mod2pip 0.11.0

Enhanced pip requirements.txt generator with dynamic import detection and conda support

mod2pip is an enhanced version of pipreqs that generates pip requirements.txt files based on imports in your project. It addresses the main limitations of the original pipreqs by providing:

✅ Enhanced Import Detection - Catches dynamic imports, conditional imports, and late imports that standard tools miss

✅ Conda Environment Support - Properly detects packages in conda environments and custom virtual environments

✅ Transitive Dependencies - Experimental support for resolving indirect dependencies

✅ Better Local Package Detection - Supports editable packages, namespace packages, and non-standard installations

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