zedda 0.3.1

Zero Effort Data Analysis — 1TB files in seconds, C++ parallel core

Zero Effort Data Analysis

Profile any dataset in seconds — powered by a C++ parallel engine.
CSV • Parquet • Arrow  |  1TB files  |  One line of code

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Why ZEDDA?

Every Data Scientist's first step is understanding the data. But existing tools force a painful tradeoff:

Tool	500MB CSV	5GB Parquet	RAM Usage
Pandas Profiling	12 min	❌ Crash	8 GB+
ydata-profiling	8 min	❌ Crash	6 GB+
ZEDDA	3 sec	5 sec	< 200 MB

ZEDDA achieves this through a multi-threaded C++ core that processes data in parallel, combined with intelligent sampling that gives you statistically accurate results without reading every single row.

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Quick Start

Install

pip install zedda

One Line — That's It

import zedda as zd

zd.profile("transactions.csv")

Output:

⚡ zedda v0.2.0
Scanning transactions.csv...

┌─────────── Dataset Overview ⚡ SAMPLED ───────────┐
│ File:    transactions.csv                          │
│ ⚠  SAMPLED MODE  (stratified, exact nulls & range)│
│ Rows:    6,362,620                                 │
│ Cols:    11  (8 numeric, 3 string)                 │
│ Nulls:   0.0%  (0 cells)                           │
│ Scanned: 4,231 ms                                  │
└────────────────────────────────────────────────────┘

 Column           Type   Nulls   Mean (±95% CI)       Min         Max       Flags
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 step             int    0.0%    192.6 ± 0.24         1           353       ok
 amount           float  0.0%    1.793e+05 ± 701      0           1.55e+07  HIGH CARD
 oldbalanceOrg    float  0.0%    8.553e+05 ± 5,714    0           3.894e+07 ok
 isFraud          int    0.0%    0.000659 ± 5.03e-05  0           1         ok
 ...

ℹ  Means show 95% confidence interval. Null counts and min/max are exact (from Parquet footer).

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Features

🚀 Blazing Fast C++ Core

ZEDDA's profiling engine is written entirely in C++17 and compiled natively for your platform. It uses BS::thread_pool to parse data across all CPU cores simultaneously — achieving 5–8x speedup over single-threaded Python.

Python (Pandas):  1 core  → 12 seconds for 500MB
ZEDDA (C++):      8 cores → 1.5 seconds for 500MB

📊 Intelligent Auto-Sampling

Files over 500 MB automatically trigger stratified sampling — ZEDDA reads 1 million representative rows instead of the entire file. This is configurable:

# Auto (default) — ZEDDA decides based on file size
zd.profile("huge_file.csv")

# Force exact scan — no sampling, read every row
zd.profile("huge_file.csv", sample_size=-1)

# Custom sample — e.g. 5 million rows
zd.profile("huge_file.csv", sample_size=5_000_000)

Why is this safe?

• Statistics guarantees it: 1M rows is a massive sample — error margin is typically < 0.1%.
• 95% Confidence Intervals: Every mean is shown as Mean ± CI so you can see exactly how precise the estimate is.
• Parquet Footer Cheat Code: Min, Max, and Null counts are always exact — read directly from Parquet metadata in milliseconds, even for TB-scale files.

🔍 Smart Column Flags

ZEDDA automatically detects data quality issues and flags them:

Flag	Meaning	When
HIGH NULL	Column has too many missing values	Null% > 20%
CONST	Column has only one unique value	Useless for ML
HIGH CARD	Column has very high cardinality	May need encoding

⚖️ Dataset Comparison

Compare two datasets (e.g., train vs test, v1 vs v2) and detect schema changes, null rate shifts, and distribution drift:

zd.compare("train.csv", "test.csv")

⚡ zedda compare
A: train.csv  (800,000 rows)
B: test.csv   (200,000 rows)

 Column       Type A  Type B  Nulls A  Nulls B  Mean A    Mean B    Drift
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 age          int     int     0.0%     0.0%     29.7      29.4      ok
 fare         float   float   0.0%     2.1%     32.2      35.8      SHIFT
 cabin        str     MISSING 77.1%    —        —         —         REMOVED
 embarked     str     str     0.2%     0.0%     —         —         ok

• DRIFT: Mean shifted significantly (z-score > 1.0) — model retraining may be needed.
• SHIFT: Moderate change detected (z-score > 0.3).
• NEW / REMOVED: Column added or dropped between datasets.

🖥️ CLI — Profile From Your Terminal

No Python script needed. Profile any file directly from the command line:

# Profile a file
zedda run data.csv

# Compare two files
zedda compare train.csv test.csv

# Quick file info (rows, size)
zedda info data.csv

# AI-powered insights (requires OPENAI_API_KEY)
zedda run data.csv --ai

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API Reference

zd.profile(path, sample_size=None)

Scan a file, print a beautiful terminal report, and return the result.

result = zd.profile("data.csv")
# Prints colored table to terminal
# Returns DatasetProfile object

zd.scan(path, sample_size=None)

Scan a file and return the result without printing.

p = zd.scan("data.parquet")

# Access dataset-level stats
print(p.num_rows)         # 6362620
print(p.num_cols)         # 11
print(p.scan_time_ms)     # 4231.5
print(p.is_sampled)       # True

