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A modern Python package and dependency manager supporting the latest PEP standards
A system-level, binary package and environment manager running on all major operating systems and platforms.
A set of tools to keep your pinned Python dependencies fresh.
Simple Python version management
a Hassle-Free Python Experience
Quick Overview
Pipenv is a Python dependency management tool that aims to bring the best of all packaging worlds to the Python world. It automatically creates and manages a virtualenv for your projects, as well as adds/removes packages from your Pipfile as you install/uninstall packages. It also generates a Pipfile.lock, which is used to produce deterministic builds.
Pros
- Combines pip and virtualenv into a single tool
- Automatically manages a Pipfile and Pipfile.lock for dependency tracking
- Provides a simple command-line interface for managing dependencies
- Supports development and production environments with separate dependencies
Cons
- Can be slower than traditional pip + virtualenv setup for large projects
- Some users report occasional inconsistencies or bugs
- Learning curve for developers accustomed to traditional Python workflow
- Limited support for certain advanced use cases compared to poetry or conda
Code Examples
- Creating a new project:
pipenv --python 3.9
This command creates a new virtual environment using Python 3.9.
- Installing packages:
pipenv install requests
pipenv install pytest --dev
These commands install the 'requests' package for production and 'pytest' for development.
- Running a script in the virtual environment:
pipenv run python my_script.py
This command runs 'my_script.py' within the Pipenv-managed virtual environment.
Getting Started
- Install Pipenv:
pip install pipenv
- Create a new project:
mkdir my_project
cd my_project
pipenv --python 3.9
- Install dependencies:
pipenv install requests
- Activate the virtual environment:
pipenv shell
- Run your Python script:
python my_script.py
Competitor Comparisons
A modern Python package and dependency manager supporting the latest PEP standards
Pros of PDM
- Faster dependency resolution and installation
- Better PEP 582 support for isolated environments
- More modern codebase and actively maintained
Cons of PDM
- Smaller community and ecosystem compared to Pipenv
- Less mature and potentially less stable
- Steeper learning curve for users familiar with Pipenv
Code Comparison
PDM:
[tool.pdm]
python_requires = ">=3.7"
dependencies = [
"requests>=2.25.0",
"sqlalchemy~=1.4.0",
]
Pipenv:
[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"
[packages]
requests = ">=2.25.0"
sqlalchemy = "~=1.4.0"
[requires]
python_version = "3.7"
Both PDM and Pipenv aim to simplify Python dependency management, but they differ in their approach and features. PDM is newer and focuses on modern Python practices, while Pipenv has been around longer and has a larger user base. PDM's performance advantages and PEP 582 support make it attractive for some users, but Pipenv's maturity and wider adoption may be preferable for others. The choice between the two depends on specific project requirements and personal preferences.
A system-level, binary package and environment manager running on all major operating systems and platforms.
Pros of conda
- Supports multiple programming languages, not just Python
- Handles non-Python dependencies (e.g., C libraries) more effectively
- Better suited for data science and scientific computing environments
Cons of conda
- Larger installation size and potentially slower package resolution
- Can be more complex to use, especially for beginners
- Less integrated with standard Python tools (e.g., pip, virtualenv)
Code comparison
conda:
conda create --name myenv python=3.8
conda activate myenv
conda install numpy pandas
pipenv:
pipenv --python 3.8
pipenv shell
pipenv install numpy pandas
Both tools aim to simplify dependency management and environment creation, but conda offers a more comprehensive solution for complex scientific computing setups, while pipenv focuses on providing a streamlined workflow for Python projects. conda's ability to manage non-Python packages makes it particularly useful for data science and machine learning projects, whereas pipenv's tight integration with pip and its lock file system may be preferable for traditional Python development.
A set of tools to keep your pinned Python dependencies fresh.
Pros of pip-tools
- Lightweight and focused on dependency management
- Generates deterministic requirements files
- Integrates well with existing pip workflows
Cons of pip-tools
- Lacks built-in virtual environment management
- Doesn't provide a unified command for installing and managing dependencies
Code comparison
pip-tools:
pip-compile requirements.in
pip-sync requirements.txt
Pipenv:
pipenv install
pipenv shell
pip-tools focuses on generating and syncing requirements files, while Pipenv provides a more comprehensive environment and dependency management solution. pip-tools is often preferred for its simplicity and integration with existing pip-based workflows, whereas Pipenv offers a more feature-rich experience with virtual environment handling built-in.
pip-tools is particularly useful for projects that require fine-grained control over dependencies and want to maintain separate files for top-level and transitive dependencies. Pipenv, on the other hand, aims to simplify Python packaging by combining dependency management and virtual environment creation into a single tool.
