laziest-import 0.1.0rc3

Automatic lazy import library - use common libraries without explicit import statements

laziest-import

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Zero-Configuration Lazy Import Library

Import and use any installed（for the not installed, it uses pip to auto install if you permitted) module with a single line

A magical way to import Python modules - just use them!

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Table of Contents

• Key Features
• Quick Start
• Installation
• Examples
• Configuration
• API Reference
• Troubleshooting
• How It Works
• Contributing
• License

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Key Features

Feature	Badge	Description
Lazy Loading		Modules import only on first access
Background Index		Symbol index builds in background thread
Auto-Correction		Typo correction (nump → numpy)
Symbol Search		Search symbols across all modules
Strict Mode		AmbiguousSymbolError on symbol conflicts
Multi-Level Cache		Three-tier caching for speed
Dependency Analysis		Analyze module dependencies
Performance Benchmark		Benchmark imports and functions
CLI Interface		laziest-import freeze / init commands
1055+ Tests		Comprehensive test coverage
1000+ Aliases		Predefined for common packages

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Installation

# Stable version
pip install laziest-import

# Pre-release version (latest features)
pip install --pre laziest-import

# From source (latest development)
pip install git+https://github.com/ChidcGithub/Laziest-import.git

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Quick Start

Method 1: Wildcard Import (Recommended)

from laziest_import import *

# Data Science
arr = np.array([1, 2, 3]) # numpy
df = pd.DataFrame({'a': [1, 2]}) # pandas
plt.plot([1, 2, 3]); plt.show() # matplotlib

# Standard Library
print(os.getcwd()) # os
data = json.dumps({'key': 'value'}) # json
result = math.sqrt(16) # math

# Submodules (auto-loading)
svd_result = np.linalg.svd(matrix) # numpy.linalg.svd()

Method 2: Namespace Prefix (Recommended)

from laziest_import import lz

arr = lz.np.array([1, 2, 3])
df = lz.pd.DataFrame({'a': [1, 2]})

Method 3: Lazy Proxy (with Auto-Correction)

from laziest_import import lazy

# Automatic typo correction
arr = lazy.nump.array([1, 2, 3]) # nump -> numpy 
df = lazy.pnda.DataFrame() # pnda -> pandas 
arr2 = lazy.nupi.array([4, 5, 6]) # nupi -> numpy 

# Submodule shortcuts
layer = lazy.nn.Linear(10, 5) # nn -> torch.nn 
relu = lazy.F.relu(tensor) # F -> torch.nn.functional 

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Key Features

Feature	Description
Modular architecture	Clean 13-module _api/ package for maintainability
Lazy loading	Modules import only on first access, reducing startup overhead
Lazy function loading	Symbol functions loaded on-demand for faster startup
Background index build	Symbol index builds in background thread
Symbol sharding	Large packages split into shards for faster access
Submodule support	np.linalg.svd() chains submodules automatically
Auto-discovery	Unregistered names search installed modules automatically
Typo correction	Misspelling auto-correction (nump → numpy, matplotlip → matplotlib)
Abbreviation expansion	300+ abbreviations (nn → torch.nn, F → torch.nn.functional)
Fuzzy matching	Typo correction via Levenshtein distance algorithm
Strict mode	Raise AmbiguousSymbolError on symbol conflicts
CLI interface	laziest-import freeze and laziest-import init
Symbol location	lz.which() finds where symbols are defined
Auto-install	Optional: missing modules can be pip-installed automatically
Multi-level cache	Three-tier caching (stdlib/third-party/memory) for fast lookups
Cache persistence	Symbol index saved to disk with configurable TTL
Cache statistics	Track hits/misses and optimize performance
Version checking	Automatic compatibility warnings for aliases/mappings
Type hints support	LazySymbol.__class_getitem__ for generic type hints
Dependency tree analysis	lz.dependency_tree() - analyze module dependencies
Performance benchmarking	lz.benchmark() - benchmark functions and imports
Dependency pre-analysis	Scan code to predict required imports
Import profiler	Record module load times and memory usage
Environment detection	Detect virtual environments (venv/conda/virtualenv)
Conflict visualization	Find and display symbol conflicts across modules
Persistent preferences	Save/load user preferences to ~/.laziestrc
1000+ aliases	Predefined aliases for common packages
1055+ tests	Comprehensive test coverage

