mod2pip 0.11.0

Installation

pip install mod2pip

For minimal installation without Jupyter notebook support:

pip install --no-deps mod2pip
pip install yarg==0.1.9 docopt==0.6.2

Usage

Usage:
    mod2pip [options] [<path>]

Arguments:
    <path>                The path to the directory containing the application files for which a requirements file
                          should be generated (defaults to the current working directory)

Options:
    --use-local           Use ONLY local package info instead of querying PyPI
    --pypi-server <url>   Use custom PyPi server
    --proxy <url>         Use Proxy, parameter will be passed to requests library
    --debug               Print debug information
    --ignore <dirs>...    Ignore extra directories, each separated by a comma
    --no-follow-links     Do not follow symbolic links in the project
    --encoding <charset>  Use encoding parameter for file open
    --savepath <file>     Save the list of requirements in the given file
    --print               Output the list of requirements in the standard output
    --force               Overwrite existing requirements.txt
    --diff <file>         Compare modules in requirements.txt to project imports
    --clean <file>        Clean up requirements.txt by removing modules that are not imported in project
    --mode <scheme>       Enables dynamic versioning with <compat>, <gt> or <no-pin> schemes
                          <compat> | e.g. Flask~=1.1.2
                          <gt>     | e.g. Flask>=1.1.2
                          <no-pin> | e.g. Flask
    --scan-notebooks      Look for imports in jupyter notebook files
    --enhanced-detection  Enable enhanced import detection (dynamic imports, conda packages)
    --include-transitive  Include transitive dependencies (experimental)
    --transitive-depth <n> Maximum depth for transitive dependency resolution (default: 2)

Enhanced Features

Dynamic Import Detection

mod2pip can detect imports that traditional tools miss:

# Dynamic imports
module_name = "pandas"
pd = importlib.import_module(module_name)

# Conditional imports
try:
    import tensorflow as tf
except ImportError:
    tf = None

# Late imports in functions
def process_data():
    import scipy.stats as stats
    return stats.norm()

Conda Environment Support

Works seamlessly with conda environments and detects conda-installed packages that pip-based tools often miss.

Enhanced Usage Examples

# Basic usage with enhanced detection
mod2pip --enhanced-detection

# Include transitive dependencies
mod2pip --enhanced-detection --include-transitive

# Use only local packages (faster, no PyPI queries)
mod2pip --enhanced-detection --use-local

# Print to stdout instead of file
mod2pip --enhanced-detection --print

Example Output

$ mod2pip --enhanced-detection /home/project/location
INFO: Using enhanced detection for conda packages and dynamic imports
Successfully saved requirements file in /home/project/location/requirements.txt

Contents of requirements.txt

beautifulsoup4==4.14.3
numpy==2.4.0
pandas==2.3.3
requests==2.32.5
tensorflow==2.20.0

Why mod2pip over pip freeze?

• pip freeze only saves packages installed with pip install in your environment

• pip freeze saves ALL packages in the environment, including unused ones (without virtualenv)

• pip freeze misses packages installed via conda or other package managers

• pip freeze cannot detect dynamically imported packages

• mod2pip analyzes your actual code imports and generates minimal, accurate requirements

Why mod2pip over pipreqs?

• Better Import Detection: Catches dynamic imports, conditional imports, and late imports

• Conda Support: Works properly with conda environments and conda-installed packages

• Transitive Dependencies: Optional resolution of indirect dependencies

• Enhanced Local Detection: Supports editable packages, namespace packages, and custom installations

• More Accurate: Reduces “missing packages” issues common with pipreqs