The choice between the two often depends on project requirements, team preferences, and existing infrastructure. Some developers appreciate pip-tools' straightforward approach, while others prefer Pipenv's all-in-one solution.
Simple Python version management
Pros of pyenv
- Manages multiple Python versions on a single system
- Allows switching between Python versions per project or globally
- Lightweight and focused on Python version management
Cons of pyenv
- Does not handle package management or virtual environments directly
- Requires manual setup of virtual environments for each project
- Limited to Python version management, lacking broader project management features
Code Comparison
pyenv:
pyenv install 3.9.0
pyenv global 3.9.0
python --version
pipenv:
pipenv --python 3.9
pipenv install requests
pipenv run python main.py
pyenv focuses on managing Python versions, while pipenv combines package management, virtual environment creation, and dependency resolution. pyenv is more suitable for users who need fine-grained control over Python versions across their system, whereas pipenv provides a more comprehensive solution for project-specific environment and dependency management.
a Hassle-Free Python Experience
Pros of Rye
- Faster dependency resolution and installation
- Built-in support for multiple Python versions
- Simpler project configuration with
pyproject.toml
Cons of Rye
- Less mature and potentially less stable than Pipenv
- Smaller community and ecosystem
Code Comparison
Rye (pyproject.toml
):
[project]
name = "my_project"
version = "0.1.0"
dependencies = [
"requests>=2.25.1",
]
Pipenv (Pipfile
):
[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"
[packages]
requests = ">=2.25.1"
[dev-packages]
[requires]
python_version = "3.9"
Rye uses a more standardized pyproject.toml
file for project configuration, while Pipenv uses its custom Pipfile
format. Rye's approach aligns better with modern Python packaging standards and provides a more consistent experience across different tools in the Python ecosystem.
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Pipenv: Python Development Workflow for Humans
Pipenv is a Python virtualenv management tool that supports a multitude of systems and nicely bridges the gaps between pip, python (using system python, pyenv or asdf) and virtualenv. Linux, macOS, and Windows are all first-class citizens in pipenv.
Pipenv automatically creates and manages a virtualenv for your projects, as well as adds/removes packages from your Pipfile
as you install/uninstall packages. It also generates a project Pipfile.lock
, which is used to produce deterministic builds.
Pipenv is primarily meant to provide users and developers of applications with an easy method to arrive at a consistent working project environment.
The problems that Pipenv seeks to solve are multi-faceted:
- You no longer need to use
pip
andvirtualenv
separately: they work together. - Managing a
requirements.txt
file with package hashes can be problematic. Pipenv usesPipfile
andPipfile.lock
to separate abstract dependency declarations from the last tested combination. - Hashes are documented in the lock file which are verified during install. Security considerations are put first.
- Strongly encourage the use of the latest versions of dependencies to minimize security risks arising from outdated components.
- Gives you insight into your dependency graph (e.g.
$ pipenv graph
). - Streamline development workflow by supporting local customizations with
.env
files.
Table Of Contents
Installation
Pipenv can be installed with Python 3.7 and above.
For most users, we recommend installing Pipenv using pip
:
pip install --user pipenv
Or, if you're using Fedora:
sudo dnf install pipenv
Or, if you're using FreeBSD:
pkg install py39-pipenv
Or, if you're using Gentoo:
sudo emerge pipenv
Or, if you're using Void Linux:
sudo xbps-install -S python3-pipenv
Alternatively, some users prefer to use Pipx:
pipx install pipenv
Or, some users prefer to use Python pip module
python -m pip install pipenv
Refer to the documentation for latest instructions.
â¨ð°â¨
Features
- Enables truly deterministic builds, while easily specifying only what you want.
- Generates and checks file hashes for locked dependencies.
- Automatically install required Pythons, if
pyenv
orasdf
is available. - Automatically finds your project home, recursively, by looking for a
Pipfile
. - Automatically generates a
Pipfile
, if one doesn't exist. - Automatically creates a virtualenv in a standard location.
- Automatically adds/removes packages to a
Pipfile
when they are installed/uninstalled. - Automatically loads
.env
files, if they exist.
For command reference, see Commands.
Basic Concepts
- A virtualenv will automatically be created, when one doesn't exist.
- When no parameters are passed to
install
, all packages[packages]
specified will be installed. - Otherwise, whatever virtualenv defaults to will be the default.