What's New

v0.1.0-rc3 (Current)

• Phase 5 — Fake Code Audit & Fix: All placeholder/fake code replaced with real logic
• Fixed assert True tests: 4 tests replaced with real assertions
• Fixed silent except Exception: pass: 4 tests, HookList.__call__(), _benchmark.py warmup/measure, _state_setters.py narrowed to except ImportError
• Fixed 48 zero-assertion smoke tests: Added type/value assertions
• Fixed _jupyter.py unload_ipython_extension(): No-op pass replaced with unregister_magics()
• Fixed _cache/_api.py invalidate_package_cache(): Added missing _STDLIB_SYMBOL_CACHE cleanup
• Fixed circular import in _build_known_modules_cache(): Skip scanning CWD (''/'.' paths) to prevent re-import of scripts in working directory
• 1055 tests: All passing, comprehensive coverage

v0.1.0-rc2

• OOP API Migration: 23 test files migrated from module-level API to lz singleton
• Fixed LazyImport.__getattr__: Negative cache check before expensive lookups
• Fixed reset_all(): Now reloads aliases, updates __all__, AND rebuilds symbol index
• Raised _build_symbol_index defaults: max_modules=500, timeout=60.0 for complete index coverage

v0.1.0-rc1

• Phase 4 — Alias System Upgrade: Data unification, JSON format upgrade, smart suggestions, filtered search, smart error hints, context-aware fuzzy matching, and conflict resolution
• Data Unification: 108 aliases merged from mappings/abbreviations.json into aliases/*.json; abbreviations.json and submodules.json removed
• JSON Format Upgrade: _meta metadata (category, description) added to all 27 alias letter files
• AliasNamespace API: lz.alias.list(category=), lz.alias.search(pattern, by_module=), lz.alias.suggest(package=/installed=/pattern=)
• Smart Error Hints: Typo detection with Levenshtein distance — lz.nummpy suggests Did you mean numpy?
• Context-Aware Fuzzy Matching: Loaded modules get priority bonus during auto-search
• Conflict Detection: register_alias() warns on alias overwrite
• Bug Fixes: Fixed __repr__ NameError crash (critical); fixed alias search fallback importing wrong module (critical); fixed opencv/cv2 infinite cycle; fixed sage/sagemath pointing to non-existent module; fixed 4 circular alias chains; fixed 80+ hyphenated alias values (used PyPI names instead of importable module names); fixed 70+ hyphenated alias keys (unreachable via Python syntax); fixed indentation bug hiding symbol-not-found fallback; removed 48 lines of dead code; fixed _suggest_for_package() duplicate return bug
• 1055+ tests: Comprehensive test coverage

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Examples

Symbol Search & Location

import laziest_import as lz

# Search for a symbol across all modules
results = lz.search_symbol('DataFrame')
for result in results:
 print(f"{result.module_name}.{result.symbol_name}")

# Find where a symbol is defined
loc = lz.which('sqrt')
print(f"Found at: {loc}") # numpy.sqrt

# Find all occurrences
locs = lz.which_all('sqrt')
for loc in locs:
 print(f"{loc.module_name}.{loc.symbol_name}")

Dependency Tree Analysis

import laziest_import as lz

# Analyze a module's dependency tree
tree = lz.dependency_tree('numpy', max_depth=2)
print(f"Total modules: {tree.total_modules}")
print(f"Stdlib: {tree.stdlib_count}, Third-party: {tree.third_party_count}")

# Print formatted tree
lz.print_dependency_tree(tree)

Performance Benchmarking

import laziest_import as lz

# Benchmark a function
result = lz.benchmark(
 lambda: sum(range(10000)),
 name="sum_test",
 iterations=100,
 warmup=10
)
print(f"Avg: {result.avg_time*1000:.4f}ms")
print(f"Min: {result.min_time*1000:.4f}ms")
print(f"Max: {result.max_time*1000:.4f}ms")

# Benchmark module imports
report = lz.benchmark_imports(['numpy', 'pandas', 'matplotlib'])
lz.print_benchmark_report(report)

Strict Mode (Symbol Conflict Detection)

from laziest_import import lz

# Enable strict mode
lz.symbol.config.strict = True

# Multiple modules define `sqrt` — raises AmbiguousSymbolError
# result = lz.sqrt  # Error: sqrt found in numpy, math, scipy, ...

