parquet, .json. Also runs as python -m safedata check ....

Function reference

Agent loop

• Agent(model, max_retries=3, isolate=True, timeout=10.0, allow_row_reduction=False) (isolate/timeout/allow_row_reduction pass through to run_safely).
• agent.ask(df, question, verbose=False) → result with .answer, .blocked, .reason, .attempts, .tokens.

Connecting a model: wrap(call, clean=...), extract_code(text), ModelError.

Running code safely

• run_safely(code, df, result_var="result", isolate=True, timeout=10.0, allow_row_reduction=False) runs on a copy, blocks unsafe ops, checks invariants, returns the result. Raises SafetyError. If the subprocess runner is unavailable, the in-process fallback still enforces timeout via a thread.
• check_code(code) → CodeCheck(.safe, .reason); screens without running.

Looking at the data: summarize(df, redact_pii=True, mask_columns=None), report(df, path=None).

Structured analysis: validate, Issue, suggest_fixes, explain_issue, quality_score, ai_readiness, privacy_report, infer_columns, build_safe_prompt.

Tokens: token_savings(df), token_stats(df), estimate_tokens(text).

License

MIT