filoma 1.10.1

Modular Python tool for profiling files, analyzing directory structures, and inspecting image data

Fast, multi-backend file/directory profiling and data preparation.

pip install filoma

Installation • Documentation • Interactive CLI • Quickstart • Cookbook • Roboflow Dataset Demo • Source Code

📖 New to Filoma? Check out the Cookbook for practical, copy-paste recipes for common tasks!

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filoma helps you analyze file directory trees, inspect file metadata, and prepare your data for exploration. It can achieve this blazingly fast using the best available backend (Rust, fd, or pure Python) ⚡🍃

Key Features

• 🚀 High-Performance Backends: Automatic selection of Rust, fd, or Python for the best performance.
• 📈 DataFrame Integration: Convert scan results to Polars (or pandas) DataFrames for powerful analysis.
• 📊 Rich Directory Analysis: Get detailed statistics on file counts, extensions, sizes, and more.
• 🔍 Smart File Search: Use regex and glob patterns to find files with FdFinder.
• 🖼️ File/Image Profiling: Extract metadata and statistics from various file formats.
• 🏗️ Architectural Clarity: High-level visual flows for discovery and processing. 📖 Architecture Documentation →
• 🖥️ Interactive CLI: Beautiful terminal interface for filesystem exploration and DataFrame analysis 📖 CLI Documentation →

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⚡ Quick Start & Capabilities

filoma provides a unified API for all your filesystem analysis needs. Whether you're inspecting a single file or a million-file directory, it stays fast and intuitive.

1. Simple File & Image Profiling

Extract rich metadata and statistics from any file or image with a single call.

import filoma as flm

# Profile any file
info = flm.probe_file("README.md")
print(info)

📄 See Metadata Output

Filo(
    path=PosixPath('README.md'), 
    size=12237, 
    mode_str='-rw-rw-r--', 
    owner='user', 
    modified=datetime.datetime(2025, 12, 30, 22, 45, 53), 
    is_file=True,
    ...
)

For images, probe_image automatically extracts shapes, types, and pixel statistics.

2. Blazingly Fast Directory Analysis

Scan entire directory trees in milliseconds. filoma automatically picks the fastest available backend (Rust → fd → Python).

# Analyze a directory
analysis = flm.probe('.')

# Print a high-level summary
analysis.print_summary()

📂 See Directory Summary Table

 Directory Analysis: /project (🦀 Rust (Parallel)) - 0.60s
┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ Metric                   ┃ Value                ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ Total Files              │ 57,225               │
│ Total Folders            │ 3,427                │
│ Total Size               │ 2,084.90 MB          │
│ Average Files per Folder │ 16.70                │
│ Maximum Depth            │ 14                   │
│ Empty Folders            │ 103                  │
│ Analysis Time            │ 0.60s                │
│ Processing Speed         │ 102,114 items/sec    │
└──────────────────────────┴──────────────────────┘

# Or get a detailed report with extensions and folder stats
analysis.print_report()

📊 See Detailed Directory Report

          File Extensions
┏━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━┓
┃ Extension  ┃ Count  ┃ Percentage ┃
┡━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━┩
│ .py        │ 240    │ 12.8%      │
│ .jpg       │ 1,204  │ 64.2%      │
│ .json      │ 431    │ 23.0%      │
│ .svg       │ 28,674 │ 50.1%      │
└────────────┴────────┴────────────┘

          Common Folder Names
┏━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ Folder Name   ┃ Occurrences ┃
┡━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ src           │ 1           │
│ tests         │ 1           │
│ docs          │ 1           │
│ notebooks     │ 1           │
└───────────────┴─────────────┘

          Empty Folders (3 found)
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Path                                       ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ /project/data/raw/empty_set_A              │
│ /project/logs/old/unused                   │
│ /project/temp/scratch                      │
└────────────────────────────────────────────┘

3. DataFrames & Enrichment

Convert scan results to Polars DataFrames for advanced analysis. Use .enrich() to instantly add path components, file stats, and hierarchy data.

# Scan and get an enriched filoma.DataFrame (Polars)
df = flm.probe_to_df('src', enrich=True)

print(df.head(2))

📊 See Enriched DataFrame Output

filoma.DataFrame with 2 rows
shape: (2, 18)
┌───────────────────┬───────┬────────┬───────────────┬───┬─────────┬───────┬────────┬────────┐
│ path              ┆ depth ┆ parent ┆ name          ┆ … ┆ inode   ┆ nlink ┆ sha256 ┆ xattrs │
│ ---               ┆ ---   ┆ ---    ┆ ---           ┆   ┆ ---     ┆ ---   ┆ ---    ┆ ---    │
│ str               ┆ i64   ┆ str    ┆ str           ┆   ┆ i64     ┆ i64   ┆ str    ┆ str    │
╞═══════════════════╪═══════╪════════╪═══════════════╪═══╪═════════╪═══════╪════════╪════════╡
│ src/async_scan.rs ┆ 1     ┆ src    ┆ async_scan.rs ┆ … ┆ 7601121 ┆ 1     ┆ null   ┆ {}     │
│ src/filoma        ┆ 1     ┆ src    ┆ filoma        ┆ … ┆ 7603126 ┆ 8     ┆ null   ┆ {}     │
└───────────────────┴───────┴────────┴───────────────┴───┴─────────┴───────┴────────┴────────┘

✨ Enriched columns added: parent, name, stem, suffix, size_bytes, modified_time, 
   created_time, is_file, is_dir, owner, group, mode_str, inode, nlink, sha256, xattrs, depth

• Seamless Pandas Integration: Just use df.pandas for instant conversion.
• Lazy Loading: import filoma is cheap; heavy dependencies load only when needed.

License

Shield:

This work is licensed under a Creative Commons Attribution 4.0 International License.

Contributing

Contributions welcome! Please check the issues for planned features and bug reports.

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filoma - Fast, multi-backend file/directory profiling and data preparation.