# Use prefer() to resolve ambiguity
lz.symbol.prefer("sqrt", "numpy")
result = lz.sqrt(16)  # Now resolves to numpy.sqrt

CLI: Freeze & Init

# Scan project and freeze alias usage
laziest-import freeze

# Generate .laziestrc config file
laziest-import init

The freeze command produces imports.laziest.json — a manifest of all lazy imports used across your project, ideal for CI validation.

Auto-Install (Optional)

from laziest_import import *

# Enable auto-install
lz.enable_auto_install()

# Accessing uninstalled modules triggers installation
arr = np.array([1, 2, 3]) # If numpy missing, prompts to install

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Configuration

User Configuration

import laziest_import as lz

# Create default config file
lz.create_rc_file()

# Load config
config = lz.load_rc_config()

# Get specific value
value = lz.get_rc_value('debug', default=False)

# Config info
info = lz.get_rc_info()

Cache Configuration

import laziest_import as lz

# Set custom cache directory
lz.set_cache_dir('./my_cache')

# Configure cache settings
lz.set_cache_config(
 symbol_index_ttl=3600, # Symbol index TTL: 1 hour
 stdlib_cache_ttl=2592000, # Stdlib cache TTL: 30 days
 max_cache_size_mb=200 # Max cache size: 200 MB
)

# Get cache statistics
stats = lz.get_cache_stats()
print(f"Hit rate: {stats['hit_rate']:.1%}")

# View cache status
info = lz.get_file_cache_info()
print(f"Cache size: {info['cache_size_mb']:.2f} MB")

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API Reference

Alias Management

Function	Description
register_alias(alias, module_name)	Register an alias
register_aliases(dict)	Register multiple aliases
unregister_alias(alias)	Remove an alias
list_loaded()	List loaded modules
list_available()	List all available aliases
get_module(alias)	Get module object
clear_cache()	Clear memory cache

Symbol Search

Function	Description
enable_symbol_search()	Enable symbol search
disable_symbol_search()	Disable symbol search
search_symbol(name)	Search for classes/functions
rebuild_symbol_index()	Rebuild symbol index
which(symbol)	Find symbol location
which_all(symbol)	Find all symbol locations

Auto-Install

Function	Description
enable_auto_install()	Enable auto-install
disable_auto_install()	Disable auto-install
install_package(name)	Install a package manually
set_pip_index(url)	Set mirror URL

Cache Management

Function	Description
get_cache_version()	Get cache version
set_cache_config(...)	Configure cache settings
get_cache_config()	Get cache configuration
get_cache_stats()	Get cache statistics
reset_cache_stats()	Reset cache statistics
invalidate_package_cache(pkg)	Invalidate package cache
get_file_cache_info()	Get file cache info
clear_file_cache()	Clear file cache
set_cache_dir(path)	Set cache directory

Analysis & Profiling

Function	Description
analyze_file(path)	Analyze Python file for imports
analyze_source(code)	Analyze source code string
analyze_directory(path)	Analyze all files in directory
start_profiling()	Start import profiler
stop_profiling()	Stop import profiler
get_profile_report()	Get profiling report
print_profile_report()	Print formatted report
dependency_tree(module)	Analyze module dependency tree
print_dependency_tree(tree)	Print dependency tree
benchmark(func)	Benchmark a function
benchmark_imports(modules)	Benchmark module imports
detect_environment()	Detect Python environment
show_environment()	Display environment info
find_symbol_conflicts()	Find symbol conflicts
show_conflicts()	Display conflicts table

Preferences

Function	Description
set_symbol_preference(name, module)	Set symbol preference
get_symbol_preference(name)	Get symbol preference
clear_symbol_preference(name)	Clear symbol preference
save_preferences()	Save preferences to file
load_preferences()	Load preferences from file
apply_preferences(prefs)	Apply loaded preferences
clear_preferences()	Clear all preferences

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Troubleshooting

Common Issues

Q: Module not found (AttributeError)

AttributeError: module 'laziest_import' has no attribute 'mymodule'

Solution: The module is not registered. Use lz.enable_auto_search() to enable auto-discovery, or register it manually:

lz.register_alias('mymodule', 'mypackage.mymodule')