# Access column-level stats
for col in p.columns:
    print(col.name)       # "amount"
    print(col.type_str)   # "float"
    print(col.mean)       # 179329.4
    print(col.stddev)     # 603858.2
    print(col.val_min)    # 0.0
    print(col.val_max)    # 15500000.0
    print(col.null_pct)   # 0.0
    print(col.unique_approx)  # 978372

zd.compare(path_a, path_b, sample_size=None)

Compare two datasets side by side with drift detection.

zd.compare("january_sales.csv", "february_sales.csv")

Parameters

Parameter	Type	Default	Description
path	str	required	Path to CSV, Parquet, or Arrow file
sample_size	int	None	Max rows to sample. None = auto, -1 = read all

Supported Formats

Format	Extension	Zero-Copy
CSV	.csv	—
Parquet	.parquet	✅ via Arrow C Data Interface
Arrow IPC	.arrow	✅ via Arrow C Data Interface

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How It Works

┌──────────────────────────────────────────────────────────┐
│                    Python API Layer                       │
│           zd.profile() / zd.scan() / zd.compare()        │
└────────────────────────┬─────────────────────────────────┘
                         │
              ┌──────────▼──────────┐
              │   Auto-Sampling     │
              │   Decision Engine   │
              │   (>500MB = sample) │
              └──────────┬──────────┘
                         │
          ┌──────────────┼──────────────┐
          ▼              ▼              ▼
    ┌──────────┐  ┌───────────┐  ┌──────────┐
    │ CSV Path │  │  Parquet   │  │  Arrow   │
    │          │  │  Path      │  │  Path    │
    └────┬─────┘  └─────┬─────┘  └────┬─────┘
         │              │              │
         ▼              ▼              ▼
    ┌──────────┐  ┌───────────┐  ┌──────────┐
    │ C++ Multi│  │ PyArrow   │  │ PyArrow  │
    │ Threaded │  │ Stratified│  │ Batched  │
    │ Chunked  │  │ Row Group │  │ Reader   │
    │ Parser   │  │ Sampling  │  │          │
    └────┬─────┘  └─────┬─────┘  └────┬─────┘
         │              │              │
         └──────────────┼──────────────┘
                        ▼
              ┌───────────────────┐
              │  C++ Profile      │
              │  Builder Engine   │
              │  (BS::thread_pool)│
              │  ──────────────── │
              │  • Welford Online │
              │    Mean/Variance  │
              │  • HyperLogLog   │
              │    Unique Approx  │
              │  • Streaming     │
              │    Min/Max/Nulls  │
              └─────────┬─────────┘
                        ▼
              ┌───────────────────┐
              │  DatasetProfile   │
              │  Result Object    │
              └─────────┬─────────┘
                        ▼
              ┌───────────────────┐
              │  Rich Terminal    │
              │  Pretty Printer   │
              │  (colored tables) │
              └───────────────────┘

Key Algorithms

Component	Algorithm	Why
Mean & Variance	Welford's Online Algorithm	Numerically stable, single-pass
Unique Count	HyperLogLog (approx)	O(1) memory, works on billions of values
Thread Pool	BS::thread_pool	Zero-overhead, lock-free task scheduling
Parquet I/O	Arrow C Data Interface	True zero-copy — no serialization
Sampling	Stratified Row Groups	Covers start, middle, and end of file

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Project Structure

zedda/
├── src/core/               # C++ engine
│   ├── profile_builder.cpp  # Multi-threaded profiling logic
│   ├── arrow_profiler.cpp   # Arrow C Data Interface consumer
│   └── stream_reader.cpp    # CSV chunked reader
├── include/zedda/           # C++ headers
│   ├── profile_builder.hpp
│   ├── profile_result.hpp   # DatasetProfile struct
│   ├── stream_reader.hpp
│   └── BS_thread_pool.hpp   # Thread pool (MIT, header-only)
├── python/zedda/            # Python package
│   ├── __init__.py          # Public API (profile, scan, compare)
│   └── cli.py               # Typer CLI app
├── tests/                   # Test suites
├── CMakeLists.txt           # Build configuration
└── pyproject.toml           # Package metadata

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Development

Build from Source

# Clone with submodules
git clone --recursive https://github.com/prince3235/fasteda.git
cd fasteda

# Install in editable mode
pip install -e . --no-build-isolation

# Run tests
python -X utf8 tests/test_phase3.py

Requirements

• Python ≥ 3.9
• C++ Compiler with C++17 support (MSVC 19+, GCC 9+, Clang 10+)
• CMake ≥ 3.21

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Roadmap

• Phase 1 — Multi-threaded CSV parsing (5–8x speedup)
• Phase 2 — Zero-copy Parquet via Arrow C Data Interface
• Phase 3 — Intelligent Sampling Engine (1TB support)
• Phase 4 — SIMD/AVX-512 vectorized numeric parsing
• Phase 5 — Interactive HTML reports & dashboards
• Phase 6 — AI-powered data insights (GPT integration)

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Contributing

We welcome contributions! Here's how:

1. Fork the repo
2. Create your feature branch (git checkout -b feat/amazing-feature)
3. Commit your changes (git commit -m 'feat: add amazing feature')
4. Push to the branch (git push origin feat/amazing-feature)
5. Open a Pull Request

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License

MIT License — see LICENSE for details.

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Built with ❤️ and C++ by Prince Patel