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pip VS pipenv

Compare pip VS pipenv and see what are their differences

pip logo pip

The PyPA recommended tool for installing Python packages.

pipenv logo pipenv

Python Development Workflow for Humans. Contribute to pypa/pipenv development by creating an account on GitHub.
  • pip Landing page
    Landing page //
    2023-08-23
  • pipenv Landing page
    Landing page //
    2023-08-26

pip features and specs

  • Ease of Use
    pip is straightforward to use with simple command-line instructions for installing and managing Python packages.
  • Wide Adoption
    pip is the standard package manager for Python, widely adopted and supported across platforms, ensuring reliability and community support.
  • Dependency Management
    pip automatically handles package dependencies, downloading and installing them alongside the desired package.
  • Integration with PyPI
    pip seamlessly integrates with the Python Package Index (PyPI), giving access to thousands of packages.
  • Virtual Environment Support
    pip works well with virtual environments, allowing users to manage packages in isolated Python environments.

Possible disadvantages of pip

  • Limited Advanced Features
    pip focuses on simplicity and may lack some advanced package management features found in more sophisticated tools.
  • Version Conflicts
    While pip handles dependencies, it can sometimes lead to version conflicts when two packages require different versions of the same dependency.
  • Lack of System Package Awareness
    pip does not interact with system package managers, which can lead to situations where packages are duplicated or out of sync.
  • Performance with Large Projects
    Managing dependencies in large-scale projects can become cumbersome with pip, as it wasn't initially designed for such complex environments.

pipenv features and specs

  • Integrated Workflow
    Pipenv combines the functionalities of pip and virtualenv, providing a seamless environment for package installation and management, making the development workflow more efficient and organized.
  • Automatic Virtual Environment Management
    Automatically creates and manages a virtual environment for projects, ensuring that dependencies are maintained separately and do not interfere with the system Python or other projects.
  • Lock File Generation
    Generates a Pipfile.lock to ensure deterministic builds, making sure that installations are consistent across different environments or deployments.
  • User-Friendly Package Installation
    Simplifies package installation with a straightforward and intuitive interface. Pipenv handles both direct package specification and environment management in a unified manner.
  • Environment Consistency
    By using the Pipfile and Pipfile.lock, Pipenv ensures that all developers working on a project have a consistent set of dependencies, reducing 'it works on my machine' issues.
  • Dependency Resolution
    Pipenv uses an advanced dependency resolver, helping to avoid dependency conflicts that can occur with complex package requirements.

Possible disadvantages of pipenv

  • Performance Overhead
    The dependency resolution process can sometimes be slow, which might be noticeable in larger projects or when installing multiple packages at once.
  • Limited Flexibility
    Pipenv abstracts away some of pip and virtualenv’s flexibility, which might limit advanced configurations or setups required by more complex projects.
  • Complexity for Simple Projects
    May add unnecessary complexity for simple or small projects where virtualenv and pip would suffice without additional layers.
  • Slower Updates
    Pipenv may lag behind updates compared to pip and virtualenv due to its additional integration layer, meaning it might not always provide immediate support for the latest Python packaging developments.
  • Learning Curve
    Requires initial learning and adjustment for developers who are accustomed to using pip and virtualenv separately, potentially slowing down onboarding for new team members.

pip videos

PIP Lancets Review #pip #piplancetreview #diabetes

More videos:

  • Review - Filling out the PIP Review Form
  • Review - My Tips for Your Personal Independence Payment Review | Disability | PIP

pipenv videos

Pipenv Crash Course

More videos:

  • Tutorial - How to use Pipenv to Manage Python Dependencies (Tutorial)
  • Review - venv, pyenv, pypi, pip, pipenv, pyWTF?

Category Popularity

0-100% (relative to pip and pipenv)
Front End Package Manager
Kids
74 74%
26% 26
Package Manager
69 69%
31% 31
Developer Tools
53 53%
47% 47

User comments

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Social recommendations and mentions

Based on our record, pip should be more popular than pipenv. It has been mentiond 19 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

pip mentions (19)

  • PYMODINS
    Use the package manager pip to Install pymodins. - Source: dev.to / 10 months ago
  • How to build a new Harlequin adapter with Poetry
    To get the most out of this guide, you should have a basic understanding of virtual environments, Python packages and modules, and pip. Our objectives are to:. - Source: dev.to / 10 months ago
  • The ultimate guide to creating a secure Python package
    You need a build system to render the files you publish in the Python package. You can use a build frontend, such as pip, or a build backend, such as setuptools, Flit, Hatchling, or PDM. - Source: dev.to / 12 months ago
  • Let’s build AI-tools with the help of AI and Typescript!
    Package installer for Python (pip), we use this for installing the Python-based packages, such as Jupyter Lab, and we're going to use this for installing other Python-based tools like the Chroma DB vector database. - Source: dev.to / about 1 year ago
  • GrandTourer – a CLI tool for easily launching applications on macOS
    Use the package manager pip to install GrandTourer. GrandTourer requires Python >=3.8. Source: over 1 year ago
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pipenv mentions (6)

  • Generate pip requirements.txt file based on imports of any project
    https://github.com/pypa/pipenv Pipenv was last updated 10 hours ago. Looks like it's still an active project to me. - Source: Hacker News / 7 months ago
  • Adding Virtual Environments to Git Repo
    Pipenv solves this by having both kinds of requirement files: Pipfile lists package names and known constraints on which versions can be used, while Pipfile.lock gives specific package versions with hashes. Theoretically the Pipfile (and its lockfile) format were supposed to be a standard that many different tools could use, but I haven't seen it get adopted much outside of pipenv itself, so I'm not sure if it's... Source: about 2 years ago
  • Top 10 Python security best practices
    Alternatively, you can look into Pipenv, which has a lot more tools to develop secure applications with. - Source: dev.to / over 2 years ago
  • Why and how to use conda?
    I’m partial to pipenv but it does depend on pyenv (which works on Windows albeit via WSL, no?). Source: about 3 years ago
  • How to make a Python package in 2021
    I think I went through the same progression — thinking pipenv was the official solution before deciding it isn’t. To add to the confusion, I just realized that pipenv [1] is currently owned by the Python Packaging Authority (PyPA) which also owns the official pip [2] and virtualenv [3]. [1]: https://github.com/pypa/pipenv [2]: https://github.com/pypa/pip [3]: https://github.com/pypa/virtualenv. - Source: Hacker News / about 4 years ago
View more

What are some alternatives?

When comparing pip and pipenv, you can also consider the following products

Conda - Binary package manager with support for environments.

Python Poetry - Python packaging and dependency manager.

Python Package Index - A repository of software for the Python programming language

envd - Machine learning development environment for data science and AI/ML engineering teams.

Scoop - A command-line installer for Windows

Anaconda - Anaconda is the leading open data science platform powered by Python.