dataprof
High-performance data profiling and quality assessment
dataprof is a Rust and Python library for profiling tabular data. It computes column-level statistics, detects data types and patterns, and assesses data quality across dimensions informed by ISO 8000 and ISO/IEC 25012, all with bounded memory usage that lets you profile datasets far larger than your available RAM.
It is built for the first ten minutes with unfamiliar data: find sparse columns, unstable types, duplicate keys, stale timestamps, and suspicious values before they turn into pipeline bugs.
What dataprof answers quickly
| Question | What you get back |
|---|---|
| Which columns are thin, empty, or structurally broken? | Null counts, completeness metrics, and schema shape in one pass |
| Did this feed drift or spike somewhere suspicious? | Numeric summaries, outlier signals, and range checks |
| Are these IDs really unique or just pretending to be keys? | Distinct counts, uniqueness ratios, and duplicate warnings |
| Are my timestamps plausible and fresh? | Future-date detection, stale-data signals, and timeliness scoring |
| Did parsing silently go wrong? | Type inference, pattern matches, format violations, and source metadata |
Pick your entry point
| You are doing this | Start with |
|---|---|
| Embedding profiling in a Rust service, ETL job, or batch tool | cargo add dataprof and Profiler::new().analyze_file(...) |
| Inspecting files in notebooks, validation scripts, or data apps | uv pip install dataprof and dp.profile(...) |
| Profiling streams, remote Parquet, or database queries | Rust feature flags, or a source-built Python extension with async/database features enabled |
Start in 30 Seconds
Python
uv pip install dataprof
Requires Python 3.10 or newer.
The pre-built PyPI wheels have no Python dependencies. Everything below runs on a bare pip install dataprof: local files, dicts, row dicts, bytes buffers, and every export in this section. Install the pandas extra only for the pandas-typed exports (to_dataframe(), describe() as a DataFrame) and for Parquet byte buffers. Async profiling, including HTTP URLs and remote Parquet, is in the wheel too; database connectors are the one documented feature that still needs a source build.
1. Profile
import dataprof as dp
report = dp.profile("data.csv")
print(f"{report.rows} rows, {report.columns} columns")
# Ad-hoc inputs work the same way, with no pandas involved
scratch = dp.profile({"age": [31, 42, 29], "city": ["Rome", "Milan", "Rome"]})
incoming = dp.profile(b"age,city\n31,Rome\n", format="csv")
Too big to profile fully? Get the shape first, then commit:
structure = dp.analyze_structure("data.csv") # cheap first pass
print(structure.format, len(structure.columns), structure.row_count.count)
2. Interpret
print(f"quality={report.quality_score:.1f}") # 0-100, weighted across assessed dimensions
print(report.quality_summary()) # per-dimension scores
age = report["age"]
print(age.data_type, age.mean, age.null_percentage)
3. Export
report.save("report.json") # full report, reloadable
print(report.to_markdown()) # a table for a PR comment or a notebook
4. Compare
Profile before and after a cleaning step, or yesterday against today:
before = dp.ProfileReport.load("report.json")
after = dp.profile("data_clean.csv")
delta = before.compare(after) # what changed, per column
5. Hand it to an agent
A token-bounded summary of shape, quality flags, and schema. Values matching a sensitive pattern are never echoed, and no raw cell values are included unless you ask:
print(report.to_llm_context(max_tokens=500))
Three problems, solved end to end
Each example generates its own data and runs from a clean checkout. Every number they print is real profiler output, and CI runs all six on every push.
| Scenario | What it shows | Run it |
|---|---|---|
| Messy CSV inspection · Python | A duplicated key, null-heavy columns, a negative price, and PII flagged but never printed | cargo run --example messy_csv_inspection |
| ETL quality gate · Python | Accept or reject a daily drop on thresholds, with the rejection reason in the log | cargo run --example etl_quality_gate |
| Before/after cleaning · Python | Save a baseline report, diff it against the cleaned data, and check the defects really went away | cargo run --example before_after_cleaning |
See examples/README.md for the Python commands and a note on two quality metrics that surprise people.
