nb-path 2.2

A Python path library that gives filesystem operations superpowers

nb_path: A Python Path Library with Filesystem Superpowers

[English](README.md) | [简体中文](README.zh.md)

nb_path is a super-enhanced version of Python's standard pathlib.Path. It fully inherits all the elegant features of pathlib (including the / operator) and seamlessly integrates advanced file operations from shutil, compression/decompression from zipfile, hash calculation from hashlib, dynamic module loading from importlib, and even includes powerful built-in features like grep search and rsync-style directory synchronization.

Its design philosophy is: to turn all common path-related operations into methods of the path object itself, enabling an extremely fluid chain of calls.

🆚 Comparison with pathlib

nb_path is not just a simple wrapper around pathlib; it's a powerful superset.

Feature	pathlib.Path	nb_path.NbPath	Advantage
Basic Path Operations	✅	✅	nb_path fully inherits and is compatible with all pathlib features
Advanced File/Dir Ops	❌	✅	Built-in methods like copy_to, move_to, delete, empty
Ensure Parent Exists	❌	✅	ensure_parent() method prevents FileNotFoundError
Compression/Decompression	❌	✅	zip_to() and unzip_to() for easy archive handling
Content Search (grep)	❌	✅	grep() method for efficient text search in files or directories
Intelligent Dir Sync	❌	✅	sync_to() method for rsync-style incremental synchronization
Network File Download	❌	✅	download_from_url() method to download a file directly to the path
Project Root Discovery	❌	✅	find_project_root() and find_git_root() to end path headaches
Dynamic Module Import	❌	✅	import_as_module() is a powerful tool for plugin development
Convenient Temp Files/Dirs	❌	✅	tempfile() and tempdir() context managers with auto-cleanup
Utility Toolkit	❌	✅	Built-in utilities like hash(), size_human(), expand()

✨ Core Features

• Fully pathlib Compatible: Seamless migration, zero learning curve.
• Powerful File/Directory Operations: copy_to, move_to, delete, empty, ensure_parent, etc., are more intuitive than shutil.
• Smart Compression & Decompression: zip_to() and unzip_to() for easy handling of ZIP files.
• Built-in grep Functionality: The grep() method allows for efficient text/regex searches in files or entire directories.
• Intelligent Directory Sync: The sync_to() method, a lightweight rsync, can intelligently synchronize two directories.
• Network File Download: download_from_url() downloads a file from a URL directly to the specified path.
• Project Root Discovery: find_project_root() and find_git_root() eliminate tedious relative path calculations.
• Dynamic Module Import: import_as_module() can dynamically import any .py file as a module, a powerful tool for plugin-based development.
• Convenient Temp Files/Dirs: tempfile() and tempdir() context managers return fully-featured NbPath objects and handle cleanup automatically.
• Utility Toolkit: hash(), size_human(), expand(), and more to meet various daily development needs.

🚀 Installation

pip install nb-path

⚡ Quick Start: Elegant Chaining

Imagine this common automation task: download a ZIP archive, extract it, find a specific file, process its content, and then save it to the project's output directory.

With nb_path, the entire process can be done in one go:

from nb_path import NbPath

# Simulate a data source URL
MOCK_URL = "https://example.com/data.zip" 

# Perform all operations in a temporary, auto-cleaning workspace
with NbPath.tempdir(prefix="data-processing-") as workspace:
    print(f"Created temporary workspace: {workspace}")

    # Core operations: download -> unzip -> find in unzipped dir -> read -> process
    unzipped_dir = (
        (workspace / "downloaded.zip")
        .download_from_url(MOCK_URL, overwrite=True)
        .unzip_to(workspace / "unzipped")
    )

    processed_content = (
        unzipped_dir.rglob_files("data.txt")[0].read_text().upper()
    )

    # Save the processed result to the project's output directory
    output_file = (
        (NbPath.self_py_dir() / "output" / "report.txt")
        .ensure_parent()
        .write_text(processed_content)
    )

    print(f"Processing complete, result saved to: {output_file}")

print("Temporary workspace has been automatically cleaned up.")

This example perfectly demonstrates the core advantages of nb_path: high cohesion, high readability, and high efficiency.

📖 API Guide

Here is a detailed guide to the main features of nb_path with examples.

1. File and Directory Operations

from nb_path import NbPath

# Ensure parent directory exists, then create an empty file
p = NbPath("data/reports/2024/sales.csv").ensure_parent().touch()

# Copy the file
p_copy = p.copy_to("data/reports/2024/sales_backup.csv")

# Move the file
p_moved = p_copy.move_to("data/archive/sales_2024.csv")

# Delete the file
p_moved.delete()

# Create a directory and then empty it
report_dir = NbPath("data/reports").empty()

# Recursively delete the entire directory tree
report_dir.delete()

2. Text and Data I/O

nb_path inherits read_text/write_text and read_bytes/write_bytes from pathlib and defaults to utf-8 encoding for text operations.

p = NbPath("config.txt")

# Write text
p.write_text("setting=enabled")

# Read text
content = p.read_text()
print(content)  # "setting=enabled"

3. Search and Discovery

Recursively Find Files/Directories

src_dir = NbPath("./my_project")

