f2a 1.1.0

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

f2a — File to Analysis

One line of code → Full statistical analysis + interactive HTML report. 24+ file formats, HuggingFace datasets, 6 languages, 20+ analysis modules, 50+ visualizations.

Generated from f2a.analyze("lerobot/roboturk") — a single line of code.

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Live Sample Report

📊 View Sample Report (lerobot/roboturk) ← GitHub Pages (recommended)

A fully self-contained interactive HTML report generated from the lerobot/roboturk dataset.

Alternative: Download raw HTML and open in your browser.

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Installation

pip install f2a

For advanced analyses (UMAP, ADF tests):

pip install f2a[advanced]

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Quick Start

import f2a

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

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

# ── Access results ───────────────────────────────────
report.stats.summary             # Descriptive statistics (DataFrame)
report.stats.correlation_matrix  # Correlation matrix
report.stats.advanced_stats      # Advanced analysis results
report.schema.columns            # Column type information
report.to_dict()                 # Everything as a dictionary

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Example: Analyzing a HuggingFace Dataset

import f2a

report = f2a.analyze("https://huggingface.co/datasets/lerobot/roboturk")

shape: (187507, 11) | subsets: 1
  default/train: (187507, 11)

report.show()

╔══════════════════════════════════════════════════════════╗
║  f2a Analysis Report — lerobot/roboturk                 ║
╠══════════════════════════════════════════════════════════╣
║  Rows: 187,507  ·  Columns: 11                         ║
║  Numeric: 9  ·  Categorical: 0  ·  Text: 0             ║
║  Datetime: 0  ·  Boolean: 0                             ║
╚══════════════════════════════════════════════════════════╝

# Save interactive HTML report (2.5 MB self-contained file)
path = report.to_html("output/")
print(path)
# → output/lerobot_roboturk_20260317_090024_report.html

📊 View this report live

# Access statistics programmatically
report.stats.summary
#          timestamp   episode_index  frame_index  ...
# count   187507.00      187507.00    187507.00   ...
# mean         ...           ...          ...     ...
# std          ...           ...          ...     ...

report.stats.correlation_matrix
#                   timestamp  episode_index  frame_index  ...
# timestamp          1.000000      0.978193     0.054412  ...
# episode_index      0.978193      1.000000    -0.003887  ...

# Advanced analysis results
report.stats.advanced_stats.keys()
# dict_keys(['advanced_distribution', 'advanced_correlation', 'clustering',
#            'dimreduction', 'feature_insights', 'advanced_anomaly', ...])

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Multi-Subset HuggingFace Datasets

Datasets with multiple configs and splits are automatically discovered and analyzed.

report = f2a.analyze("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}")

# Load specific subset
report = f2a.analyze("FINAL-Bench/ALL-Bench-Leaderboard", config="agent", split="train")

The HTML report generates tabbed navigation — each subset/split gets its own analysis page.

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HTML Report Features

report.to_html() generates a single self-contained HTML file (no external dependencies) with:

📑 Two-Depth Tab Navigation

[Subset/Split Tabs]
  └── [Basic] | [Advanced]
        ├── Basic: 13 analysis sections
        └── Advanced: 10 advanced analysis sections

🎯 Interactive Elements

Feature	Description
Metric Tooltips	Hover any table header to see a detailed explanation of the metric
Method Info Modals	Click the ⓘ button on each section to see a detailed beginner-friendly explanation
Image Zoom Modal	Click any chart to view full-size with zoom/pan/drag support
Draggable Tables	Wide tables support horizontal drag-scrolling with sticky first column
6-Language i18n	English, Korean, Chinese, Japanese, German, French — switch in the header
Dark/Light Theme	Automatic system preference detection + manual toggle
Responsive Layout	Works on desktop, tablet, and mobile

📖 Beginner-Friendly Descriptions

Every section and every metric includes:

• Detailed modal descriptions with HTML formatting, examples, and analogies
• Beginner tips (초심자 팁 / Anfänger-Tipp / Conseil débutant / 初心者向けヒント / 初学者提示)
• Interpretation guidance — what does this number actually mean?
• All descriptions are fully translated into 6 languages (not machine-translated placeholders)

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Analysis Modules

Basic Analysis (13 sections)

Section	Key Metrics
Overview	Row/column count, type distribution, memory usage
Data Quality	Completeness, uniqueness, consistency, validity (0–100%)
Preprocessing	Applied steps, before/after comparison
Descriptive Statistics	Mean, median, std, SE, CV, MAD, min/max, quartiles, IQR, skewness, kurtosis
Distribution Analysis	Shapiro-Wilk, D'Agostino, KS, Anderson-Darling normality tests
Correlation Analysis	Pearson, Spearman, Kendall matrices, Cramér's V, VIF
Missing Data	Per-column missing ratio, row distribution, pattern analysis
Outlier Detection	IQR method, Z-score method, per-column outlier stats
Categorical Analysis	Frequency, entropy, normalized entropy, chi-square independence
Feature Importance	Variance ranking, mean absolute correlation, mutual information
PCA	Explained variance, scree plot, loadings heatmap, biplot
Duplicates	Exact duplicate rows, column-wise uniqueness
Warnings	High correlation, high missing ratio, constant columns