Q: Slow first import The symbol index may be building. Check status with:

lz.is_index_building() # True if building
lz.wait_for_index(30) # Wait up to 30 seconds

Q: Typo correction not working Ensure the module is in the alias list:

lz.register_alias('nump', 'numpy') # Add misspelling
arr = lz.nump.array([1, 2, 3]) # Now works

Q: Symbol conflicts (same name in multiple modules) Use module hints or preferences:

lz.set_symbol_preference('DataFrame', 'pandas') # Prefer pandas
result = lz.DataFrame # Gets pandas.DataFrame

Debug Mode

Enable detailed logging:

lz.enable_debug_mode()
import laziest_import as lz
arr = lz.np.array([1, 2, 3]) # See import details in logs

Cache Issues

Clear caches if experiencing stale data:

lz.clear_cache() # Clear memory cache
lz.clear_file_cache() # Clear disk cache
lz.rebuild_symbol_index() # Rebuild symbol index

Performance Tips

1. First run: ~2s to build index
2. Cached run: ~0.003s (700x faster!)
3. Use lz.enable_background_build() to avoid blocking on first import
4. For CI/CD, set LAZY_BG_BUILD=1 to pre-build cache

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How It Works

Architecture

1. Proxy objects: Each alias maps to a LazyModule proxy
2. On-demand import: Real import triggers on first attribute access via __getattr__
3. Caching: Imported modules cache within the proxy object
4. Chain proxies: LazySubmodule handles recursive lazy loading
5. Fuzzy search: Levenshtein distance algorithm for fault-tolerant matching

Multi-Level Cache Architecture

The library uses a three-tier caching system for optimal performance:

Cache Level	Description	Default TTL
Stdlib cache	Standard library symbols	7 days
Third-party cache	Installed package symbols	24 hours
Memory cache	Hot cache for current session	Session

Cache files are stored in ~/.laziest_import/cache/ and automatically:

• Expire based on TTL settings
• Clean up when exceeding size limit (default: 100 MB)
• Invalidate on Python version changes

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Predefined Aliases

Data Science

np, pd, plt, sns, scipy

Machine Learning

torch, tf, keras, sklearn, xgboost, lightgbm

Deep Learning

transformers, langchain, llama_index

Web Frameworks

flask, django, fastapi, starlette

HTTP Clients

requests, httpx, aiohttp

Databases

sqlalchemy, pymongo, redis, duckdb

Cloud Services

boto3 (AWS), google.cloud, azure

Image Processing

cv2, PIL.Image, skimage

GUI

PyQt6, tkinter, flet, nicegui

DevOps

docker, kubernetes, ansible

NLP

spacy, nltk, transformers

Visualization

plotly, bokeh, streamlit, gradio

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Contributing

We love contributions! Check out our Contributing Guide for more information.

How to Contribute

1. Fork the repo and create your branch from main
2. Read our Code of Conduct
3. Make sure your code lints (flake8)
4. Add tests for any new functionality
5. Ensure the test suite passes (pytest tests/)
6. Commit your changes
7. Push to your branch and open a Pull Request!

Development Setup

# Clone the repo
git clone https://github.com/ChidcGithub/Laziest-import.git
cd Laziest-import

# Install in development mode
pip install -e ".[dev,test]"

# Run tests
pytest tests/ -v

# Run linting
flake8 laziest_import/

Good First Issues

Looking for a place to start? Check out:

• Good first issues: Issues labeled "good first issue"
• Help wanted: Issues labeled "help wanted"

Ways to Contribute

• Report bugs
• Propose new features
• Improve documentation
• Fix existing issues
• Add more tests
• Help with translations
• Design improvements

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Contributors

Thanks goes to these wonderful people:

Made with contrib.rocks.

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Star History

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Get In Touch

• Email: (your email)
• GitHub: ChidcGithub/Laziest-import
• Discussions: GitHub Discussions
• Issues: GitHub Issues

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Acknowledgments

• Thanks to all the contributors who have helped make this project better!
• Inspired by the Python community's love for clean, simple APIs

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Show Your Support

If you find this project useful, please consider:

• Starring this repo on GitHub
• Reporting bugs or suggesting features
• Sharing it with friends and colleagues
• Blogging about it if you use it
• Contributing to the project

Thank you!

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Made by Chidc and contributors with LOVE