Rust
[dependencies]
dataprof = "0.11"
# or: dataprof = { version = "0.11", default-features = false }
Minimum supported Rust version: 1.96.
use dataprof::Profiler;
let report = Profiler::new().analyze_file("data.csv")?;
println!("Rows: {}", report.execution.rows_processed);
println!("Quality: {:.1}%", report.quality_score().unwrap_or(0.0));
for col in &report.column_profiles {
println!("{} {:?} nulls={}", col.name, col.data_type, col.null_count);
}
Why it feels modern
- Fast first-pass signal -- surface null pockets, type drift, duplicate keys, and outliers quickly
- True streaming -- bounded-memory profiling with online algorithms for files bigger than RAM
- Multi-format by default -- move from CSV and JSON to Parquet, live databases, DataFrames, and Arrow batches without changing tools
- Two polished entry points -- a compact Rust facade and a Python package that feels natural in notebooks
- Async-ready -- Rust async APIs and a Python async module that ships in the wheel cover stream pipelines, services, and remote Parquet sources
- Explainable quality assessment -- seven selectively requestable dimensions, including validity and decimal-scale precision, with inspectable facts behind every score
Feature Flags
| Feature | Description |
|---|---|
arrow |
Arrow-backed columnar engine |
parquet (default) |
Parquet profiling; includes arrow |
async-streaming |
Async profiling engine with tokio |
parquet-async |
Profile Parquet files over HTTP; includes parquet and async-streaming |
database |
Database profiling (connection handling, retry, SSL) |
postgres |
PostgreSQL connector (includes database) |
mysql |
MySQL/MariaDB connector (includes database) |
sqlite |
SQLite connector (includes database) |
all-db |
All three database connectors |
For the leanest Rust build, use default-features = false or cargo --no-default-features instead of a separate minimal alias.
Supported Formats
| Format | Engine | Notes |
|---|---|---|
| CSV | Incremental, Columnar | Auto-detects , ; | \t delimiters |
| JSON | Incremental | Array-of-objects |
| JSONL / NDJSON | Incremental | One object per line |
| Parquet | Columnar | Reads metadata for schema/count without scanning rows |
| Database query | Async | PostgreSQL, MySQL, SQLite via connection string |
| pandas / polars DataFrame | Columnar | Python API only |
| Arrow RecordBatch | Columnar | Via PyCapsule (zero-copy) or Rust API |
| dict / list of dicts | Columnar | Python API only; no dependencies |
| bytes / BytesIO | Columnar | Python API only; requires format=. CSV, JSON and JSONL need no dependencies; Parquet bytes need the pandas extra |
| Async byte stream | Incremental | Any AsyncRead source (HTTP, WebSocket, etc.) |
Reported columns follow source order in every format and on every transport: the CSV header, the Parquet/Arrow schema, and for JSON/JSONL the first record's field order, with fields that only appear in later records appended where they were first seen. Converting a dataset between formats does not reshuffle the report.
Quality Metrics
dataprof reports seven quality dimensions informed by concepts in ISO 8000-8 and ISO/IEC 25012:
| Dimension | What it measures |
|---|---|
| Completeness | Missing-cell percentage, share of rows with no nulls, columns past the null threshold |
| Consistency | Data type consistency, format violations, encoding issues |
| Uniqueness | Duplicate rows, key uniqueness, high-cardinality warnings |
| Accuracy | Outlier ratio, range violations, negative values in positive-only columns |
| Timeliness | Future dates, stale data ratio, temporal ordering violations in explicit temporal columns |
| Validity | Conformance to confidently detected semantic patterns, such as email or identifier formats |
| Precision | Consistency of effective decimal places within floating-point columns |
The overall quality score (0 -- 100) is dataprof's weighted average of the
dimensions that had data to assess. The aggregation formula is not mandated or
certified by ISO. Rust callers can customize the relative weights through
IsoQualityConfig::score_weights; default weights are 0.25 / 0.20 / 0.15 /
0.15 / 0.10 / 0.10 / 0.05 in the table order above.
Documentation
Start here
- Why dataprof? -- an honest comparison with Polars/pandas
describe()and ydata-profiling, including when to prefer them - Getting Started -- the shortest path from mystery dataset to useful signal
- Examples Cookbook -- focused Rust and Python recipes you can adapt quickly
- Agent Workflows -- copy-paste guidance for AGENTS.md, Cursor rules, and Claude Code skills
Integrate it
- Python API Guide -- files, DataFrames, Arrow interop, exports, and optional source-built async/database features
- Database Connectors -- PostgreSQL, MySQL, SQLite setup and connection patterns
Understand it
- Crate Redesign Notes -- what the facade owns and why the workspace is split this way
- Release notes -- upgrade checklist, migration details, and known limitations
- Contributing
- Changelog
Historical
- Archived CLI Guide -- pre-0.8 reference only
Citing dataprof
Use the Cite this repository button in the GitHub sidebar, which generates
APA and BibTeX from CITATION.cff, or read that file directly.
The citation is for the software. dataprof has no associated publication or DOI,
so CITATION.cff carries no preferred-citation entry and the generated BibTeX
is a @misc software record. Reproducible benchmark material lives in
scalcom2026-dataprof.
License
Dual-licensed under either the MIT License or the Apache License, Version 2.0, at your option.
1 maintainer
Metadata
1 maintainer
Release files for dataprof 0.11.0
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