fastdateinfer 0.1.3

Fast, consensus-based date format inference

fastdateinfer

Fast, consensus-based date format inference written in Rust with Python bindings.

Why?

The problem: Is 01/02/2025 January 2nd or February 1st?

Library	Approach	Problem
pandas	dayfirst=True hint	You must know the format
dateutil	Guess per-element	Inconsistent results
hidateinfer	Consensus voting	Correct, but slow

The solution: If your data contains 15/03/2025, we know it's DD/MM/YYYY (15 can't be a month). This insight applies to ALL dates, resolving ambiguous ones like 01/02/2025.

fastdateinfer implements this consensus algorithm in Rust — 270x faster than hidateinfer.

Installation

pip install fastdateinfer

Quick Start

import fastdateinfer

# Infer format from dates
result = fastdateinfer.infer(["15/03/2025", "01/02/2025", "28/12/2025"])
print(result.format)      # %d/%m/%Y
print(result.confidence)  # 1.0

# Just get the format string
fmt = fastdateinfer.infer_format(["2025-01-15", "2025-03-20"])
print(fmt)  # %Y-%m-%d

# Use with pandas
import pandas as pd
dates = ["15/03/2025", "01/02/2025", "28/12/2025"]
fmt = fastdateinfer.infer_format(dates)
df = pd.to_datetime(dates, format=fmt)

Benchmarks

vs hidateinfer (Python)

Tested on 29,351 real-world dates across multiple formats:

Library	Time	Speedup
fastdateinfer	22.5 ms	—
hidateinfer	6,075 ms	270x slower

vs pandas / polars

Comparison on synthetic data (DD/MM/YYYY format):

Dates	fastdateinfer	pandas (explicit)	pandas (mixed)	Ratio
100	0.05 ms	0.24 ms	0.25 ms	5x faster
1,000	0.48 ms	0.97 ms	1.02 ms	2x faster
10,000	0.74 ms	2.14 ms	2.20 ms	3x faster
100,000	3.39 ms	17.00 ms	17.50 ms	5x faster

Note: fastdateinfer does format inference while pandas just parses a known format. Yet fastdateinfer is faster because it samples intelligently (consensus converges with ~1000 dates).

Scaling

Dates	Time	Per-date
1,000	0.48 ms	0.48 µs
10,000	0.74 ms	0.07 µs
100,000	3.39 ms	0.03 µs
1,000,000	~35 ms	0.03 µs

Performance is sublinear due to smart sampling — only ~1000 dates are fully analyzed regardless of input size.

Supported Formats

Format	Example	Output
European	15/03/2025	%d/%m/%Y
American	03/15/2025	%m/%d/%Y
ISO 8601	2025-03-15	%Y-%m-%d
ISO datetime	2025-03-15T10:30:00	%Y-%m-%dT%H:%M:%S
Month name	15 Mar 2025	%d %b %Y
Month name (full)	15 March 2025	%d %B %Y
Month first	Mar 15, 2025	%b %d, %Y
2-digit year	15/03/25	%d/%m/%y
With time	15/03/25 10.30.00	%d/%m/%y %H.%M.%S
Month-year only	March, 2025	%B, %Y
Day-month only	15/Mar	%d/%b

API Reference

infer(dates, prefer_dayfirst=True, min_confidence=0.0, strict=False)

Infer date format from a list of date strings.

Arguments:

• dates: List of date strings
• prefer_dayfirst: Use DD/MM for fully ambiguous dates (default: True)
• min_confidence: Minimum confidence threshold (default: 0.0)
• strict: Raise error if any date doesn't match (default: False)

Returns: InferResult with:

• format: strptime format string
• confidence: float between 0.0 and 1.0
• token_types: list of resolved token types

result = fastdateinfer.infer(["01/02/2025", "03/04/2025"], prefer_dayfirst=False)
print(result.format)  # %m/%d/%Y (American format)

infer_format(dates, prefer_dayfirst=True)

Convenience function that returns only the format string.

fmt = fastdateinfer.infer_format(["2025-01-15", "2025-03-20"])
print(fmt)  # %Y-%m-%d

infer_batch(columns, prefer_dayfirst=True)

Infer formats for multiple columns at once.

results = fastdateinfer.infer_batch({
    "transaction_date": ["15/03/2025", "01/02/2025"],
    "created_at": ["2025-01-15T10:30:00", "2025-01-16T14:45:00"],
    "value_date": ["15-Mar-2025", "01-Feb-2025"]
})

for col, result in results.items():
    print(f"{col}: {result.format}")
# transaction_date: %d/%m/%Y
# created_at: %Y-%m-%dT%H:%M:%S
# value_date: %d-%b-%Y

How It Works

1. Tokenize: Split "15/03/2025" into [15, /, 03, /, 2025]
2. Constrain: 15 can only be Day (>12), 03 could be Day or Month, 2025 is Year
3. Vote: Across all dates, count evidence for each position
4. Resolve: Position 1 has strong Day evidence → Position 2 must be Month
5. Format: Output %d/%m/%Y

The key insight: consensus converges quickly. Even with 1 million dates, we only need to analyze ~1000 to determine the format with high confidence.

Use Cases

CSV/Data Processing

import pandas as pd
import fastdateinfer

# Read raw data
df = pd.read_csv("data.csv")

# Detect format automatically
fmt = fastdateinfer.infer_format(df["date"].dropna().tolist())

# Parse with detected format
df["date"] = pd.to_datetime(df["date"], format=fmt)

Multi-format Data Pipeline

# Different columns may have different formats
results = fastdateinfer.infer_batch({
    col: df[col].dropna().astype(str).tolist()
    for col in ["date", "value_date", "created_at"]
})

for col, result in results.items():
    df[col] = pd.to_datetime(df[col], format=result.format)

Validation

# Ensure high confidence
result = fastdateinfer.infer(dates, min_confidence=0.9)
if result.confidence < 0.9:
    raise ValueError(f"Low confidence: {result.confidence}")

Comparison

Feature	fastdateinfer	hidateinfer	pandas	dateutil
Consensus-based	✅	✅	❌	❌
Speed (10k dates)	0.74 ms	200 ms	2 ms*	N/A
Returns strptime format	✅	✅	❌	❌
Batch inference	✅	❌	❌	❌
Type hints	✅	❌	✅	✅
Pure Rust core	✅	❌	❌	❌

*pandas time is for parsing only (you must already know the format)

Building from Source

# Prerequisites
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
pip install maturin

# Clone and build
git clone https://github.com/coledrain/fastdateinfer
cd fastdateinfer
maturin develop --release

# Run tests
cargo test

License

MIT License. See LICENSE for details.

Contributing

Contributions welcome! Please open an issue or PR on GitHub.

Acknowledgments

• Inspired by hidateinfer
• Built with PyO3 for Python bindings
• Built for high-volume data processing pipelines