# Find all Python files
py_files = src_dir.rglob_files("*.py")

# Find all directories named 'tests'
test_dirs = src_dir.rglob_dirs("tests")

grep: Search for Content in Files

This is one of nb_path's "killer features".

import sys
project_dir = NbPath("./my_project")

# 1. Search for the string "import requests" in all .py files
for result in project_dir.grep("import requests", file_pattern="*.py", is_regex=False):
    print(f"{result.path.name}:{result.line_number}: {result.line_content.strip()}")

# 2. Use a regular expression to find all Flask routes
for result in project_dir.grep(r"@app\.route\(['\"](.*?)['\"]\)", file_pattern="*.py"):
    print(f"Found route: {result.match.group(1)}")

# 3. Search with 2 lines of context before and after
for result in project_dir.grep("important_logic", context=2, file_pattern="*.py"):
    print("-" * 20)
    for num, line_text in result.context_lines:
        prefix = ">>" if num == result.line_number else "  "
        sys.stdout.write(f"{prefix} {num:4d}: {line_text.rstrip()}\n")

4. Project and Path Navigation

# Automatically find the root of the Git repository containing the current file
git_root = NbPath(__file__).find_git_root()

# Find the project root based on marker files (e.g., 'pyproject.toml')
project_root = NbPath().find_project_root()

# Dynamically get the caller's file path or directory path
current_file = NbPath.self_py_file()
current_dir = NbPath.self_py_dir()

# Expand environment variables and user directories
# NbPath('$HOME/.config/my_app').expand() -> /home/user/.config/my_app
# NbPath('~/.bashrc').expand() -> /home/user/.bashrc
config_path = NbPath("$HOME/.config").expand()

5. Compression and Decompression

assets_dir = NbPath("./assets")

# Compress the entire directory into a ZIP file
zip_file = assets_dir.zip_to("assets_archive.zip", overwrite=True)

# Extract the ZIP file to a specified directory
unzipped_dir = zip_file.unzip_to("./unzipped_assets")

6. Network and Synchronization

Download a File from a URL

# Download an image and display a progress bar
image_path = NbPath("python_logo.png").download_from_url(
    "https://www.python.org/static/community_logos/python-logo-master-v3-TM.png",
    overwrite=True
)
print(f"Image downloaded to: {image_path}, Size: {image_path.size_human()}")

sync_to: Intelligent Directory Synchronization

This method only copies new or modified files, making it highly efficient.

source_dir = NbPath("./src")
deploy_dir = NbPath("./deploy")

# Synchronize the source directory to the deployment directory
# delete_extraneous=True will delete extra files in the destination (mirroring)
source_dir.sync_to(deploy_dir, delete_extraneous=True, ignore_patterns=['*.pyc', '__pycache__'])

# Perform a dry run to see what would change without actually modifying any files
print("\n--- Performing a dry run ---")
source_dir.sync_to(deploy_dir, delete_extraneous=True, dry_run=True)

7. Temporary Files and Directories

nb_path provides more user-friendly context managers than the standard library, and they return NbPath objects.

# Create a temporary configuration file
with NbPath.tempfile(suffix=".txt", prefix="config_") as tmp_file:
    print(f"Temporary file: {tmp_file}")
    tmp_file.write_text("temporary setting")
    # The file is automatically deleted when this block is exited

# Create a temporary plugin workspace
with NbPath.tempdir(prefix="plugin_") as tmp_dir:
    print(f"Temporary directory: {tmp_dir}")
    (tmp_dir / "plugin.py").write_text("print('hello from plugin')")
    # The directory and all its contents are automatically deleted here

# For debugging, you can prevent cleanup
with NbPath.tempdir(cleanup=False) as persistent_tmp_dir:
    persistent_tmp_dir.joinpath("log.txt").write_text("some debug info")
    print(f"This directory will NOT be deleted: {persistent_tmp_dir}")
assert persistent_tmp_dir.exists()

8. Dynamic Module Import (Advanced Feature)

This is one of the most unique features of nb_path, very useful for building plugin systems or dynamically loading scripts.

from nb_path import NbPathPyImporter

# Import any .py file as a module
plugin_path = NbPathPyImporter("./plugins/my_plugin.py")
my_plugin_module = plugin_path.import_as_module()

# Call a function from the plugin
my_plugin_module.run()

# Automatically import all .py files in a directory
plugins_dir = NbPathPyImporter("./plugins")
plugins_dir.auto_import_pyfiles_in_dir()

9. Utilities

p = NbPath("my_large_file.dat")
p.write_bytes(b"0" * 5 * 1024 * 1024) # Write 5MB of data

# Get file size in bytes
print(p.size())  # 5242880

# Get human-readable file size
print(p.size_human())  # "5.0 MB"

# Calculate file hash
print(p.hash())  # 'f3a3535...' (sha256)
print(p.hash('md5')) # 'a74f6...' (md5)

Contributing

Contributions of any kind are welcome! If you have good ideas, feature suggestions, or have found a bug, please feel free to submit an Issue or Pull Request.

License

This project is open-sourced under the MIT License.