Shell Completion
To enable completion in fish, add this to your configuration ~/.config/fish/completions/pipenv.fish
:
eval (env _PIPENV_COMPLETE=fish_source pipenv)
There is also a fish plugin, which will automatically activate your subshells for you!
Alternatively, with zsh, add this to your configuration ~/.zshrc
:
eval "$(_PIPENV_COMPLETE=zsh_source pipenv)"
Alternatively, with bash, add this to your configuration ~/.bashrc
or ~/.bash_profile
:
eval "$(_PIPENV_COMPLETE=bash_source pipenv)"
Magic shell completions are now enabled!
Usage
$ pipenv --help
Usage: pipenv [OPTIONS] COMMAND [ARGS]...
Options:
--where Output project home information.
--venv Output virtualenv information.
--py Output Python interpreter information.
--envs Output Environment Variable options.
--rm Remove the virtualenv.
--bare Minimal output.
--man Display manpage.
--support Output diagnostic information for use in
GitHub issues.
--site-packages / --no-site-packages
Enable site-packages for the virtualenv.
[env var: PIPENV_SITE_PACKAGES]
--python TEXT Specify which version of Python virtualenv
should use.
--clear Clears caches (pipenv, pip). [env var:
PIPENV_CLEAR]
-q, --quiet Quiet mode.
-v, --verbose Verbose mode.
--pypi-mirror TEXT Specify a PyPI mirror.
--version Show the version and exit.
-h, --help Show this message and exit.
Usage Examples:
Create a new project using Python 3.7, specifically:
$ pipenv --python 3.7
Remove project virtualenv (inferred from current directory):
$ pipenv --rm
Install all dependencies for a project (including dev):
$ pipenv install --dev
Create a lockfile containing pre-releases:
$ pipenv lock --pre
Show a graph of your installed dependencies:
$ pipenv graph
Check your installed dependencies for security vulnerabilities:
$ pipenv check
Install a local setup.py into your virtual environment/Pipfile:
$ pipenv install -e .
Use a lower-level pip command:
$ pipenv run pip freeze
Commands:
check Checks for PyUp Safety security vulnerabilities and against
PEP 508 markers provided in Pipfile.
clean Uninstalls all packages not specified in Pipfile.lock.
graph Displays currently-installed dependency graph information.
install Installs provided packages and adds them to Pipfile, or (if no
packages are given), installs all packages from Pipfile.
lock Generates Pipfile.lock.
open View a given module in your editor.
requirements Generate a requirements.txt from Pipfile.lock.
run Spawns a command installed into the virtualenv.
scripts Lists scripts in current environment config.
shell Spawns a shell within the virtualenv.
sync Installs all packages specified in Pipfile.lock.
uninstall Uninstalls a provided package and removes it from Pipfile.
update Runs lock, then sync.
upgrade Update the lock of the specified dependency / sub-dependency,
but does not actually install the packages.
verify Verify the hash in Pipfile.lock is up-to-date.
Locate the project:
$ pipenv --where
/Users/kennethreitz/Library/Mobile Documents/com~apple~CloudDocs/repos/kr/pipenv/test
Locate the virtualenv:
$ pipenv --venv
/Users/kennethreitz/.local/share/virtualenvs/test-Skyy4vre
Locate the Python interpreter:
$ pipenv --py
/Users/kennethreitz/.local/share/virtualenvs/test-Skyy4vre/bin/python
Install packages:
$ pipenv install
Creating a virtualenv for this project...
...
No package provided, installing all dependencies.
Virtualenv location: /Users/kennethreitz/.local/share/virtualenvs/test-EJkjoYts
Installing dependencies from Pipfile.lock...
...
To activate this project's virtualenv, run the following:
$ pipenv shell
Installing from git:
You can install packages with pipenv from git and other version control systems using URLs formatted according to the following rule:
<vcs_type>+<scheme>://<location>/<user_or_organization>/<repository>@<branch_or_tag>#<package_name>
The only optional section is the @<branch_or_tag>
section. When using git over SSH, you may use the shorthand vcs and scheme alias git+git@<location>:<user_or_organization>/<repository>@<branch_or_tag>#<package_name>
. Note that this is translated to git+ssh://git@<location>
when parsed.
Valid values for <vcs_type>
include git
, bzr
, svn
, and hg
. Valid values for <scheme>
include http,
, https
, ssh
, and file
. In specific cases you also have access to other schemes: svn
may be combined with svn
as a scheme, and bzr
can be combined with sftp
and lp
.
Note that it is strongly recommended that you install any version-controlled dependencies in editable mode, using pipenv install -e
, in order to ensure that dependency resolution can be performed with an up to date copy of the repository each time it is performed, and that it includes all known dependencies.
