Zero-Effort Data Discovery & Analysis
C++17-powered EDA and data cleaning engine for Python.
Guide · API reference · How it works · Contributing · Security
What is Zedda
Zedda profiles, cleans, and validates datasets from a single Python call. Its core is written in C++17 and streams data in constant memory, so it scales from a 900-row CSV to a terabyte-scale Parquet file without changing how you use it.
Installation
pip install --upgrade pip # Ensure pip >= 22.3 for abi3 wheel recognition
pip install zedda # CSV only — zero native deps
pip install "zedda[parquet]" # adds Parquet/Arrow/Feather support
pip install "zedda[clean]" # adds fuzzy typo detection
pip install "zedda[ai]" # adds AI Q&A (zd.ask with Groq/OpenAI)
pip install "zedda[parquet,clean,ai]" # everything together
Platform support:
| Platform | Base install | [parquet] extra |
|---|---|---|
| Linux x86_64 (manylinux_2_28 / glibc >= 2.28 / musl) | ✅ prebuilt wheel | ✅ prebuilt pyarrow wheel |
| Linux ARM64 (aarch64) | ✅ prebuilt wheel | ✅ prebuilt pyarrow wheel |
| macOS Intel (x86_64) | ✅ prebuilt wheel | ✅ prebuilt pyarrow wheel |
| macOS Apple Silicon (ARM64) | ✅ prebuilt wheel | ✅ prebuilt pyarrow wheel |
| Windows x86_64 | ✅ prebuilt wheel | ✅ prebuilt pyarrow wheel |
| Windows ARM64 | ✅ prebuilt wheel | ⚠️ pyarrow has no win_arm64 wheel — Parquet requires manual build |
| Python 3.13 free-threaded (cp313t) | ⚠️ Not yet supported — use standard Python 3.13 |
conda users: Zedda is not yet on conda-forge. Install via pip inside your conda environment:
conda activate myenv pip install zedda
High-Performance Builds (x86-64-v3 / AVX2)
Prebuilt wheels on PyPI support all 64-bit x86 CPUs with runtime AVX2/AVX-512 dispatch in the scanner. For full compiler auto-vectorization across the entire C++ engine on modern CPUs (Intel Haswell+, AMD Zen+):
CMAKE_ARGS="-DZEDDA_TARGET_X86_64_V3=ON -DZEDDA_ENABLE_LTO=ON" pip install --no-binary zedda zedda
How to use
import zedda as zd
zd.profile("data.csv") # full EDA report in terminal
zd.scan("data.csv") # silent scan for CI/CD pipelines
zd.compare("train.csv", "test.csv") # train/test drift detection
zd.fix("data.csv", apply=True) # generate or apply pandas fix code
zd.ask("data.csv", "any nulls here?") # plain-English dataset Q&A
zd.ml_ready("data.csv") # readiness score for ML training
zd.warnings("data.csv") # list all issues ranked by severity
zd.clean("data.csv", output="clean.csv") # safe, backed-up auto-clean
zd.merge(["jan.csv", "feb.csv"], "out") # safely combine multiple files
zd.report("data.csv", output="rep.html") # export full report to offline HTML
Every function also accepts a pandas DataFrame directly — a file path is
never required.
import pandas as pd
df = pd.read_csv("data.csv")
zd.profile(df)
See the full list of available functions in the API reference, and how the underlying engine works in How it works.
Installation from source
git clone https://github.com/Zedda-Labs/Zedda.git --recursive
cd Zedda
# C++17 build tools (cmake, ninja) are required
pip install cmake ninja
pip install -e ".[dev]"
pytest tests/
Enterprise Data Quality FAQ
1. How does ZEDDA prevent false-positive cleaning recommendations?
ZEDDA evaluates statistical distributions across single-pass scans before flagging issues:
- ID Columns: Flagged only when cardinality is 100.0% unique across all rows.
- Zero-Variance Columns: Flagged only when a single distinct value spans 100.0% of non-null cells.
- Outliers: Identified using IQR bounds (
1.5 * IQR) combined with skewness thresholds. - Multicollinearity: Evaluated via single-pass Pearson correlation matrices (
|r| >= 0.95).
2. Is auto-cleaning (`zd.clean()`) safe for production data ingestion?
Yes. zd.clean() is fully deterministic, atomic, and safe:
- Automatic Backup: Creates
{filename}.zedda-backupprior to mutating any dataset. - Audit Trail: Outputs
{stem}.audit.jsoncontaining exact before/after row counts, quality scores, applied transformations, and timing logs. - Rollback Support: Run
zd.clean.undo("filename.csv")at any time to instantly restore the original dataset from backup.
3. How does ZEDDA integrate into CI/CD pipelines and orchestrators?
- Programmatic Python: Use
zd.scan("data.csv")inside Airflow / Prefect tasks to access.to_dict()and.to_json()metadata objects. - CLI Ingestion: Run
zedda scan data.csv --jsoninside GitHub Actions or GitLab CI to pipe structured JSON metadata directly into downstream data warehouse checks. - Air-Gapped Compliance:
zd.report()generates completely self-contained HTML files (zero CDN links, zero tracking, zero external network calls) suitable for banking and healthcare environments.
Contributing
Issues and pull requests are welcome. See CONTRIBUTING.md.
License
MIT — see LICENSE.
Metadata
Release files for zedda 0.4.9
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| zedda-0.4.9.tar.gz | 1.8 MB | Details |
Built distributions (wheels)
Total release size: 15.3 MB
Release files / zedda-0.4.9.tar.gz
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