Advanced Analysis (10 sections)

Section	Techniques
Advanced Distribution	Best-fit distribution selection (7 candidates), power transform analysis, Jarque-Bera test, ECDF, KDE bandwidth optimization
Advanced Correlation	Partial correlation, mutual information matrix, bootstrap confidence intervals, correlation network graph
Clustering	K-Means (elbow method), DBSCAN, hierarchical clustering (dendrogram), cluster profiling
Dimensionality Reduction	t-SNE, UMAP (optional), Factor Analysis
Feature Insights	Interaction detection, monotonic relationships, optimal binning, cardinality analysis, data leakage detection
Anomaly Detection	Isolation Forest, Local Outlier Factor (LOF), Mahalanobis distance, ensemble consensus
Statistical Tests	Levene, Kruskal-Wallis, Mann-Whitney U, chi-square goodness-of-fit, Grubbs test, ADF stationarity
Insight Engine	Auto-generated prioritized natural-language insights
Cross Analysis	Outlier × cluster intersection, Simpson's paradox detection
ML Readiness	Multi-dimensional ML-readiness scoring, encoding recommendations, data type suggestions

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Visualizations (50+)

Category	Charts
Distribution	Histogram + KDE, boxplots, violin plots, Q-Q plots
Correlation	Heatmap (Pearson/Spearman/Kendall), partial correlation heatmap, MI heatmap, bootstrap CI plot, network graph
Missing	Missing matrix, bar chart, heatmap
Outlier	Box plots with outlier markers, scatter plots
Categorical	Bar charts, frequency tables
PCA	Scree plot, cumulative variance, loadings heatmap, biplot
Clustering	Elbow curve, silhouette plot, cluster scatter, dendrogram, cluster profiles
Advanced Distribution	ECDF, power transform comparison, KDE bandwidth grid
Dimensionality Reduction	t-SNE scatter, Factor Analysis loadings
Anomaly	Isolation Forest scores, LOF scores, Mahalanobis distances, consensus heatmap
Quality	Radar chart (4 dimensions), per-column quality bars
Insights	Insight summary cards, cross-analysis Venn diagrams

All charts are inline base64 PNG — no external image files needed.

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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, full URL, or org/dataset pattern

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Configuration

from f2a import AnalysisConfig

# ── Preset configs ───────────────────────────────────
config = AnalysisConfig.fast()        # Skip PCA, feature importance, advanced
config = AnalysisConfig.minimal()     # Descriptive + missing only
config = AnalysisConfig.basic_only()  # All basic on, all advanced off

# ── Custom config ────────────────────────────────────
config = AnalysisConfig(
    advanced=True,
    clustering=True,
    advanced_anomaly=True,
    statistical_tests=True,
    insight_engine=True,
    cross_analysis=True,
    ml_readiness=True,
    outlier_method="zscore",          # "iqr" (default) or "zscore"
    outlier_threshold=3.0,            # Z-score cutoff
    correlation_threshold=0.9,        # High-correlation warning threshold
    pca_max_components=10,
    max_cluster_k=10,                 # Max K for K-Means elbow search
    tsne_perplexity=30.0,
    bootstrap_iterations=1000,
    max_sample_for_advanced=5000,     # Subsample for expensive analyses
)

report = f2a.analyze("data.csv", config=config)

Config Options

Option	Default	Description
descriptive	True	Basic descriptive statistics
distribution	True	Distribution & normality tests
correlation	True	Correlation matrices
outlier	True	Outlier detection
categorical	True	Categorical variable analysis
feature_importance	True	Feature importance ranking
pca	True	PCA analysis
duplicates	True	Duplicate detection
quality_score	True	Data quality scoring
advanced	True	Master toggle for all advanced analyses
advanced_distribution	True	Best-fit distribution, ECDF, power transform
advanced_correlation	True	Partial correlation, MI matrix, bootstrap CI
clustering	True	K-Means, DBSCAN, hierarchical
advanced_dimreduction	True	t-SNE, UMAP, Factor Analysis
feature_insights	True	Interaction & leakage detection
advanced_anomaly	True	Isolation Forest, LOF, Mahalanobis
statistical_tests	True	Levene, Kruskal-Wallis, Grubbs, ADF
insight_engine	True	Auto-generated insights
cross_analysis	True	Cross-dimensional analysis
column_role	True	Column role detection
ml_readiness	True	ML readiness scoring