Below is an example usage which installs the git repository located at https://github.com/requests/requests.git
from tag v2.19.1
as package name requests
:
$ pipenv install -e git+https://github.com/requests/requests.git@v2.19#egg=requests
Creating a Pipfile for this project...
Installing -e git+https://github.com/requests/requests.git@v2.19.1#egg=requests...
[...snipped...]
Adding -e git+https://github.com/requests/requests.git@v2.19.1#egg=requests to Pipfile's [packages]...
[...]
You can read more about pip's implementation of vcs support here.
Install a dev dependency:
$ pipenv install pytest --dev
Installing pytest...
...
Adding pytest to Pipfile's [dev-packages]...
Show a dependency graph:
$ pipenv graph
requests==2.18.4
- certifi [required: >=2017.4.17, installed: 2017.7.27.1]
- chardet [required: >=3.0.2,<3.1.0, installed: 3.0.4]
- idna [required: >=2.5,<2.7, installed: 2.6]
- urllib3 [required: <1.23,>=1.21.1, installed: 1.22]
Generate a lockfile:
$ pipenv lock
Assuring all dependencies from Pipfile are installed...
Locking [dev-packages] dependencies...
Locking [packages] dependencies...
Note: your project now has only default [packages] installed.
To install [dev-packages], run: $ pipenv install --dev
Install all dev dependencies:
$ pipenv install --dev
Pipfile found at /Users/kennethreitz/repos/kr/pip2/test/Pipfile. Considering this to be the project home.
Pipfile.lock out of date, updating...
Assuring all dependencies from Pipfile are installed...
Locking [dev-packages] dependencies...
Locking [packages] dependencies...
Uninstall everything:
$ pipenv uninstall --all
No package provided, un-installing all dependencies.
Found 25 installed package(s), purging...
...
Environment now purged and fresh!
Use the shell:
$ pipenv shell
Loading .env environment variables...
Launching subshell in virtual environment. Type 'exit' or 'Ctrl+D' to return.
$ â¯
PURPOSE AND ADVANTAGES OF PIPENV
To understand the problems that Pipenv solves, it's useful to show how Python package management has evolved.
Take yourself back to the first Python iteration. We had Python, but there was no clean way to install packages.
Then came Easy Install, a package that installs other Python packages with relative ease. But it came with a catch: it wasn't easy to uninstall packages that were no longer needed.
Enter pip, which most Python users are familiar with. pip lets us install and uninstall packages. We could specify versions, run pip freeze > requirements.txt to output a list of installed packages to a text file, and use that same text file to install everything an app needed with pip install -r requirements.txt.
But pip didn't include a way to isolate packages from each other. We might work on apps that use different versions of the same libraries, so we needed a way to enable that.
Pipenv aims to solve several problems. First, the problem of needing the pip library for package installation, plus a library for creating a virtual environment, plus a library for managing virtual environments, plus all the commands associated with those libraries. That's a lot to manage. Pipenv ships with package management and virtual environment support, so you can use one tool to install, uninstall, track, and document your dependencies and to create, use, and organize your virtual environments. When you start a project with it, Pipenv will automatically create a virtual environment for that project if you aren't already using one.
Pipenv accomplishes this dependency management by abandoning the requirements.txt norm and trading it for a new document called a Pipfile. When you install a library with Pipenv, a Pipfile for your project is automatically updated with the details of that installation, including version information and possibly the Git repository location, file path, and other information.
Second, Pipenv wants to make it easier to manage complex interdependencies.
Using Pipenv, which gives you Pipfile, lets you avoid these problems by managing dependencies for different environments for you. This command will install the main project dependencies:
pipenv install
Adding the --dev tag will install the dev/testing requirements:
pipenv install --dev To generate a Pipfile.lock file, run:
pipenv lock
You can also run Python scripts with Pipenv. To run a top-level Python script called hello.py, run:
pipenv run python hello.py
And you will see your expected result in the console.
To start a shell, run:
pipenv shell
If you would like to convert a project that currently uses a requirements.txt file to use Pipenv, install Pipenv and run:
pipenv install requirements.txt
This will create a Pipfile and install the specified requirements.
Documentation
Documentation resides over at pipenv.pypa.io.
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Top Related Projects
A modern Python package and dependency manager supporting the latest PEP standards
A system-level, binary package and environment manager running on all major operating systems and platforms.
A set of tools to keep your pinned Python dependencies fresh.
Simple Python version management
a Hassle-Free Python Experience
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