f2a 0.1.1

File to Analysis — Automatically perform descriptive statistical analysis and visualization from any data source

f2a - File to Analysis

One line of code to analyze any data file. Automatic descriptive statistics, visualizations, and interactive HTML reports from 24+ file formats and HuggingFace datasets.

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Installation

pip install f2a

All formats (HuggingFace, Excel, Parquet, SPSS, DuckDB, etc.) are supported out of the box. No extras needed.

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Quick Start

import f2a

# Local file
report = f2a.analyze("data/sales.csv")
report.show()                    # Print summary to console
report.to_html("output/")       # Save interactive HTML report

# HuggingFace dataset (multiple input styles)
report = f2a.analyze("https://huggingface.co/datasets/imdb")
report = f2a.analyze("hf://imdb")
report = f2a.analyze("imdb")    # org/dataset pattern auto-detected

# Access results programmatically
report.stats.summary             # Summary statistics (DataFrame)
report.stats.correlation_matrix  # Correlation matrix
report.schema.columns            # Column type information
report.to_dict()                 # Everything as a dictionary

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Multi-Subset HuggingFace Datasets

Datasets with multiple subsets (configs) and splits are automatically discovered and analyzed individually.

# Auto-discover all subsets x splits
report = f2a.analyze("https://huggingface.co/datasets/FINAL-Bench/ALL-Bench-Leaderboard")
print(f"Total: {report.shape[0]} rows across {len(report.subsets)} subsets")

for s in report.subsets:
    print(f"  {s.subset}/{s.split}: {s.shape[0]} rows x {s.shape[1]} cols")

# Load specific subset via URL path
report = f2a.analyze("https://huggingface.co/datasets/FINAL-Bench/ALL-Bench-Leaderboard/viewer/agent")

# Or via explicit parameters
report = f2a.analyze("FINAL-Bench/ALL-Bench-Leaderboard", config="agent", split="train")

The HTML report includes tabbed navigation so each subset/split gets its own analysis page.

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HTML Report

report.to_html() generates a self-contained HTML file with:

• Overview cards - row count, column count, type breakdown, memory usage
• Summary statistics table - horizontally scrollable with drag support and sticky first column
• Visualizations - distribution histograms, boxplots, correlation heatmap, missing data matrix
• Warnings - high correlation alerts, high missing ratio alerts
• Tabbed UI for multi-subset datasets

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Supported Formats (24+)

Category	Formats
Delimited	.csv .tsv .txt .dat .tab .fwf
JSON	.json .jsonl .ndjson
Spreadsheet	.xlsx .xls .xlsm .xlsb
OpenDocument	.ods
Columnar	.parquet .pq .feather .ftr .arrow .ipc .orc
HDF5	.hdf .hdf5 .h5
Statistical	.dta (Stata) .sas7bdat .xpt (SAS) .sav .zsav (SPSS)
Database	.sqlite .sqlite3 .db .duckdb
Pickle	.pkl .pickle
Markup	.xml .html .htm
HuggingFace	hf:// / URL / org/dataset

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Analysis Features

Feature	Details
Descriptive Statistics	Mean, median, std, min/max, quartiles, unique count, mode
Distribution Analysis	Skewness, kurtosis, normality tests
Correlation Analysis	Pearson, Spearman, multicollinearity warnings
Missing Data	Per-column missing ratio, overall missing alerts
Type Inference	Auto-detect numeric, categorical, text, datetime, boolean
Visualization	Histograms, boxplots, correlation heatmaps, missing data matrix

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API Reference

f2a.analyze(source, **kwargs)

Parameter	Description
source	File path, URL, or HuggingFace dataset identifier
config	HuggingFace dataset config/subset name (optional)
split	HuggingFace dataset split name (optional)
**kwargs	Additional arguments passed to the data loader

Returns: AnalysisReport

AnalysisReport

Attribute / Method	Description
.shape	(rows, columns) tuple
.schema	Column types and metadata
.stats	Statistical analysis results
.viz	Visualization access
.subsets	List of SubsetReport (multi-subset HF datasets)
.warnings	List of warning messages
.show()	Print summary to console
.to_html(output_dir)	Save interactive HTML report
.to_dict()	Export all results as dictionary

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License

MIT License - See LICENSE for details.