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API Reference

f2a.analyze(source, **kwargs) → AnalysisReport

Parameter	Type	Description
source	str	File path, URL, or HuggingFace dataset identifier
config	AnalysisConfig	Analysis configuration (optional)
config	str	HuggingFace dataset config/subset name (optional)
split	str	HuggingFace dataset split name (optional)

AnalysisReport

Attribute / Method	Type	Description
.shape	tuple[int, int]	(total_rows, columns)
.schema	SchemaInfo	Column types and metadata
.stats	StatsResult	All statistical results
.stats.summary	DataFrame	Descriptive statistics table
.stats.correlation_matrix	DataFrame	Correlation matrix
.stats.advanced_stats	dict	Advanced analysis results
.subsets	list[SubsetReport]	Per-subset results (multi-subset HF datasets)
.warnings	list[str]	Analysis warnings
.show()	—	Print summary to console
.to_html(output_dir)	Path	Save interactive HTML report
.to_dict()	dict	Export all results as dictionary

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Project Structure

f2a/
├── __init__.py              # Public API: analyze(), AnalysisConfig
├── _version.py
├── core/
│   ├── analyzer.py          # Main analysis orchestrator
│   ├── config.py            # AnalysisConfig dataclass
│   ├── loader.py            # 24+ format data loader
│   ├── preprocessor.py      # Data preprocessing pipeline
│   └── schema.py            # Schema inference
├── stats/                   # 20 analysis modules
│   ├── descriptive.py       # Mean, median, std, quartiles, etc.
│   ├── distribution.py      # Normality tests, skew/kurtosis
│   ├── correlation.py       # Pearson, Spearman, Kendall, VIF
│   ├── missing.py           # Missing data analysis
│   ├── outlier.py           # IQR / Z-score outlier detection
│   ├── categorical.py       # Frequency, entropy, chi-square
│   ├── feature_importance.py
│   ├── pca_analysis.py
│   ├── duplicates.py
│   ├── quality.py           # 4-dimension quality scoring
│   ├── advanced_distribution.py
│   ├── advanced_correlation.py
│   ├── advanced_anomaly.py  # Isolation Forest, LOF, Mahalanobis
│   ├── advanced_dimreduction.py  # t-SNE, UMAP, Factor Analysis
│   ├── clustering.py        # K-Means, DBSCAN, hierarchical
│   ├── feature_insights.py  # Interaction, leakage detection
│   ├── statistical_tests.py # Levene, KW, Mann-Whitney, ADF
│   ├── insight_engine.py    # Auto insight generation
│   ├── cross_analysis.py    # Cross-dimensional analysis
│   ├── column_role.py       # Column role inference
│   └── ml_readiness.py      # ML readiness scoring
├── viz/                     # 15 visualization modules
│   ├── plots.py             # Base plot utilities
│   ├── theme.py             # Consistent theming
│   ├── dist_plots.py
│   ├── corr_plots.py
│   ├── missing_plots.py
│   ├── outlier_plots.py
│   ├── categorical_plots.py
│   ├── pca_plots.py
│   ├── quality_plots.py
│   ├── cluster_plots.py
│   ├── advanced_dist_plots.py
│   ├── advanced_corr_plots.py
│   ├── advanced_anomaly_plots.py
│   ├── dimreduction_plots.py
│   ├── insight_plots.py
│   └── cross_plots.py
├── report/
│   ├── generator.py         # HTML report generator
│   └── i18n.py              # 6-language translations
└── utils/
    ├── exceptions.py
    ├── logging.py
    ├── type_inference.py
    └── validators.py

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Internationalization (i18n)

The HTML report supports 6 languages with a language selector in the header:

Language	Code	Description Quality
🇺🇸 English	en	Full detailed descriptions with beginner tips
🇰🇷 Korean	ko	Full detailed descriptions with 초심자 팁
🇨🇳 Chinese	zh	Full detailed descriptions with 初学者提示
🇯🇵 Japanese	ja	Full detailed descriptions with 初心者向けヒント
🇩🇪 German	de	Full detailed descriptions with Anfänger-Tipp
🇫🇷 French	fr	Full detailed descriptions with Conseil débutant

Each language includes:

• ~120 metric tooltip translations — hover any table header
• ~50 section modal descriptions — click the ⓘ button on each section
• All UI labels, buttons, and messages

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Requirements

• Python ≥ 3.10
• Core: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn
• Formats: datasets (HuggingFace), openpyxl, pyarrow, pyreadstat, tables, odfpy, lxml, duckdb
• UI: rich, jinja2
• Optional: networkx, umap-learn, statsmodels (install with pip install f2a[advanced])

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Development

# Clone and install
git clone https://github.com/CocoRoF/f2a.git
cd f2a
pip install -e ".[dev]"

# Run tests (88 tests)
pytest git_action/tests/ -q

# Lint
ruff check f2a/

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License

MIT License — See LICENSE for details.